Search is not available for this dataset
title
string | abstract
string | url
string | category
string | prediction
string | probability
float64 | arxiv_id
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|---|---|---|---|---|---|---|
Reporting existing datasets for automatic epilepsy diagnosis and seizure detection
|
More than 50 million individuals are affected by epilepsy, a chronic
neurological disorder characterized by unprovoked, recurring seizures and
psychological symptoms. Researchers are working to automatically detect or
predict epileptic episodes through Electroencephalography (EEG) signal
analysis, and machine, and deep learning methods. Good quality, open-source,
and free EEG data acts as a catalyst in this ongoing battle to manage this
disease. This article presents 40+ publicly available EEG datasets for adult
and pediatric human populations from 2001-2023. A comparative analysis and
discussion on open and private EEG datasets have been done based on objective
parameters in this domain. Bonn and CHB-MIT remain the benchmark datasets used
for the automatic detection of epileptic and seizure EEG signals. Meta-data has
also been released for large EEG data like CHB-MIT. This article will be
updated every year to report the progress and changing trends in the
development of EEG datasets in this field.
|
http://arxiv.org/abs/2306.12292v1
|
eess.SP
|
new_dataset
| 0.99396 |
2306.12292
|
Winter Wheat Crop Yield Prediction on Multiple Heterogeneous Datasets using Machine Learning
|
Winter wheat is one of the most important crops in the United Kingdom, and
crop yield prediction is essential for the nation's food security. Several
studies have employed machine learning (ML) techniques to predict crop yield on
a county or farm-based level. The main objective of this study is to predict
winter wheat crop yield using ML models on multiple heterogeneous datasets,
i.e., soil and weather on a zone-based level. Experimental results demonstrated
their impact when used alone and in combination. In addition, we employ
numerous ML algorithms to emphasize the significance of data quality in any
machine-learning strategy.
|
http://arxiv.org/abs/2306.11946v1
|
cs.LG
|
not_new_dataset
| 0.992214 |
2306.11946
|
A Deep Learning Model for Heterogeneous Dataset Analysis -- Application to Winter Wheat Crop Yield Prediction
|
Western countries rely heavily on wheat, and yield prediction is crucial.
Time-series deep learning models, such as Long Short Term Memory (LSTM), have
already been explored and applied to yield prediction. Existing literature
reported that they perform better than traditional Machine Learning (ML)
models. However, the existing LSTM cannot handle heterogeneous datasets (a
combination of data which varies and remains static with time). In this paper,
we propose an efficient deep learning model that can deal with heterogeneous
datasets. We developed the system architecture and applied it to the real-world
dataset in the digital agriculture area. We showed that it outperforms the
existing ML models.
|
http://arxiv.org/abs/2306.11942v1
|
cs.LG
|
not_new_dataset
| 0.992006 |
2306.11942
|
Evaluation of Chinese-English Machine Translation of Emotion-Loaded Microblog Texts: A Human Annotated Dataset for the Quality Assessment of Emotion Translation
|
In this paper, we focus on how current Machine Translation (MT) tools perform
on the translation of emotion-loaded texts by evaluating outputs from Google
Translate according to a framework proposed in this paper. We propose this
evaluation framework based on the Multidimensional Quality Metrics (MQM) and
perform a detailed error analysis of the MT outputs. From our analysis, we
observe that about 50% of the MT outputs fail to preserve the original emotion.
After further analysis of the errors, we find that emotion carrying words and
linguistic phenomena such as polysemous words, negation, abbreviation etc., are
common causes for these translation errors.
|
http://arxiv.org/abs/2306.11900v1
|
cs.CL
|
new_dataset
| 0.994139 |
2306.11900
|
GIO: Gradient Information Optimization for Training Dataset Selection
|
It is often advantageous to train models on a subset of the available train
examples, because the examples are of variable quality or because one would
like to train with fewer examples, without sacrificing performance. We present
Gradient Information Optimization (GIO), a scalable, task-agnostic approach to
this data selection problem that requires only a small set of (unlabeled)
examples representing a target distribution. GIO begins from a natural,
information-theoretic objective that is intractable in practice. Our
contribution is in showing that it can be made highly scalable through a simple
relaxation of the objective and a highly efficient implementation. In
experiments with machine translation, spelling correction, and image
recognition, we show that GIO delivers outstanding results with very small
train sets. These findings are robust to different representation models and
hyperparameters for GIO itself. GIO is task- and domain-agnostic and can be
applied out-of-the-box to new datasets and domains.
|
http://arxiv.org/abs/2306.11670v1
|
cs.LG
|
not_new_dataset
| 0.992043 |
2306.11670
|
EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks
|
Brain-Machine Interfacing (BMI) has greatly benefited from adopting machine
learning methods for feature learning that require extensive data for training,
which are often unavailable from a single dataset. Yet, it is difficult to
combine data across labs or even data within the same lab collected over the
years due to the variation in recording equipment and electrode layouts
resulting in shifts in data distribution, changes in data dimensionality, and
altered identity of data dimensions. Our objective is to overcome this
limitation and learn from many different and diverse datasets across labs with
different experimental protocols. To tackle the domain adaptation problem, we
developed a novel machine learning framework combining graph neural networks
(GNNs) and transfer learning methodologies for non-invasive Motor Imagery (MI)
EEG decoding, as an example of BMI. Empirically, we focus on the challenges of
learning from EEG data with different electrode layouts and varying numbers of
electrodes. We utilise three MI EEG databases collected using very different
numbers of EEG sensors (from 22 channels to 64) and layouts (from custom
layouts to 10-20). Our model achieved the highest accuracy with lower standard
deviations on the testing datasets. This indicates that the GNN-based transfer
learning framework can effectively aggregate knowledge from multiple datasets
with different electrode layouts, leading to improved generalization in
subject-independent MI EEG classification. The findings of this study have
important implications for Brain-Computer-Interface (BCI) research, as they
highlight a promising method for overcoming the limitations posed by
non-unified experimental setups. By enabling the integration of diverse
datasets with varying electrode layouts, our proposed approach can help advance
the development and application of BMI technologies.
|
http://arxiv.org/abs/2306.13109v1
|
eess.SP
|
not_new_dataset
| 0.992204 |
2306.13109
|
SeFNet: Bridging Tabular Datasets with Semantic Feature Nets
|
Machine learning applications cover a wide range of predictive tasks in which
tabular datasets play a significant role. However, although they often address
similar problems, tabular datasets are typically treated as standalone tasks.
The possibilities of using previously solved problems are limited due to the
lack of structured contextual information about their features and the lack of
understanding of the relations between them. To overcome this limitation, we
propose a new approach called Semantic Feature Net (SeFNet), capturing the
semantic meaning of the analyzed tabular features. By leveraging existing
ontologies and domain knowledge, SeFNet opens up new opportunities for sharing
insights between diverse predictive tasks. One such opportunity is the Dataset
Ontology-based Semantic Similarity (DOSS) measure, which quantifies the
similarity between datasets using relations across their features. In this
paper, we present an example of SeFNet prepared for a collection of predictive
tasks in healthcare, with the features' relations derived from the SNOMED-CT
ontology. The proposed SeFNet framework and the accompanying DOSS measure
address the issue of limited contextual information in tabular datasets. By
incorporating domain knowledge and establishing semantic relations between
features, we enhance the potential for meta-learning and enable valuable
insights to be shared across different predictive tasks.
|
http://arxiv.org/abs/2306.11636v1
|
cs.LG
|
not_new_dataset
| 0.992129 |
2306.11636
|
On Evaluating Multilingual Compositional Generalization with Translated Datasets
|
Compositional generalization allows efficient learning and human-like
inductive biases. Since most research investigating compositional
generalization in NLP is done on English, important questions remain
underexplored. Do the necessary compositional generalization abilities differ
across languages? Can models compositionally generalize cross-lingually? As a
first step to answering these questions, recent work used neural machine
translation to translate datasets for evaluating compositional generalization
in semantic parsing. However, we show that this entails critical semantic
distortion. To address this limitation, we craft a faithful rule-based
translation of the MCWQ dataset from English to Chinese and Japanese. Even with
the resulting robust benchmark, which we call MCWQ-R, we show that the
distribution of compositions still suffers due to linguistic divergences, and
that multilingual models still struggle with cross-lingual compositional
generalization. Our dataset and methodology will be useful resources for the
study of cross-lingual compositional generalization in other tasks.
|
http://arxiv.org/abs/2306.11420v1
|
cs.CL
|
new_dataset
| 0.986937 |
2306.11420
|
DICES Dataset: Diversity in Conversational AI Evaluation for Safety
|
Machine learning approaches often require training and evaluation datasets
with a clear separation between positive and negative examples. This risks
simplifying and even obscuring the inherent subjectivity present in many tasks.
Preserving such variance in content and diversity in datasets is often
expensive and laborious. This is especially troubling when building safety
datasets for conversational AI systems, as safety is both socially and
culturally situated. To demonstrate this crucial aspect of conversational AI
safety, and to facilitate in-depth model performance analyses, we introduce the
DICES (Diversity In Conversational AI Evaluation for Safety) dataset that
contains fine-grained demographic information about raters, high replication of
ratings per item to ensure statistical power for analyses, and encodes rater
votes as distributions across different demographics to allow for in-depth
explorations of different aggregation strategies. In short, the DICES dataset
enables the observation and measurement of variance, ambiguity, and diversity
in the context of conversational AI safety. We also illustrate how the dataset
offers a basis for establishing metrics to show how raters' ratings can
intersects with demographic categories such as racial/ethnic groups, age
groups, and genders. The goal of DICES is to be used as a shared resource and
benchmark that respects diverse perspectives during safety evaluation of
conversational AI systems.
|
http://arxiv.org/abs/2306.11247v1
|
cs.HC
|
new_dataset
| 0.994477 |
2306.11247
|
AVOIDDS: Aircraft Vision-based Intruder Detection Dataset and Simulator
|
Designing robust machine learning systems remains an open problem, and there
is a need for benchmark problems that cover both environmental changes and
evaluation on a downstream task. In this work, we introduce AVOIDDS, a
realistic object detection benchmark for the vision-based aircraft
detect-and-avoid problem. We provide a labeled dataset consisting of 72,000
photorealistic images of intruder aircraft with various lighting conditions,
weather conditions, relative geometries, and geographic locations. We also
provide an interface that evaluates trained models on slices of this dataset to
identify changes in performance with respect to changing environmental
conditions. Finally, we implement a fully-integrated, closed-loop simulator of
the vision-based detect-and-avoid problem to evaluate trained models with
respect to the downstream collision avoidance task. This benchmark will enable
further research in the design of robust machine learning systems for use in
safety-critical applications. The AVOIDDS dataset and code are publicly
available at
$\href{https://purl.stanford.edu/hj293cv5980}{purl.stanford.edu/hj293cv5980}$
and
$\href{https://github.com/sisl/VisionBasedAircraftDAA}{github.com/sisl/VisionBasedAircraftDAA}$,
respectively.
|
http://arxiv.org/abs/2306.11203v1
|
cs.CV
|
new_dataset
| 0.994541 |
2306.11203
|
BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets
|
Biomedical relation extraction (RE) is the task of automatically identifying
and characterizing relations between biomedical concepts from free text. RE is
a central task in biomedical natural language processing (NLP) research and
plays a critical role in many downstream applications, such as literature-based
discovery and knowledge graph construction. State-of-the-art methods were used
primarily to train machine learning models on individual RE datasets, such as
protein-protein interaction and chemical-induced disease relation. Manual
dataset annotation, however, is highly expensive and time-consuming, as it
requires domain knowledge. Existing RE datasets are usually domain-specific or
small, which limits the development of generalized and high-performing RE
models. In this work, we present a novel framework for systematically
addressing the data heterogeneity of individual datasets and combining them
into a large dataset. Based on the framework and dataset, we report on BioREx,
a data-centric approach for extracting relations. Our evaluation shows that
BioREx achieves significantly higher performance than the benchmark system
trained on the individual dataset, setting a new SOTA from 74.4% to 79.6% in
F-1 measure on the recently released BioRED corpus. We further demonstrate that
the combined dataset can improve performance for five different RE tasks. In
addition, we show that on average BioREx compares favorably to current
best-performing methods such as transfer learning and multi-task learning.
Finally, we demonstrate BioREx's robustness and generalizability in two
independent RE tasks not previously seen in training data: drug-drug N-ary
combination and document-level gene-disease RE. The integrated dataset and
optimized method have been packaged as a stand-alone tool available at
https://github.com/ncbi/BioREx.
|
http://arxiv.org/abs/2306.11189v1
|
cs.CL
|
new_dataset
| 0.991113 |
2306.11189
|
GlyphNet: Homoglyph domains dataset and detection using attention-based Convolutional Neural Networks
|
Cyber attacks deceive machines into believing something that does not exist
in the first place. However, there are some to which even humans fall prey. One
such famous attack that attackers have used over the years to exploit the
vulnerability of vision is known to be a Homoglyph attack. It employs a primary
yet effective mechanism to create illegitimate domains that are hard to
differentiate from legit ones. Moreover, as the difference is pretty
indistinguishable for a user to notice, they cannot stop themselves from
clicking on these homoglyph domain names. In many cases, that results in either
information theft or malware attack on their systems. Existing approaches use
simple, string-based comparison techniques applied in primary language-based
tasks. Although they are impactful to some extent, they usually fail because
they are not robust to different types of homoglyphs and are computationally
not feasible because of their time requirement proportional to the string
length. Similarly, neural network-based approaches are employed to determine
real domain strings from fake ones. Nevertheless, the problem with both methods
is that they require paired sequences of real and fake domain strings to work
with, which is often not the case in the real world, as the attacker only sends
the illegitimate or homoglyph domain to the vulnerable user. Therefore,
existing approaches are not suitable for practical scenarios in the real world.
In our work, we created GlyphNet, an image dataset that contains 4M domains,
both real and homoglyphs. Additionally, we introduce a baseline method for a
homoglyph attack detection system using an attention-based convolutional Neural
Network. We show that our model can reach state-of-the-art accuracy in
detecting homoglyph attacks with a 0.93 AUC on our dataset.
|
http://arxiv.org/abs/2306.10392v1
|
cs.CR
|
new_dataset
| 0.994462 |
2306.10392
|
Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses
|
The ongoing deployment of the fifth generation (5G) wireless networks
constantly reveals limitations concerning its original concept as a key driver
of Internet of Everything (IoE) applications. These 5G challenges are behind
worldwide efforts to enable future networks, such as sixth generation (6G)
networks, to efficiently support sophisticated applications ranging from
autonomous driving capabilities to the Metaverse. Edge learning is a new and
powerful approach to training models across distributed clients while
protecting the privacy of their data. This approach is expected to be embedded
within future network infrastructures, including 6G, to solve challenging
problems such as resource management and behavior prediction. This survey
article provides a holistic review of the most recent research focused on edge
learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the
existing surveys on machine learning for 6G IoT security and machine
learning-associated threats in three different learning modes: centralized,
federated, and distributed. Then, we provide an overview of enabling emerging
technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of
existing research on attacks against machine learning and classify threat
models into eight categories, including backdoor attacks, adversarial examples,
combined attacks, poisoning attacks, Sybil attacks, byzantine attacks,
inference attacks, and dropping attacks. In addition, we provide a
comprehensive and detailed taxonomy and a side-by-side comparison of the
state-of-the-art defense methods against edge learning vulnerabilities.
