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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Esteban Parra Mohammad Alahmad Ashley Ellis Sonia Haiduc Department of Mathematics and Computer Science Belmont University Nashville TN USA Department of Software Engineering University of Jeddah Jeddah Saudi Arabia Department of Computer Science Florida State University Tallahassee Florida USA

software developers are often using instant messaging platforms to communicate with each other and other stakeholders. Among these platforms, Gitter has emerged as a popular choice and the messages it contains can reveal important information to researchers studying open-source software systems. Uncovering what developers are communicating about through Gitter is an essential first step towards successfully understanding and leveraging this information. This paper builds upon our previously published paper (Parra et al. 2020), which introduced GitterCom for the first time and presented a study of the messages it contains with the goal of observing how developers and other stakeholders communicate about software using Gitter in the context of Gitter communities dedicated to the active development of open source software systems on GitHub.

关键词： Instant messaging Stakeholders Open source software software development management

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Efficient Resource Allocation Using Service Function Chain in Agricultural Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Yao, Jiamin Qiao, Sibo Xie, Yu Kumari, Saru Lin, Qiao Khan, Fazlullah Shanghai Maritime University College of Information Engineering Shanghai201306 China Tiangong University School of Software Tianjin300387 China Chaudhary Charan Singh University Department of Mathematics Meerut India University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China

As the level of intelligence in agricultural consumer electronics continues to advance, data-driven devices are often faced with challenges such as resource shortages, high real-time requirements, and complex data processing. In particular, the diversity and variability of massive agricultural consumer electronics in the agricultural Internet of Things (IoT) complicate the resource allocation process. Traditional centralized cloud computing architectures often struggle to meet these demands. To address these issues, this paper proposes a resource collaborative allocation method for agricultural consumer electronics based on Service Function Chain (SFC). First, we formalize the resource requirements of various agricultural consumer electronics applications based on SFC in a resource mapping model. Subsequently, we develop a multidimensional resource balancing model with computing resources, storage resources, and bandwidth resources requirements of agricultural consumer electronics. We also devise a fine-grained end-to-end latency model to comprehensively consider the effective utilization of multidimensional resources and latency guarantees for agricultural consumer electronics. Finally, we employ a deep reinforcement learning algorithm with experience replay to optimize the resource allocation strategy in real-time, thereby enhancing the agricultural consumer electronics system's responsiveness and resource utilization efficiency. Experimental results demonstrate that our proposed strategy outperforms existing methods in terms of improving resource utilization and reducing latency. © 1975-2011 IEEE.

关键词： Chains

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Eliciting Requirements from Stakeholders’ Responses Using Natural Language Processing

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computer Modeling in engineering & Sciences 2021年第4期127卷 99-116页

作者： Mohammed Lafi Bilal Hawashin Shadi AlZu’bi Department of Software Engineering Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmman11733Jordan Department of Computer Information Systems Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science Faculty of Science and Information TechnologyAl-Zaytoonah University of JordanAmman11733Jordan

Most software systems have different stakeholders with a variety of *** process of collecting requirements from a large number of stakeholders is vital but *** propose an efficient,automatic approach to collecting requirements from different stakeholders’responses to a specific *** use natural language processing techniques to get the stakeholder response that represents most other stakeholders’*** study improves existing practices in three ways:Firstly,it reduces the human effort needed to collect the requirements;secondly,it reduces the time required to carry out this task with a large number of stakeholders;thirdly,it underlines the importance of using of data mining techniques in various software engineering *** approach uses tokenization,stop word removal,and word lemmatization to create a list of frequently accruing *** then creates a similarity matrix to calculate the score value for each response and selects the answer with the highest *** experiments show that using this approach significantly reduces the time and effort needed to collect requirements and does so with a sufficient degree of accuracy.

关键词： software requirements requirements elicitation natural language processing

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Optimized Drug-Drug Interaction Extraction With BioGPT and Focal Loss-Based Attention

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4560-4570页

作者： Zhu Yuan Shuailiang Zhang Huiyun Zhang Ping Xie Yaxun Jia Department of Information Management The National Police University for Criminal Justice Baoding China Department of Computer Engineering Henan Institute of Economics and Trade Zhengzhou China School of Software Henan University Kaifeng China Computer College Qinghai Normal University Xining China Department of Radiation Oncology Shanghai East Hospital Tongji University School of Medicine Shanghai China

Drug-drug interactions (DDIs) are a significant focus in biomedical research and clinical practice due to their potential to compromise treatment outcomes or cause adverse effects. While deep learning approaches have advanced DDI extraction, challenges such as severe class imbalance and the complexity of biomedical relationships persist. This study introduces BioFocal-DDI, a framework combining BioGPT for data augmentation, BioBERT and BiLSTM for contextual and sequential feature extraction, and Relational Graph Convolutional Networks (ReGCN) for relational modeling. To address class imbalance, a Focal Loss-based Attention mechanism is employed to enhance learning on underrepresented and challenging instances. Evaluated on the DDI Extraction 2013 dataset, BioFocal-DDI achieves a precision of 86.75%, recall of 86.53%, and an F1 Score of 86.64%. These results suggest that the proposed method is effective in improving DDI extraction.

