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What can we learn from quality assurance badges in open-source software?

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Science China(Information Sciences) 2024年第4期67卷 146-163页

作者： Feng LI Yiling LOU Xin TAN Zhenpeng CHEN Jinhao DONG Yang LI Xuanzhi WANG Dan HAO Lu ZHANG MoE Key Lab of HCST School of Computer SciencePeking University department of computer Science Purdue University School of computer Science & Engineering Beihang University(BUAA) department of computer Science University College London(UCL)

In the development of open-source software(OSS), many developers use badges to give an overview of the software and share some key features/metrics conveniently. Among various badges, quality assurance(QA) badges make up a large proportion and are the most prevalent because QA is of vital importance in software development, and ineffective QA may lead to anomalies or defects. In this paper, we focus on QA badges in open-source projects, which present quality assurance information directly and instantly,and aim to produce some interesting findings and provide practical implications. We collect and analyze 100000 projects written in popular programming languages from GitHub and conduct a comprehensive empirical study both inside and outside QA badges. Inside QA badges, we build a category classification for all QA badges based on the properties they focus on, which shows the types of QA badges developers use. Then,we analyze the frequency of the properties that QA badges focus on, and property combinations, too, which present their use status. We find that QA badges focus on various properties while developers give different preferences to different properties. The use status also differs between different programming languages. For example, projects written in C focus on Security to a great extent. Our findings also provide implications for developers and badge providers. Outside QA badges, we conduct a correlation analysis between QA badges and some software metrics that have potential relationships with code quality, contribution quality, and popularity. We find that QA badges have statistically significant correlations with various software metrics.

关键词： quality assurance badge open-source software code quality empirical study

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Controllable multi-domain semantic artwork synthesis

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Computational Visual Media 2024年第2期10卷 355-373页

作者： Yuantian Huang Satoshi Iizuka Edgar Simo-Serra Kazuhiro Fukui department of computer Science University of TsukubaTsukuba 305-8577Japan department of computer Science and Engineering Waseda UniversityTokyo 169-8050Japan

We present a novel framework for the multidomain synthesis of artworks from semantic *** of the main limitations of this challenging task is the lack of publicly available segmentation datasets for art *** address this problem,we propose a dataset called ArtSem that contains 40,000 images of artwork from four different domains,with their corresponding semantic label *** first extracted semantic maps from landscape photography and used a conditional generative adversarial network(GAN)-based approach for generating high-quality artwork from semantic maps without requiring paired training ***,we propose an artwork-synthesis model using domain-dependent variational encoders for high-quality multi-domain ***,the model was improved and complemented with a simple but effective normalization method based on jointly normalizing semantics and style,which we call spatially style-adaptive normalization(SSTAN).Compared to the previous methods,which only take semantic layout as the input,our model jointly learns style and semantic information representation,improving the generation quality of artistic *** results indicate that our model learned to separate the domains in the latent ***,we can perform fine-grained control of the synthesized artwork by identifying hyperplanes that separate the different ***,by combining the proposed dataset and approach,we generated user-controllable artworks of higher quality than that of existing approaches,as corroborated by quantitative metrics and a user study.

关键词： semantic artwork synthesis generative adversarial network(GAN) datasets non-photorealistic rendering

A blockchain based privacy-preserving federated learning scheme for Internet of Vehicles

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Digital Communications and Networks 2024年第1期10卷 126-134页

作者： Naiyu Wang Wenti Yang Xiaodong Wang Longfei Wu Zhitao Guan Xiaojiang Du Mohsen Guizani School of Control and computer Engineering North China Electric Power UniversityChina department of Mathematics&computer Science Fayetteville State UniversityFayettevilleNCUSA department of Electrical and computer Engineering Stevens Institute of TechnologyHobokenNJ07030USA department of computer Science and Engineering Qatar UniversityDohaQatar

The application of artificial intelligence technology in Internet of Vehicles(lov)has attracted great research interests with the goal of enabling smart transportation and traffic ***,concerns have been raised over the security and privacy of the tons of traffic and vehicle *** this regard,Federated Learning(FL)with privacy protection features is considered a highly promising ***,in the FL process,the server side may take advantage of its dominant role in model aggregation to steal sensitive information of users,while the client side may also upload malicious data to compromise the training of the global *** existing privacy-preserving FL schemes in IoV fail to deal with threats from both of these two sides at the same *** this paper,we propose a Blockchain based Privacy-preserving Federated Learning scheme named BPFL,which uses blockchain as the underlying distributed framework of *** improve the Multi-Krum technology and combine it with the homomorphic encryption to achieve ciphertext-level model aggregation and model filtering,which can enable the verifiability of the local models while achieving ***,we develop a reputation-based incentive mechanism to encourage users in IoV to actively participate in the federated learning and to practice *** security analysis and performance evaluations are conducted to show that the proposed scheme can meet the security requirements and improve the performance of the FL model.