Finally, as new attacks and defense technologies are realized, new research and
future overall prospects for 6G-enabled IoT are discussed.
|
http://arxiv.org/abs/2306.10309v1
|
cs.CR
|
not_new_dataset
| 0.992275 |
2306.10309
|
Flow-Bench: A Dataset for Computational Workflow Anomaly Detection
|
A computational workflow, also known as workflow, consists of tasks that must
be executed in a specific order to attain a specific goal. Often, in fields
such as biology, chemistry, physics, and data science, among others, these
workflows are complex and are executed in large-scale, distributed, and
heterogeneous computing environments that are prone to failures and performance
degradations. Therefore, anomaly detection for workflows is an important
paradigm that aims to identify unexpected behavior or errors in workflow
execution. This crucial task to improve the reliability of workflow executions
must be assisted by machine learning-based techniques. However, such
application is limited, in large part, due to the lack of open datasets and
benchmarking. To address this gap, we make the following contributions in this
paper: (1) we systematically inject anomalies and collect raw execution logs
from workflows executing on distributed infrastructures; (2) we summarize the
statistics of new datasets, as well as a set of open datasets, and provide
insightful analyses; (3) we benchmark unsupervised anomaly detection techniques
by converting workflows into both tabular and graph-structured data. Our
findings allow us to examine the effectiveness and efficiencies of the
benchmark methods and identify potential research opportunities for improvement
and generalization. The dataset and benchmark code are available online with
MIT License for public usage.
|
http://arxiv.org/abs/2306.09930v1
|
cs.DC
|
new_dataset
| 0.994468 |
2306.09930
|
DISC: a Dataset for Integrated Sensing and Communication in mmWave Systems
|
In this paper we present DISC, a dataset of millimeter-wave channel impulse
response measurements for integrated human activity sensing and communication.
This is the first dataset collected with a software-defined radio testbed that
transmits 60 GHz IEEE 802-11ay-compliant packets and estimates the channel
response including reflections of the signal on the moving body parts of
subjects moving in an indoor environment. The provided data contain the
contribution of 7 subjects performing 4 different activities. Differently from
available radar-based millimeter-wave sensing datasets, our measurements are
collected using both uniform packet transmission times and sparse traffic
patterns from real Wi-Fi deployments. Thanks to these unique characteristics,
DISC serves as a multi-purpose benchmarking tool for machine learning-based
human activity recognition, radio frequency gait analysis, and sparse sensing
algorithms for next-generation integrated sensing and communication.
|
http://arxiv.org/abs/2306.09469v1
|
eess.SP
|
new_dataset
| 0.994489 |
2306.09469
|
Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization
|
Fairness (also known as equity interchangeably) in machine learning is
important for societal well-being, but limited public datasets hinder its
progress. Currently, no dedicated public medical datasets with imaging data for
fairness learning are available, though minority groups suffer from more health
issues. To address this gap, we introduce Harvard Glaucoma Fairness
(Harvard-GF), a retinal nerve disease dataset with both 2D and 3D imaging data
and balanced racial groups for glaucoma detection. Glaucoma is the leading
cause of irreversible blindness globally with Blacks having doubled glaucoma
prevalence than other races. We also propose a fair identity normalization
(FIN) approach to equalize the feature importance between different identity
groups. Our FIN approach is compared with various the-state-of-the-art fairness
learning methods with superior performance in the racial, gender, and ethnicity
fairness tasks with 2D and 3D imaging data, which demonstrate the utilities of
our dataset Harvard-GF for fairness learning. To facilitate fairness
comparisons between different models, we propose an equity-scaled performance
measure, which can be flexibly used to compare all kinds of performance metrics
in the context of fairness. The dataset and code are publicly accessible via
\url{https://ophai.hms.harvard.edu/datasets/harvard-glaucoma-fairness-3300-samples/}.
|
http://arxiv.org/abs/2306.09264v2
|
cs.CV
|
new_dataset
| 0.994415 |
2306.09264
|
DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning
|
Pragmatic reasoning plays a pivotal role in deciphering implicit meanings
that frequently arise in real-life conversations and is essential for the
development of communicative social agents. In this paper, we introduce a novel
challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic
reasoning and situated conversational understanding. Compared with previous
works that treat different figurative expressions (e.g. metaphor, sarcasm) as
individual tasks, DiPlomat provides a cohesive framework towards general
pragmatic understanding. Our dataset is created through the utilization of
Amazon Mechanical Turk ( AMT ), resulting in a total of 4, 177 multi-turn
dialogues. In conjunction with the dataset, we propose two tasks, Pragmatic
Identification and Reasoning (PIR) and Conversational Question Answering (CQA).
Experimental results with state-of-the-art (SOTA) neural architectures reveal
several significant findings: 1) large language models ( LLMs) exhibit poor
performance in tackling this subjective domain; 2) comprehensive comprehension
of context emerges as a critical factor for establishing benign human-machine
interactions; 3) current models defect in the application of pragmatic
reasoning. As a result, we call on more attention to improve the ability of
context understanding, reasoning, and implied meaning modeling.
|
http://arxiv.org/abs/2306.09030v2
|
cs.CL
|
new_dataset
| 0.994505 |
2306.09030
|
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators
|
Modern climate projections lack adequate spatial and temporal resolution due
to computational constraints. A consequence is inaccurate and imprecise
predictions of critical processes such as storms. Hybrid methods that combine
physics with machine learning (ML) have introduced a new generation of higher
fidelity climate simulators that can sidestep Moore's Law by outsourcing
compute-hungry, short, high-resolution simulations to ML emulators. However,
this hybrid ML-physics simulation approach requires domain-specific treatment
and has been inaccessible to ML experts because of lack of training data and
relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset
designed for hybrid ML-physics research. It comprises multi-scale climate
simulations, developed by a consortium of climate scientists and ML
researchers. It consists of 5.7 billion pairs of multivariate input and output
vectors that isolate the influence of locally-nested, high-resolution,
high-fidelity physics on a host climate simulator's macro-scale physical state.
The dataset is global in coverage, spans multiple years at high sampling
frequency, and is designed such that resulting emulators are compatible with
downstream coupling into operational climate simulators. We implement a range
of deterministic and stochastic regression baselines to highlight the ML
challenges and their scoring. The data
(https://huggingface.co/datasets/LEAP/ClimSim_high-res,
https://huggingface.co/datasets/LEAP/ClimSim_low-res, and
https://huggingface.co/datasets/LEAP/ClimSim_low-res_aqua-planet) and code
(https://leap-stc.github.io/ClimSim) are released openly to support the
development of hybrid ML-physics and high-fidelity climate simulations for the
benefit of science and society.
|
http://arxiv.org/abs/2306.08754v3
|
cs.LG
|
new_dataset
| 0.994532 |
2306.08754
|
MMASD: A Multimodal Dataset for Autism Intervention Analysis
|
Autism spectrum disorder (ASD) is a developmental disorder characterized by
significant social communication impairments and difficulties perceiving and
presenting communication cues. Machine learning techniques have been broadly
adopted to facilitate autism studies and assessments. However, computational
models are primarily concentrated on specific analysis and validated on private
datasets in the autism community, which limits comparisons across models due to
privacy-preserving data sharing complications. This work presents a novel
privacy-preserving open-source dataset, MMASD as a MultiModal ASD benchmark
dataset, collected from play therapy interventions of children with Autism.
MMASD includes data from 32 children with ASD, and 1,315 data samples segmented
from over 100 hours of intervention recordings. To promote public access, each
data sample consists of four privacy-preserving modalities of data; some of
which are derived from original videos: (1) optical flow, (2) 2D skeleton, (3)
3D skeleton, and (4) clinician ASD evaluation scores of children, e.g., ADOS
scores. MMASD aims to assist researchers and therapists in understanding
children's cognitive status, monitoring their progress during therapy, and
customizing the treatment plan accordingly. It also has inspiration for
downstream tasks such as action quality assessment and interpersonal synchrony
estimation. MMASD dataset can be easily accessed at
https://github.com/Li-Jicheng/MMASD-A-Multimodal-Dataset-for-Autism-Intervention-Analysis.
|
http://arxiv.org/abs/2306.08243v3
|
cs.CV
|
new_dataset
| 0.994475 |
2306.08243
|
Assessing the Effectiveness of GPT-3 in Detecting False Political Statements: A Case Study on the LIAR Dataset
|
The detection of political fake statements is crucial for maintaining
information integrity and preventing the spread of misinformation in society.
Historically, state-of-the-art machine learning models employed various methods
for detecting deceptive statements. These methods include the use of metadata
(W. Wang et al., 2018), n-grams analysis (Singh et al., 2021), and linguistic
(Wu et al., 2022) and stylometric (Islam et al., 2020) features. Recent
advancements in large language models, such as GPT-3 (Brown et al., 2020) have
achieved state-of-the-art performance on a wide range of tasks. In this study,
we conducted experiments with GPT-3 on the LIAR dataset (W. Wang et al., 2018)
and achieved higher accuracy than state-of-the-art models without using any
additional meta or linguistic features. Additionally, we experimented with
zero-shot learning using a carefully designed prompt and achieved near
state-of-the-art performance. An advantage of this approach is that the model
provided evidence for its decision, which adds transparency to the model's
decision-making and offers a chance for users to verify the validity of the
evidence provided.
|
http://arxiv.org/abs/2306.08190v1
|
cs.CL
|
not_new_dataset
| 0.992134 |
2306.08190
|
Getting the Most from Eye-Tracking: User-Interaction Based Reading Region Estimation Dataset and Models
|
A single digital newsletter usually contains many messages (regions). Users'
reading time spent on, and read level (skip/skim/read-in-detail) of each
message is important for platforms to understand their users' interests,
personalize their contents, and make recommendations. Based on accurate but
expensive-to-collect eyetracker-recorded data, we built models that predict
per-region reading time based on easy-to-collect Javascript browser tracking
data.
With eye-tracking, we collected 200k ground-truth datapoints on participants
reading news on browsers. Then we trained machine learning and deep learning
models to predict message-level reading time based on user interactions like
mouse position, scrolling, and clicking. We reached 27\% percentage error in
reading time estimation with a two-tower neural network based on user
interactions only, against the eye-tracking ground truth data, while the
heuristic baselines have around 46\% percentage error. We also discovered the
benefits of replacing per-session models with per-timestamp models, and adding
user pattern features. We concluded with suggestions on developing
message-level reading estimation techniques based on available data.
|
http://arxiv.org/abs/2306.07455v1
|
cs.HC
|
new_dataset
| 0.993873 |
2306.07455
|
RB-Dust -- A Reference-based Dataset for Vision-based Dust Removal
|
Dust in the agricultural landscape is a significant challenge and influences,
for example, the environmental perception of autonomous agricultural machines.
Image enhancement algorithms can be used to reduce dust. However, these require
dusty and dust-free images of the same environment for validation. In fact, to
date, there is no dataset that we are aware of that addresses this issue.
Therefore, we present the agriscapes RB-Dust dataset, which is named after its
purpose of reference-based dust removal. It is not possible to take pictures
from the cabin during tillage, as this would cause shifts in the images.
Because of this, we built a setup from which it is possible to take images from
a stationary position close to the passing tractor. The test setup was based on
a half-sided gate through which the tractor could drive. The field tests were
carried out on a farm in Bavaria, Germany, during tillage. During the field
tests, other parameters such as soil moisture and wind speed were controlled,
as these significantly affect dust development. We validated our dataset with
contrast enhancement and image dehazing algorithms and analyzed the
generalizability from recordings from the moving tractor. Finally, we
demonstrate the application of dust removal based on a high-level vision task,
such as person classification. Our empirical study confirms the validity of
RB-Dust for vision-based dust removal in agriculture.
|
http://arxiv.org/abs/2306.07244v1
|
cs.CV
|
new_dataset
| 0.994516 |
2306.07244
|
Generating Synthetic Datasets by Interpolating along Generalized Geodesics
|
Data for pretraining machine learning models often consists of collections of
heterogeneous datasets. Although training on their union is reasonable in
agnostic settings, it might be suboptimal when the target domain -- where the
model will ultimately be used -- is known in advance. In that case, one would
ideally pretrain only on the dataset(s) most similar to the target one. Instead
of limiting this choice to those datasets already present in the pretraining
collection, here we explore extending this search to all datasets that can be
synthesized as `combinations' of them. We define such combinations as
multi-dataset interpolations, formalized through the notion of generalized
geodesics from optimal transport (OT) theory. We compute these geodesics using
a recent notion of distance between labeled datasets, and derive alternative
interpolation schemes based on it: using either barycentric projections or
optimal transport maps, the latter computed using recent neural OT methods.
These methods are scalable, efficient, and -- notably -- can be used to
interpolate even between datasets with distinct and unrelated label sets.
Through various experiments in transfer learning in computer vision, we
demonstrate this is a promising new approach for targeted on-demand dataset
synthesis.
|
http://arxiv.org/abs/2306.06866v1
|
cs.LG
|
not_new_dataset
| 0.992183 |
2306.06866
|
Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Dataset Labeling
|
Study Objective: Machine learning models have advanced medical image
processing and can yield faster, more accurate diagnoses. Despite a wealth of
available medical imaging data, high-quality labeled data for model training is
lacking. We investigated whether a gamified crowdsourcing platform enhanced
with inbuilt quality control metrics can produce lung ultrasound clip labels
comparable to those from clinical experts.
Methods: 2,384 lung ultrasound clips were retrospectively collected from 203
patients. Six lung ultrasound experts classified 393 of these clips as having
no B-lines, one or more discrete B-lines, or confluent B-lines to create two
sets of reference standard labels (195 training set clips and 198 test set
clips). Sets were respectively used to A) train users on a gamified
crowdsourcing platform, and B) compare concordance of the resulting crowd
labels to the concordance of individual experts to reference standards.
Results: 99,238 crowdsourced opinions on 2,384 lung ultrasound clips were
collected from 426 unique users over 8 days. On the 198 test set clips, mean
labeling concordance of individual experts relative to the reference standard
was 85.0% +/- 2.0 (SEM), compared to 87.9% crowdsourced label concordance
(p=0.15). When individual experts' opinions were compared to reference standard
labels created by majority vote excluding their own opinion, crowd concordance
was higher than the mean concordance of individual experts to reference
standards (87.4% vs. 80.8% +/- 1.6; p<0.001).
