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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Kumar, Gaurav Devayani, G. Saraswathi, C. Melba Kani, R. Dongre, Sandeep Mallick, M. S. Mohamed School of Computer Application Lovely Professional University Phagwara India M.Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India School of Management Studies Bannari Amman Institute of Technology Erode India Mar Ephraem college of Engineering and Technology Kanyakumari India Nagpur India Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798331508456

Online retailers must monitor and respond to changing consumer trends to stay competitive. AI-driven customization's revolutionary impact on market trends is the focus of this study on online buying and customer behavior. As AI advances, e-commerce platforms will be able to customize user experiences. Preprocessing, feature extraction, and model training comprise the AI-driven personalization process. Customer data from online marketplaces is collected first. It manages missing values, balance the data, encode categorical variables, scale the features, and minimize the dimensions as part of our thorough data preprocessing to ensure fair and applicable results. To trained the algorithms with CNN and Convolutional LSTM Based on key criteria, the AI model beat LSTM and CNN. It provides personalized insights and recommendations with an average accuracy of 92.03%. Personalized experiences and product suggestions enabled by AI-powered personalization have transformed e-commerce. It's important to use smart algorithms to engage customers and examine large databases. This proposed approach to shows that AI can transform online shopping and improve customer experiences. © 2025 IEEE.

关键词： Marketplaces

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Research on Emotion Holder Extraction for Chinese Texts 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Sun, Yawei Shi, Yu Han, Xu School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing100876 China Institute of Scientific and Technical Information of China Beijing100038 China

ISBN: (纸本)9780000000002

Emotion holders are the entities in text that exhibit emotions, and their analysis is crucial for understanding emotional expressions in text. However, existing studies have not fully considered the phenomenon of co-reference among emotion holders, and the lack of Chinese corpora has also hindered the development of this research area. This paper constructs a dataset for the extraction of emotion holders from Chinese texts, effectively resolving the co-reference issues within the data. Additionally, a model that integrates semantic, emotional, and part-of-speech features is proposed. It achieves efficient extraction and resolution of emotion holders, surpassing baseline models in various performance metrics. Ablation studies further validate the rationality of the model design. © 2024 China National Conference on Computational Linguistics.

关键词： Computational linguistics

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Repairing Reed-Solomon Codes over Prime Fields via Exponential Sums

Repairing Reed-Solomon Codes over Prime Fields via Exponenti...

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IEEE International Symposium on Information Theory

作者： Roni Con Noah Shutty Itzhak Tamo Mary Wootters Blavatnik School of Computer Science Tel Aviv University Stanford Institute for Theoretical Physics Stanford University Department of Electrical Engineering-Systems Tel Aviv University Department of Computer Science and Electrical Engineering Stanford University

This paper presents several repair schemes for lowrate Reed Solomon (RS) codes over prime fields that can repair any node by downloading a constant number of bits from each surviving node. The resulting total bandwidth is higher than the bandwidth incurred during the trivial repair; however, this is still interesting in the context of leakage-resilient secret sharing. In that language, our results give attacks that show that k-out-of-n Shamir’s Secret Sharing over prime fields for small k is not leakage resilient, even if the parties only leak a constant number of bits. To the best of our knowledge, these are the first such *** another application, we provide decoding schemes for RS codes over prime fields, where the entire RS codeword is recovered by transmitting a constant number of bits from each *** results follow from a novel connection between exponential sums and repair of RS codes. In particular, we show that nontrivial bounds on certain exponential sums imply the existence of efficient nonlinear repair schemes for RS codes over prime fields.

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Leveraging Large Vision-Language Model For Better Automatic Web GUI Testing

arXiv

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arXiv 2024年

作者： Wang, Siyi Wang, Sinan Fan, Yujia Li, Xiaolei Liu, Yepang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Hong Kong University of Science and Technology Hong Kong

With the rapid development of web technology, more and more software applications have become web-based in the past decades. To ensure software quality and user experience, various techniques have been proposed to automatically test web applications by interacting with their GUIs. To achieve high functional coverage, web GUI testing tools often need to generate high-quality text inputs and interact with the associated GUI elements (e.g., click submit buttons). However, developing a holistic approach that solves both subtasks is challenging because the web GUI context can be complicated and highly dynamic, which is hard to process programmatically. The recent development of large vision-language models (LVLM) provides new opportunities to handle these longstanding problems. We in this paper propose VETL, the first LVLM-driven end-to-end web testing technique. With LVLM’s scene understanding capabilities, VETL can generate valid and meaningful text inputs focusing on the local context, while avoiding the need to extract precise textual attributes. The selection of associated GUI elements is formulated as a visual question answering problem, allowing LVLM to capture the logical connection between the input box and the relevant element based on visual instructions. Further, the GUI exploration is guided by a multi-armed bandit module employing a curiosity-oriented strategy. Experiments show that VETL is effective in exploring web state/action spaces and detecting bugs. Compared with WebExplor, the state-of-the-art web testing technique, VETL can discover 25% more unique web actions on benchmark websites. Moreover, it can expose functional bugs in top-ranking commercial websites, which have been confirmed by the website maintainers. Our work makes the first attempt of leveraging LVLM in end-to-end GUI testing, demonstrating promising results of this research direction. Copyright © 2024, The Authors. All rights reserved.

