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arXiv 2024年

作者： de Dieu, Musengamana Jean Liang, Peng Shahin, Mojtaba Khan, Arif Ali School of Computer Science Wuhan University Wuhan China School of Computing Technologies RMIT University Melbourne Australia M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland

Technical Questions and Answers (Q&A) sites, such as Stack Overflow (SO), accumulate a significant variety of information related to software development in posts from users. To ensure the quality of this information, SO encourages its users to review posts through various mechanisms (e.g., question and answer revision processes). Although Architecture Related Posts (ARPs) communicate architectural information that has a system-wide impact on development, little is known about how SO users revise information shared in ARPs. To fill this gap, we conducted an empirical study to understand how users revise Architecture Related Questions (ARQs) on SO. By following a rigorous procedure consisting of data collection and filtering, we manually checked 13,205 ARPs and finally identified 4,114 ARQs that contain revision information. Our main findings are that: (1) The revision of ARQs is not prevalent in SO, and an ARQ revision starts soon after this question is posted (i.e., from 1 minute onward). Moreover, the revision of an ARQ occurs before and after this question receives its first answer/architecture solution, with most revisions beginning before the first architecture solution is posted. Both Question Creators (QCs) and non-QCs actively participate in ARQ revisions, with most revisions being made by QCs. (2) A variety of information (14 categories) is missing and further provided in ARQs after being posted, among which design context, component dependency, and architecture concern are dominant information. (3) Clarify the understanding of architecture under design and improve the readability of architecture problem are the two major purposes of the further provided information in ARQs. (4) The further provided information in ARQs has several impacts on the quality of answers/architecture solutions, including making architecture solution useful, making architecture solution informative, making architecture solution relevant, among others. Our findings (1) provide resea

关键词： software design

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Low-Complexity Beamforming Design for Null Space-based Simultaneous Wireless Information and Power Transfer Systems

arXiv

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arXiv 2025年

作者： Luo, Cheng Hu, Jie Xiang, Luping K., Yang Sichuan Chengdu China State Key Laboratory of Novel Software Technology Nanjing University Nanjing210008 China School of Intelligent Software and Engineering Nanjing University Suzhou Campus Suzhou China School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom

Simultaneous wireless information and power transfer (SWIPT) is a promising technology for the upcoming sixth-generation (6G) communication networks, enabling internet of things (IoT) devices and sensors to extend their operational lifetimes. In this paper, we propose a SWIPT scheme by projecting the interference signals from both intra-wireless information transfer (WIT) and inter-wireless energy transfer (WET) into the null space, simplifying the system into a point-to-point WIT and WET problem. Upon further analysis, we confirm that dedicated energy beamforming is unnecessary. In addition, we develop a low-complexity algorithm to solve the problem efficiently, further reducing computational overhead. Numerical results validate our analysis, showing that the computational complexity is reduced by 97.5% and 99.96% for the cases of KI = KE = 2, M = 4 and KI = KE = 16, M = 64, respectively. Copyright © 2025, The Authors. All rights reserved.

关键词： Inductive power transmission

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Container Anomaly Detection System Based on Rule Mining and Matching

Container Anomaly Detection System Based on Rule Mining and ...

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2022 International Conference on Blockchain Technology and Information Security, ICBCTIS 2022

作者： Yu, Daisong Xie, Yulai Long, Huachao Jin, Minpeng Li, Xuan School of Cyber Science and Engineering Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan China Wuhan National Lab for Optoelectronics F311 Wuhan China School of Computer Science Huazhong University of Science and Technology Wuhan China Nsfocus Inc. A9-3/F Optical Valley Software Park Wuhan China

ISBN: (数字)9781665496919

ISBN: (纸本)9781665496919

Compared with virtual machine technology, container technology is more and more popular by virtue of its excellent convenience and reliable security isolation. Containers will encounter various anomalies during operation. For maintenance personnel, a large number of abnormal logs makes the work of anomaly analysis very difficult, so anomaly detection technology is needed. In this paper, a container anomaly detection system based on rule mining and matching is proposed, which collects and simplifies the container's logs generated in the abnormal time period, then delivers them to association rule mining algorithm for rule mining and display rules to the maintainer who makes a decision after a simple analysis. The experimental results show that the rule mining algorithm can process log entries in less time consumption compared with traditional association rule mining algorithm, and container anomaly detection system can effectively help system administrator to locate anomalies. © 2022 IEEE.