Conclusion: Crowdsourced labels for B-line classification via a gamified
approach achieved expert-level quality. Scalable, high-quality labeling
approaches may facilitate training dataset creation for machine learning model
development.
|
http://arxiv.org/abs/2306.06773v1
|
cs.CY
|
not_new_dataset
| 0.991899 |
2306.06773
|
Neural Architecture Design and Robustness: A Dataset
|
Deep learning models have proven to be successful in a wide range of machine
learning tasks. Yet, they are often highly sensitive to perturbations on the
input data which can lead to incorrect decisions with high confidence,
hampering their deployment for practical use-cases. Thus, finding architectures
that are (more) robust against perturbations has received much attention in
recent years. Just like the search for well-performing architectures in terms
of clean accuracy, this usually involves a tedious trial-and-error process with
one additional challenge: the evaluation of a network's robustness is
significantly more expensive than its evaluation for clean accuracy. Thus, the
aim of this paper is to facilitate better streamlined research on architectural
design choices with respect to their impact on robustness as well as, for
example, the evaluation of surrogate measures for robustness. We therefore
borrow one of the most commonly considered search spaces for neural
architecture search for image classification, NAS-Bench-201, which contains a
manageable size of 6466 non-isomorphic network designs. We evaluate all these
networks on a range of common adversarial attacks and corruption types and
introduce a database on neural architecture design and robustness evaluations.
We further present three exemplary use cases of this dataset, in which we (i)
benchmark robustness measurements based on Jacobian and Hessian matrices for
their robustness predictability, (ii) perform neural architecture search on
robust accuracies, and (iii) provide an initial analysis of how architectural
design choices affect robustness. We find that carefully crafting the topology
of a network can have substantial impact on its robustness, where networks with
the same parameter count range in mean adversarial robust accuracy from
20%-41%. Code and data is available at http://robustness.vision/.
|
http://arxiv.org/abs/2306.06712v1
|
cs.LG
|
new_dataset
| 0.994279 |
2306.06712
|
Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine Perception
|
We introduce the Aria Digital Twin (ADT) - an egocentric dataset captured
using Aria glasses with extensive object, environment, and human level ground
truth. This ADT release contains 200 sequences of real-world activities
conducted by Aria wearers in two real indoor scenes with 398 object instances
(324 stationary and 74 dynamic). Each sequence consists of: a) raw data of two
monochrome camera streams, one RGB camera stream, two IMU streams; b) complete
sensor calibration; c) ground truth data including continuous
6-degree-of-freedom (6DoF) poses of the Aria devices, object 6DoF poses, 3D eye
gaze vectors, 3D human poses, 2D image segmentations, image depth maps; and d)
photo-realistic synthetic renderings. To the best of our knowledge, there is no
existing egocentric dataset with a level of accuracy, photo-realism and
comprehensiveness comparable to ADT. By contributing ADT to the research
community, our mission is to set a new standard for evaluation in the
egocentric machine perception domain, which includes very challenging research
problems such as 3D object detection and tracking, scene reconstruction and
understanding, sim-to-real learning, human pose prediction - while also
inspiring new machine perception tasks for augmented reality (AR) applications.
To kick start exploration of the ADT research use cases, we evaluated several
existing state-of-the-art methods for object detection, segmentation and image
translation tasks that demonstrate the usefulness of ADT as a benchmarking
dataset.
|
http://arxiv.org/abs/2306.06362v2
|
cs.CV
|
new_dataset
| 0.994546 |
2306.06362
|
Machine Learning Based Missing Values Imputation in Categorical Datasets
|
This study explored the use of machine learning algorithms for predicting and
imputing missing values in categorical datasets. We focused on ensemble models
that use the error correction output codes (ECOC) framework, including
SVM-based and KNN-based ensemble models, as well as an ensemble classifier that
combines SVM, KNN, and MLP models. We applied these algorithms to three
datasets: the CPU dataset, the hypothyroid dataset, and the Breast Cancer
dataset. Our experiments showed that the machine learning algorithms were able
to achieve good performance in predicting and imputing the missing values, with
some variations depending on the specific dataset and missing value pattern.
The ensemble models using the error correction output codes (ECOC) framework
were particularly effective in improving the accuracy and robustness of the
predictions, compared to individual models. However, there are also challenges
and limitations to using deep learning for missing value imputation, including
the need for large amounts of labeled data and the potential for overfitting.
Further research is needed to evaluate the effectiveness and efficiency of deep
learning algorithms for missing value imputation and to develop strategies for
addressing the challenges and limitations that may arise.
|
http://arxiv.org/abs/2306.06338v1
|
cs.LG
|
not_new_dataset
| 0.992207 |
2306.06338
|
2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning
|
Recent research in computational imaging largely focuses on developing
machine learning (ML) techniques for image reconstruction, which requires
large-scale training datasets consisting of measurement data and ground-truth
images. However, suitable experimental datasets for X-ray Computed Tomography
(CT) are scarce, and methods are often developed and evaluated only on
simulated data. We fill this gap by providing the community with a versatile,
open 2D fan-beam CT dataset suitable for developing ML techniques for a range
of image reconstruction tasks. To acquire it, we designed a sophisticated,
semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray
CT setup. A diverse mix of samples with high natural variability in shape and
density was scanned slice-by-slice (5000 slices in total) with high angular and
spatial resolution and three different beam characteristics: A high-fidelity, a
low-dose and a beam-hardening-inflicted mode. In addition, 750
out-of-distribution slices were scanned with sample and beam variations to
accommodate robustness and segmentation tasks. We provide raw projection data,
reference reconstructions and segmentations based on an open-source data
processing pipeline.
|
http://arxiv.org/abs/2306.05907v1
|
eess.IV
|
new_dataset
| 0.994512 |
2306.05907
|
DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data
|
Previous work on learning physical systems from data has focused on
high-resolution grid-structured measurements. However, real-world knowledge of
such systems (e.g. weather data) relies on sparsely scattered measuring
stations. In this paper, we introduce a novel simulated benchmark dataset,
DynaBench, for learning dynamical systems directly from sparsely scattered data
without prior knowledge of the equations. The dataset focuses on predicting the
evolution of a dynamical system from low-resolution, unstructured measurements.
We simulate six different partial differential equations covering a variety of
physical systems commonly used in the literature and evaluate several machine
learning models, including traditional graph neural networks and point cloud
processing models, with the task of predicting the evolution of the system. The
proposed benchmark dataset is expected to advance the state of art as an
out-of-the-box easy-to-use tool for evaluating models in a setting where only
unstructured low-resolution observations are available. The benchmark is
available at https://anonymous.4open.science/r/code-2022-dynabench/.
|
http://arxiv.org/abs/2306.05805v2
|
cs.LG
|
new_dataset
| 0.994491 |
2306.05805
|
JABBERWOCK: A Tool for WebAssembly Dataset Generation and Its Application to Malicious Website Detection
|
Machine learning is often used for malicious website detection, but an
approach incorporating WebAssembly as a feature has not been explored due to a
limited number of samples, to the best of our knowledge. In this paper, we
propose JABBERWOCK (JAvascript-Based Binary EncodeR by WebAssembly Optimization
paCKer), a tool to generate WebAssembly datasets in a pseudo fashion via
JavaScript. Loosely speaking, JABBERWOCK automatically gathers JavaScript code
in the real world, convert them into WebAssembly, and then outputs vectors of
the WebAssembly as samples for malicious website detection. We also conduct
experimental evaluations of JABBERWOCK in terms of the processing time for
dataset generation, comparison of the generated samples with actual WebAssembly
samples gathered from the Internet, and an application for malicious website
detection. Regarding the processing time, we show that JABBERWOCK can construct
a dataset in 4.5 seconds per sample for any number of samples. Next, comparing
10,000 samples output by JABBERWOCK with 168 gathered WebAssembly samples, we
believe that the generated samples by JABBERWOCK are similar to those in the
real world. We then show that JABBERWOCK can provide malicious website
detection with 99\% F1-score because JABBERWOCK makes a gap between benign and
malicious samples as the reason for the above high score. We also confirm that
JABBERWOCK can be combined with an existing malicious website detection tool to
improve F1-scores. JABBERWOCK is publicly available via GitHub
(https://github.com/c-chocolate/Jabberwock).
|
http://arxiv.org/abs/2306.05698v1
|
cs.CR
|
not_new_dataset
| 0.992107 |
2306.05698
|
Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean
|
We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire
modeling in the Mediterranean. Mesogeos integrates variables representing
wildfire drivers (meteorology, vegetation, human activity) and historical
records of wildfire ignitions and burned areas for 17 years (2006-2022). It is
designed as a cloud-friendly spatio-temporal dataset, namely a datacube,
harmonizing all variables in a grid of 1km x 1km x 1-day resolution. The
datacube structure offers opportunities to assess machine learning (ML) usage
in various wildfire modeling tasks. We extract two ML-ready datasets that
establish distinct tracks to demonstrate this potential: (1) short-term
wildfire danger forecasting and (2) final burned area estimation given the
point of ignition. We define appropriate metrics and baselines to evaluate the
performance of models in each track. By publishing the datacube, along with the
code to create the ML datasets and models, we encourage the community to foster
the implementation of additional tracks for mitigating the increasing threat of
wildfires in the Mediterranean.
|
http://arxiv.org/abs/2306.05144v1
|
cs.CV
|
new_dataset
| 0.994537 |
2306.05144
|
SANGEET: A XML based Open Dataset for Research in Hindustani Sangeet
|
It is very important to access a rich music dataset that is useful in a wide
variety of applications. Currently, available datasets are mostly focused on
storing vocal or instrumental recording data and ignoring the requirement of
its visual representation and retrieval. This paper attempts to build an
XML-based public dataset, called SANGEET, that stores comprehensive information
of Hindustani Sangeet (North Indian Classical Music) compositions written by
famous musicologist Pt. Vishnu Narayan Bhatkhande. SANGEET preserves all the
required information of any given composition including metadata, structural,
notational, rhythmic, and melodic information in a standardized way for easy
and efficient storage and extraction of musical information. The dataset is
intended to provide the ground truth information for music information research
tasks, thereby supporting several data-driven analysis from a machine learning
perspective. We present the usefulness of the dataset by demonstrating its
application on music information retrieval using XQuery, visualization through
Omenad rendering system. Finally, we propose approaches to transform the
dataset for performing statistical and machine learning tasks for a better
understanding of Hindustani Sangeet. The dataset can be found at
https://github.com/cmisra/Sangeet.
|
http://arxiv.org/abs/2306.04148v1
|
cs.SD
|
new_dataset
| 0.994489 |
2306.04148
|
VR.net: A Real-world Dataset for Virtual Reality Motion Sickness Research
|
Researchers have used machine learning approaches to identify motion sickness
in VR experience. These approaches demand an accurately-labeled, real-world,
and diverse dataset for high accuracy and generalizability. As a starting point
to address this need, we introduce `VR.net', a dataset offering approximately
12-hour gameplay videos from ten real-world games in 10 diverse genres. For
each video frame, a rich set of motion sickness-related labels, such as
camera/object movement, depth field, and motion flow, are accurately assigned.
Building such a dataset is challenging since manual labeling would require an
infeasible amount of time. Instead, we utilize a tool to automatically and
precisely extract ground truth data from 3D engines' rendering pipelines
without accessing VR games' source code. We illustrate the utility of VR.net
through several applications, such as risk factor detection and sickness level
prediction. We continuously expand VR.net and envision its next version
offering 10X more data than the current form. We believe that the scale,
accuracy, and diversity of VR.net can offer unparalleled opportunities for VR
motion sickness research and beyond.
|
http://arxiv.org/abs/2306.03381v1
|
cs.AI
|
new_dataset
| 0.994542 |
2306.03381
|
AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions
|
Antibodies have become an important class of therapeutic agents to treat
human diseases. To accelerate therapeutic antibody discovery, computational
methods, especially machine learning, have attracted considerable interest for
predicting specific interactions between antibody candidates and target
antigens such as viruses and bacteria. However, the publicly available datasets
in existing works have notable limitations, such as small sizes and the lack of
non-binding samples and exact amino acid sequences. To overcome these
limitations, we have developed AVIDa-hIL6, a large-scale dataset for predicting
antigen-antibody interactions in the variable domain of heavy chain of heavy
chain antibodies (VHHs), produced from an alpaca immunized with the human
interleukin-6 (IL-6) protein, as antigens. By leveraging the simple structure
of VHHs, which facilitates identification of full-length amino acid sequences
by DNA sequencing technology, AVIDa-hIL6 contains 573,891 antigen-VHH pairs
with amino acid sequences. All the antigen-VHH pairs have reliable labels for
binding or non-binding, as generated by a novel labeling method. Furthermore,
via introduction of artificial mutations, AVIDa-hIL6 contains 30 different
mutants in addition to wild-type IL-6 protein. This characteristic provides
opportunities to develop machine learning models for predicting changes in
antibody binding by antigen mutations. We report experimental benchmark results
on AVIDa-hIL6 by using neural network-based baseline models. The results
indicate that the existing models have potential, but further research is
needed to generalize them to predict effective antibodies against unknown
mutants. The dataset is available at https://avida-hil6.cognanous.com.
|
http://arxiv.org/abs/2306.03329v1
|
cs.LG
|
new_dataset
| 0.994501 |
2306.03329
|
Towards Coding Social Science Datasets with Language Models
|
Researchers often rely on humans to code (label, annotate, etc.) large sets
of texts. This kind of human coding forms an important part of social science
research, yet the coding process is both resource intensive and highly variable
from application to application. In some cases, efforts to automate this
process have achieved human-level accuracies, but to achieve this, these
attempts frequently rely on thousands of hand-labeled training examples, which
makes them inapplicable to small-scale research studies and costly for large
ones. Recent advances in a specific kind of artificial intelligence tool -
language models (LMs) - provide a solution to this problem. Work in computer
science makes it clear that LMs are able to classify text, without the cost (in
financial terms and human effort) of alternative methods. To demonstrate the
possibilities of LMs in this area of political science, we use GPT-3, one of
the most advanced LMs, as a synthetic coder and compare it to human coders. We
find that GPT-3 can match the performance of typical human coders and offers
benefits over other machine learning methods of coding text. We find this
across a variety of domains using very different coding procedures. This
provides exciting evidence that language models can serve as a critical advance
in the coding of open-ended texts in a variety of applications.
|
http://arxiv.org/abs/2306.02177v1
|
cs.AI
|
not_new_dataset
| 0.992015 |
2306.02177
|
DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation
|
Many machine learning problems require performing dataset valuation, i.e. to
quantify the incremental gain, to some relevant pre-defined utility, of
aggregating an individual dataset to others. As seminal examples, dataset
valuation has been leveraged in collaborative and federated learning to create
incentives for data sharing across several data owners. The Shapley value has
recently been proposed as a principled tool to achieve this goal due to formal
axiomatic justification. Since its computation often requires exponential time,
standard approximation strategies based on Monte Carlo integration have been
considered. Such generic approximation methods, however, remain expensive in
some cases. In this paper, we exploit the knowledge about the structure of the
dataset valuation problem to devise more efficient Shapley value estimators. We
propose a novel approximation of the Shapley value, referred to as discrete
uniform Shapley (DU-Shapley) which is expressed as an expectation under a
discrete uniform distribution with support of reasonable size. We justify the
relevancy of the proposed framework via asymptotic and non-asymptotic
theoretical guarantees and show that DU-Shapley tends towards the Shapley value
when the number of data owners is large. The benefits of the proposed framework
are finally illustrated on several dataset valuation benchmarks. DU-Shapley
outperforms other Shapley value approximations, even when the number of data
owners is small.
