检索结果-内蒙古大学图书馆

International Conference on Computer Supported Cooperative Work in Design

作者： Jiayi Zhong Yaling Tang Jiawei Chen Xiaokang Zhou Xiaocong Xiao Guosheng Kang Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan Hunan China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

The industrial Internet integrates industrial systems with Internet technologies, significantly enhancing production efficiency and reducing costs through collaboration with intelligent devices. Under the context of the industrial Internet, a production process typically comprises multiple tasks, each task requiring one or more types of production factors, which makes the service composition of production factors more complex and challenging compared to the traditional service composition under Web environments. To optimize the service composition of production factors under industrial Internet, this paper proposes a collaboration-aware service composition optimization model by considering the collaboration relationships among services. Specifically, both historical collaboration and present collaboration are considered, where the historical collaboration relationships among services include the collaboration between two directly-followed tasks and collaboration within one task, and the present collaboration relationships include the collaboration among the selected services in present service composition. To derive the optimal service composition plan, we propose using the advanced teaching-learning-based optimization algorithm (TLBO). Extensive experiments are conducted to compare with other population-based optimization methods under a real-world ship production process to verify the superiority of our approach. Experimental results show that the integration of collaboration relationships improves the overall quality of the optimized service composition plans in terms of time and cost, and the selected TLBO is more effective to derive the optimized service composition plans than other population-based optimization methods.

关键词： Costs Federated learning Optimization models Metaheuristics Collaboration Production Quality of service Internet Marine vehicles

来源：评论

学校读者我要写书评

暂无评论

Secure software Development in Google Colab

Secure Software Development in Google Colab

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Visual Perturbation for Text-Based Person Search 39

Visual Perturbation for Text-Based Person Search

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Application of Deep Learning Algorithm for Prediction of Flood Severity

Application of Deep Learning Algorithm for Prediction of Flo...

引用

Sustainable computing and Smart Systems (ICSCSS), International Conference on

作者： C Jamunadevi N Naveen M Manimaran M Janani Department of Computer Technology PG Kongu Engineering College Erode Department of IT Excel Engineering College Namakkal School of Computing Science and Engineering VIT Bhopal University Bhopal Madhya Pradesh India M.Sc (Software Systems) Kongu Engineering College Erode

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Natural disasters like floods have caused immense harm to lives and resources, underscoring the need for accurate prediction methods to mitigate their impact. Meteorological flood indices such as the standardized precipitation index help identify flood occurrences and severity levels, enabling proactive measures. While past research primarily used statistical methods like Least Square-Support Vector Machine (LS-SVM), these often lack accuracy for long-term forecasts. Advancements in machine learning, particularly artificial neural networks and deep neural networks, offer more accurate predictions over extended periods. Long Short-Term Memory (LSTM) models and Convolutional Neural Networks (CNN) are used to predict flood indices, handling real-time nonlinear data effectively. Benchmarking trials have shown that an LSTM and CNN configuration produced the best predictions, with an accuracy, prediction, MSE, and RMSE of 97.93,0.89, 0.01, and 0.10 respectively.

关键词： Accuracy Weather forecasting Predictive models Surfaces Data models Vectors Floods

来源：评论

学校读者我要写书评

暂无评论

A Survey on IEEE 802.15.7 MAC Protocols for Visible Light Communication

A Survey on IEEE 802.15.7 MAC Protocols for Visible Light Co...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Practical and Efficient Model Extraction of Sentiment Analysis APIs

Practical and Efficient Model Extraction of Sentiment Analys...

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Multiple-Path Learning Neural Network Model for Code Completion

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Crowdsourcing for Data Labeling with Heterogeneous Crowd 39

Hierarchical Crowdsourcing for Data Labeling with Heterogene...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Cybersecurity and Smart Cities: Current Status and Future

Cybersecurity and Smart Cities: Current Status and Future

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：