关键词： Application programs

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Content Moderation and the Formation of Online Communities: A Theoretical Framework

arXiv

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arXiv 2023年

作者： Dwork, Cynthia Hays, Chris Kleinberg, Jon Raghavan, Manish Department of Computer Science Harvard University United States Institute for Data Systems and Society Massachusetts Institute of Technology United States Departments of Computer Science and Information Science Cornell University United States Sloan School of Management Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

We study the impact of content moderation policies in online communities. In our theoretical model, a platform chooses a content moderation policy and individuals choose whether or not to participate in the community according to the fraction of user content that aligns with their preferences. The effects of content moderation, at first blush, might seem obvious: it restricts speech on a platform. However, when user participation decisions are taken into account, its effects can be more subtle - and counter-intuitive. For example, our model can straightforwardly demonstrate how moderation policies may increase participation and diversify content available on the platform. In our analysis, we explore a rich set of interconnected phenomena related to content moderation in online communities. We first characterize the effectiveness of a natural class of moderation policies for creating and sustaining communities. Building on this, we explore how resource-limited or ideological platforms might set policies, how communities are affected by differing levels of personalization, and competition between platforms. Our model provides a vocabulary and mathematically tractable framework for analyzing platform decisions about content moderation. Copyright © 2023, The Authors. All rights reserved.

关键词： Online systems

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Self-Supervised Signed Graph Attention Network for Social Recommendation

Self-Supervised Signed Graph Attention Network for Social Re...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qin Zhao Gang Liu Fuli Yang Ru Yang Zuliang Kou Dong Wang Department of Computer Science and Technology Shanghai Normal University Shanghai China Shanghai Newtouch Software Company Limited Shanghai China School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai China

In recent times, social recommendation has become a popular technique in recommender systems due to its ability to enhance the accuracy of recommendations by leveraging the social relationships among users. Despite its widespread use, the prevalent social recommendation methods are often marred by sparsity and noise issues that negatively impact their practicality. Additionally, these methods fail to consider complex user interactions, which could potentially provide additional information. To address these limitations, this paper proposes a novel self-supervised signed graph attention network (SSAN) that incorporates user attitudes in the construction of higher-order user relations. This approach integrates signed networks in the formation of reasonable higher-order local neighborhood relations and aggregates user interests in different social relations through graph convolution and balance theory. Furthermore, two self-supervised signals, derived from social theory, are designed and incorporated into the recommendation framework to better exploit the rich structural and semantic information in social relationship graphs. Empirical results on three publicly available datasets demonstrate that SSAN outperforms existing state-of-the-art social recommendation methods.

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scDCMAC:adaptive feedback clustering of scRNA-seq data with deep gene correlation mining

scDCMAC:adaptive feedback clustering of scRNA-seq data with ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shanchen Pang Ruiqian Wang Wenhao Wu Hengxiao Li Jiarui Wu Yuanyuan Zhang Shudong Wang College of Computer Science and Technology Qingdao Institute of Software China University of Petroleum (East China) Qingdao China School of Information and Control Engineering QINGDAO University of Technology Qingdao China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Cluster assignment is crucial for analyzing single-cell RNA sequencing (scRNA-seq) data, essential for studying cell diversity and biological functions at the single-cell level. Deep learning-based clustering methods have been extensively applied in scRNA-seq data analysis. However, current deep models frequently overlook the deep exploration of potential gene correlation, resulting in networks lacking guidance from gene correlation information when learning hidden layer features. This paper proposes an adaptive feedback fuzzy clustering method based on deep gene correlation mining(scDCMAC). The deep gene correlation mining network employs contrast learning to compute the contrast loss on two cell similarity matrices, constructed from denoised autoencoder hidden layer features and original input features, to uncover potential gene correlation. The adaptive feedback clustering network further denoises the hidden layer features using a self-attention mechanism, computes the affiliation matrix, and supervises the denoising autoencoder’s dimensionality reduction using the affiliation matrix. Experiments on 13 real scRNA-seq datasets demonstrate that scDCMAC excels in clustering accuracy and stability, showing great potential in scRNA-seq sample analysis.