关键词： Anomaly detection

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MoCo: Urban User Mobile Contact Detection Based on Cellular Signaling Trace

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IEEE Transactions on Mobile Computing 2025年

作者： Duan, Sijing Lyu, Feng Zhang, Jing Lu, Huali Yang, Peng Wu, Huaqing Zhang, Yaoxue Shen, Xuemin Central South University School of Computer Science and Engineering Hunan ChangSha410083 China Tsinghua University Department of Computer Science and Technology Beijing China Huazhong University of Science and Technology School of Electronic Information and Communications Hubei Wuhan430074 China University of Calgary Department of Electrical and Software Engineering CalgaryABT2N 1N4 Canada Tsinghua University Department of Computer Science and Technology Haidian District Beijing100084 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Mobile contact exhibits user co-traveling events within the same transportation tool, which is crucial for resident profiling, face-to-face interaction detection, etc. In this paper, we investigate urban user mobile contact detection with cellular signaling traces, which is cost-efficient to enable large-scale detection. Specifically, we develop a data collection platform to collect substantial user signaling traces, covering different types of road scenarios within a city. With the collected traces, we perform systematic data analysis to reveal several technical challenges, which are sparsity of signaling trajectory, remote base station noise, and fuzzy matching difficulties. To address challenges, we propose a mobile contact detection method named MoCo. In MoCo framework, we first conduct data denoising to remove the noise from remote base stations. Then, we devise a spatio-temporal filter to eliminate unlikely mobile contact traces in both spatial and temporal domains, reducing the computational overhead. Finally, we design a detection network that integrates the submodules of data alignment, feature encoder, spatio-temporal representation learner, and user mobile contact detector. Extensive evaluation results demonstrate the superiority of MoCo in comparison with state-of-the-art baselines. Robust experiments show that MoCo can work efficiently in different transportation modes and urban densities. © 2002-2012 IEEE.

关键词： Urban transportation

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NESec: A Modified-UI Honeyword Generation Strategy for Mitigating Targeted Guessing Attacks 7

NESec: A Modified-UI Honeyword Generation Strategy for Mitig...

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7th Cyber Security in Networking Conference, CSNet 2023

作者： Chakraborty, Nilesh Shojafar, Mohammad Chen, Zhuangzhuang Leung, Victor C. M. Li, Jianqiang Zulkernine, Mohammad School of Computing Queen's University K7L 3N6 Canada Institute for Communication Systems University of Surrey 5GIC & 6GIC GU27XH United Kingdom College of Computer Science and Software Engineering Shenzhen University 518060 China

ISBN: (纸本)9798350342871

By harnessing Graphics Processing Unit (GPU), Field-programmable Gate Array (FPGA), and advanced cracking techniques, the success rates of server-side threats on passwords have reached unprecedented levels. Honeywords, also known as decoy passwords, have emerged as a promising detection strategy against this threat scenario. However, existing work falls short in creating a trap to counter targeted guessing attackers (TgA) who exploit users' personal information to bypass honeyword-based traps. In this paper, we introduce two fundamental honeyword generation modules, namely NSecure-modifiedUI and ESecure-modifiedUI. Building upon these primary modules, we propose a hybrid honeyword-based strategy named NESec, which significantly enhances the ability to detect TgA 's activities. A comparative analysis showcases the usability advantages and security benefits of the proposed NESec approach. © 2023 IEEE.

关键词： Graphics processing unit

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Performance Analysis of IRS-Assisted Multi-Cell Data and Energy Integrated Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Zhang, Bingxin Yang, Kun Wang, Kezhi Zhang, Guopeng Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China Nanjing University School of Intelligent Software and Engineering Suzhou Campus Suzhou215163 China Brunel University London Department of Computer Science Middlesex UxbridgeUB8 3PH United Kingdom China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China

Intelligent reflecting surface (IRS) can significantly enhance the performance of data and energy integrated networks (DEIN) by adjusting its amplitude and/or phase. However, there is a lack of comprehensive performance analysis model for realistic DEIN where multiple cells exist rather than only one cell as assumed by most existing work. In this paper, we consider an IRS-assisted multi-cell DEIN. Specifically, in the downlink wireless energy transfer (WET) stage, the hybrid access point (HAP) in each cell broadcasts radio frequency (RF) energy signals to edge user equipments (UEs). Subsequently, during the uplink wireless information transfer (WIT) stage, the edge UEs employ the harvested energy to send their information to the HAP. We first represent the statistical characteristics of the signal-to-interference-plus-noise ratio (SINR) at the edge UE. Then, we derive the closed-form expressions for outage probability, ergodic rate and average symbol error probability of the edge UE in the typical cell. To gain more insights, we obtain the minimum required number of reflection elements and a sub-optimal solution for time allocation coefficients. Finally, extensive numerical results are provided to validate the correctness of the theoretical results. © 2002-2012 IEEE.

