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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Abdulwadood Alawadhi Abdullah Almogahed Mohd. Hasbullah Omar InterNetWorks Research Laboratory School of Computing Universiti Utara Malaysia Sintok-Kedah Malaysia Department of Software Engineering Faculty of Engineering and Information Technolog Taiz University Taiz Yemen

Networks that use visible light communication (VLC) are suitable for meeting 5G and 6G criteria. The VLC is a new wireless high-speed communication technology. To meet the bandwidth requirements of bandwidth-hungry devices, VLC makes use of the illumination infrastructure. VLC is an excellence alternative to communication 6G technologies because of its immunology to electromagnetic interference, high data rate, high reuse factor and capacity to operate in an unlicensed band. Although the standard of IEEE 802.15.7 would provide detailed physical and medium access control layer standards, the emerging VLC technology confronts MAC issues. However, MAC-specific solutions in wireless networks need not resolve VLC network issues. Full-duplex capability, deafness, channel use, hidden nodes, and connectivity should all be considered in VLC MAC approaches.

关键词： IEEE 802.15 Standard 6G mobile communication Measurement Visualization Technological innovation Wireless networks Full-duplex system

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Secure software Development in Google Colab

Secure Software Development in Google Colab

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AI IoT Congress (AIIoT), World

作者： Tarun Potluri Yong Shi Hossain Shahriar Dan Lo Reza Parizi Hongmei Chi Kai Qian College of Computing and Software Engineering Kennesaw State University USA Department of Computer and Information Sciences Florida A&M University Tallahassee FL

software is said to be secure if it has been developed and designed with security as its top priority. This entails implementing secure coding practices, performing vulnerability testing and repair, as well as continuing to monitor and maintain the product to ensure the program remains secure over time. Since it may lessen the likelihood of security breakdowns, protect sensitive data and infrastructure, and maintain users' and clients' trust, secure software is essential. As technology advances and more aspects of our lives rely on it, the need for secure software has become more critical to preserving the safety and security of our digital environment. In this paper, we discuss vulnerabilities in software development such as SQL Injection and XSS Attacks, analyze the insecure cases related to them, and demonstrate the prevention approaches which are implemented in Google Colab with relevant libraries.

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A Multiple-Path Learning Neural Network Model for Code Completion

A Multiple-Path Learning Neural Network Model for Code Compl...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Liu, Yi Liu, Jianxun Zhang, Xiangping Hu, Haize Hunan University of Science and Technology Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Xiangtan411201 China Hunan University of Humanities Science and Technology School of Informationn Loudi417000 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350304855

Code completion, which can accelerate the software development process and improve the quality of software products, is an essential part of today's integrated development environments. It has become an important research topic in the field of software engineering. Recent studies have shown that the method of code completion based on the Abstract Syntax Tree (AST) learns syntactic information about the code, which helps to improve the accuracy of code completion. However, when modeling neural networks for ASTs, the sequencing operation of nodes leads to the loss of their hierarchical structure information. Meanwhile, traditional neural networks cannot predict many Out-of-Vocabulary (OoV) words in the terminal node values of AST. To alleviate the above problem, in this paper, we propose a Multiple-Path Learning neural network model for code completion (MPL) based on an AST by learning from a large-scale corpus. In this model, multiple paths such as context path, root path, and terminal node path are established to understand different code features required for node prediction and improve code representation ability. Based on the principle of program local repeatability, it also adopts a replication mechanism to copy the appropriate OoV words from the local terminal node path as the prediction result, further improving the prediction accuracy. The experimental results show that the MPL model has better performance than existing methods on the code completion task. © 2023 IEEE.

关键词： Neural network models

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Practical and Efficient Model Extraction of Sentiment Analysis APIs

Practical and Efficient Model Extraction of Sentiment Analys...

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International Conference on software engineering (ICSE)

作者： Weibin Wu Jianping Zhang Victor Junqiu Wei Xixian Chen Zibin Zheng Irwin King Michael R. Lyu School of Software Engineering Sun Yat-sen University Department of Computer Science and Engineering The Chinese University of Hong Kong Department of Computing The Hong Kong Polytechnic University Tencent

