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ICT Instruction and Access Impediments on Women from a Disadvantaged Urban Community: A South African Perspective 1

8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Moletsane, Ramadile Liebenberg, Janet Smit, Imelda Vaal University of Technology Vanderbijlpark South Africa Unit for Data Science and Computing North-West University Potchefstroom South Africa

ISBN: (数字)9789819758104

ISBN: (纸本)9789819758098

There is much value in not neglecting women for the socio-economic development of any society. Without access to Information Communication Technology (ICT) education and training women will continue to be excluded from social and economic leadership opportunities. Women lack ICT access and have fewer ICT-related training opportunities than men. The deliverance of women from all practices of repression could assist in bridging the gap between genders. This paper aims to establish which antecedents inhibit the access to instruction of ICT experienced by women in a disadvantaged urban community and to propose a solution. In this qualitative study, data was gathered through semi-structured interviews and purposive sampling was used to identify the women in a disadvantaged urban community. The data was analysed using qualitative content analysis assisted by *** software. The most reported issue as a barrier to ICT instruction and access is cost. Here cost is linked to Internet access, instruction tuition and personal purchases of the information and communication equipment. The least reported issues include social class and gender. The study recognized the sad situation faced by women from disadvantaged communities, regardless of progressive laws for women in South Africa. It is therefore recommended that awareness programmes be put in place to remind women about their constitutional rights. Education about human rights should be prioritized and enforced by the government and organizations that advocate women’s emancipation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Digital divide ICT education Inequality in ICT Knowledge gap Women empowerment

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Movie Reviews using Sentiment Analysis with Natural Language API 8

Movie Reviews using Sentiment Analysis with Natural Language...

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8th IEEE International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2023

作者： Athish, V.P. Rajeswari, D. Sree Nandha, S. Srm Institute of Science and Technology School of Computing Dept. of Data Science and Business Systems Tamilnadu Kattankulathur India

ISBN: (纸本)9798350347791

Sentiment Analysis is the process of analyzing sentiment of a piece of text by identifying polarity or writer's attitude towards any topic or product such as positive, neutral or negative. This paper uses sentiment analysis on movie reviews in twitter data, which is the most dominant public social media and its data is accessible using their own Twitter API. Words and misspellings make sentiment analysis for twitter data, harder due to the existence of slang words and bad spelling. This work uses Hadoop for the various features it provides over the API. The proposed work uses the knowledge base approach using lexica analyzer for sentiment analysis of movie reviews. The assigned score for movies is 100. The proposed work get all the positive, negative and neutral comments to judge "how good"or "how bad"the movie is. © 2023 IEEE.

关键词： Sentiment analysis

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Analysis of the Influence of Green Artificial Intelligence on Agricultural and Biological Fields Using Bibliometrics 5

Analysis of the Influence of Green Artificial Intelligence o...

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5th International Conference on Artificial Intelligence and data sciences, AiDAS 2024

作者： Anand, Jonitha Ab Ghani, Hadhrami Yusoff, Nooraini Kumar Thoti, Kiran Universiti Malaysia Faculty of Data Science & Computing Kelantan Malaysia M.S. Ramaiah Institute of Management Bangalore India

ISBN: (纸本)9798331528553

The research examines how Green Artificial Intelligence (AI) impacts agricultural and biological domains by analysing relevant literature using bibliometrics. Green AI, which prioritizes sustainability and environmental awareness, is proving to be an asset in tackling the issues in agriculture and biology. The investigation examines a wide range of academic publications, patents, and research papers to identify significant trends, research topics, and the influence of Green AI on agricultural and biological fields. The research shows an increasing amount of literature investigating the combination of AI technology with green principles to improve resource management, boost agricultural output, and reduce environmental effects. This study uses analysis of citation networks, co-authorship patterns, and theme grouping to get insights into the present status and future trends of research in the fields of Green AI, agriculture, and biology. The data was extracted from the database maintained by Scopus using the PRISM Model. 11,142 article data points were used for the analysis of Green AI. This bibliometric study enhances comprehension of the changing environment of Green AI applications in agricultural and biological areas, providing vital recommendations for academics, policymakers, and practitioners working towards sustainable development objectives. © 2024 IEEE.

