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

作者： Ponnusamy, Rahul Pannerselvam, Kathiravan Rajiakodi, Saranya Kumaresan, Prasanna Kumar Thavareesan, Sajeetha Sivagnanam, Bhuvaneswari Anshid, K.A. Kumar, Susminu S. Buitelaar, Paul Chakravarthi, Bharathi Raja Data Science Institute University of Galway Ireland Department of Computer Science Central University of Tamil Nadu India Department of Computing Eastern University Sri Lanka WMO Imam Gazzali Arts and Science College Kerala India Revemax International Pvt.Ltd. Bangalore India

ISBN: (纸本)9782493814104

In this digital era, memes have become a prevalent online expression, humor, sarcasm, and social commentary. However, beneath their surface lies concerning issues such as the propagation of misogyny, gender-based bias, and harmful stereotypes. To overcome these issues, we introduced MDMD (Misogyny Detection Meme dataset) in this paper. This article focuses on creating an annotated dataset with detailed annotation guidelines to delve into online misogyny within the Tamil and Malayalam-speaking communities. Through analyzing memes, we uncover the intricate world of gender bias and stereotypes in these communities, shedding light on their manifestations and impact. This dataset, along with its comprehensive annotation guidelines, is a valuable resource for understanding the prevalence, origins, and manifestations of misogyny in various contexts, aiding researchers, policymakers, and organizations in developing effective strategies to combat gender-based discrimination and promote equality and inclusivity. It enables a deeper understanding of the issue and provides insights that can inform strategies for cultivating a more equitable and secure online environment. This work represents a crucial step in raising awareness and addressing gender-based discrimination in the digital space. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Classification (of information)

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MatrikalinDiabetes: User-Centered Design of a mHealth App for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women 26th

MatrikalinDiabetes: User-Centered Design of a mHealth App ...

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26th International Conference on Human-Computer Interaction, HCII 2024

作者： Ratul, Mohammad Arshad Hossain Yanoor Bristy, Tunisha Sayeed, Noorjahan Islam, Ashraful Chaudhry, Beenish Moalla Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana United States

ISBN: (纸本)9783031768088

This study employs the User-Centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects approximately 10% of pregnant women globally and around 35% within Bangladesh, as reported by the World Health Organization (WHO). Despite its high prevalence, there is a significant lack of awareness and education about GDM in Bangladesh, compounded by language barriers that make existing digital solutions less accessible. The MatrikalinDiabetes mHealth app aims to overcome these barriers by providing comprehensive management and educational resources for GDM in the Bangla language, addressing both pre-pregnancy and post-pregnancy needs. To ensure the app meets the actual needs of potential users, personas were created based on a literature review, and a survey was conducted. This process informed the development of both a low-fidelity, paper-based prototype and a high-fidelity digital prototype. Key features of the MatrikalinDiabetes app include tracking of food and water intake, physical activity monitoring, reminders, GDM education, chat forums for community support, blood glucose level (BGL) monitoring, an SOS button for emergency contacts, and quizzes for user engagement. Preliminary feedback indicated that 53.8% of participants favored the integrated features and expressed willingness to use and recommend the app. The survey of the high-fidelity prototype yielded positive responses, with 57.9% of participants strongly agreeing and 42.1% agreeing that the features were beneficial. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, aiding in health management, enhancing GDM understanding, and promoting a healthier lifestyle among pregnant women in Bangladesh. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

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Convergence Analysis of Hierarchical Split Federated Learning

Convergence Analysis of Hierarchical Split Federated Learnin...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Hualei Du, Jun Hou, Xiangwang Jiang, Chunxiao Wang, Jintao Niyato, Dusit Tsinghua University Department of Electronic Engineering Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China Nanyang Technological University College of Computing and Data Science Nanyang Avenue 639798 Singapore