关键词： data and energy integrated network (DEIN) Intelligent reflecting surface (IRS) multi-cell performance analysis wireless energy transfer (WET)

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Data security and privacy computing in artificial intelligence

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Journal of Information and Intelligence 2024年第2期2卷 99-101页

作者： Dengguo Feng Hui Li Rongxing Lu Zheli Liu Jianbing Ni Hui Zhu Institute of Software Academy of SciencesBeijing 100190China State Key Laboratory of Integrated Services Networks School of Cyber EngineeringXidian UniversityXi’an 710071China Faculty of Computer Science University of New BrunswickFredericton E3B 5A3Canada College of Computer Science Nankai UniversityTianjin 300071China Department of Electrical and Computer Engineering Queen’s UniversityKingston K7L 3N6Canada

Artificial intelligence(AI)is a field of computer science dedicated to creating systems and algorithms that can perform tasks typically requiring human intelligence,such as learning,problem-solving,language understanding,and decision-making,contributing to a wide array of applications across diverse *** development of AI,such as machine learning and deep learning,has revolutionized data processing and *** transformation is rapidly changing human life and has allowed for many practical AI based applications,including biometric recognition,text/sentimental analysis,and attack detection in the fields of health care,finance,autonomous vehicles,personalized ***,the potential benefits of AI are hindered by issues,such as insecurity and privacy violations in data processing and communication.

关键词： such has finance

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Fine-Grained Alignment in Vision-and-Language Navigation through Bayesian Optimization

arXiv

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arXiv 2024年

作者： Song, Yuhang Gianni, Mario Yang, Chenguang Lin, Kunyang Chiu, Te-Chuan Nguyen, Anh Lee, Chun-Yi Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan Department of Computer Science National Tsing Hua University Hsinchu300 Taiwan Department of Computer Science National Tsing Hua University Taiwan School of Software Engineering South China University of Technology Guangzhou510006 China

This paper addresses the challenge of fine-grained alignment in Vision-and-Language Navigation (VLN) tasks, where robots navigate realistic 3D environments based on natural language instructions. Current approaches use contrastive learning to align language with visual trajectory sequences. Nevertheless, they encounter difficulties with fine-grained vision negatives. To enhance cross-modal embeddings, we introduce a novel Bayesian Optimization-based adversarial optimization framework for creating fine-grained contrastive vision samples. To validate the proposed methodology, we conduct a series of experiments to assess the effectiveness of the enriched embeddings on fine-grained vision negatives. We conduct experiments on two common VLN benchmarks R2R and REVERIE, experiments on the them demonstrate that these embeddings benefit navigation, and can lead to a promising performance enhancement. Our source code and trained models are available at: https://***/r/FGVLN. © 2024, CC BY.

关键词： Contrastive Learning

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Development of a Secure Cloud-based Breast Cancer Diagnosis System 22

Development of a Secure Cloud-based Breast Cancer Diagnosis ...

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6th International Conference on Cloud and Big Data Computing, ICCBDC 2022

作者： Amin, Fazal-E. Hussain, Muhammad Ali, Zulfiqar Busaleh, Mariam Al Sultan, Sarah A. Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh11541 Saudi Arabia School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Department of Radiology College of Medicine King Khalid University Hospital King Saud University Riyadh Saudi Arabia

ISBN: (纸本)9781450396578

Breast cancer is one of the most common cancers that cause death in women. Breast cancer was the most common cancer in 2020, with 2.26 million new cases. Cancer mortalities can be reduced by early detection and treatment. There are two components of early detection: early diagnosis and screening. Our work is related to early diagnosis. Early diagnosis helps to start treatment early, which is more effective and less expensive. The lives of cancer patients can be improved significantly by early detection and avoiding treatment delays. This paper presents the design and development of a secure cloud-based breast cancer diagnosis system. The proposed system uses using convolutional neural network for breast mass classification. Fragile zero watermarking will be implemented to secure the transmission of mammographs. The proposed system will enable women living in remote areas to get the facility for the routine initial screening near their homes without making their identity and medical data vulnerable. It will reduce the cost and time by avoiding frequent visits of the patients to specialized health care centers. On the other hand, it will help enhance the quality of service at specialized healthcare centers by reducing the number of patients in the initial screening. © 2022 ACM.

关键词： Deep learning

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Task Consistent Prototype Learning for Incremental Few-shot Semantic Segmentation

arXiv

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arXiv 2024年

作者： Xu, Wenbo Wu, Yanan Jiang, Haoran Wang, Yang Wu, Qiang Zhang, Jian Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated examples. Typical incremental approaches encounter a challenge that the objective of the base training phase (fitting base classes with sufficient instances) does not align with the incremental learning phase (rapidly adapting to new classes with less forgetting). This disconnect can result in suboptimal performance in the incremental setting. This study introduces a meta-learning-based prototype approach that encourages the model to learn how to adapt quickly while preserving previous knowledge. Concretely, we mimic the incremental evaluation protocol during the base training session by sampling a sequence of pseudo-incremental tasks. Each task in the simulated sequence is trained using a meta-objective to enable rapid adaptation without forgetting. To enhance discrimination among class prototypes, we introduce prototype space redistribution learning, which dynamically updates class prototypes to establish optimal inter-prototype boundaries within the prototype space. Extensive experiments on iFSS datasets built upon PASCAL and COCO benchmarks show the advanced performance of the proposed approach, offering valuable insights for addressing iFSS challenges. © 2024, CC BY-NC-SA.

关键词： Semantic Segmentation

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