关键词： Federated learning Blockchain Privacy-preservation Homomorphic encryption Internetof vehicles

Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes

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computers, Materials & Continua 2024年第1期78卷 1207-1225页

作者： Zahid Farooq Khan Muhammad Ramzan Mudassar Raza Muhammad Attique Khan Khalid Iqbal Taerang Kim Jae-Hyuk Cha department of computer Science COMSATS University IslamabadWah CampusWah47040Pakistan department of computer Science and Mathematics Lebanese American UniversityBeirut13-5053Lebanon department of computer Science HITEC UniversityTaxila47080Pakistan department of computer Science COMSATS University IslamabadAttock CampusAttock43600Pakistan department of computer Science Hanyang UniversitySeoul04763Korea

Accurate detection and classification of artifacts within the gastrointestinal(GI)tract frames remain a significant challenge in medical image *** science combined with artificial intelligence is advancing to automate the diagnosis and treatment of numerous *** to this is the development of robust algorithms for image classification and detection,crucial in designing sophisticated systems for diagnosis and *** study makes a small contribution to endoscopic image *** proposed approach involves multiple operations,including extracting deep features from endoscopy images using pre-trained neural networks such as Darknet-53 and ***,feature optimization utilizes the binary dragonfly algorithm(BDA),with the fusion of the obtained feature *** fused feature set is input into the ensemble subspace k nearest neighbors(ESKNN)*** Kvasir-V2 benchmark dataset,and the COMSATS University Islamabad(CUI)Wah private dataset,featuring three classes of endoscopic stomach images were *** assessments considered various feature selection techniques,including genetic algorithm(GA),particle swarm optimization(PSO),salp swarm algorithm(SSA),sine cosine algorithm(SCA),and grey wolf optimizer(GWO).The proposed model excels,achieving an overall classification accuracy of 98.25% on the Kvasir-V2 benchmark and 99.90% on the CUI Wah private *** approach holds promise for developing an automated computer-aided system for classifying GI tract syndromes through endoscopy images.

关键词： Feature fusion Darknet-53 Xception binary dragonfly algorithm ensemble

WebFLex:A Framework for Web Browsers-Based Peer-to-Peer Federated Learning Systems Using WebRTC

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computers, Materials & Continua 2024年第3期78卷 4177-4204页

作者： Mai Alzamel Hamza Ali Rizvi Najwa Altwaijry Isra Al-Turaiki department of computer Science College of Computer and Information SciencesKing Saud UniversityRiyadhKingdom of Saudi Arabia department of computer Science and Engineering Punjab Engineering CollegeChandigarhIndia

Scalability and information personal privacy are vital for training and deploying large-scale deep learning *** learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web ***,relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client ***,information relating to the training dataset can possibly be extracted from the distributed weights,potentially reducing the privacy of the local data used for *** this research paper,we aim to investigate the challenges of scalability and data privacy to increase the efficiency of distributed training *** a result,we propose a web-federated learning exchange(WebFLex)framework,which intends to improve the decentralization of the federated learning *** is additionally developed to secure distributed and scalable federated learning systems that operate in web browsers across heterogeneous ***,WebFLex utilizes peer-to-peer interactions and secure weight exchanges utilizing browser-to-browser web real-time communication(WebRTC),efficiently preventing the need for a main central *** has actually been measured in various setups using the MNIST *** results show WebFLex’s ability to improve the scalability of federated learning systems,allowing a smooth increase in the number of participating devices without central data *** addition,WebFLex can maintain a durable federated learning procedure even when faced with device disconnections and network ***,it improves data privacy by utilizing artificial noise,which accomplishes an appropriate balance between accuracy and privacy preservation.