Despite their stunning performance, developing deep learning models from scratch is a formidable task. Therefore, it popularizes Machine-Learning-as-a-Service (MLaaS), where general users can access the trained models of MLaaS providers via Application Programming Interfaces (APIs) on a pay-per-query basis. Unfortunately, the success of MLaaS is under threat from model extraction attacks, where attackers intend to extract a local model of equivalent functionality to the target MLaaS model. However, existing studies on model extraction of text analytics APIs frequently assume adversaries have strong knowledge about the victim model, like its architecture and parameters, which hardly holds in practice. Besides, since the attacker's and the victim's training data can be considerably discrepant, it is non-trivial to perform efficient model extraction. In this paper, to advance the understanding of such attacks, we propose a framework, PEEP, for practical and efficient model extraction of sentiment analysis APIs with only query access. Specifically, PEEP features a learning-based scheme, which employs out-of-domain public corpora and a novel query strategy to construct proxy training data for model extraction. Besides, PEEP introduces a greedy search algorithm to settle an appropriate architecture for the extracted model. We conducted extensive experiments with two victim models across three datasets and two real-life commercial sentiment analysis APIs. Experimental results corroborate that PEEP can consistently outperform the state-of-the-art baselines in terms of effectiveness and efficiency.

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Hierarchical Crowdsourcing for Data Labeling with Heterogeneous Crowd 39

Hierarchical Crowdsourcing for Data Labeling with Heterogene...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Zhang, Haodi Huang, Wenxi Su, Zhenhan Chen, Junyang Jiang, Di Fan, Lixin Zhang, Chen Lian, Defu Wu, Kaishun Shenzhen University College of Computer Science and Software Engineering China Shenzhen University WeBank Institute of Financial Technology China Webank Co. Ltd. China Hong Kong Polytechnic University Department of Computing Hong Kong University of Science and Technology of China School of Data Science China

ISBN: (纸本)9798350322279

With the rapid and continuous development of data-driven technologies such as supervised learning, high-quality labeled data sets are commonly required by many applications. Due to the easiness of crowdsourcing small tasks with low cost, a straightforward solution for label quality improvement is to collect multiple labels from a crowd, and then aggregate the answers. The aggregation strategies include majority voting and its many variants, EM-based approaches, Graph Neural Nets and so on. However, due to the uncertainty information loss and commonly existing task correlations, the aggregated labels usually contain errors and may damnify the downstream model *** address the above problem, we propose a hierarchical crowdsourcing framework1 for data labeling with noisy answers about correlated data. We make use of the heterogeneity of the labeling crowd and form an initialization-checking-update loop to improve the quality of labeled data. We formalize and successfully solve the core optimization problem, namely, selecting a proper set of checking tasks for each round. We prove that maximizing the expected quality improvement is equivalent to minimizing the conditional entropy of the observations given the crowdsourced answer families for the selected task set, which is NP-hard to solve. Therefore, we design an efficient approximation algorithm and conduct a series of experiments on real data. The experimental results show that the proposed method effectively improves the quality of the labeled data sets as well as the SOTA performance, yet without extra human labor costs. © 2023 IEEE.

关键词： Approximation algorithms

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Visual Perturbation for Text-Based Person Search 39

Visual Perturbation for Text-Based Person Search

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Pengcheng Yu, Xiaohan Bai, Xiao Zheng, Jin School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Jiangxi Research Institute Beihang University Beijing China School of Computing Macquarie University Sydney Australia

ISBN: (纸本)157735897X

Text-based person search aims at locating a person described by natural language in uncropped scene images. Recent works for TBPS mainly focus on aligning multi-granularity vision and language representations, neglecting a key discrepancy between training and inference where the former learns to unify vision and language features where the visual side covers all clues described by language, yet the latter matches image-text pairs where the images may capture only part of the described clues due to perturbations such as occlusions, background clutters and misaligned boundaries. To alleviate this issue, we present ViPer: a Visual Perturbation network that learns to match language descriptions with perturbed visual clues. On top of a CLIP-driven baseline, we design three visual perturbation modules: (1) Spatial ViPer that varies person proposals and produces visual features with misaligned boundaries, (2) Attentive ViPer that estimates visual attention on the fly and manipulates attentive visual tokens within a proposal to produce global features under visual perturbations, and (3) Fine-grained ViPer that learns to recover masked visual clues from detailed language descriptions to encourage matching language features with perturbed visual features at the fine granularity. This overall framework thus simulates real-world scenarios at the training stage to minimize the discrepancy and improve the generalization ability of the model. Experimental results demonstrate that the proposed method clearly surpasses previous TBPS methods on the PRW-TBPS and CUHK-SYSU-TBPS datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Cybersecurity and Smart Cities: Current Status and Future

Cybersecurity and Smart Cities: Current Status and Future

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Emerging Trends in engineering, Sciences and Technology (ICES&T), IEEE International Conference on

