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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： M. Maragatharajan Shanmugapriya S M. B. Sudhan Deeptha R. Sankar Muthu Paramasivam School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputhur Tamilnadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu Department of Information Technology SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu India Distinguished Software Engineer Palo Alto Networks Inc Santa Clara California USA

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

Strokes or cardiovascular disorders continue to be a major global health concern. Proactive intervention and timely detection greatly reduce the morbidity and death related to strokes. The goal of this research is to build a prediction model using machine learning techniques to assess a person’s risk of stroke based on a variety of sociodemographic and health characteristics. The dataset used in this study consists of a large collection of medical records. It contains important details including age, gender, BMI, smoking habits, heart disease, hypertension, and the type of residential property. Using this information, a Decision Tree Classifier is trained to predict the likelihood of strokes, enabling early detection and customized treatment plans. A Decision Tree Classifier with a predetermined maximum depth is used in the proposed methodology’s rigorous data preprocessing, which includes handling missing values, encoding categorical features, and developing models. Both the training and testing datasets are used to evaluate the model’s performance using evaluation measures including accuracy, precision, and recall. The model’s insights provide important characteristics that influence the prediction of stroke risk, assisting medical professionals in focused risk assessment and preventive intervention. The proposed method has the potential to improve healthcare practices by facilitating prompt interventions and personalized treatment for stroke risk individuals. The proposed model has resulted in 86.78% accuracy for this dataset by using the traditional machine learning algorithms. This work highlights the value of predictive modelling in the healthcare industry by demonstrating how it may improve early detection techniques and lessen the impact of strokes on public health.

关键词： Training Machine learning algorithms Machine learning Predictive models Stroke (medical condition) Real-time systems Risk management

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A survey on applying machine learning to enhance trust in mobile adhoc networks

A survey on applying machine learning to enhance trust in mo...

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2020 International Research Conference on Smart computing and Systems engineering, SCSE 2020

作者： Jinarajadasa, G.M. Liyange, S.R. University of Kelaniya Faculty of Computing and Technology Department of Software Engineering Sri Lanka

ISBN: (纸本)9781728172491

Mobile ad-hoc networks (MANETs) play a vital role in the increasingly networked world where it has many applications in plenty of important fields including military sector, business applications, social networks such as Vehicular Networks (VANETs), and other intelligent systems. Because of the dynamic nature of mobile ad-hoc networks, they are more tent to be objected to the various malicious attacks. Over the recent past decades, a certain amount of researches has been done to increase reliable and trustworthy communications in a MANET environment. Over the proposed solutions, Machine learning applications have significant results. Hence based on those, a critical analysis of existing machine learning-based trust approaches for mobile ad-hoc networks are presented here. The focus of this survey is to classify and evaluate the existing trust mechanisms and to provide guidance for future research work in the area. © 2020 IEEE.

关键词： Vehicular ad hoc networks

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Reinforcement Learning-based Contention Window Adjustment for Wireless Body Area Networks

Reinforcement Learning-based Contention Window Adjustment fo...

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Big Data Analytics and Practices (IBDAP), International Conference on

作者： Jung-Hyok Kwon Dongwan Kim Eui-Jik Kim Smart Computing Laboratory Hallym University Chuncheon South Korea Department of Electronic Engineering Dong-A University Busan South Korea Division of Software Hallym University Chuncheon South Korea

This paper presents a reinforcement learning-based contention window adjustment for wireless body area networks (WBANs), abbreviated RL-CWA. The RL-CWA uses Q-learning to adaptively adjust the contention window according to the acknowledgment reception, thereby reducing the number of collisions. In addition, RL-CWA maintains multiple Q-tables to take into account minimum and maximum contention windows that vary depending on the user priority (UP) of the traffic. To verify the superiority of RL-CWA, an experimental simulation was conducted. The results showed that the RL-CWA obtains better performance than the existing IEEE 802.15.6 MAC.

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Enhancing software Reliability Growth Modeling: A Comprehensive Analysis of Historical Datasets and Optimal Model Selections

Enhancing Software Reliability Growth Modeling: A Comprehens...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Taehyoun Kim Duksan Ryu Jongmoon Baik Agency for Defense Development Daejeon Republic of Korea Department of software engineering Jeonbuk national university Jeonju-si Jeonbuk-do Republic of Korea School of Computing Korea Advanced Institute of Science and Technology Daejeon Republic of Korea

ISBN: (数字)9798350365634

ISBN: (纸本)9798350365641

It requires historical datasets from diverse environments to build accurate and robust software reliability growth models. However, it is very difficult to collect such datasets, particularly in the industrial domain, due to confidentiality and security considerations. To solve this issue, we conducted a thorough investigation of historical datasets for software reliability growth modeling. We searched IEEE Xplore, a prominent digital library, using the keyword “software Reliability Growth Model” for publications up to 2022 and gathered 127 non-redundant historical datasets. In this paper, we present a comprehensive analysis of these datasets, which helps advance software reliability growth modeling. We applied seven representative software reliability growth models and found that the Generalized Goel model, which has a concave curve, was the most frequently chosen optimal model for Failure-Count type datasets. However, It is notable that S-shaped curve models remained preferred for the majority of datasets. Categorizing datasets by collection phases, application types, source types, and data flow trends resulted in varied optimal model selections across classifications. These insights contribute to the advancement of software reliability growth modeling, fostering informed decision-making in software development and maintenance.