关键词： Federated learning web browser privacy deep learning

Skill enhancement learning with knowledge distillation

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Science China(Information Sciences) 2024年第8期67卷 206-220页

作者： Naijun LIU Fuchun SUN Bin FANG Huaping LIU department of computer Science and Technology Tsinghua University

Skill learning through reinforcement learning has significantly progressed in recent years. However, it often struggles to efficiently find optimal or near-optimal policies due to the inherent trial-and-error exploration in reinforcement learning. Although algorithms have been proposed to enhance skill learning efficacy, there is still much room for improvement in terms of skill learning performance and training stability. In this paper, we propose an algorithm called skill enhancement learning with knowledge distillation(SELKD), which integrates multiple actors and multiple critics for skill learning. SELKD employs knowledge distillation to establish a mutual learning mechanism among actors. To mitigate critic overestimation bias,we introduce a novel target value calculation method. We also perform theoretical analysis to ensure the convergence of SELKD. Finally, experiments are conducted on several continuous control tasks, illustrating the effectiveness of the proposed algorithm.

关键词： skill learning enhancement learning reinforcement learning knowledge distillation

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SHA: QoS-Aware Software and Hardware Auto-Tuning for Database Systems

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Journal of computer Science & Technology 2024年第2期39卷 369-383页

作者：李进陈全唐晓新过敏意 department of computer Science Shanghai Jiao Tong UniversityShanghai 200240China department of computer Science and Technology Shanghai University of Finance and EconomicsShanghai 200433China

While databases are widely-used in commercial user-facing services that have stringent quality-of-service(QoS)requirement,it is crucial to ensure their good performance and minimize the hardware usage at the same *** investigation shows that the optimal DBMS(database management system)software configuration varies for different user request patterns(i.e.,workloads)and hardware *** is challenging to identify the optimal software and hardware configurations for a database workload,because DBMSs have hundreds of tunable knobs,the effect of tuning a knob depends on other knobs,and the dependency relationship changes under different hardware *** this paper,we propose SHA,a software and hardware auto-tuning system for *** is comprised of a scaling-based performance predictor,a reinforcement learning(RL)based software tuner,and a QoS-aware resource *** performance predictor predicts its optimal performance with different hardware configurations and identifies the minimum amount of resources for satisfying its performance *** software tuner fine-tunes the DBMS software knobs to optimize the performance of the *** resource reallocator assigns the saved resources to other applications to improve resource utilization without incurring QoS violation of the database *** results show that SHA improves the performance of database workloads by 9.9%on average compared with a state-of-the-art solution when the hardware configuration is fixed,and improves 43.2%of resource utilization while ensuring the QoS.

关键词： auto-tuning database configuration joint tuning utilization quality-of-service(QoS)

Leveraging User-Generated Comments and Fused BiLSTM Models to Detect and Predict Issues with Mobile Apps

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computers, Materials & Continua 2024年第4期79卷 735-759页

作者： Wael M.S.Yafooz Abdullah Alsaeedi department of computer Science College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user ***, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.

关键词： Mobile apps issues play store user comments deep learning LSTM bidirectional LSTM

CToMP: a cycle-task-oriented memory protection scheme for unmanned systems

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Science China(Information Sciences) 2024年第6期67卷 399-417页

作者： Chengyan MA Ning XI Di LU Yebo FENG Jianfeng MA School of Cyber Engineering Xidian University School of computer Science and Technology Xidian University department of computer and Information Science University of Oregon

Memory corruption attacks(MCAs) refer to malicious behaviors of system intruders that modify the contents of a memory location to disrupt the normal operation of computing systems, causing leakage of sensitive data or perturbations to ongoing processes. Unlike general-purpose systems, unmanned systems cannot deploy complete security protection schemes, due to their limitations in size, cost and *** in unmanned systems are particularly difficult to defend against. Furthermore, MCAs have diverse and unpredictable attack interfaces in unmanned systems, severely impacting digital and physical sectors. In this paper, we first generalize, model and taxonomize MCAs found in unmanned systems currently, laying the foundation for designing a portable and general defense approach. According to different attack mechanisms,we found that MCAs are mainly categorized into two types — return2libc and return2shellcode. To tackle return2libc attacks, we model the erratic operation of unmanned systems with cycles and then propose a cycle-task-oriented memory protection(CToMP) approach to protect control flows from tampering. To defend against return2shellcode attacks, we introduce a secure process stack with a randomized memory address by leveraging the memory pool to prevent Shellcode from being executed. Moreover, we discuss the mechanism by which CTo MP resists the return-oriented programming(ROP) attack, a novel variant of return2libc attacks. Finally, we implement CTo MP on CUAV V5+ with Ardupilot and Crazyflie. The evaluation and security analysis results demonstrate that the proposed approach CTo MP is resilient to various MCAs in unmanned systems with low footprints and system overhead.

关键词： unmanned system memory corruption attack memory protection system security randomized memory address

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