|
http://arxiv.org/abs/2306.02071v1
|
cs.AI
|
not_new_dataset
| 0.992206 |
2306.02071
|
DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection
|
The tremendous recent advances in generative artificial intelligence
techniques have led to significant successes and promise in a wide range of
different applications ranging from conversational agents and textual content
generation to voice and visual synthesis. Amid the rise in generative AI and
its increasing widespread adoption, there has been significant growing concern
over the use of generative AI for malicious purposes. In the realm of visual
content synthesis using generative AI, key areas of significant concern has
been image forgery (e.g., generation of images containing or derived from
copyright content), and data poisoning (i.e., generation of adversarially
contaminated images). Motivated to address these key concerns to encourage
responsible generative AI, we introduce the DeepfakeArt Challenge, a
large-scale challenge benchmark dataset designed specifically to aid in the
building of machine learning algorithms for generative AI art forgery and data
poisoning detection. Comprising of over 32,000 records across a variety of
generative forgery and data poisoning techniques, each entry consists of a pair
of images that are either forgeries / adversarially contaminated or not. Each
of the generated images in the DeepfakeArt Challenge benchmark dataset has been
quality checked in a comprehensive manner. The DeepfakeArt Challenge is a core
part of GenAI4Good, a global open source initiative for accelerating machine
learning for promoting responsible creation and deployment of generative AI for
good.
|
http://arxiv.org/abs/2306.01272v2
|
cs.CV
|
new_dataset
| 0.994516 |
2306.01272
|
MindBigData 2023 MNIST-8B The 8 billion datapoints Multimodal Dataset of Brain Signals
|
MindBigData 2023 MNIST-8B is the largest, to date (June 1st 2023), brain
signals open dataset created for Machine Learning, based on EEG signals from a
single subject captured using a custom 128 channels device, replicating the
full 70,000 digits from Yaan LeCun et all MNIST dataset. The brain signals were
captured while the subject was watching the pixels of the original digits one
by one on a screen and listening at the same time to the spoken number 0 to 9
from the real label. The data, collection procedures, hardware and software
created are described in detail, background extra information and other related
datasets can be found at our previous paper MindBigData 2022: A Large Dataset
of Brain Signals.
|
http://arxiv.org/abs/2306.00455v1
|
cs.LG
|
new_dataset
| 0.994517 |
2306.00455
|
Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges
|
The deep learning, which is a dominating technique in artificial
intelligence, has completely changed the image understanding over the past
decade. As a consequence, the sea ice extraction (SIE) problem has reached a
new era. We present a comprehensive review of four important aspects of SIE,
including algorithms, datasets, applications, and the future trends. Our review
focuses on researches published from 2016 to the present, with a specific focus
on deep learning-based approaches in the last five years. We divided all
relegated algorithms into 3 categories, including classical image segmentation
approach, machine learning-based approach and deep learning-based methods. We
reviewed the accessible ice datasets including SAR-based datasets, the
optical-based datasets and others. The applications are presented in 4 aspects
including climate research, navigation, geographic information systems (GIS)
production and others. It also provides insightful observations and inspiring
future research directions.
|
http://arxiv.org/abs/2306.00303v1
|
cs.CV
|
not_new_dataset
| 0.992056 |
2306.00303
|
Exploring the Vulnerabilities of Machine Learning and Quantum Machine Learning to Adversarial Attacks using a Malware Dataset: A Comparative Analysis
|
The burgeoning fields of machine learning (ML) and quantum machine learning
(QML) have shown remarkable potential in tackling complex problems across
various domains. However, their susceptibility to adversarial attacks raises
concerns when deploying these systems in security sensitive applications. In
this study, we present a comparative analysis of the vulnerability of ML and
QML models, specifically conventional neural networks (NN) and quantum neural
networks (QNN), to adversarial attacks using a malware dataset. We utilize a
software supply chain attack dataset known as ClaMP and develop two distinct
models for QNN and NN, employing Pennylane for quantum implementations and
TensorFlow and Keras for traditional implementations. Our methodology involves
crafting adversarial samples by introducing random noise to a small portion of
the dataset and evaluating the impact on the models performance using accuracy,
precision, recall, and F1 score metrics. Based on our observations, both ML and
QML models exhibit vulnerability to adversarial attacks. While the QNNs
accuracy decreases more significantly compared to the NN after the attack, it
demonstrates better performance in terms of precision and recall, indicating
higher resilience in detecting true positives under adversarial conditions. We
also find that adversarial samples crafted for one model type can impair the
performance of the other, highlighting the need for robust defense mechanisms.
Our study serves as a foundation for future research focused on enhancing the
security and resilience of ML and QML models, particularly QNN, given its
recent advancements. A more extensive range of experiments will be conducted to
better understand the performance and robustness of both models in the face of
adversarial attacks.
|
http://arxiv.org/abs/2305.19593v1
|
cs.LG
|
not_new_dataset
| 0.991994 |
2305.19593
|
VSTAR: A Video-grounded Dialogue Dataset for Situated Semantic Understanding with Scene and Topic Transitions
|
Video-grounded dialogue understanding is a challenging problem that requires
machine to perceive, parse and reason over situated semantics extracted from
weakly aligned video and dialogues. Most existing benchmarks treat both
modalities the same as a frame-independent visual understanding task, while
neglecting the intrinsic attributes in multimodal dialogues, such as scene and
topic transitions. In this paper, we present Video-grounded Scene&Topic AwaRe
dialogue (VSTAR) dataset, a large scale video-grounded dialogue understanding
dataset based on 395 TV series. Based on VSTAR, we propose two benchmarks for
video-grounded dialogue understanding: scene segmentation and topic
segmentation, and one benchmark for video-grounded dialogue generation.
Comprehensive experiments are performed on these benchmarks to demonstrate the
importance of multimodal information and segments in video-grounded dialogue
understanding and generation.
|
http://arxiv.org/abs/2305.18756v1
|
cs.CV
|
new_dataset
| 0.994436 |
2305.18756
|
A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets
|
The development of large language models (LLMs) such as ChatGPT has brought a
lot of attention recently. However, their evaluation in the benchmark academic
datasets remains under-explored due to the difficulty of evaluating the
generative outputs produced by this model against the ground truth. In this
paper, we aim to present a thorough evaluation of ChatGPT's performance on
diverse academic datasets, covering tasks like question-answering, text
summarization, code generation, commonsense reasoning, mathematical
problem-solving, machine translation, bias detection, and ethical
considerations. Specifically, we evaluate ChatGPT across 140 tasks and analyze
255K responses it generates in these datasets. This makes our work the largest
evaluation of ChatGPT in NLP benchmarks. In short, our study aims to validate
the strengths and weaknesses of ChatGPT in various tasks and provide insights
for future research using LLMs. We also report a new emergent ability to follow
multi-query instructions that we mostly found in ChatGPT and other
instruction-tuned models. Our extensive evaluation shows that even though
ChatGPT is capable of performing a wide variety of tasks, and may obtain
impressive performance in several benchmark datasets, it is still far from
achieving the ability to reliably solve many challenging tasks. By providing a
thorough assessment of ChatGPT's performance across diverse NLP tasks, this
paper sets the stage for a targeted deployment of ChatGPT-like LLMs in
real-world applications.
|
http://arxiv.org/abs/2305.18486v4
|
cs.CL
|
not_new_dataset
| 0.992341 |
2305.18486
|
TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore
|
Total Defence is a defence policy combining and extending the concept of
military defence and civil defence. While several countries have adopted total
defence as their defence policy, very few studies have investigated its
effectiveness. With the rapid proliferation of social media and digitalisation,
many social studies have been focused on investigating policy effectiveness
through specially curated surveys and questionnaires either through digital
media or traditional forms. However, such references may not truly reflect the
underlying sentiments about the target policies or initiatives of interest.
People are more likely to express their sentiment using communication mediums
such as starting topic thread on forums or sharing memes on social media. Using
Singapore as a case reference, this study aims to address this research gap by
proposing TotalDefMeme, a large-scale multi-modal and multi-attribute meme
dataset that captures public sentiments toward Singapore's Total Defence
policy. Besides supporting social informatics and public policy analysis of the
Total Defence policy, TotalDefMeme can also support many downstream multi-modal
machine learning tasks, such as aspect-based stance classification and
multi-modal meme clustering. We perform baseline machine learning experiments
on TotalDefMeme and evaluate its technical validity, and present possible
future interdisciplinary research directions and application scenarios using
the dataset as a baseline.
|
http://arxiv.org/abs/2305.17911v1
|
cs.SI
|
new_dataset
| 0.994474 |
2305.17911
|
InDL: A New Dataset and Benchmark for In-Diagram Logic Interpretation based on Visual Illusion
|
This paper introduces a novel approach to evaluating deep learning models'
capacity for in-diagram logic interpretation. Leveraging the intriguing realm
of visual illusions, we establish a unique dataset, InDL, designed to
rigorously test and benchmark these models. Deep learning has witnessed
remarkable progress in domains such as computer vision and natural language
processing. However, models often stumble in tasks requiring logical reasoning
due to their inherent 'black box' characteristics, which obscure the
decision-making process. Our work presents a new lens to understand these
models better by focusing on their handling of visual illusions -- a complex
interplay of perception and logic. We utilize six classic geometric optical
illusions to create a comparative framework between human and machine visual
perception. This methodology offers a quantifiable measure to rank models,
elucidating potential weaknesses and providing actionable insights for model
improvements. Our experimental results affirm the efficacy of our benchmarking
strategy, demonstrating its ability to effectively rank models based on their
logic interpretation ability. As part of our commitment to reproducible
research, the source code and datasets will be made publicly available at
https://github.com/rabbit-magic-wh/InDL
|
http://arxiv.org/abs/2305.17716v4
|
cs.CV
|
new_dataset
| 0.994493 |
2305.17716
|
HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language
|
This paper presents HaVQA, the first multimodal dataset for visual
question-answering (VQA) tasks in the Hausa language. The dataset was created
by manually translating 6,022 English question-answer pairs, which are
associated with 1,555 unique images from the Visual Genome dataset. As a
result, the dataset provides 12,044 gold standard English-Hausa parallel
sentences that were translated in a fashion that guarantees their semantic
match with the corresponding visual information. We conducted several baseline
experiments on the dataset, including visual question answering, visual
question elicitation, text-only and multimodal machine translation.
|
http://arxiv.org/abs/2305.17690v1
|
cs.CL
|
new_dataset
| 0.994489 |
2305.17690
|
ArPanEmo: An Open-Source Dataset for Fine-Grained Emotion Recognition in Arabic Online Content during COVID-19 Pandemic
|
Emotion recognition is a crucial task in Natural Language Processing (NLP)
that enables machines to comprehend the feelings conveyed in the text. The
applications of emotion recognition are diverse, including mental health
diagnosis, student support, and the detection of online suspicious behavior.
Despite the substantial amount of literature available on emotion recognition
in various languages, Arabic emotion recognition has received relatively little
attention, leading to a scarcity of emotion-annotated corpora. This paper
presents the ArPanEmo dataset, a novel dataset for fine-grained emotion
recognition of online posts in Arabic. The dataset comprises 11,128 online
posts manually labeled for ten emotion categories or neutral, with Fleiss'
kappa of 0.71. It targets a specific Arabic dialect and addresses topics
related to the COVID-19 pandemic, making it the first and largest of its kind.
Python's packages were utilized to collect online posts related to the COVID-19
pandemic from three sources: Twitter, YouTube, and online newspaper comments
between March 2020 and March 2022. Upon collection of the online posts, each
one underwent a semi-automatic classification process using a lexicon of
emotion-related terms to determine whether it belonged to the neutral or
emotional category. Subsequently, manual labeling was conducted to further
categorize the emotional data into fine-grained emotion categories.
|
http://arxiv.org/abs/2305.17580v1
|
cs.CL
|
new_dataset
| 0.994488 |
2305.17580
|
Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence
|
Despite the progress in deep learning networks, efficient learning at the
edge (enabling adaptable, low-complexity machine learning solutions) remains a
critical need for defense and commercial applications. We envision a pipeline
to utilize large neuroimaging datasets, including maps of the brain which
capture neuron and synapse connectivity, to improve machine learning
approaches. We have pursued different approaches within this pipeline
structure. First, as a demonstration of data-driven discovery, the team has
developed a technique for discovery of repeated subcircuits, or motifs. These
were incorporated into a neural architecture search approach to evolve network
architectures. Second, we have conducted analysis of the heading direction
circuit in the fruit fly, which performs fusion of visual and angular velocity
features, to explore augmenting existing computational models with new insight.
Our team discovered a novel pattern of connectivity, implemented a new model,
and demonstrated sensor fusion on a robotic platform. Third, the team analyzed
circuitry for memory formation in the fruit fly connectome, enabling the design
of a novel generative replay approach. Finally, the team has begun analysis of
connectivity in mammalian cortex to explore potential improvements to
transformer networks. These constraints increased network robustness on the
most challenging examples in the CIFAR-10-C computer vision robustness
benchmark task, while reducing learnable attention parameters by over an order
of magnitude. Taken together, these results demonstrate multiple potential
approaches to utilize insight from neural systems for developing robust and
efficient machine learning techniques.
|
http://arxiv.org/abs/2305.17300v1
|
cs.NE
|
not_new_dataset
| 0.9922 |
2305.17300
|
BIG-C: a Multimodal Multi-Purpose Dataset for Bemba
|
We present BIG-C (Bemba Image Grounded Conversations), a large multimodal
dataset for Bemba. While Bemba is the most populous language of Zambia, it
exhibits a dearth of resources which render the development of language
technologies or language processing research almost impossible. The dataset is
comprised of multi-turn dialogues between Bemba speakers based on images,
transcribed and translated into English. There are more than 92,000
utterances/sentences, amounting to more than 180 hours of audio data with
corresponding transcriptions and English translations. We also provide
baselines on speech recognition (ASR), machine translation (MT) and speech
translation (ST) tasks, and sketch out other potential future multimodal uses
of our dataset. We hope that by making the dataset available to the research
community, this work will foster research and encourage collaboration across
the language, speech, and vision communities especially for languages outside
the "traditionally" used high-resourced ones. All data and code are publicly
available: https://github.com/csikasote/bigc.
|
http://arxiv.org/abs/2305.17202v1
|
cs.CL
|
new_dataset
| 0.994515 |
2305.17202
|
DataChat: Prototyping a Conversational Agent for Dataset Search and Visualization
|
Data users need relevant context and research expertise to effectively search
for and identify relevant datasets. Leading data providers, such as the
Inter-university Consortium for Political and Social Research (ICPSR), offer
standardized metadata and search tools to support data search. Metadata
standards emphasize the machine-readability of data and its documentation.