关键词： Adaptation models Analytical models Accuracy software quality Reliability engineering Data models Libraries

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Automated Machine Learning for Enhanced software Reliability Growth Modeling: A Comparative Analysis with Traditional SRGMs

Automated Machine Learning for Enhanced Software Reliability...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

ISBN: (数字)9798350365634

ISBN: (纸本)9798350365641

Traditional software Reliability Growth Models (SRGMs) depend on unrealistic assumptions, which makes it difficult to capture the complexities of modern software development. Recent advancements in artificial intelligence have introduced new modeling techniques, but these methods are complex and require careful algorithm selection and hyperparameter tuning. Automated Machine Learning (AutoML) has emerged as a promising solution to streamline this process. However, its application in the field of software reliability growth modeling remains unexplored. In this study, we explore the effectiveness of AutoML in enhancing software reliability growth modeling and compare its performance with traditional SRGMs. We employ two prominent AutoML packages, Auto-sklearn and H2O AutoML, and leverage twelve project datasets to answer three research questions: (1) the impact of various AutoML package options on software reliability growth modeling, (2) the identification of the optimal AutoML approach for modeling software reliability growth, and (3) the overall effectiveness of AutoML in software reliability growth modeling. We found that using ensemble options enhanced predictive performance across multiple projects. Auto-sklearn with the ensemble option emerged as the most effective approach when evaluated based on End-point Prediction values, while H2O AutoML with the ensemble option demonstrated superior performance based on Mean Squared Error values. Additionally, AutoML packages with ensemble options demonstrated more accurate predictive performance compared to traditional SRGMs across the majority of datasets. Our study highlights the potential of AutoML to enhance software reliability growth modeling and provides insights for future research and practical applications in software engineering.

关键词： Water Analytical models software algorithms software quality software systems software reliability Security

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SCALAR: A Part-of-speech Tagger for Identifiers

arXiv

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arXiv 2025年

作者： Newman, Christian D. Scholten, Brandon Testa, Sophia Behler, Joshua A.C. Banabilah, Syreen Collard, Michael L. Decker, Michael J. Mkaouer, Mohamed Wiem Zampieri, Marcos AlOmar, Eman Abdullah Alsuhaibani, Reem Peruma, Anthony Maletic, Jonathan I. Department of Software Engineering Rochester Institute of Technology RochesterNY United States Department of Computer Science Kent State University KentOH United States Department of Computer Science University of Akron AkronOH United States Department of Software Engineering Bowling Green State University Bowling GreenOH United States Department of Computer Science University of Michigan Flint FlintMI United States George Mason University School of Computing FairfaxVA United States Stevens Institute of Technology School of Engineering and Science HobokenNJ United States Prince Sultan University Department of Software Engineering Riyadh Saudi Arabia University of Hawaii at Manoa Department of Information and Computer Sciences HonoluluHI United States

The paper presents the Source Code Analysis and Lexical Annotation Runtime (SCALAR), a tool specialized for mapping (annotating) source code identifier names to their corresponding part-of-speech tag sequence (grammar pattern). SCALAR’s internal model is trained using scikit-learn’s GradientBoostingClassifier in conjunction with a manually-curated oracle of identifier names and their grammar patterns. This specializes the tagger to recognize the unique structure of the natural language used by developers to create all types of identifiers (e.g., function names, variable names etc.). SCALAR’s output is compared with a previous version of the tagger, as well as a modern off-the-shelf part-of-speech tagger to show how it improves upon other taggers’ output for annotating identifiers. The code is available on Github1 © 2025, CC BY.

关键词： Computer software maintenance

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LALISA: Adaptive Bitrate Ladder Optimization in HTTP-based Adaptive Live Streaming

LALISA: Adaptive Bitrate Ladder Optimization in HTTP-based A...