关键词： Biotic

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A knowledge-enhanced interest segment division attention network for click-through rate prediction

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Neural computing and Applications 2024年第34期36卷 21817-21837页

作者： Liu, Zhanghui Chen, Shijie Chen, Yuzhong Su, Jieyang Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Click-through rate (CTR) prediction aims to estimate the probability of a user clicking on a particular item, making it one of the core tasks in various recommendation platforms. In such systems, user behavior data are crucial for capturing user interests, which has garnered significant attention from both academia and industry, leading to the development of various user behavior modeling methods. However, existing models still face unresolved issues, as they fail to capture the complex diversity of user interests at the semantic level, refine user interests effectively, and uncover users’ potential interests. To address these challenges, we propose a novel model called knowledge-enhanced Interest segment division attention network (KISDAN), which can effectively and comprehensively model user interests. Specifically, to leverage the semantic information within user behavior sequences, we employ the structure of a knowledge graph to divide user behavior sequence into multiple interest segments. To provide a comprehensive representation of user interests, we further categorize user interests into strong and weak interests. By leveraging both the knowledge graph and the item co-occurrence graph, we explore users’ potential interests from two perspectives. This methodology allows KISDAN to better understand the diversity of user interests. Finally, we extensively evaluate KISDAN on three benchmark datasets, and the experimental results consistently demonstrate that the KISDAN model outperforms state-of-the-art models across various evaluation metrics, which validates the effectiveness and superiority of KISDAN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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Beware of Road Markings: A New Adversarial Patch Attack to Monocular Depth Estimation 38

Beware of Road Markings: A New Adversarial Patch Attack to M...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Hangcheng Wu, Zhenhu Wang, Hao Han, Xingshuo Guo, Shangwei Xiang, Tao Zhang, Tianwei College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science Beijing University of Posts and Telecommunications China College of Computer Science Chongqing University China

Monocular Depth Estimation (MDE) enables the prediction of scene depths from a single RGB image, having been widely integrated into production-grade autonomous driving systems, e.g., Tesla Autopilot. Current adversarial attacks to MDE models focus on attaching an optimized adversarial patch to a designated obstacle. Although effective, this approach presents two inherent limitations: its reliance on specific obstacles and its limited malicious impact. In contrast, we propose a pioneering attack to MDE models that decouples obstacles from patches physically and deploys optimized patches on roads, thereby extending the attack scope to arbitrary traffic participants. This approach is inspired by our groundbreaking discovery: various MDE models with different architectures, trained for autonomous driving, heavily rely on road regions when predicting depths for different obstacles. Based on this discovery, we design the Adversarial Road Marking (AdvRM) attack, which camouflages patches as ordinary road markings and deploys them on roads, thereby posing a continuous threat within the environment. Experimental results from both dataset simulations and real-world scenarios demonstrate that AdvRM is effective, stealthy, and robust against various MDE models, achieving about 1.507 of Mean Relative Shift Ratio (MRSR) over 8 MDE models. The code is available at this Github Repo. © 2024 Neural information processing systems foundation. All rights reserved.

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Resource Allocation for Stable LLM Training in Mobile Edge computing 24

Resource Allocation for Stable LLM Training in Mobile Edge C...

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2024 International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile computing, MobiHoc 2024

作者： Liu, Chang Zhao, Jun Graduate College Nanyang Technological University Singapore Singapore College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798400705212

As mobile devices increasingly become focal points for advanced applications, edge computing presents a viable solution to their inherent computational limitations, particularly in deploying large language models (LLMs). However, despite the advancements in edge computing, significant challenges remain in efficient training and deploying LLMs due to the computational demands and data privacy concerns associated with these models. This paper explores a collaborative training framework that integrates mobile users with edge servers to optimize resource allocation, thereby enhancing both performance and efficiency. Our approach leverages parameter-efficient fine-tuning (PEFT) methods, allowing mobile users to adjust the initial layers of the LLM while edge servers handle the more demanding latter layers. Specifically, we formulate a multi-objective optimization problem to minimize the total energy consumption and delay during training. We also address the common issue of instability in model performance by incorporating stability enhancements into our objective function. Through novel fractional programming technique, we achieve a stationary point for the formulated problem. Simulations demonstrate that our method reduces the energy consumption as well as the latency, and increases the reliability of LLMs across various mobile settings. © 2024 Copyright held by the owner/author(s).

关键词： Mobile edge computing

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Elliptic Curve Diffie-Hellman based Privacy-Preserving Deduplication for Big data in Cloud Systems

Elliptic Curve Diffie-Hellman based Privacy-Preserving Dedup...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Madasu, Sairam Murugesan, Prakash Jaganathan, Humashankar Vellathur Pamulaparthyvenkata, Saigurudatta Masters of Science in Computer Science Cloud Computing and Artificial Intelligence United States Artificial Intelligence and Data Verizon United States Cbl Cgq Atlanta United States Big Data Independent Researcher United States

ISBN: (纸本)9798350360660

data deduplication is crucial for cloud computing, particularly in managing the expansive growth of outsourced mobile data. Some existing approaches are vulnerable to brute-force attacks and single-point attacks, posing significant security risks. Furthermore, it causes substantial complexity on the resource-constrained edge nodes and the lack of support for cross-domain deduplication. To overcome this problem, this research aims to propose an effective and secure big data deduplication approach in cloud computing. This research proposes the novel combined encryption algorithm of the Elliptic Curve with Diffie Hellman (ECDH) for data privacy preservation and deduplication. The effectiveness and the security of the ECDH approach permit it to manage the enhanced data volumes in the multi-domain framework without significant performance degradation. The experimental results show that the proposed ECDH approach attains the effective communication overhead of data upload and data retrieval of 5.98Kb and 1.39Kb on the data size of 2Kb respectively as compared to the existing methods EC and DH. © 2024 IEEE.