ISBN: (纸本)9798350351255

Federated Learning (FL) enables distributed intelligence in Internet of Things (IoT) networks, facilitating decentralized machine learning without the need for exchanging raw data. However, the growing complexity of training models significantly hinders their deployment on resource-constrained IoT devices. To address this challenge, Split Federated Learning (SFL) has emerged as a promising solution by partitioning the entire model into client-side and server-side sub-models to alleviate the computational burden on IoT devices. Considering that the client-edge-cloud architecture can enhance data privacy, support connections to a wider range of devices, and reduce communication costs, we explore a hierarchical SFL (HierSFL) system. This system is supported by a HierSFL algorithm that allows for different aggregation frequencies between the client-side and server-side sub-models. Then, we present a convergence analysis of HierSFL that quantifies the effects of client-side and server-side model aggregation on learning performance, providing a theoretical foundation. Empirical experiments verify the theoretical analysis and demonstrate the superiority of the hierarchical architecture within a wireless IoT network. In particular, it is validated that adopting different aggregation frequencies can enhance the training performance. Moreover, the HierSFL algorithm outperforms traditional hierarchical FL algorithm, achieving superior test accuracy in a shorter time. © 2024 IEEE.

关键词： Federated learning

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Enhancing data Accessibility in COVID Research - A Synthetic data Generator for the RADx data Hub 1

Enhancing Data Accessibility in COVID Research - A Synthetic...

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1st IEEE International Conference on Artificial Intelligence for Medicine, Health and Care, AIMHC 2024

作者： Ahmad, Asiyah Roupe, Griffin Samsa, Leigh Ann Krishnamurthy, Ashok Hubal, Robert Computer Science University of North Carolina at Chapel Hill Chapel Hill United States School of Data Science and Society University of North Carolina at Chapel Hill Chapel Hill United States Renaissance Computing Institute Chapel Hill United States

ISBN: (纸本)9798350371987

The NIH RADx data Hub serves as an access platform for COVID-19 research studies funded by the U.S. National Institutes of Health (NIH). The RADx data Hub contains a range of patient-related information, including diagnostic test data, demographic details, and clinical records. Controlled access to data safeguards patient privacy, but also presents challenges for users unfamiliar with the platform, developers seeking to test processes, and curators aiming to showcase its features. Meanwhile, using actual patient data raises confidentiality concerns and timing challenges, such as needing to get requisite approval. To address these limitations, synthetic datasets support generation of simulated patients, enabling effective learning and testing of the system with data closely resembling real study data. This paper describes a dynamic synthetic data generator for COVID-19 research data based on RADx codebooks and external sources. The generator empowers RADx data Hub users to model diverse patient cohorts, generate hypotheses, and attain a better grasp of existing COVID-19 data that they may access through the RADx data Hub. ©2024 IEEE.

关键词： COVID-19

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Autonomous Emergency Landing on 3D Terrains: Approaches for Monocular Vision-based UAVs 8

Autonomous Emergency Landing on 3D Terrains: Approaches for ...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Du, Weiming Lin, Junmou Du, Binqing Borhan, Uddin Md. Li, Jianqiang Chen, Jie College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9798350300178

With the increasing use of unmanned aerial vehicles (UAVs) in a variety of applications, their safety has become a critical concern. UAVs face numerous emergencies during missions, in these situations, the UAVs need to autonomously find a suitable landing site, plan flight routes, and avoid obstacles in unstructured environments. However, due to the limitations of computing power and sensors, it is challenging to achieve this goal. This research aims to explore the monocular emergency autonomous landing algorithm. Specifically, this work focuses on extracting depth and vision information. A topology information extractor is designed to convert image to graph and assess the connectivity of terrain. Additionally, a depth information extractor is designed to calculate the slope and roughness of the ground. A 3D topology optimizer is designed to optimize the graph by depth information and evaluate the landing suitability by a heuristic strategy. To make the action decision, a hybrid decision network (DGN) based on 3D topology information is proposed. Finally, this work build a simulated scenario based on real scene to verify the efficiency of DGN. The results of the experiment show that DGN outperforms its counterparts in terms of action prediction accuracy and landing success rate. © 2023 IEEE.

