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检索条件"机构=Data and Data Science"

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Reflecting the Male Gaze: Quantifying Female Objectification in 19th and 20th Century Novels 30

Reflecting the Male Gaze: Quantifying Female Objectification...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Luo, Kexin Mao, Yue Zhang, Bei Hao, Sophie Center for Data Science New York University United States

ISBN: (纸本)9782493814104

Inspired by the concept of the male gaze (Mulvey, 1975) in literature and media studies, this paper proposes a framework for analyzing gender bias in terms of female objectification-the extent to which a text portrays female individuals as objects of visual pleasure. Our framework measures female objectification along two axes. First, we compute an agency bias score that indicates whether male entities are more likely to appear in the text as grammatical agents than female entities. Next, by analyzing the word embedding space induced by a text (Caliskan et al., 2017), we compute an appearance bias score that indicates whether female entities are more closely associated with appearance-related words than male entities. Applying our framework to 19th and 20th century novels reveals evidence of female objectification in literature: we find that novels written from a male perspective systematically objectify female characters, while novels written from a female perspective do not exhibit statistically significant objectification of any gender. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： bias gender humanities literature representational harms sexism stereotypes

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Integrating Heterogeneous Gene Expression data through Knowledge Graphs for Improving Diabetes Prediction 7

Integrating Heterogeneous Gene Expression Data through Knowl...

引用

7th Workshop on Semantic Web Solutions for Large-Scale Biomedical data Analytics, SeWeBMeDa 2024

作者： Sousa, Rita T. Paulheim, Heiko Data and Web Science Group Universität Mannheim Germany

Diabetes is a worldwide health issue affecting millions of people. Machine learning methods have shown promising results in improving diabetes prediction, particularly through the analysis of diverse data types, namely gene expression data. While gene expression data can provide valuable insights, challenges arise from the fact that the sample sizes in expression datasets are usually limited, and the data from different datasets with different gene expressions cannot be easily combined. This work proposes a novel approach to address these challenges by integrating multiple gene expression datasets and domain-specific knowledge using knowledge graphs, a unique tool for biomedical data integration. KG embedding methods are then employed to generate vector representations, serving as inputs for a classifier. Experiments demonstrated the efficacy of our approach, revealing improvements in diabetes prediction when integrating multiple gene expression datasets and domain-specific knowledge about protein functions and interactions. Copyright © 2024 for this paper by its authors.

关键词： Forecasting

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Darknet Traffic Classification Using Machine Learning

Darknet Traffic Classification Using Machine Learning

引用

2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Pookpun, Phenphitcha Kosolsombat, Somkiat Luangwiriya, Taweewat Thammasat University Data Science And Innovation Pathum Thani Thailand

ISBN: (纸本)9798350370096

The Darknet is an overlay network that is difficult to access. It requires special software to prevent tracking by Internet Service Providers (ISP) and malicious actors. It is often associated with illegal activities. Therefore, a study was conducted on classifying Darknet Traffic using Machine Learning to detect user behaviors and identify potentially harmful activities. The CIC-Darknet2020 dataset was utilized, comprising 8 classes of user behavior. It is Browsing, Chat, Email, File-Transfer, P2P, Audio, Video, and VoIP. Due to the dataset's imbalance, SMOTE and ADASYN techniques were employed. After applying Machine Learning, it was observed that Random Forest achieved the highest accuracy in all experiments, reaching up to 93.1%. © 2024 IEEE.

关键词： Internet service providers

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Refining BERT Adversarial Attacks with Projected Gradient Descent 4

Refining BERT Adversarial Attacks with Projected Gradient De...