There are opportunities to enhance dataset search by improving users' ability
to learn about, and make sense of, information about data. Prior research has
shown that context and expertise are two main barriers users face in
effectively searching for, evaluating, and deciding whether to reuse data. In
this paper, we propose a novel chatbot-based search system, DataChat, that
leverages a graph database and a large language model to provide novel ways for
users to interact with and search for research data. DataChat complements data
archives' and institutional repositories' ongoing efforts to curate, preserve,
and share research data for reuse by making it easier for users to explore and
learn about available research data.
|
http://arxiv.org/abs/2305.18358v1
|
cs.IR
|
not_new_dataset
| 0.989342 |
2305.18358
|
DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions
|
Modern machine learning relies on datasets to develop and validate research
ideas. Given the growth of publicly available data, finding the right dataset
to use is increasingly difficult. Any research question imposes explicit and
implicit constraints on how well a given dataset will enable researchers to
answer this question, such as dataset size, modality, and domain. We
operationalize the task of recommending datasets given a short natural language
description of a research idea, to help people find relevant datasets for their
needs. Dataset recommendation poses unique challenges as an information
retrieval problem; datasets are hard to directly index for search and there are
no corpora readily available for this task. To facilitate this task, we build
the DataFinder Dataset which consists of a larger automatically-constructed
training set (17.5K queries) and a smaller expert-annotated evaluation set (392
queries). Using this data, we compare various information retrieval algorithms
on our test set and present a superior bi-encoder retriever for text-based
dataset recommendation. This system, trained on the DataFinder Dataset, finds
more relevant search results than existing third-party dataset search engines.
To encourage progress on dataset recommendation, we release our dataset and
models to the public.
|
http://arxiv.org/abs/2305.16636v2
|
cs.IR
|
new_dataset
| 0.99418 |
2305.16636
|
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation
|
Paraphrase generation is a long-standing task in natural language processing
(NLP). Supervised paraphrase generation models, which rely on human-annotated
paraphrase pairs, are cost-inefficient and hard to scale up. On the other hand,
automatically annotated paraphrase pairs (e.g., by machine back-translation),
usually suffer from the lack of syntactic diversity -- the generated paraphrase
sentences are very similar to the source sentences in terms of syntax. In this
work, we present ParaAMR, a large-scale syntactically diverse paraphrase
dataset created by abstract meaning representation back-translation. Our
quantitative analysis, qualitative examples, and human evaluation demonstrate
that the paraphrases of ParaAMR are syntactically more diverse compared to
existing large-scale paraphrase datasets while preserving good semantic
similarity. In addition, we show that ParaAMR can be used to improve on three
NLP tasks: learning sentence embeddings, syntactically controlled paraphrase
generation, and data augmentation for few-shot learning. Our results thus
showcase the potential of ParaAMR for improving various NLP applications.
|
http://arxiv.org/abs/2305.16585v1
|
cs.CL
|
new_dataset
| 0.994407 |
2305.16585
|
CVB: A Video Dataset of Cattle Visual Behaviors
|
Existing image/video datasets for cattle behavior recognition are mostly
small, lack well-defined labels, or are collected in unrealistic controlled
environments. This limits the utility of machine learning (ML) models learned
from them. Therefore, we introduce a new dataset, called Cattle Visual
Behaviors (CVB), that consists of 502 video clips, each fifteen seconds long,
captured in natural lighting conditions, and annotated with eleven visually
perceptible behaviors of grazing cattle. We use the Computer Vision Annotation
Tool (CVAT) to collect our annotations. To make the procedure more efficient,
we perform an initial detection and tracking of cattle in the videos using
appropriate pre-trained models. The results are corrected by domain experts
along with cattle behavior labeling in CVAT. The pre-hoc detection and tracking
step significantly reduces the manual annotation time and effort. Moreover, we
convert CVB to the atomic visual action (AVA) format and train and evaluate the
popular SlowFast action recognition model on it. The associated preliminary
results confirm that we can localize the cattle and recognize their frequently
occurring behaviors with confidence. By creating and sharing CVB, our aim is to
develop improved models capable of recognizing all important behaviors
accurately and to assist other researchers and practitioners in developing and
evaluating new ML models for cattle behavior classification using video data.
|
http://arxiv.org/abs/2305.16555v2
|
cs.CV
|
new_dataset
| 0.994576 |
2305.16555
|
AUC Optimization from Multiple Unlabeled Datasets
|
Weakly supervised learning aims to empower machine learning when the perfect
supervision is unavailable, which has drawn great attention from researchers.
Among various types of weak supervision, one of the most challenging cases is
to learn from multiple unlabeled (U) datasets with only a little knowledge of
the class priors, or U$^m$ learning for short. In this paper, we study the
problem of building an AUC (area under ROC curve) optimization model from
multiple unlabeled datasets, which maximizes the pairwise ranking ability of
the classifier. We propose U$^m$-AUC, an AUC optimization approach that
converts the U$^m$ data into a multi-label AUC optimization problem, and can be
trained efficiently. We show that the proposed U$^m$-AUC is effective
theoretically and empirically.
|
http://arxiv.org/abs/2305.15776v3
|
cs.LG
|
not_new_dataset
| 0.992218 |
2305.15776
|
Towards Solving Cocktail-Party: The First Method to Build a Realistic Dataset with Ground Truths for Speech Separation
|
Speech separation is very important in real-world applications such as
human-machine interaction, hearing aids devices, and automatic meeting
transcription. In recent years, a significant improvement occurred towards the
solution based on deep learning. In fact, much attention has been drawn to
supervised learning methods using synthetic mixtures datasets despite their
being not representative of real-world mixtures. The difficulty in building a
realistic dataset led researchers to use unsupervised learning methods, because
of their ability to handle realistic mixtures directly. The results of
unsupervised learning methods are still unconvincing. In this paper, a method
is introduced to create a realistic dataset with ground truth sources for
speech separation. The main challenge in designing a realistic dataset is the
unavailability of ground truths for speakers signals. To address this, we
propose a method for simultaneously recording two speakers and obtaining the
ground truth for each. We present a methodology for benchmarking our realistic
dataset using a deep learning model based on Bidirectional Gated Recurrent
Units (BGRU) and clustering algorithm. The experiments show that our proposed
dataset improved SI-SDR (Scale Invariant Signal to Distortion Ratio) by 1.65 dB
and PESQ (Perceptual Evaluation of Speech Quality) by approximately 0.5. We
also evaluated the effectiveness of our method at different distances between
the microphone and the speakers and found that it improved the stability of the
learned model.
|
http://arxiv.org/abs/2305.15758v1
|
cs.SD
|
new_dataset
| 0.99368 |
2305.15758
|
SciReviewGen: A Large-scale Dataset for Automatic Literature Review Generation
|
Automatic literature review generation is one of the most challenging tasks
in natural language processing. Although large language models have tackled
literature review generation, the absence of large-scale datasets has been a
stumbling block to the progress. We release SciReviewGen, consisting of over
10,000 literature reviews and 690,000 papers cited in the reviews. Based on the
dataset, we evaluate recent transformer-based summarization models on the
literature review generation task, including Fusion-in-Decoder extended for
literature review generation. Human evaluation results show that some
machine-generated summaries are comparable to human-written reviews, while
revealing the challenges of automatic literature review generation such as
hallucinations and a lack of detailed information. Our dataset and code are
available at https://github.com/tetsu9923/SciReviewGen.
|
http://arxiv.org/abs/2305.15186v1
|
cs.CL
|
new_dataset
| 0.99438 |
2305.15186
|
Calc-X: Enriching Arithmetical Chain-of-Thoughts Datasets by Interaction with Symbolic Systems
|
This report overviews our ongoing work in enriching chain-of-thoughts
datasets requiring arithmetical reasoning with the integration of
non-parametric components, such as a calculator. We conduct an analysis of
prominent relevant datasets such as GSM8K, Ape210K, AQuA-RAT, and MathQA and
propose a machine-processable HTML-like format specifically tailored for
working with semi-structured chains. By converting the datasets into this
unified format, we enable the effective integration of large language models
and symbolic systems, empowering them to tackle arithmetical reasoning tasks
more efficiently.
|
http://arxiv.org/abs/2305.15017v1
|
cs.LG
|
not_new_dataset
| 0.990214 |
2305.15017
|
Sāmayik: A Benchmark and Dataset for English-Sanskrit Translation
|
Sanskrit is a low-resource language with a rich heritage. Digitized Sanskrit
corpora reflective of the contemporary usage of Sanskrit, specifically that too
in prose, is heavily under-represented at present. Presently, no such
English-Sanskrit parallel dataset is publicly available. We release a dataset,
S\={a}mayik, of more than 42,000 parallel English-Sanskrit sentences, from four
different corpora that aim to bridge this gap. Moreover, we also release
benchmarks adapted from existing multilingual pretrained models for
Sanskrit-English translation. We include training splits from our contemporary
dataset and the Sanskrit-English parallel sentences from the training split of
Itih\={a}sa, a previously released classical era machine translation dataset
containing Sanskrit.
|
http://arxiv.org/abs/2305.14004v1
|
cs.CL
|
new_dataset
| 0.994475 |
2305.14004
|
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation
|
We present a large-scale video subtitle translation dataset, BigVideo, to
facilitate the study of multi-modality machine translation. Compared with the
widely used How2 and VaTeX datasets, BigVideo is more than 10 times larger,
consisting of 4.5 million sentence pairs and 9,981 hours of videos. We also
introduce two deliberately designed test sets to verify the necessity of visual
information: Ambiguous with the presence of ambiguous words, and Unambiguous in
which the text context is self-contained for translation. To better model the
common semantics shared across texts and videos, we introduce a contrastive
learning method in the cross-modal encoder. Extensive experiments on the
BigVideo show that: a) Visual information consistently improves the NMT model
in terms of BLEU, BLEURT, and COMET on both Ambiguous and Unambiguous test
sets. b) Visual information helps disambiguation, compared to the strong text
baseline on terminology-targeted scores and human evaluation. Dataset and our
implementations are available at https://github.com/DeepLearnXMU/BigVideo-VMT.
|
http://arxiv.org/abs/2305.18326v3
|
cs.CV
|
new_dataset
| 0.994431 |
2305.18326
|
TeCS: A Dataset and Benchmark for Tense Consistency of Machine Translation
|
Tense inconsistency frequently occurs in machine translation. However, there
are few criteria to assess the model's mastery of tense prediction from a
linguistic perspective. In this paper, we present a parallel tense test set,
containing French-English 552 utterances. We also introduce a corresponding
benchmark, tense prediction accuracy. With the tense test set and the
benchmark, researchers are able to measure the tense consistency performance of
machine translation systems for the first time.
|
http://arxiv.org/abs/2305.13740v1
|
cs.CL
|
new_dataset
| 0.994371 |
2305.13740
|
Evaluating Model Performance in Medical Datasets Over Time
|
Machine learning (ML) models deployed in healthcare systems must face data
drawn from continually evolving environments. However, researchers proposing
such models typically evaluate them in a time-agnostic manner, splitting
datasets according to patients sampled randomly throughout the entire study
time period. This work proposes the Evaluation on Medical Datasets Over Time
(EMDOT) framework, which evaluates the performance of a model class across
time. Inspired by the concept of backtesting, EMDOT simulates possible training
procedures that practitioners might have been able to execute at each point in
time and evaluates the resulting models on all future time points. Evaluating
both linear and more complex models on six distinct medical data sources
(tabular and imaging), we show how depending on the dataset, using all
historical data may be ideal in many cases, whereas using a window of the most
recent data could be advantageous in others. In datasets where models suffer
from sudden degradations in performance, we investigate plausible explanations
for these shocks. We release the EMDOT package to help facilitate further works
in deployment-oriented evaluation over time.
|
http://arxiv.org/abs/2305.13426v2
|
cs.LG
|
not_new_dataset
| 0.992188 |
2305.13426
|
Leveraging Human Feedback to Scale Educational Datasets: Combining Crowdworkers and Comparative Judgement
|
Machine Learning models have many potentially beneficial applications in
education settings, but a key barrier to their development is securing enough
data to train these models. Labelling educational data has traditionally relied
on highly skilled raters using complex, multi-class rubrics, making the process
expensive and difficult to scale. An alternative, more scalable approach could
be to use non-expert crowdworkers to evaluate student work, however,
maintaining sufficiently high levels of accuracy and inter-rater reliability
when using non-expert workers is challenging. This paper reports on two
experiments investigating using non-expert crowdworkers and comparative
judgement to evaluate complex student data. Crowdworkers were hired to evaluate
student responses to open-ended reading comprehension questions. Crowdworkers
were randomly assigned to one of two conditions: the control, where they were
asked to decide whether answers were correct or incorrect (i.e., a categorical
judgement), or the treatment, where they were shown the same question and
answers, but were instead asked to decide which of two candidate answers was
more correct (i.e., a comparative/preference-based judgement). We found that
using comparative judgement substantially improved inter-rater reliability on
both tasks. These results are in-line with well-established literature on the
benefits of comparative judgement in the field of educational assessment, as
well as with recent trends in artificial intelligence research, where
comparative judgement is becoming the preferred method for providing human
feedback on model outputs when working with non-expert crowdworkers. However,
to our knowledge, these results are novel and important in demonstrating the
beneficial effects of using the combination of comparative judgement and
crowdworkers to evaluate educational data.
|
http://arxiv.org/abs/2305.12894v1
|
cs.CL
|
not_new_dataset
| 0.992043 |
2305.12894
|
Productive Crop Field Detection: A New Dataset and Deep Learning Benchmark Results
|
In precision agriculture, detecting productive crop fields is an essential
practice that allows the farmer to evaluate operating performance separately
and compare different seed varieties, pesticides, and fertilizers. However,
manually identifying productive fields is often a time-consuming and
error-prone task. Previous studies explore different methods to detect crop
fields using advanced machine learning algorithms, but they often lack good
quality labeled data. In this context, we propose a high-quality dataset
generated by machine operation combined with Sentinel-2 images tracked over
time. As far as we know, it is the first one to overcome the lack of labeled
samples by using this technique. In sequence, we apply a semi-supervised
classification of unlabeled data and state-of-the-art supervised and
self-supervised deep learning methods to detect productive crop fields
automatically. Finally, the results demonstrate high accuracy in Positive
Unlabeled learning, which perfectly fits the problem where we have high
confidence in the positive samples. Best performances have been found in
Triplet Loss Siamese given the existence of an accurate dataset and Contrastive
Learning considering situations where we do not have a comprehensive labeled
dataset available.