作者： Muhammad Yasir Habib Haseeb Ahmad Qureshi Shujahat Ali Khan Zara Mansoor Abdul Rehman Chishti Dept. of Electronic Engineering Faculty of Engineering The Islamia University of Bahawalpur Dept. of Computing and Informatics Faculty of Computing Bournemouth University Bournemouth United Kingdom College of Engineering IT and Environment Charles Darwin University Darwin Australia Dept. of Software Engineering Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Dept. of Information and Communication Engineering Faculty of Engineering The Islamia University of Bahawalpur Bahawalpur Pakistan

A Smart city implements the latest IoT and information and communication technologies (ICT) to improve the quality of urban city administrations, decrease expenditures, asset management and interconnect citizens of a Smart city. Smart cities offer numerous advantages like improved energy productivity, management, healthcare facilities, efficient transport systems, proper waste and water management, and individual security. Nonetheless, this reliance on ICT and IoT technologies makes a Smart city prone to digital cyber assaults. These technologies are vulnerable to many security issues like Information theft, Eaves-dropping attack, Denial of service, Communication delays, Data manipulation, IoT security attacks, Communication interception, Jamming, Sensor failure, insecure API, and Remote exploitation. This research study intends to address opinions on cybersecurity technologies, vulnerabilities, and cybercrime awareness based on the systematic literature review “PRISMA Model” as our research method and help researchers and practitioners to look for innovative Smart City solutions. Our research endeavors to momentarily depict the central ideas of digital security and protection issues related to Smart city areas and uncover digital cyber-attacks that focus on Smart city communities in the literature. In brief, the focus of this research is to explore and review the aspects of Smart city cybersecurity issues, Smart city vulnerabilities related to information security, and provide a comprehensive research framework that will help the researchers and practitioners explore this area of research.

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Instructors' Perspectives on Capstone Courses in computing Fields: A Mixed-Methods Study 2024

Instructors' Perspectives on Capstone Courses in Computing F...

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29th Working Group Reports on Innovation and Technology in Computer Science Education, ITiCSE WGR 2024

作者： Hooshangi, Sara Shakil, Asma Dasgupta, Subhasish Davis, Karen C.C. Farghally, Mohammed Fitzpatrick, KellyAnn Gutica, Mirela Hardt, Ryan Riddle, Steve Seyam, Mohammed Department of Computer Science Virginia Tech BlacksburgVA United States School of Computer Science University of Auckland Auckland New Zealand Info Systems and Tech Mgmt Dept George Washington University WashingtonDC United States Computer Science and Software Engineering Dept. Miami University OxfordOH United States School of Computing Instruction Georgia Institute of Technology AtlantaGA United States School of Computing & Academic Studies British Columbia Institute of Technology BurnabyBC Canada Computer & Information Science University of St. Thomas Saint PaulVA United States School of Computing Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional de

关键词： Students

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A Sample Detection Model Applying IoT Technology

A Sample Detection Model Applying IoT Technology

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Li Wang Shu Huang Computer and Information Engineering College Wuhan Railway Vocational College of Technology Wuhan Hubei China School of Computing Wuhan Vocational College of Software and Engineering Wuhan Hubei China

This article first analyzes the development trend of IoT technology to determine its process framework in the industry. Then, through sample analysis, the application value and direction of intelligent instruments in sample analysis are identified. Finally, by integrating intelligent instruments in sample analysis with IoT technology, the application of intelligent instruments in sample analysis under IoT technology is studied. This article is based on the application of Internet of Things technology to intelligent instruments, obtaining a new type of intelligent instrument and exploring its application in blood cells. It mainly conducts comparative experiments on the counting and classification of blood cells. The experimental results of the comparison between the model algorithm and instrument method in this article indicate that the algorithm of this model has a certain degree of accuracy and effectiveness, and the intelligent analysis in this article further improves the research on samples.

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Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Zhang, Xiyue Xue, Xiaoyong Du, Xiaoning Xie, Xiaofei Liu, Yang Sun, Meng University of Oxford Department of Computer Science United Kingdom Peking University Department of Information and Computing Science School of Mathematical Science China Monash University Department of Software Systems and Cybersecurity Australia Singapore Management University School of Computing and Information Systems Singapore Nanyang Technological University School of Computer Science and Engineering Singapore

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify dissimilarities between clients, identifying model deviations caused by backdoor injections. We also propose an iterative algorithm to progressively detect and exclude malicious clients as outliers based on these dissimilarity measurements. Evaluations across a range of benchmark tasks demonstrate that our approach outperforms state-of-the-art methods in detection accuracy and defense effectiveness. When deployed for runtime protection, our approach effectively eliminates backdoor injections with marginal overheads. © 2025 IEEE.

关键词： Federated learning

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