引用

4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Waghela, Hetvi Sen, Jaydip Rakshit, Sneha Praxis Business School Department of Data Science Kolkata India

ISBN: (纸本)9798350354218

Adversarial attacks against deep learning models represent a major threat to the security and reliability of natural language processing (NLP) systems. In this paper, we propose modifying the BERT-Attack framework, integrating Projected Gradient Descent (PGD) to enhance its effectiveness and robustness. The original BERT-Attack, designed for generating adversarial examples against BERT-based models, suffers from limitations such as a fixed perturbation budget and a lack of consideration for semantic similarity. The proposed approach in this work, PGD-BERT-Attack, addresses these limitations by leveraging PGD to iteratively generate adversarial examples while ensuring both imperceptibility and semantic similarity to the original input. Extensive experiments are conducted to evaluate the performance of PGD-BERT-Attack compared to the original BERT-Attack and other baseline methods. The results demonstrate that PGD-BERT-Attack achieves higher success rates in causing misclassification while maintaining low perceptual changes. Furthermore, PGD-BERT-Attack produces adversarial instances that exhibit greater semantic resemblance to the initial input, enhancing their applicability in real-world scenarios. Overall, the proposed modification offers a more effective and robust approach to adversarial attacks on BERT-based models, thus contributing to the advancement of defense against attacks on NLP systems. © 2024 IEEE.

关键词： Adversarial machine learning

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SOFT CONTRASTIVE LEARNING FOR TIME SERIES 12

SOFT CONTRASTIVE LEARNING FOR TIME SERIES

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Lee, Seunghan Park, Taeyoung Lee, Kibok Department of Statistics and Data Science Yonsei University Korea Republic of

Contrastive learning has shown to be effective to learn representations from time series in a self-supervised way. However, contrasting similar time series instances or values from adjacent timestamps within a time series leads to ignore their inherent correlations, which results in deteriorating the quality of learned representations. To address this issue, we propose SoftCLT, a simple yet effective soft contrastive learning strategy for time series. This is achieved by introducing instance-wise and temporal contrastive loss with soft assignments ranging from zero to one. Specifically, we define soft assignments for 1) instance-wise contrastive loss by the distance between time series on the data space, and 2) temporal contrastive loss by the difference of timestamps. SoftCLT is a plug-and-play method for time series contrastive learning that improves the quality of learned representations without bells and whistles. In experiments, we demonstrate that SoftCLT consistently improves the performance in various downstream tasks including classification, semi-supervised learning, transfer learning, and anomaly detection, showing state-of-the-art performance. Code is available at this repository: https://***/seunghan96/softclt. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Time series

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PhD Forum Abstract: Minimizing Carbon Footprint for Timely E-Truck Transportation 11

PhD Forum Abstract: Minimizing Carbon Footprint for Timely E...

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11th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, BuildSys 2024

作者： Su, Junyan Department of Data Science City University of HongKong Hong Kong

ISBN: (纸本)9798400707063

Electrifying heavy-duty trucks is essential for decarbonizing the transportation sector and combating climate change. To fully leverage the advantages of electric trucks (E-Trucks), we need to optimize the operational carbon footprint by carefully planning routes, speeds, and charging stops, despite complex logistics and tight schedules. We propose a novel stage-expanded graph formulation that simplifies these challenges, allowing us to develop an efficient algorithm with strong performance guarantees. Utilizing real-world data from U.S. highway network, we conduct extensive simulation and show that our approach not only enhances the inherent 44% carbon reduction from electrification alone but adds an extra 24%, totaling a 68% decrease. © 2024 Copyright held by the owner/author(s).

关键词： Carbon footprint

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Research on a fast sharing solution for real-time traffic conditions based on federated learning 4

Research on a fast sharing solution for real-time traffic co...

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4th International Conference on Green Communication, Network, and Internet of Things, CNIoT 2024

作者： Ou, Zhenyang Shao, Xuping Faculty of Data Science City University of Macau 999078 China

ISBN: (纸本)9781510685710

Federated learning has resolved the issue of privacy-protected data sharing among vehicles in vehicular network environments. However, traditional federated learning schemes suffer from long convergence times and significant network resource consumption. To fulfill the future needs for real-time traffic condition data and to alleviate the bandwidth pressure on backbone networks, this paper introduces a privacy-preserving federated learning scheme for rapid sharing of real-time traffic conditions. This system begins by designing a method that involves selecting high-quality networking partners and optimizing the federated network structure of vehicles, thereby enhancing the efficiency with which vehicles obtain real-time traffic condition data. The results demonstrate that the data update efficiency of this system exceeds that of conventional vehicular network federated learning mechanisms. It provides multiple benefits, such as reduced potential attack windows, improved data timeliness, and lower communication costs. © 2024 SPIE.