|
http://arxiv.org/abs/2305.11990v2
|
cs.CV
|
new_dataset
| 0.994537 |
2305.11990
|
MiraBest: A Dataset of Morphologically Classified Radio Galaxies for Machine Learning
|
The volume of data from current and future observatories has motivated the
increased development and application of automated machine learning
methodologies for astronomy. However, less attention has been given to the
production of standardised datasets for assessing the performance of different
machine learning algorithms within astronomy and astrophysics. Here we describe
in detail the MiraBest dataset, a publicly available batched dataset of 1256
radio-loud AGN from NVSS and FIRST, filtered to $0.03 < z < 0.1$, manually
labelled by Miraghaei and Best (2017) according to the Fanaroff-Riley
morphological classification, created for machine learning applications and
compatible for use with standard deep learning libraries. We outline the
principles underlying the construction of the dataset, the sample selection and
pre-processing methodology, dataset structure and composition, as well as a
comparison of MiraBest to other datasets used in the literature. Existing
applications that utilise the MiraBest dataset are reviewed, and an extended
dataset of 2100 sources is created by cross-matching MiraBest with other
catalogues of radio-loud AGN that have been used more widely in the literature
for machine learning applications.
|
http://arxiv.org/abs/2305.11108v1
|
astro-ph.IM
|
new_dataset
| 0.994551 |
2305.11108
|
Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction
|
Most research in Relation Extraction (RE) involves the English language,
mainly due to the lack of multi-lingual resources. We propose Multi-CrossRE,
the broadest multi-lingual dataset for RE, including 26 languages in addition
to English, and covering six text domains. Multi-CrossRE is a machine
translated version of CrossRE (Bassignana and Plank, 2022), with a sub-portion
including more than 200 sentences in seven diverse languages checked by native
speakers. We run a baseline model over the 26 new datasets and--as sanity
check--over the 26 back-translations to English. Results on the back-translated
data are consistent with the ones on the original English CrossRE, indicating
high quality of the translation and the resulting dataset.
|
http://arxiv.org/abs/2305.10985v1
|
cs.CL
|
new_dataset
| 0.994547 |
2305.10985
|
Solar Active Region Magnetogram Image Dataset for Studies of Space Weather
|
In this dataset we provide a comprehensive collection of magnetograms (images
quantifying the strength of the magnetic field) from the National Aeronautics
and Space Administration's (NASA's) Solar Dynamics Observatory (SDO). The
dataset incorporates data from three sources and provides SDO Helioseismic and
Magnetic Imager (HMI) magnetograms of solar active regions (regions of large
magnetic flux, generally the source of eruptive events) as well as labels of
corresponding flaring activity. This dataset will be useful for image analysis
or solar physics research related to magnetic structure, its evolution over
time, and its relation to solar flares. The dataset will be of interest to
those researchers investigating automated solar flare prediction methods,
including supervised and unsupervised machine learning (classical and deep),
binary and multi-class classification, and regression. This dataset is a
minimally processed, user configurable dataset of consistently sized images of
solar active regions that can serve as a benchmark dataset for solar flare
prediction research.
|
http://arxiv.org/abs/2305.09492v2
|
astro-ph.SR
|
new_dataset
| 0.994557 |
2305.09492
|
Ship-D: Ship Hull Dataset for Design Optimization using Machine Learning
|
Machine learning has recently made significant strides in reducing design
cycle time for complex products. Ship design, which currently involves years
long cycles and small batch production, could greatly benefit from these
advancements. By developing a machine learning tool for ship design that learns
from the design of many different types of ships, tradeoffs in ship design
could be identified and optimized. However, the lack of publicly available ship
design datasets currently limits the potential for leveraging machine learning
in generalized ship design. To address this gap, this paper presents a large
dataset of thirty thousand ship hulls, each with design and functional
performance information, including parameterization, mesh, point cloud, and
image representations, as well as thirty two hydrodynamic drag measures under
different operating conditions. The dataset is structured to allow human input
and is also designed for computational methods. Additionally, the paper
introduces a set of twelve ship hulls from publicly available CAD repositories
to showcase the proposed parameterizations ability to accurately reconstruct
existing hulls. A surrogate model was developed to predict the thirty two wave
drag coefficients, which was then implemented in a genetic algorithm case study
to reduce the total drag of a hull by sixty percent while maintaining the shape
of the hulls cross section and the length of the parallel midbody. Our work
provides a comprehensive dataset and application examples for other researchers
to use in advancing data driven ship design.
|
http://arxiv.org/abs/2305.08279v2
|
cs.LG
|
new_dataset
| 0.994549 |
2305.08279
|
Anomaly Detection Dataset for Industrial Control Systems
|
Over the past few decades, Industrial Control Systems (ICSs) have been
targeted by cyberattacks and are becoming increasingly vulnerable as more ICSs
are connected to the internet. Using Machine Learning (ML) for Intrusion
Detection Systems (IDS) is a promising approach for ICS cyber protection, but
the lack of suitable datasets for evaluating ML algorithms is a challenge.
Although there are a few commonly used datasets, they may not reflect realistic
ICS network data, lack necessary features for effective anomaly detection, or
be outdated. This paper presents the 'ICS-Flow' dataset, which offers network
data and process state variables logs for supervised and unsupervised ML-based
IDS assessment. The network data includes normal and anomalous network packets
and flows captured from simulated ICS components and emulated networks. The
anomalies were injected into the system through various attack techniques
commonly used by hackers to modify network traffic and compromise ICSs. We also
proposed open-source tools, `ICSFlowGenerator' for generating network flow
parameters from Raw network packets. The final dataset comprises over
25,000,000 raw network packets, network flow records, and process variable
logs. The paper describes the methodology used to collect and label the dataset
and provides a detailed data analysis. Finally, we implement several ML models,
including the decision tree, random forest, and artificial neural network to
detect anomalies and attacks, demonstrating that our dataset can be used
effectively for training intrusion detection ML models.
|
http://arxiv.org/abs/2305.09678v1
|
cs.CR
|
new_dataset
| 0.994502 |
2305.09678
|
Collection Space Navigator: An Interactive Visualization Interface for Multidimensional Datasets
|
We introduce the Collection Space Navigator (CSN), a browser-based
visualization tool to explore, research, and curate large collections of visual
digital artifacts that are associated with multidimensional data, such as
vector embeddings or tables of metadata. Media objects such as images are often
encoded as numerical vectors, for e.g. based on metadata or using machine
learning to embed image information. Yet, while such procedures are widespread
for a range of applications, it remains a challenge to explore, analyze, and
understand the resulting multidimensional spaces in a more comprehensive
manner. Dimensionality reduction techniques such as t-SNE or UMAP often serve
to project high-dimensional data into low dimensional visualizations, yet
require interpretation themselves as the remaining dimensions are typically
abstract. Here, the Collection Space Navigator provides a customizable
interface that combines two-dimensional projections with a set of configurable
multidimensional filters. As a result, the user is able to view and investigate
collections, by zooming and scaling, by transforming between projections, by
filtering dimensions via range sliders, and advanced text filters. Insights
that are gained during the interaction can be fed back into the original data
via ad hoc exports of filtered metadata and projections. This paper comes with
a functional showcase demo using a large digitized collection of classical
Western art. The Collection Space Navigator is open source. Users can
reconfigure the interface to fit their own data and research needs, including
projections and filter controls. The CSN is ready to serve a broad community.
|
http://arxiv.org/abs/2305.06809v1
|
cs.CV
|
not_new_dataset
| 0.962312 |
2305.06809
|
BanglaBook: A Large-scale Bangla Dataset for Sentiment Analysis from Book Reviews
|
The analysis of consumer sentiment, as expressed through reviews, can provide
a wealth of insight regarding the quality of a product. While the study of
sentiment analysis has been widely explored in many popular languages,
relatively less attention has been given to the Bangla language, mostly due to
a lack of relevant data and cross-domain adaptability. To address this
limitation, we present BanglaBook, a large-scale dataset of Bangla book reviews
consisting of 158,065 samples classified into three broad categories: positive,
negative, and neutral. We provide a detailed statistical analysis of the
dataset and employ a range of machine learning models to establish baselines
including SVM, LSTM, and Bangla-BERT. Our findings demonstrate a substantial
performance advantage of pre-trained models over models that rely on manually
crafted features, emphasizing the necessity for additional training resources
in this domain. Additionally, we conduct an in-depth error analysis by
examining sentiment unigrams, which may provide insight into common
classification errors in under-resourced languages like Bangla. Our codes and
data are publicly available at https://github.com/mohsinulkabir14/BanglaBook.
|
http://arxiv.org/abs/2305.06595v3
|
cs.CL
|
new_dataset
| 0.994482 |
2305.06595
|
Spectral Clustering on Large Datasets: When Does it Work? Theory from Continuous Clustering and Density Cheeger-Buser
|
Spectral clustering is one of the most popular clustering algorithms that has
stood the test of time. It is simple to describe, can be implemented using
standard linear algebra, and often finds better clusters than traditional
clustering algorithms like $k$-means and $k$-centers. The foundational
algorithm for two-way spectral clustering, by Shi and Malik, creates a
geometric graph from data and finds a spectral cut of the graph.
In modern machine learning, many data sets are modeled as a large number of
points drawn from a probability density function. Little is known about when
spectral clustering works in this setting -- and when it doesn't. Past
researchers justified spectral clustering by appealing to the graph Cheeger
inequality (which states that the spectral cut of a graph approximates the
``Normalized Cut''), but this justification is known to break down on large
data sets.
We provide theoretically-informed intuition about spectral clustering on
large data sets drawn from probability densities, by proving when a continuous
form of spectral clustering considered by past researchers (the unweighted
spectral cut of a probability density) finds good clusters of the underlying
density itself. Our work suggests that Shi-Malik spectral clustering works well
on data drawn from mixtures of Laplace distributions, and works poorly on data
drawn from certain other densities, such as a density we call the `square-root
trough'.
Our core theorem proves that weighted spectral cuts have low weighted
isoperimetry for all probability densities. Our key tool is a new Cheeger-Buser
inequality for all probability densities, including discontinuous ones.
|
http://arxiv.org/abs/2305.06541v1
|
cs.LG
|
not_new_dataset
| 0.992087 |
2305.06541
|
WikiSQE: A Large-Scale Dataset for Sentence Quality Estimation in Wikipedia
|
Wikipedia can be edited by anyone and thus contains various quality
sentences. Therefore, Wikipedia includes some poor-quality edits, which are
often marked up by other editors. While editors' reviews enhance the
credibility of Wikipedia, it is hard to check all edited text. Assisting in
this process is very important, but a large and comprehensive dataset for
studying it does not currently exist. Here, we propose WikiSQE, the first
large-scale dataset for sentence quality estimation in Wikipedia. Each sentence
is extracted from the entire revision history of Wikipedia, and the target
quality labels were carefully investigated and selected. WikiSQE has about 3.4
M sentences with 153 quality labels. In the experiment with automatic
classification using competitive machine learning models, sentences that had
problems with citation, syntax/semantics, or propositions were found to be more
difficult to detect. In addition, we conducted automated essay scoring
experiments to evaluate the generalizability of the dataset. We show that the
models trained on WikiSQE perform better than the vanilla model, indicating its
potential usefulness in other domains. WikiSQE is expected to be a valuable
resource for other tasks in NLP.
|
http://arxiv.org/abs/2305.05928v1
|
cs.CL
|
new_dataset
| 0.994403 |
2305.05928
|
Augmented Datasheets for Speech Datasets and Ethical Decision-Making
|
Speech datasets are crucial for training Speech Language Technologies (SLT);
however, the lack of diversity of the underlying training data can lead to
serious limitations in building equitable and robust SLT products, especially
along dimensions of language, accent, dialect, variety, and speech impairment -
and the intersectionality of speech features with socioeconomic and demographic
features. Furthermore, there is often a lack of oversight on the underlying
training data - commonly built on massive web-crawling and/or publicly
available speech - with regard to the ethics of such data collection. To
encourage standardized documentation of such speech data components, we
introduce an augmented datasheet for speech datasets, which can be used in
addition to "Datasheets for Datasets". We then exemplify the importance of each
question in our augmented datasheet based on in-depth literature reviews of
speech data used in domains such as machine learning, linguistics, and health.
Finally, we encourage practitioners - ranging from dataset creators to
researchers - to use our augmented datasheet to better define the scope,
properties, and limits of speech datasets, while also encouraging consideration
of data-subject protection and user community empowerment. Ethical dataset
creation is not a one-size-fits-all process, but dataset creators can use our
augmented datasheet to reflexively consider the social context of related SLT
applications and data sources in order to foster more inclusive SLT products
downstream.
|
http://arxiv.org/abs/2305.04672v1
|
cs.CY
|
not_new_dataset
| 0.98482 |
2305.04672
|
MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset
|
Relation extraction (RE) is a fundamental task in information extraction,
whose extension to multilingual settings has been hindered by the lack of
supervised resources comparable in size to large English datasets such as
TACRED (Zhang et al., 2017). To address this gap, we introduce the MultiTACRED
dataset, covering 12 typologically diverse languages from 9 language families,
which is created by machine-translating TACRED instances and automatically
projecting their entity annotations. We analyze translation and annotation
projection quality, identify error categories, and experimentally evaluate
fine-tuned pretrained mono- and multilingual language models in common transfer
learning scenarios. Our analyses show that machine translation is a viable
strategy to transfer RE instances, with native speakers judging more than 83%
of the translated instances to be linguistically and semantically acceptable.
We find monolingual RE model performance to be comparable to the English
original for many of the target languages, and that multilingual models trained
on a combination of English and target language data can outperform their
monolingual counterparts. However, we also observe a variety of translation and
annotation projection errors, both due to the MT systems and linguistic
features of the target languages, such as pronoun-dropping, compounding and
inflection, that degrade dataset quality and RE model performance.
|
http://arxiv.org/abs/2305.04582v2
|
cs.CL
|
new_dataset
| 0.994431 |
2305.04582
|
Scaling Graph-Based ANNS Algorithms to Billion-Size Datasets: A Comparative Analysis
|
Algorithms for approximate nearest-neighbor search (ANNS) have been the topic
of significant recent interest in the research community. However, evaluations
of such algorithms are usually restricted to a small number of datasets with
millions or tens of millions of points, whereas real-world applications require
algorithms that work on the scale of billions of points. Furthermore, existing
evaluations of ANNS algorithms are typically heavily focused on measuring and
optimizing for queries-per second (QPS) at a given accuracy, which can be
hardware-dependent and ignores important metrics such as build time.