关键词： Landing

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SAIF: A Comprehensive Framework for Evaluating the Risks of Generative AI in the Public Sector

arXiv

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arXiv 2025年

作者： Lee, Kyeongryul Kim, Heehyeon Whang, Joyce Jiyoung Graduate School of Data Science KAIST Korea Republic of School of Computing KAIST Korea Republic of

The rapid adoption of generative AI in the public sector, encompassing diverse applications ranging from automated public assistance to welfare services and immigration processes, highlights its transformative potential while underscoring the pressing need for thorough risk assessments. Despite its growing presence, evaluations of risks associated with AI-driven systems in the public sector remain insufficiently explored. Building upon an established taxonomy of AI risks derived from diverse government policies and corporate guidelines, we investigate the critical risks posed by generative AI in the public sector while extending the scope to account for its multimodal capabilities. In addition, we propose a Systematic data generatIon Framework for evaluating the risks of generative AI (SAIF). SAIF involves four key stages: breaking down risks, designing scenarios, applying jailbreak methods, and exploring prompt types. It ensures the systematic and consistent generation of prompt data, facilitating a comprehensive evaluation while providing a solid foundation for mitigating the risks. Furthermore, SAIF is designed to accommodate emerging jailbreak methods and evolving prompt types, thereby enabling effective responses to unforeseen risk scenarios. We believe that this study can play a crucial role in fostering the safe and responsible integration of generative AI into the public sector. Copyright © 2025, The Authors. All rights reserved.

关键词： Energy policy

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Set pair three-way overlapping community discovery algorithm for weighted social internet of things

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Digital Communications and Networks 2023年第1期9卷 3-13页

作者： Chunying Zhang Jing Ren Lu Liu Shouyue Liu Xiaoqi Li Liya Wang College of Science North China University of Science and TechnologyTangshanHebeiChina Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshanHebeiChina Hebei Key Laboratory of Data Science and Application North China University of Science and TechnologyTangshanHebeiChina The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshanHebeiChina Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and TechnologyTangshanHebeiChina

There are many problems in Social Internet of Things(IoTs),such as complex topology information,different degree of association between nodes and overlapping *** idea of set pair information grain computing and clustering is introduced to solve the above problems so as to accurately describe the similarity between nodes and fully explore the multi-community structure.A Set Pair Three-Way Overlapping Community Discovery Algorithm for Weighted Social Internet of Things(WSIoT-SPTOCD)is *** the local network structure,which fully considers the topological information between nodes,the set pair connection degree is used to analyze the identity,difference and reverse of neighbor *** similarity degree of different neighbor nodes is defined from network edge weight and node degree,and the similarity measurement method of set pair between nodes based on the local information structure is *** to the number of nodes'neighbors and the connection degree of adjacent edges,the clustering intensity of nodes is defined,and an improved algorithm for initial value selection of k-means is *** nodes are allocated according to the set pair similarity between nodes and different ***-way community structures composed of a positive domain,boundary domain and negative domain are generated ***,the overlapping node set is generated according to the calculation results of community node ***,experiments are carried out on artificial networks and real *** results show that WSIoT-SPTOCD performs well in terms of standardized mutual information,overlapping community modularity and F1.

关键词： Social internet of things Set pair analysis K-means Local information structure Overlapping community

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CryptoASCII-ASCII based cryptosystem for cloud data storage

CryptoASCII-ASCII based cryptosystem for cloud data storage

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International Conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Pavithra, M. Prakash, M. Vennila, V. Tamil Nadu Komarapalayam India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computer Science and Engineering K. S. R. College of Engineering Tamil Nadu Tiruchengode India

ISBN: (纸本)9781032669663

Cloud computing plays a huge role in IT industry, Education sector, government sector, medical field, defense management, Natural disaster control, weather reporting, road traffic maintenances, e-money transaction etc. The cloud provides wide amount of services can include delivery of data, software, hardware, infrastructure, platform, networking and so on. The merits of cloud computing are numerous to the user for data storing, accessing, maintenance in the cloud from anywhere at any time at low cost. On the other hands, the data in the cloud were not maintained by the direct owner. Despite, the data security has always been big challenge in cloud storage. data integrity, confidentiality and privacy protection are the prominent factors of the user’s side from the cloud environment. In this proposed paper, we introducing a new method CryptoASCII to provide data security in the cloud storage. By CryptoASCII, we confirmed that the user’s data are maintained with integrity and confidentiality. CryptoASCII can provide encryption and decryption algorithms to ensure data privacy as well as it provide redundancy checking proof for data integrity. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Redundancy

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Debiasing Computer Vision Models using data Augmentation based Adversarial Techniques 3

Debiasing Computer Vision Models using Data Augmentation bas...