关键词： Differential privacy

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Enhancing Arrhythmia Detection Using an Ensemble of Transformer Models for Heartbeat Classification 10th

Enhancing Arrhythmia Detection Using an Ensemble of Transf...

引用

10th International Conference on Machine Learning, Optimization, and data science, LOD 2024

作者： Malūkaitė, Jaunė Treigys, Povilas Bernatavičienė, Jolita Institute of Data Science and Digital Technologies Vilnius University Vilnius Lithuania

ISBN: (纸本)9783031824838

Cardiovascular diseases stand as the leading cause of death globally. While many irregular heartbeats are not severe, certain rhythm disorders can increase the risk of stroke or heart failure. Therefore, distinguishing between various heartbeats is important for identifying heart disorders. Recognising them accurately from electrocardiograms is challenging even for skilled doctors, and patients may not always understand that they have heart issues. This research aims to identify an ensemble of transformer models for classifying specific arrhythmia heartbeats from electrocardiogram signals since it is difficult to correctly distinguish supra-ventricular premature heartbeats from normal heartbeats. The ensemble incorporates three deep learning models with modified architectures, using different class mappings for dataset transformations. The model’s performance is evaluated by comparing recall metrics with a baseline convolutional neural network model and a transformer model trained with an under-sampled dataset for predicting three heartbeat classes. The analysis shows that the ensemble model achieved higher recall metric values for supra-ventricular premature heartbeats and ventricular premature contractions while reaching high overall and per-class metrics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Creating Pelargonium dataset for Species Classification Task 1

Creating Pelargonium Dataset for Species Classification Task

引用

IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Zhumatay, Aruzhan Alzhanov, Almas Department of Computing and Data Science Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350364378

This paper presents a comprehensive dataset of pelargonium species that can be used to train classification and recognition models. The provided dataset consists of 1600 images divided by 4 Pelargonium species. The collection process involved manual photography and conducting inquiries with individuals possessing imagery and knowledge regarding Pelargonium species. In addition, the paper provides a baseline method for pelargonium classification that uses Resnet50 deep convolutional neural network architecture achieving an F1-score of 73,2%. This dataset and the results of the baseline model could be used as a benchmark for further research and enhancements in the field. It is particularly useful for researchers in data science and artificial intelligence to create competitive machine learning models. The objective of the study is to create a dataset to contribute to the development of flower monitoring and flower recognition in general which can prove beneficial in the fields of botanical research, agriculture, and commerce. ©2024 IEEE |

关键词： Classification (of information)

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Improving Temporal Action Segmentation and Detection with Hierarchical Task Grammar 27th

Improving Temporal Action Segmentation and Detection with ...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Yihui, Qiu Rajan, Deepu College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9783031781094

Human activities are inherently task-oriented, and integrating explicit task learning into action segmentation models is hypothesized to enhance performance. However, empirical evaluations using the Ego4D Goal Step dataset reveal a paradox: the inclusion of learning tasks deteriorated model performance. This issue partially arises from limited task samples, i.e., over 50% of tasks have less than two training samples, leading to bias and overfitting in the training process. To address this, we propose a novel grammar induction method to accurately capture the hierarchical decomposition of a task with limited task samples, and use the induced grammar to guide neural predictions. Experiments demonstrate that our induction method achieves comparable results with SOTA on the Breakfast dataset with as few as two training samples for each task. Additionally, incorporating our grammar significantly boosts temporal action segmentation results for Ego4D Goal Step dataset by 8%. This approach not only mitigates data scarcity but also enhances the robustness and accuracy of action segmentation and action detection models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Context sensitive grammars

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