In this paper, we propose a set of principled measures for evaluating ANNS
algorithms which refocuses on their scalability to billion-size datasets. These
measures include ability to be efficiently parallelized, build times, and
scaling relationships as dataset size increases. We also expand on the QPS
measure with machine-agnostic measures such as the number of distance
computations per query, and we evaluate ANNS data structures on their accuracy
in more demanding settings required in modern applications, such as evaluating
range queries and running on out-of-distribution data. We optimize four
graph-based algorithms for the billion-scale setting, and in the process
provide a general framework for making many incremental ANNS graph algorithms
lock-free. We use our framework to evaluate the aforementioned graph-based ANNS
algorithms as well as two alternative approaches.
|
http://arxiv.org/abs/2305.04359v1
|
cs.IR
|
not_new_dataset
| 0.991472 |
2305.04359
|
Considerations for Ethical Speech Recognition Datasets
|
Speech AI Technologies are largely trained on publicly available datasets or
by the massive web-crawling of speech. In both cases, data acquisition focuses
on minimizing collection effort, without necessarily taking the data subjects'
protection or user needs into consideration. This results to models that are
not robust when used on users who deviate from the dominant demographics in the
training set, discriminating individuals having different dialects, accents,
speaking styles, and disfluencies. In this talk, we use automatic speech
recognition as a case study and examine the properties that ethical speech
datasets should possess towards responsible AI applications. We showcase
diversity issues, inclusion practices, and necessary considerations that can
improve trained models, while facilitating model explainability and protecting
users and data subjects. We argue for the legal & privacy protection of data
subjects, targeted data sampling corresponding to user demographics & needs,
appropriate meta data that ensure explainability & accountability in cases of
model failure, and the sociotechnical \& situated model design. We hope this
talk can inspire researchers \& practitioners to design and use more
human-centric datasets in speech technologies and other domains, in ways that
empower and respect users, while improving machine learning models' robustness
and utility.
|
http://arxiv.org/abs/2305.02081v1
|
cs.CY
|
not_new_dataset
| 0.99218 |
2305.02081
|
SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model
|
The success of the Segment Anything Model (SAM) demonstrates the significance
of data-centric machine learning. However, due to the difficulties and high
costs associated with annotating Remote Sensing (RS) images, a large amount of
valuable RS data remains unlabeled, particularly at the pixel level. In this
study, we leverage SAM and existing RS object detection datasets to develop an
efficient pipeline for generating a large-scale RS segmentation dataset, dubbed
SAMRS. SAMRS totally possesses 105,090 images and 1,668,241 instances,
surpassing existing high-resolution RS segmentation datasets in size by several
orders of magnitude. It provides object category, location, and instance
information that can be used for semantic segmentation, instance segmentation,
and object detection, either individually or in combination. We also provide a
comprehensive analysis of SAMRS from various aspects. Moreover, preliminary
experiments highlight the importance of conducting segmentation pre-training
with SAMRS to address task discrepancies and alleviate the limitations posed by
limited training data during fine-tuning. The code and dataset will be
available at https://github.com/ViTAE-Transformer/SAMRS.
|
http://arxiv.org/abs/2305.02034v2
|
cs.CV
|
new_dataset
| 0.994428 |
2305.02034
|
A Survey on Dataset Distillation: Approaches, Applications and Future Directions
|
Dataset distillation is attracting more attention in machine learning as
training sets continue to grow and the cost of training state-of-the-art models
becomes increasingly high. By synthesizing datasets with high information
density, dataset distillation offers a range of potential applications,
including support for continual learning, neural architecture search, and
privacy protection. Despite recent advances, we lack a holistic understanding
of the approaches and applications. Our survey aims to bridge this gap by first
proposing a taxonomy of dataset distillation, characterizing existing
approaches, and then systematically reviewing the data modalities, and related
applications. In addition, we summarize the challenges and discuss future
directions for this field of research.
|
http://arxiv.org/abs/2305.01975v3
|
cs.LG
|
not_new_dataset
| 0.992036 |
2305.01975
|
NorQuAD: Norwegian Question Answering Dataset
|
In this paper we present NorQuAD: the first Norwegian question answering
dataset for machine reading comprehension. The dataset consists of 4,752
manually created question-answer pairs. We here detail the data collection
procedure and present statistics of the dataset. We also benchmark several
multilingual and Norwegian monolingual language models on the dataset and
compare them against human performance. The dataset will be made freely
available.
|
http://arxiv.org/abs/2305.01957v1
|
cs.CL
|
new_dataset
| 0.99436 |
2305.01957
|
HTPS: Heterogeneous Transferring Prediction System for Healthcare Datasets
|
Medical internet of things leads to revolutionary improvements in medical
services, also known as smart healthcare. With the big healthcare data, data
mining and machine learning can assist wellness management and intelligent
diagnosis, and achieve the P4-medicine. However, healthcare data has high
sparsity and heterogeneity. In this paper, we propose a Heterogeneous
Transferring Prediction System (HTPS). Feature engineering mechanism transforms
the dataset into sparse and dense feature matrices, and autoencoders in the
embedding networks not only embed features but also transfer knowledge from
heterogeneous datasets. Experimental results show that the proposed HTPS
outperforms the benchmark systems on various prediction tasks and datasets, and
ablation studies present the effectiveness of each designed mechanism.
Experimental results demonstrate the negative impact of heterogeneous data on
benchmark systems and the high transferability of the proposed HTPS.
|
http://arxiv.org/abs/2305.01252v1
|
cs.LG
|
not_new_dataset
| 0.991726 |
2305.01252
|
S2abEL: A Dataset for Entity Linking from Scientific Tables
|
Entity linking (EL) is the task of linking a textual mention to its
corresponding entry in a knowledge base, and is critical for many
knowledge-intensive NLP applications. When applied to tables in scientific
papers, EL is a step toward large-scale scientific knowledge bases that could
enable advanced scientific question answering and analytics. We present the
first dataset for EL in scientific tables. EL for scientific tables is
especially challenging because scientific knowledge bases can be very
incomplete, and disambiguating table mentions typically requires understanding
the papers's tet in addition to the table. Our dataset, S2abEL, focuses on EL
in machine learning results tables and includes hand-labeled cell types,
attributed sources, and entity links from the PaperswithCode taxonomy for 8,429
cells from 732 tables. We introduce a neural baseline method designed for EL on
scientific tables containing many out-of-knowledge-base mentions, and show that
it significantly outperforms a state-of-the-art generic table EL method. The
best baselines fall below human performance, and our analysis highlights
avenues for improvement.
|
http://arxiv.org/abs/2305.00366v1
|
cs.CL
|
new_dataset
| 0.99456 |
2305.00366
|
A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels
|
In the last years, several machine learning-based techniques have been
proposed to monitor human movements from Wi-Fi channel readings. However, the
development of domain-adaptive algorithms that robustly work across different
environments is still an open problem, whose solution requires large datasets
characterized by strong domain diversity, in terms of environments, persons and
Wi-Fi hardware. To date, the few public datasets available are mostly obsolete
- as obtained via Wi-Fi devices operating on 20 or 40 MHz bands - and contain
little or no domain diversity, thus dramatically limiting the advancements in
the design of sensing algorithms. The present contribution aims to fill this
gap by providing a dataset of IEEE 802.11ac channel measurements over an 80 MHz
bandwidth channel featuring notable domain diversity, through measurement
campaigns that involved thirteen subjects across different environments, days,
and with different hardware. Novel experimental data is provided by blocking
the direct path between the transmitter and the monitor, and collecting
measurements in a semi-anechoic chamber (no multi-path fading). Overall, the
dataset - available on IEEE DataPort [1] - contains more than thirteen hours of
channel state information readings (23.6 GB), allowing researchers to test
activity/identity recognition and people counting algorithms.
|
http://arxiv.org/abs/2305.03170v1
|
eess.SP
|
new_dataset
| 0.994552 |
2305.03170
|
HeySQuAD: A Spoken Question Answering Dataset
|
Human-spoken questions are critical to evaluating the performance of spoken
question answering (SQA) systems that serve several real-world use cases
including digital assistants. We present a new large-scale community-shared SQA
dataset, HeySQuAD that consists of 76k human-spoken questions and 97k
machine-generated questions and corresponding textual answers derived from the
SQuAD QA dataset. The goal of HeySQuAD is to measure the ability of machines to
understand noisy spoken questions and answer the questions accurately. To this
end, we run extensive benchmarks on the human-spoken and machine-generated
questions to quantify the differences in noise from both sources and its
subsequent impact on the model and answering accuracy. Importantly, for the
task of SQA, where we want to answer human-spoken questions, we observe that
training using the transcribed human-spoken and original SQuAD questions leads
to significant improvements (12.51%) over training using only the original
SQuAD textual questions.
|
http://arxiv.org/abs/2304.13689v1
|
cs.CL
|
new_dataset
| 0.994506 |
2304.13689
|
On the redundancy in large material datasets: efficient and robust learning with less data
|
Extensive efforts to gather materials data have largely overlooked potential
data redundancy. In this study, we present evidence of a significant degree of
redundancy across multiple large datasets for various material properties, by
revealing that up to 95 % of data can be safely removed from machine learning
training with little impact on in-distribution prediction performance. The
redundant data is related to over-represented material types and does not
mitigate the severe performance degradation on out-of-distribution samples. In
addition, we show that uncertainty-based active learning algorithms can
construct much smaller but equally informative datasets. We discuss the
effectiveness of informative data in improving prediction performance and
robustness and provide insights into efficient data acquisition and machine
learning training. This work challenges the "bigger is better" mentality and
calls for attention to the information richness of materials data rather than a
narrow emphasis on data volume.
|
http://arxiv.org/abs/2304.13076v2
|
cond-mat.mtrl-sci
|
not_new_dataset
| 0.99217 |
2304.13076
|
Scalable Bilevel Optimization for Generating Maximally Representative OPF Datasets
|
New generations of power systems, containing high shares of renewable energy
resources, require improved data-driven tools which can swiftly adapt to
changes in system operation. Many of these tools, such as ones using machine
learning, rely on high-quality training datasets to construct probabilistic
models. Such models should be able to accurately represent the system when
operating at its limits (i.e., operating with a high degree of ``active
constraints"). However, generating training datasets that accurately represent
the many possible combinations of these active constraints is a particularly
challenging task, especially within the realm of nonlinear AC Optimal Power
Flow (OPF), since most active constraints cannot be enforced explicitly. Using
bilevel optimization, this paper introduces a data collection routine that
sequentially solves for OPF solutions which are ``optimally far" from
previously acquired voltage, power, and load profile data points. The routine,
termed RAMBO, samples critical data close to a system's boundaries much more
effectively than a random sampling benchmark. Simulated test results are
collected on the 30-, 57-, and 118-bus PGLib test cases.
|
http://arxiv.org/abs/2304.10912v1
|
eess.SY
|
not_new_dataset
| 0.991748 |
2304.10912
|
Application of quantum-inspired generative models to small molecular datasets
|
Quantum and quantum-inspired machine learning has emerged as a promising and
challenging research field due to the increased popularity of quantum
computing, especially with near-term devices. Theoretical contributions point
toward generative modeling as a promising direction to realize the first
examples of real-world quantum advantages from these technologies. A few
empirical studies also demonstrate such potential, especially when considering
quantum-inspired models based on tensor networks. In this work, we apply
tensor-network-based generative models to the problem of molecular discovery.
In our approach, we utilize two small molecular datasets: a subset of $4989$
molecules from the QM9 dataset and a small in-house dataset of $516$ validated
antioxidants from TotalEnergies. We compare several tensor network models
against a generative adversarial network using different sample-based metrics,
which reflect their learning performances on each task, and multiobjective
performances using $3$ relevant molecular metrics per task. We also combined
the output of the models and demonstrate empirically that such a combination
can be beneficial, advocating for the unification of classical and
quantum(-inspired) generative learning.
|
http://arxiv.org/abs/2304.10867v1
|
quant-ph
|
not_new_dataset
| 0.991291 |
2304.10867
|
HabitatDyn Dataset: Dynamic Object Detection to Kinematics Estimation
|
The advancement of computer vision and machine learning has made datasets a
crucial element for further research and applications. However, the creation
and development of robots with advanced recognition capabilities are hindered
by the lack of appropriate datasets. Existing image or video processing
datasets are unable to accurately depict observations from a moving robot, and
they do not contain the kinematics information necessary for robotic tasks.
Synthetic data, on the other hand, are cost-effective to create and offer
greater flexibility for adapting to various applications. Hence, they are
widely utilized in both research and industry. In this paper, we propose the
dataset HabitatDyn, which contains both synthetic RGB videos, semantic labels,
and depth information, as well as kinetics information. HabitatDyn was created
from the perspective of a mobile robot with a moving camera, and contains 30
scenes featuring six different types of moving objects with varying velocities.
To demonstrate the usability of our dataset, two existing algorithms are used
for evaluation and an approach to estimate the distance between the object and
camera is implemented based on these segmentation methods and evaluated through
the dataset. With the availability of this dataset, we aspire to foster further
advancements in the field of mobile robotics, leading to more capable and
intelligent robots that can navigate and interact with their environments more
effectively. The code is publicly available at
https://github.com/ignc-research/HabitatDyn.
|
http://arxiv.org/abs/2304.10854v1
|
cs.CV
|
new_dataset
| 0.994464 |
2304.10854
|
The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictions
|
We introduce dataset multiplicity, a way to study how inaccuracies,
uncertainty, and social bias in training datasets impact test-time predictions.
The dataset multiplicity framework asks a counterfactual question of what the
set of resultant models (and associated test-time predictions) would be if we
could somehow access all hypothetical, unbiased versions of the dataset. We
discuss how to use this framework to encapsulate various sources of uncertainty
in datasets' factualness, including systemic social bias, data collection
practices, and noisy labels or features. We show how to exactly analyze the
impacts of dataset multiplicity for a specific model architecture and type of
uncertainty: linear models with label errors. Our empirical analysis shows that
real-world datasets, under reasonable assumptions, contain many test samples
whose predictions are affected by dataset multiplicity. Furthermore, the choice
of domain-specific dataset multiplicity definition determines what samples are
affected, and whether different demographic groups are disparately impacted.
Finally, we discuss implications of dataset multiplicity for machine learning
practice and research, including considerations for when model outcomes should
not be trusted.
|
http://arxiv.org/abs/2304.10655v1
|
cs.LG
|
not_new_dataset
| 0.992135 |
2304.10655
|
Is augmentation effective to improve prediction in imbalanced text datasets?
|
Imbalanced datasets present a significant challenge for machine learning
models, often leading to biased predictions. To address this issue, data
augmentation techniques are widely used in natural language processing (NLP) to
generate new samples for the minority class. However, in this paper, we
challenge the common assumption that data augmentation is always necessary to
improve predictions on imbalanced datasets. Instead, we argue that adjusting
the classifier cutoffs without data augmentation can produce similar results to
oversampling techniques. Our study provides theoretical and empirical evidence
to support this claim. Our findings contribute to a better understanding of the
strengths and limitations of different approaches to dealing with imbalanced
data, and help researchers and practitioners make informed decisions about
which methods to use for a given task.
|
http://arxiv.org/abs/2304.10283v1
|
cs.CL
|
not_new_dataset
| 0.991772 |
2304.10283
|
HandCT: hands-on computational dataset for X-Ray Computed Tomography and Machine-Learning
|
Machine-learning methods rely on sufficiently large dataset to learn data
distributions. They are widely used in research in X-Ray Computed Tomography,
from low-dose scan denoising to optimisation of the reconstruction process. The
lack of datasets prevents the scalability of these methods to realistic 3D
problems. We develop a 3D procedural dataset in order to produce samples for
data-driven algorithms. It is made of a meshed model of a left hand and a
script to randomly change its anatomic properties and pose whilst conserving
realistic features. This open-source solution relies on the freeware Blender
and its Python core. Blender handles the modelling, the mesh and the generation
of the hand's pose, whilst Python processes file format conversion from obj
file to matrix and functions to scale and center the volume for further
processing. Dataset availability and quality drives research in
machine-learning. We design a dataset that weighs few megabytes, provides
truthful samples and proposes continuous enhancements using version control. We
anticipate this work to be a starting point for anatomically accurate
procedural datasets. For instance, by adding more internal features and fine
tuning their X-Ray attenuation properties.
|
http://arxiv.org/abs/2304.14412v1
|
eess.IV
|
new_dataset
| 0.994517 |
2304.14412
|
Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets
|
Dialogue acts (DAs) can represent conversational actions of tutors or
students that take place during tutoring dialogues. Automating the
identification of DAs in tutoring dialogues is significant to the design of
dialogue-based intelligent tutoring systems. Many prior studies employ machine
learning models to classify DAs in tutoring dialogues and invest much effort to
optimize the classification accuracy by using limited amounts of training data
(i.e., low-resource data scenario). However, beyond the classification
accuracy, the robustness of the classifier is also important, which can reflect
the capability of the classifier on learning the patterns from different class
distributions. We note that many prior studies on classifying educational DAs
employ cross entropy (CE) loss to optimize DA classifiers on low-resource data
with imbalanced DA distribution. The DA classifiers in these studies tend to
prioritize accuracy on the majority class at the expense of the minority class
which might not be robust to the data with imbalanced ratios of different DA
classes. To optimize the robustness of classifiers on imbalanced class
distributions, we propose to optimize the performance of the DA classifier by
maximizing the area under the ROC curve (AUC) score (i.e., AUC maximization).