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3rd Workshop on Bias, Risk, Ethical AI, Explainability and the Role of Logic and Logic Programming, BEWARE 2024

作者： Kumar, Teerath Mandal, Abhishek Leavy, Susan Little, Suzanne Mileo, Alessandra Bendechache, Malika CRT-AI School of Computing Dublin City University Dublin Ireland Insight SFI Research Center for Data Analytics School of Computing Dublin City University Dublin Ireland Insight SFI Research Center for Data Analytics School of Information and Communication Studies University College Dublin Dublin Ireland ADAPT & Lero Research Centres School of Computer Science University of Galway Galway Ireland

Deep learning models in computer vision, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have been found to exhibit significant biases related to factors such as gender and ethnicity. These biases often originate from inherent imbalances in training data predominantly sourced from the internet. In this study, we aim to address gender bias in computer vision models by curating a specialized dataset that highlights gender-related disparities. Additionally, we measure dataset diversity across six datasets (FFHQ, WIKI, IMDB, LFW, UTK Faces, diverse dataset), five professions (CEO, engineer, nurse, politician, and teacher) and different query retrieval tasks using the Image Similarity Score (ISS). To reduce learned gender biases and increase data diversity, we propose adversarial data augmentation techniques that specifically target facial regions within images. These techniques, named Partial Mix (PM), that partially mixes two gendered faces in a squared pattern, and Noise Addition (NA), that adds noise to the facial region, are designed to mitigate bias. Our experimental results demonstrate increased data diversity across the six datasets and professions, along with reduction in gender bias for CNN-based models. However, these adversarial techniques were less effective in reducing bias for Vision Transformers. This discrepancy highlights the unique challenges for bias mitigation posed by ViTs. Consistent with prior research, our findings indicate that ViTs learn from a broader set of visual cues compared to CNNs. This increased sensitivity makes ViTs more prone to amplifying biases, emphasizing the need for tailored bias mitigation strategies when deploying these models in real-world applications. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

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UAV-enabled Collaborative Secure data Transmission via Hybrid-Action Multi-Agent Deep Reinforcement Learning

UAV-enabled Collaborative Secure Data Transmission via Hybri...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liu, Saichao Sun, Geng Teng, Siyu Li, Jiahui Zhang, Chuang Wang, Jiacheng Du, Hongyang Liu, Yinqiu College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Hong Kong Baptist University Kowloon Hong Kong Waytous Inc. Beijing China

ISBN: (纸本)9798350351255

With the advancement of smart cities, smart manufacturing, and smart transportation, the Internet of Things (IoT) big data platform operating on wireless networks has emerged as a pivotal sector. In such systems, unmanned aerial vehicles (UAVs) play an indispensable support due to their flexibility and adaptability, but the energy sensitivity and limited communication capabilities pose further challenges. In this paper, we study a UAV-assisted secure communication system, where a UAV-enabled virtual antenna array (UVAA) consisting of multiple UAVs communicates with a remote mobile user (MU) by executing collaborative beamforming (CB), and then an eavesdropper exists for eavesdropping the transmission data from UVAA to MU. Then, a UAV-enabled secure communication optimization problem is formulated to maximize the total secrecy rate between the UVAA and the MU by optimizing the roles, locations and excitation current weights of UAVs. Since the considered scenario is dynamic and the UAVs need to cooperate with each other, we propose a hybrid-action multi-agent deep reinforcement learning (MADRL) algorithm (HMAPPO) to efficiently solve the optimization problem. Simulation results verify the effectiveness of the HMAPPO and illustrate that it learns the best strategy compared with other baseline methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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