关键词： Dimensionality reduction Sequential analysis Correlation Data analysis Clustering methods RNA Autoencoders Noise reduction Stability analysis Data mining

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Enhancing Security with Machine Learning-based Finger-Vein Biometric Authentication System

Enhancing Security with Machine Learning-based Finger-Vein B...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： G Prakash Babu Upendra Singh Aswal M. Sindhu Vijay Jagdish Upadhye Natrayan L Harshal Patil Department of Computer Science and Engineering Acharya Institute of Technology Bangalore Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Business Systems R.M.K. Engineering College Thiruvallur Tamil Nadu India Department of Microbiology Research and Development Cell Parul Institute of Applied Sciences Parul University Waghodia Gujarat India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India

Biometric authentication systems have gained popularity as the necessity for secure individual identification has expanded. Iris, hand geometry, fingerprints, retina, vein patterns on the fingers and palms, and voice patterns are all common biometric features. Biometric recognition systems based on finger-veins are becoming more common due to their numerous advantages, including low danger of duplication or fraud, minimum invasiveness, and excellent identification accuracy. Motivated by these advantages, our research looks into the subject of finger-vein authentication. Similar to identification, exploration of hidden patterns within data is essential for effective finger-vein recognition. The study employs two feature extraction approaches, the Local Binary Pattern (LBP) and the Gabor filter, as well as two machine learning models, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector Machine (SVM). Using finger-vein images from the SDUMLA HMT database, this study evaluates four distinct feature extraction and ML model combinations. The simulation results highlight the excellent accuracy of 97.72%, False Acceptance Rate (FAR) of 3.1%, and False Rejection Rate (FRR) of 1.41% attained by LBP with ANFIS. In addition to performance measurements, the study examines the amount of time required for each task.

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Shield-U: Safeguarding Traffic Sign Recognition Against Perturbation Attacks

Shield-U: Safeguarding Traffic Sign Recognition Against Pert...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Shengmin Xu Jianfei Sun Hangcheng Cao Yulan Gao Ziyang He Cong Wu College of Computer and Cyber Security Fujian Normal University China School of Computing and Information Systems Singapore Management University Singapore Department of Computer Science City University of Hong Kong China Division of Information Science and Engineering KTH Royal Institute of Technology Sweden School of Cyber Science and Engineering Zhengzhou University China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Traffic sign recognition systems are crucial for the navigation and situation awareness of autonomous vehicles. They leverage deep learning technologies to swiftly and accurately identify traffic signs, even in the most challenging traffic environments. However, security researchers have uncovered a critical vulnerability in these systems: learning-based TSRs are particularly susceptible to physical-world perturbation attacks. Through subtle modifications (i.e., attaching well-designed patches on traffic signs), attackers can deceive the recognition system into making erroneous judgments, which can further lead to serious traffic accidents. Although several defense mechanisms have been proposed to enhance the security of sign recognition systems, these solutions generally target only specific types of malicious perturbations and thus lack robustness. To address this issue, we present a robust defense mechanism named Shield-U, which restores traffic sign images contaminated by physical patch perturbations, providing credible data for the recognition model. In the process of implementing Shield-U, we first design a feature difference-aware perturbation generator that outputs potential sign contamination patterns. Incorporating generated perturbations during the training phase enables our restoration model to gain sufficient understanding of diverse perturbation types, thus enhancing its ability to repair various perturbed signs. Following this, we build an attention-driven restoration network to repair sign images. Finally, we evaluate the effectiveness of Shield-U using widely used sign recognition models and public datasets. The results demonstrate that our defense mechanism excels in resisting potential perturbations, increasing the average sign recognition accuracy by 50.4%.

关键词： Training Image recognition Target recognition Perturbation methods Maintenance engineering Robustness Image restoration Safety Security Public transportation

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A Data-Informed Analysis of Scalable Supervision for Safety in Autonomous Vehicle Fleets

A Data-Informed Analysis of Scalable Supervision for Safety ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Cameron Hickert Zhongxia Yan Cathy Wu Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Autonomous driving is a highly anticipated approach toward eliminating roadway fatalities. At the same time, the bar for safety is both high and costly to verify. This work considers the role of remotely-located human operators supervising a fleet of autonomous vehicles (AVs) for safety. Such a ‘scalable supervision’ concept was previously proposed to bridge the gap between still-maturing autonomy technology and the pressure to begin commercial offerings of autonomous driving. The present article proposes DISCES, a framework for Data-Informed Safety-Critical Event Simulation, to investigate the practicality of this concept from a dynamic network loading standpoint. With a focus on the safety-critical context of AVs merging into mixed-autonomy traffic, vehicular arrival processes at 1,097 highway merge points are modeled using microscopic traffic reconstruction with historical data from interstates across three California counties. Combined with a queuing theoretic model, these results characterize the dynamic supervision requirements and thereby scalability of the teleoperation approach. Across all scenarios we find reductions in operator requirements greater than 99% as compared to in-vehicle supervisors for the time period analyzed. The work also demonstrates two methods for reducing these empirical supervision requirements: (i) the use of cooperative connected AVs — which are shown to produce an average 3.67 orders-of-magnitude system reliability improvement across the scenarios studied — and (ii) aggregation across larger regions.

关键词： Road transportation Scalability System performance Predictive models Safety Reliability Vehicle dynamics Autonomous vehicles Queueing analysis Load modeling

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