关键词： Biological system modeling Feature extraction Drugs Data augmentation Data mining Training Data models Bidirectional long short term memory Semantics Attention mechanisms

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Enhanced Word Embedding with CNN using Word Order Information for Sentiment Analysis 20

Enhanced Word Embedding with CNN using Word Order Informatio...

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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Atandoh, Peter Fengli, Zhang Hakeem, Paul Atandoh Acheampong, Edward Mensah Addo, Daniel Appiah-Twum, Michael Adu-Gyamfi, Daniel Blankson, Richard Safo Giza Wi, Tohye Tewodros School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Mercer University Department of Mathematics MaconGA31207 United States School of Resources and Environment University of Electronic Science and Technology of China Chengdu China C. K. Tedam University of Technology and Applied Sciences Department of Cyber Security and Computer Engineering Technology Navrongo UE/R Ghana Summit Economics LLC Colorado United States

ISBN: (纸本)9798350318982

On several e-commerce platforms, internet users share opinions. Understanding people's sentiments and opinions is necessary. GloVe, which uses word contexts and matrix vectorization, is an effective word vector learning method. Vector-learning systems have improved with this method. However, the GloVe model ignores word order in context. This paper presents Positional Embedding, which integrates word order into GloVe word embeddings. We found that our Word Order Vector (PEWOVe) word embeddings method outperforms GloVe in sentiment classification. Amazon data was used to test the proposed technique. The proposed model is 2.7%, 2.5%, and 2.1 % more accurate than baseline models on GE, GEC, and GE+PE. © 2023 IEEE.

关键词： Sentiment analysis

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A ResNet-Based DVFS Regulator for Heterogeneous Multi-Core Mobile Processors

A ResNet-Based DVFS Regulator for Heterogeneous Multi-Core M...

引用

IEEE Region 10 International Conference TENCON

作者： Shibo Hu Xinzi Xu Yuze Chen Muyun Qin Yong Lian Yang Zhao School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Xiaomi Mobile Software Co. Ltd. Beijing China Department of Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

Dynamic voltage and frequency scaling (DVFS) is commonly used for the balance of performance and power of mobile processors. Conventional DVFS regulators take the processor cores with the same power and performance weight showing their inability in the regulation of heterogeneous processors. This paper proposes a core-aware frequency-power evaluation scheme that utilizes a multilayer perceptron (MLP) to assess the efficacy of DVFS regulation actions. Additionally, a ResNet-based DVFS regulator, trained with the assistance of the MLP, is developed for heterogeneous multi-core processors. Evaluated on Xiaomi 13 Pro powered by a Qualcomm Snapdragon 8 Gen 2, the proposed DVFS approach achieves an up to ×1.47 improvement in FPS Performance Per Watt (FPPW) compared with the Qualcomm's default DVFS governor in video recording scenario.

关键词： Program processors Regulators Power demand Multicore processing Multilayer perceptrons Regulation User experience Video recording Frequency control IEEE Regions

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The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things

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computer Methods and Programs in Biomedicine 2023年第1期241卷 107745-107745页

作者： Aminizadeh, Sarina Heidari, Arash Toumaj, Shiva Darbandi, Mehdi Navimipour, Nima Jafari Rezaei, Mahsa Talebi, Samira Azad, Poupak Unal, Mehmet Medical Faculty of Islamic Azad University of Tabriz Tabriz Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Software Engineering Haliç University Istanbul Turkey Urmia University of Medical Sciences Urmia Iran Department of Electrical and Electronic Engineering Eastern Mediterranean University Gazimagusa99628 Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Tabriz University of Medical Sciences Faculty of Surgery Tabriz Iran Department of Computer Science University of Texas at San Antonio TX United States Department of Computer Science University of Manitoba Winnipeg Canada Department of Computer Engineering Nisantasi University Istanbul Turkey