关键词： Differential privacy

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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： Yu, Erxin Li, Jing Xu, Chunpu Department of Computing The Hong Kong Polytechnic University Hong Kong Research Centre for Data Science & Artificial Intelligence Hong Kong

ISBN: (纸本)9782493814104

Social media platforms are daily exhibiting millions of events. To preliminarily predict the mainstream public reaction to these events, we study trendy response prediction to automatically generate top-liked user replies to social media events. While previous works focus on generating responses without factoring in popularity, we propose Popularity-Aligned Language Models (PopALM) to distinguish responses liked by a larger audience through reinforcement learning. Recognizing the noisy labels from user "likes", we tailor-make curriculum learning in proximal policy optimization (PPO) to help models capture the essential samples for easy-to-hard training. In experiments, we build a large-scale Weibo dataset for trendy response prediction, and its results show that PopALM can help boost the performance of advanced language models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Curricula

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Towards Harmless Rawlsian Fairness Regardless of Demographic Prior 38

Towards Harmless Rawlsian Fairness Regardless of Demographic...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xuanqian Li, Jing Tsang, Ivor W. Ong, Yew-Soon School of Computer Science and Engineering Beihang University China Institute of High-Performance Computing Agency for Science Technology and Research Singapore Centre for Frontier AI Research Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

Due to privacy and security concerns, recent advancements in group fairness advocate for model training regardless of demographic information. However, most methods still require prior knowledge of demographics. In this study, we explore the potential for achieving fairness without compromising its utility when no prior demographics are provided to the training set, namely harmless Rawlsian fairness. We ascertain that such a fairness requirement with no prior demographic information essential promotes training losses to exhibit a Dirac delta distribution. To this end, we propose a simple but effective method named VFair to minimize the variance of training losses inside the optimal set of empirical losses. This problem is then optimized by a tailored dynamic update approach that operates in both loss and gradient dimensions, directing the model towards relatively fairer solutions while preserving its intact utility. Our experimental findings indicate that regression tasks, which are relatively unexplored from literature, can achieve significant fairness improvement through VFair regardless of any prior, whereas classification tasks usually do not because of their quantized utility measurements. The implementation of our method is publicly available at https://***/wxqpxw/VFair. © 2024 Neural information processing systems foundation. All rights reserved.

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Power Optimization for Low Transmission Delay in Software Defined data Center Networks

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IEEE Transactions on Mobile computing 2025年第7期24卷 5716-5730页

作者： Zhao, Yong Cao, Jiannong Wang, Xingwei Li, Fuliang He, Qiang Liu, Xiaojie University of Electronic Science and Technology of China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Laboratory of Intelligent Collaborative Computing Chengdu611731 China Hong Kong Polytechnic University Department of Computing Hung Hom Hong Kong Northeastern University College of Computer Science and Engineering Shenyang110819 China Northeastern University College of Medicine and Biological Information Engineering Shenyang110819 China Pengcheng Laboratory Shenzhen518066 China

Software-Defined data Center Networks (SDDCNs) utilizes Software Defined Networking (SDN) as a network architecture to achieve highly flexible, programmable, and automated management of data Center Networks (DCNs). The high energy consumption of DCNs remains a persistent and significant challenge. Thus, the energy saving is crucial and imperative for DCNs. Current energy-efficient solutions primarily rely on flow consolidation and dynamic device sleeping techniques to reduce energy consumption. However, these approaches often yield long Flow Completion Time (FCT), potentially resulting in violations of service-level agreements, particularly for delay-sensitive applications. In this paper, we formulate the problem of minimizing the power consumption in SDDCNs as key objective while ensuring timely FCT for delay-sensitive applications. To solve this problem, we first introduce the Active Network Generation (ANG) approach, which generates a minimal active subnet with the least number of active devices while meeting the current traffic demand. Subsequently, we propose two algorithms based on the type of applications: the Delay-Tolerant Flow Route (DTFR) algorithm for delay-tolerant applications and the Delay-Sensitive Flow Route (DSFR) algorithm for delay-sensitive applications. Simulation results demonstrate that our propose solution achieves an energy-saving rate of up to 67.77% and significantly reduces FCT compared to benchmark solutions. © 2002-2012 IEEE.

关键词： Energy efficiency

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