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

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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SDCC: software-defined collective communication for distributed training

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science China(Information sciences) 2024年第9期67卷 105-125页

作者： Xin JIN Zhen ZHANG Yunshan JIA Yun MA Xuanzhe LIU School of computer science Peking University department of computer science and Technology Johns Hopkins University

Communication is crucial to the performance of distributed training. Today's solutions tightly couple the control and data planes and lack flexibility, generality, and performance. In this study, we present SDCC, a software-defined collective communication framework for distributed training. SDCC is based on the principle of modern systems design to effectively decouple the control plane from the data *** abstracts the operations for collective communication in distributed training with dataflow operations and unifies computing and communication with a single dataflow graph. The abstraction, together with the unification, is powerful: it enables users to easily express new and existing collective communication algorithms and optimizations, simplifies the integration with different computing engines(e.g., Py Torch and Tensor Flow) and network transports(e.g., Linux TCP and kernel bypass), and allows the system to improve performance by exploiting parallelism exposed by the dataflow graph. We further demonstrate the benefits of SDCC in four use cases.

关键词： machine learning systems distributed training deep learning collective communication software-defined networking

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Digital neuromorphic technology: current and future prospects

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National science Review 2024年第5期11卷 10-11页

作者： Steve Furber department of computer science The University of Manchester

Neuromorphic technology has diversified considerably from its origins in the seminal work by Carver Mead and his group at Caltech in the 1980s [1]. That early work focussed on the analogy between the equations describing the flow of ions in biological neurons and the equations describing the flow of carriers in field-effect transistors operating in the subthreshold region.

关键词： LOIHI

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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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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

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Improving Video Watermarking through Galois Field GF(2^(4)) Multiplication Tables with Diverse Irreducible Polynomials and Adaptive Techniques

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computers, Materials & Continua 2024年第1期78卷 1423-1442页

作者： Yasmin Alaa Hassan Abdul Monem S.Rahma department of computer science College of ScienceUniversity of BaghdadBaghdadIraq department of computer science College of ScienceAl-Maarif University CollegeAl-AnbarIraq

Video watermarking plays a crucial role in protecting intellectual property rights and ensuring content *** study delves into the integration of Galois Field(GF)multiplication tables,especially GF(2^(4)),and their interaction with distinct irreducible *** primary aim is to enhance watermarking techniques for achieving imperceptibility,robustness,and efficient execution *** research employs scene selection and adaptive thresholding techniques to streamline the watermarking *** selection is used strategically to embed watermarks in the most vital frames of the video,while adaptive thresholding methods ensure that the watermarking process adheres to imperceptibility criteria,maintaining the video's visual ***,careful consideration is given to execution time,crucial in real-world scenarios,to balance efficiency and *** Peak Signal-to-Noise Ratio(PSNR)serves as a pivotal metric to gauge the watermark's imperceptibility and video *** study explores various irreducible polynomials,navigating the trade-offs between computational efficiency and watermark *** parallel,the study pays careful attention to the execution time,a paramount consideration in real-world scenarios,to strike a balance between efficiency and *** comprehensive analysis provides valuable insights into the interplay of GF multiplication tables,diverse irreducible polynomials,scene selection,adaptive thresholding,imperceptibility,and execution *** evaluation of the proposed algorithm's robustness was conducted using PSNR and NC metrics,and it was subjected to assessment under the impact of five distinct attack *** findings contribute to the development of watermarking strategies that balance imperceptibility,robustness,and processing efficiency,enhancing the field's practicality and effectiveness.

关键词： Video watermarking galois field irreducible polynomial multiplication table scene selection adaptive thresholding

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Cross-comparative review of Machine learning for plant disease detection:apple,cassava,cotton and potato plants

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Artificial Intelligence in Agriculture 2024年第2期12卷 127-151页

作者： James Daniel Omaye Emeka Ogbuju Grace Ataguba Oluwayemisi Jaiyeoba Joseph Aneke Francisca Oladipo department of computer science Federal UniversityLokojaNigeria department of computer science Dalhousie UniversityHalifaxCanada department of computer science Hampton UniversityVirginiaUSA

Plant disease detection has played a significant role in combating plant diseases that pose a threat to global agri-culture and food *** these diseases early can help mitigate their impact and ensure healthy crop *** learning algorithms have emerged as powerful tools for accurately identifying and classifying a wide range of plant diseases from trained image datasets of affected *** algorithms,including deep learning algorithms,have shown remarkable success in recognizing disease patterns and early signs of plant *** early detection,there are other potential benefits of machine learning algorithms in overall plant disease management,such as soil and climatic condition predictions for plants,pest identification,proximity detection,and many *** the years,research has focused on using machine-learning algorithms for plant disease ***,little is known about the extent to which the research community has ex-plored machine learning algorithms to cover other significant areas of plant disease *** view of this,we present a cross-comparative review of machine learning algorithms and applications designed for plant dis-ease detection with a specific focus on four(4)economically important plants:apple,cassava,cotton,and *** conducted a systematic review of articles published between 2013 and 2023 to explore trends in the research community over the *** filtering a number of articles based on our inclusion criteria,including articles that present individual prediction accuracy for classes of disease associated with the selected plants,113 articles were considered *** these articles,we analyzed the state-of-the-art techniques,challenges,and future prospects of using machine learning for disease identification of the selected *** from our re-view show that deep learning and other algorithms performed significantly well in detecting plant *** addition,we fou

关键词： Machine learning Plant diseases Agriculture Plant disease management Convolutional neural networks

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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

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An Encoding-Decoding Framework Based on CNN for circ RNA-RBP Binding Sites Prediction

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Chinese Journal of Electronics 2024年第1期33卷 256-263页

作者： Yajing GUO Xiujuan LEI Yi PAN School of computer science Shaanxi Normal University Faculty of computer science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences department of computer science Georgia State University

Predicting RNA binding protein(RBP) binding sites on circular RNAs(circ RNAs) is a fundamental step to understand their interaction mechanism. Numerous computational methods are developed to solve this problem, but they cannot fully learn the features. Therefore, we propose circ-CNNED, a convolutional neural network(CNN)-based encoding and decoding framework. We first adopt two encoding methods to obtain two original matrices. We preprocess them using CNN before fusion. To capture the feature dependencies, we utilize temporal convolutional network(TCN) and CNN to construct encoding and decoding blocks, respectively. Then we introduce global expectation pooling to learn latent information and enhance the robustness of circ-CNNED. We perform circ-CNNED across 37 datasets to evaluate its effect. The comparison and ablation experiments demonstrate that our method is superior. In addition, motif enrichment analysis on four datasets helps us to explore the reason for performance improvement of circ-CNNED.

关键词： Proteins RNA Neural networks Encoding Robustness Decoding Convolutional neural networks

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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

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