Medical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, which generate patient indexes in hospital data networks. Innovations like distributed computing, Machine Learning (ML), blockchain, chatbots, wearables, and pattern recognition can adequately enable the collection and processing of medical data for decision-making in the healthcare era. Particularly, to assist experts in the disease diagnostic process, distributed computing is beneficial by digesting huge volumes of data swiftly and producing personalized smart suggestions. On the other side, the current globe is confronting an outbreak of COVID-19, so an early diagnosis technique is crucial to lowering the fatality rate. ML systems are beneficial in aiding radiologists in examining the incredible amount of medical images. Nevertheless, they demand a huge quantity of training data that must be unified for processing. Hence, developing Deep Learning (DL) confronts multiple issues, such as conventional data collection, quality assurance, knowledge exchange, privacy preservation, administrative laws, and ethical considerations. In this research, we intend to convey an inclusive analysis of the most recent studies in distributed computing platform applications based on five categorized platforms, including cloud computing, edge, fog, IoT, and hybrid platforms. So, we evaluated 27 articles regarding the usage of the proposed framework, deployed methods, and applications, noting the advantages, drawbacks, and the applied dataset and screening the security mechanism and the presence of the Transfer Learning (TL) method. As a result, it was proved that most recent research (about 43%) used the IoT platform as the environment for the proposed architecture, and most of the studies (about 46%) were done in 2021. In addition, the most popular utilized DL

关键词： Deep learning

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Supporting Meta-Model-Based Language Evolution and Rapid Prototyping with Automated Grammar Optimization

SSRN

引用

SSRN 2023年

作者： Zhang, Weixing Holtmann, Jörg Hebig, Regina Steghöfer, Jan-Philipp Department of Computer Science & Engineering Chalmers | University of Gothenburg Gothenburg Sweden Institute of Computer Science University of Rostock Rostock Germany XITASO GmbH IT & Software Solutions Augsburg Germany

In model-driven engineering, textual domain-specific languages (DSLs) are constructed using a meta-model and a grammar and artifacts for parsing can be generated from this *** designing such a DSL, it is often necessary to manually optimize the generated grammar. When the meta-model changes during rapid prototyping or language evolution, the regenerated grammar needs to be optimized again, causing repeated effort and potential *** compared the generated grammars of seven DSLs to their original, hand-crafted grammars. We extracted a set of optimization rules that transform the generated grammars into ones that parse the same language as the original grammars and implemented them in *** evaluate GrammarOptimizer, we applied the optimization rules to these seven languages. The tool can modify the generated grammars so that they parse the same languages as the original, hand-crafted *** addition, we optimized generated grammars for different versions of QVTo and EAST-ADL to validate the support for language evolution. The contribution of this paper is GrammarOptimizer, a novel tool for optimizing generated grammars based on meta-models. It reduces the efforts of language engineers and simplifies rapid prototyping and evolution of meta-model-based DSLs. © 2023, The Authors. All rights reserved.

关键词： Rapid prototyping

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Prediction of Motion Trajectory of a Moving Object

Prediction of Motion Trajectory of a Moving Object

引用

National Conference with International Participation (TELECOM)

作者： Ventsislav Nikolov Ivo Rakitin Computer Science and Engineering Departments Technical University of Varna Varna Bulgaria Software and Internet Technologies Department Technical University of Varna Varna Bulgaria

In this paper experimental result of prediction of a moving object in a net of discrete points is presented. The prediction is done by autoregressive model of X-axis and Y-axis independently. Such kind of prediction could be valuable for prediction of moving of autonomous robots, drones and other similar artificial objects which can have implemented some kind of intelligent behavior. Thus the moving object can predict reaching a dangerous area or erupting points in its path. The results are analyzed and conclusions are summarized.

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DCC: Differentiable Cardinality Constraints for Partial Index Tracking

arXiv

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arXiv 2024年

作者： Jo, Wooyeon Cho, Hyunsouk Department of Artificial Intelligence Seoul06177 Korea Republic of Department of Software and Computer Engineering Ajou University Suwon16499 Korea Republic of The AI Lab Inc. Seoul06177 Korea Republic of

Index tracking is a popular passive investment strategy aimed at optimizing portfolios, but fully replicating an index can lead to high transaction costs. To address this, partial replication have been proposed. However, the cardinality constraint renders the problem non-convex, non-differentiable, and often NP-hard, leading to the use of heuristic or neural network-based methods, which can be non-interpretable or have NP-hard complexity. To overcome these limitations, We propose a Differentiable Cardinality Constraint (DCC) for index tracking and introduce a floating-point precision-aware method (DCCfpp) to address implementation issues. We theoretically prove our methods calculate cardinality accurately and enforce actual cardinality with polynomial time complexity. We propose the range of the hyperparameter a ensures that DCCfpp has no error in real implementations, based on theoretical proof and experiment. Our method applied to mathematical method outperforms baseline methods across various datasets, demonstrating the effectiveness of the identified hyperparameter a. Copyright © 2024, The Authors. All rights reserved.

关键词： Heuristic methods

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