Through extensive experiments, our study provides evidence that (i) by
maximizing AUC in the training process, the DA classifier achieves significant
performance improvement compared to the CE approach under low-resource data,
and (ii) AUC maximization approaches can improve the robustness of the DA
classifier under different class imbalance ratios.
|
http://arxiv.org/abs/2304.07499v1
|
cs.CL
|
not_new_dataset
| 0.992221 |
2304.07499
|
Vax-Culture: A Dataset for Studying Vaccine Discourse on Twitter
|
Vaccine hesitancy continues to be a main challenge for public health
officials during the COVID-19 pandemic. As this hesitancy undermines vaccine
campaigns, many researchers have sought to identify its root causes, finding
that the increasing volume of anti-vaccine misinformation on social media
platforms is a key element of this problem. We explored Twitter as a source of
misleading content with the goal of extracting overlapping cultural and
political beliefs that motivate the spread of vaccine misinformation. To do
this, we have collected a data set of vaccine-related Tweets and annotated them
with the help of a team of annotators with a background in communications and
journalism. Ultimately we hope this can lead to effective and targeted public
health communication strategies for reaching individuals with anti-vaccine
beliefs. Moreover, this information helps with developing Machine Learning
models to automatically detect vaccine misinformation posts and combat their
negative impacts. In this paper, we present Vax-Culture, a novel Twitter
COVID-19 dataset consisting of 6373 vaccine-related tweets accompanied by an
extensive set of human-provided annotations including vaccine-hesitancy stance,
indication of any misinformation in tweets, the entities criticized and
supported in each tweet and the communicated message of each tweet. Moreover,
we define five baseline tasks including four classification and one sequence
generation tasks, and report the results of a set of recent transformer-based
models for them. The dataset and code are publicly available at
https://github.com/mrzarei5/Vax-Culture.
|
http://arxiv.org/abs/2304.06858v3
|
cs.SI
|
new_dataset
| 0.994557 |
2304.06858
|
ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition
|
Sign languages are used as a primary language by approximately 70 million
D/deaf people world-wide. However, most communication technologies operate in
spoken and written languages, creating inequities in access. To help tackle
this problem, we release ASL Citizen, the first crowdsourced Isolated Sign
Language Recognition (ISLR) dataset, collected with consent and containing
83,399 videos for 2,731 distinct signs filmed by 52 signers in a variety of
environments. We propose that this dataset be used for sign language dictionary
retrieval for American Sign Language (ASL), where a user demonstrates a sign to
their webcam to retrieve matching signs from a dictionary. We show that
training supervised machine learning classifiers with our dataset advances the
state-of-the-art on metrics relevant for dictionary retrieval, achieving 63%
accuracy and a recall-at-10 of 91%, evaluated entirely on videos of users who
are not present in the training or validation sets. An accessible PDF of this
article is available at the following link:
https://aashakadesai.github.io/research/ASLCitizen_arxiv_updated.pdf
|
http://arxiv.org/abs/2304.05934v2
|
cs.CV
|
new_dataset
| 0.994518 |
2304.05934
|
A Multi-Institutional Open-Source Benchmark Dataset for Breast Cancer Clinical Decision Support using Synthetic Correlated Diffusion Imaging Data
|
Recently, a new form of magnetic resonance imaging (MRI) called synthetic
correlated diffusion (CDI$^s$) imaging was introduced and showed considerable
promise for clinical decision support for cancers such as prostate cancer when
compared to current gold-standard MRI techniques. However, the efficacy for
CDI$^s$ for other forms of cancers such as breast cancer has not been as
well-explored nor have CDI$^s$ data been previously made publicly available.
Motivated to advance efforts in the development of computer-aided clinical
decision support for breast cancer using CDI$^s$, we introduce Cancer-Net BCa,
a multi-institutional open-source benchmark dataset of volumetric CDI$^s$
imaging data of breast cancer patients. Cancer-Net BCa contains CDI$^s$
volumetric images from a pre-treatment cohort of 253 patients across ten
institutions, along with detailed annotation metadata (the lesion type, genetic
subtype, longest diameter on the MRI (MRLD), the Scarff-Bloom-Richardson (SBR)
grade, and the post-treatment breast cancer pathologic complete response (pCR)
to neoadjuvant chemotherapy). We further examine the demographic and tumour
diversity of the Cancer-Net BCa dataset to gain deeper insights into potential
biases. Cancer-Net BCa is publicly available as a part of a global open-source
initiative dedicated to accelerating advancement in machine learning to aid
clinicians in the fight against cancer.
|
http://arxiv.org/abs/2304.05623v1
|
eess.IV
|
new_dataset
| 0.994491 |
2304.05623
|
NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation
|
77% of adults over 50 want to age in place today, presenting a major
challenge to ensuring adequate nutritional intake. It has been reported that
one in four older adults that are 65 years or older are malnourished and given
the direct link between malnutrition and decreased quality of life, there have
been numerous studies conducted on how to efficiently track nutritional intake
of food. Recent advancements in machine learning and computer vision show
promise of automated nutrition tracking methods of food, but require a large
high-quality dataset in order to accurately identify the nutrients from the
food on the plate. Unlike existing datasets, a collection of 3D models with
nutritional information allow for view synthesis to create an infinite number
of 2D images for any given viewpoint/camera angle along with the associated
nutritional information. In this paper, we develop a methodology for collecting
high-quality 3D models for food items with a particular focus on speed and
consistency, and introduce NutritionVerse-3D, a large-scale high-quality
high-resolution dataset of 105 3D food models, in conjunction with their
associated weight, food name, and nutritional value. These models allow for
large quantity food intake scenes, diverse and customizable scene layout, and
an infinite number of camera settings and lighting conditions.
NutritionVerse-3D is publicly available as a part of an open initiative to
accelerate machine learning for nutrition sensing.
|
http://arxiv.org/abs/2304.05619v1
|
cs.CV
|
new_dataset
| 0.994549 |
2304.05619
|
Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines
|
Through this paper, we introduce a novel driver cognitive load assessment
dataset, CL-Drive, which contains Electroencephalogram (EEG) signals along with
other physiological signals such as Electrocardiography (ECG) and Electrodermal
Activity (EDA) as well as eye tracking data. The data was collected from 21
subjects while driving in an immersive vehicle simulator, in various driving
conditions, to induce different levels of cognitive load in the subjects. The
tasks consisted of 9 complexity levels for 3 minutes each. Each driver reported
their subjective cognitive load every 10 seconds throughout the experiment. The
dataset contains the subjective cognitive load recorded as ground truth. In
this paper, we also provide benchmark classification results for different
machine learning and deep learning models for both binary and ternary label
distributions. We followed 2 evaluation criteria namely 10-fold and
leave-one-subject-out (LOSO). We have trained our models on both hand-crafted
features as well as on raw data.
|
http://arxiv.org/abs/2304.04273v1
|
cs.LG
|
new_dataset
| 0.99446 |
2304.04273
|
The Saudi Privacy Policy Dataset
|
This paper introduces the Saudi Privacy Policy Dataset, a diverse compilation
of Arabic privacy policies from various sectors in Saudi Arabia, annotated
according to the 10 principles of the Personal Data Protection Law (PDPL); the
PDPL was established to be compatible with General Data Protection Regulation
(GDPR); one of the most comprehensive data regulations worldwide. Data were
collected from multiple sources, including the Saudi Central Bank, the Saudi
Arabia National United Platform, the Council of Health Insurance, and general
websites using Google and Wikipedia. The final dataset includes 1,000 websites
belonging to 7 sectors, 4,638 lines of text, 775,370 tokens, and a corpus size
of 8,353 KB. The annotated dataset offers significant reuse potential for
assessing privacy policy compliance, benchmarking privacy practices across
industries, and developing automated tools for monitoring adherence to data
protection regulations. By providing a comprehensive and annotated dataset of
privacy policies, this paper aims to facilitate further research and
development in the areas of privacy policy analysis, natural language
processing, and machine learning applications related to privacy and data
protection, while also serving as an essential resource for researchers,
policymakers, and industry professionals interested in understanding and
promoting compliance with privacy regulations in Saudi Arabia.
|
http://arxiv.org/abs/2304.02757v1
|
cs.CL
|
new_dataset
| 0.994539 |
2304.02757
|
Statistics of extreme events in coarse-scale climate simulations via machine learning correction operators trained on nudged datasets
|
This work presents a systematic framework for improving the predictions of
statistical quantities for turbulent systems, with a focus on correcting
climate simulations obtained by coarse-scale models. While high resolution
simulations or reanalysis data are available, they cannot be directly used as
training datasets to machine learn a correction for the coarse-scale climate
model outputs, since chaotic divergence, inherent in the climate dynamics,
makes datasets from different resolutions incompatible. To overcome this
fundamental limitation we employ coarse-resolution model simulations nudged
towards high quality climate realizations, here in the form of ERA5 reanalysis
data. The nudging term is sufficiently small to not pollute the coarse-scale
dynamics over short time scales, but also sufficiently large to keep the
coarse-scale simulations close to the ERA5 trajectory over larger time scales.
The result is a compatible pair of the ERA5 trajectory and the weakly nudged
coarse-resolution E3SM output that is used as input training data to machine
learn a correction operator. Once training is complete, we perform free-running
coarse-scale E3SM simulations without nudging and use those as input to the
machine-learned correction operator to obtain high-quality (corrected) outputs.
The model is applied to atmospheric climate data with the purpose of predicting
global and local statistics of various quantities of a time-period of a decade.
Using datasets that are not employed for training, we demonstrate that the
produced datasets from the ML-corrected coarse E3SM model have statistical
properties that closely resemble the observations. Furthermore, the corrected
coarse-scale E3SM output for the frequency of occurrence of extreme events,
such as tropical cyclones and atmospheric rivers are presented. We present
thorough comparisons and discuss limitations of the approach.
|
http://arxiv.org/abs/2304.02117v1
|
physics.ao-ph
|
not_new_dataset
| 0.992065 |
2304.02117
|
Distance-based Analysis of Machine Learning Prediction Reliability for Datasets in Materials Science and Other Fields
|
Despite successful use in a wide variety of disciplines for data analysis and
prediction, machine learning (ML) methods suffer from a lack of understanding
of the reliability of predictions due to the lack of transparency and black-box
nature of ML models. In materials science and other fields, typical ML model
results include a significant number of low-quality predictions. This problem
is known to be particularly acute for target systems which differ significantly
from the data used for ML model training. However, to date, a general method
for characterization of the difference between the predicted and training
system has not been available. Here, we show that a simple metric based on
Euclidean feature space distance and sampling density allows effective
separation of the accurately predicted data points from data points with poor
prediction accuracy. We show that the metric effectiveness is enhanced by the
decorrelation of the features using Gram-Schmidt orthogonalization. To
demonstrate the generality of the method, we apply it to support vector
regression models for various small data sets in materials science and other
fields. Our method is computationally simple, can be used with any ML learning
method and enables analysis of the sources of the ML prediction errors.
Therefore, it is suitable for use as a standard technique for the estimation of
ML prediction reliability for small data sets and as a tool for data set
design.
|
http://arxiv.org/abs/2304.01146v1
|
cond-mat.mtrl-sci
|
not_new_dataset
| 0.992094 |
2304.01146
|
LAHM : Large Annotated Dataset for Multi-Domain and Multilingual Hate Speech Identification
|
Current research on hate speech analysis is typically oriented towards
monolingual and single classification tasks. In this paper, we present a new
multilingual hate speech analysis dataset for English, Hindi, Arabic, French,
German and Spanish languages for multiple domains across hate speech - Abuse,
Racism, Sexism, Religious Hate and Extremism. To the best of our knowledge,
this paper is the first to address the problem of identifying various types of
hate speech in these five wide domains in these six languages. In this work, we
describe how we created the dataset, created annotations at high level and low
level for different domains and how we use it to test the current
state-of-the-art multilingual and multitask learning approaches. We evaluate
our dataset in various monolingual, cross-lingual and machine translation
classification settings and compare it against open source English datasets
that we aggregated and merged for this task. Then we discuss how this approach
can be used to create large scale hate-speech datasets and how to leverage our
annotations in order to improve hate speech detection and classification in
general.
|
http://arxiv.org/abs/2304.00913v1
|
cs.CL
|
new_dataset
| 0.994545 |
2304.00913
|
PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels
|
Allowing organizations to share their data for training of machine learning
(ML) models without unintended information leakage is an open problem in
practice. A promising technique for this still-open problem is to train models
on the encoded data. Our approach, called Privately Encoded Open Datasets with
Public Labels (PEOPL), uses a certain class of randomly constructed transforms
to encode sensitive data. Organizations publish their randomly encoded data and
associated raw labels for ML training, where training is done without knowledge
of the encoding realization. We investigate several important aspects of this
problem: We introduce information-theoretic scores for privacy and utility,
which quantify the average performance of an unfaithful user (e.g., adversary)
and a faithful user (e.g., model developer) that have access to the published
encoded data. We then theoretically characterize primitives in building
families of encoding schemes that motivate the use of random deep neural
networks. Empirically, we compare the performance of our randomized encoding
scheme and a linear scheme to a suite of computational attacks, and we also
show that our scheme achieves competitive prediction accuracy to raw-sample
baselines. Moreover, we demonstrate that multiple institutions, using
independent random encoders, can collaborate to train improved ML models.
|
http://arxiv.org/abs/2304.00047v1
|
cs.LG
|
not_new_dataset
| 0.992232 |
2304.00047
|
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