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IEEE Symposium on Network Operations and Management

作者： Farzad Tashtarian Abdelhak Bentaleb Hadi Amirpour Babak Taraghi Christian Timmerer Hermann Hellwagner Roger Zimmermann Christian Doppler Laboratory ATHENA Alpen-Adria-Universitat Klagenfurt Austria Department of Computer Science and Software Engineering Concordia University Canada School of Computing National University of Singapore Singapore

Video content in Live HTTP Adaptive Streaming (HAS) is typically encoded using a pre-defined, fixed set of bitrate-resolution pairs (termed Bitrate Ladder), allowing play-back devices to adapt to changing network conditions using an adaptive bitrate (ABR) algorithm. However, using a fixed one-size-fits-all solution when faced with various content complexities, heterogeneous network conditions, viewer device resolutions and locations, does not result in an overall maximal viewer quality of experience (QoE). Here, we consider these factors and design LALISA, an efficient framework for dynamic bitrate ladder optimization in live HAS. LALISA dynamically changes a live video session’s bitrate ladder, allowing improvements in viewer QoE and savings in encoding, storage, and bandwidth costs. LALISA is independent of ABR algorithms and codecs, and is deployed along the path between viewers and the origin server. In particular, it leverages the latest developments in video analytics to collect statistics from video players, content delivery networks and video encoders, to perform bitrate ladder tuning. We evaluate the performance of LALISA against existing solutions in various video streaming scenarios using a trace-driven testbed. Evaluation results demonstrate significant improvements in encoding computation (24.4%) and bandwidth (18.2%) costs with an acceptable QoE.

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Model checking ontology-driven reasoning agents using strategy and abstraction

Model checking ontology-driven reasoning agents using strate...

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作者： Rakib, Abdur Faruqui, Rokan Uddin Department of Computer Science and Creative Technologies The University of the West of England Bristol United Kingdom Department of Computing and Software McMaster University Hamilton Canada Department of Computer Science and Engineering University of Chittagong Chittagong Bangladesh

We present a framework for the modelling, specification, and verification of ontology-driven multi-agent rule-based systems (MASs). We assume that each agent executes in a separate process and that they communicate via message passing. The proposed approach makes use of abstract specifications to model the behaviour of some of the agents in the system and exploits information about the reasoning strategy adopted by the agents. Abstract specifications are given as Linear Temporal Logic (LTL) formulas which describe the external behaviour of the agents, allowing their temporal behaviour to be compactly modelled. Both abstraction and strategy have been combined in an automated model checking encoding tool Tovrba for rule-based multi-agent systems which allows the system designer to specify information about agents' interaction, behaviour, and execution strategy at different levels of abstraction. The Tovrba tool generates an encoding of the system for the Maude LTL model checker, allowing properties of the system to be verified. © 2019 John Wiley & Sons, Ltd.

关键词： Model checking

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AutoDebloater: Automated Android App Debloating 23

AutoDebloater: Automated Android App Debloating

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Proceedings of the 38th IEEE/ACM International Conference on Automated software engineering

作者： Jiakun Liu Xing Hu Ferdian Thung Shahar Maoz Eran Toch Debin Gao David Lo School of Computing and Information Systems Singapore Management University Singapore School of Software Technology Zhejiang University China School of Computer Science Tel Aviv University Israel Department of Industrial Engineering Tel Aviv University Israel

ISBN: (纸本)9798350329964

Android applications are getting bigger with an increasing number of features. However, not all the features are needed by a specific user. The unnecessary features can increase the attack surface and cost additional resources (e.g., storage and memory). Therefore, it is important to remove unnecessary features from Android applications. However, it is difficult for the end users to fully explore the apps to identify the unnecessary features, and there is no off-the-shelf tool available to assist users to debloat the apps by themselves. In this work, we propose AutoDebloater to debloat Android applications automatically for end users. AutoDebloater is a web application that can be accessed by end-users through a web browser. In particular, AutoDebloater can automatically explore an app and identify the transitions between activities. Then, AutoDebloater will present the Activity Transition Graph to users and ask them to select the activities they do not want to keep. Finally, AutoDebloater will remove the activities that are selected by users from the app. We conducted a user study on five Android apps downloaded from three categories (i.e., Finance, Tools, and Navigation) in Google Play and F-Droid. The results show that users are satisfied with AutoDebloater in terms of the stability of the debloated apps and the ability of AutoDebloater to identify features that are never noticed before. The tool is available at http://***. The code is available at https://***/jiakun-liu/autodebloater/ and the demonstration video can be found at https://***/Gmz0-p2n9D4.

关键词： android

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SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning

arXiv

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arXiv 2025年

作者： Li, Nan Wang, Xiaolu Du, Xiao Cai, Puyu Wang, Ting Engineering Research Center of Software/Hardware Co-Design Technology and Application Ministry of Education the Shangh ai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Software Engineering Institute East China Normal University Shanghai200050 China Computer Science Department New York University New YorkNY10012 United States

Federated Learning (FL) enables clients to collaboratively train machine learning models without sharing local data, preserving privacy in diverse environments. While traditional FL approaches preserve privacy, they often struggle with high computational and communication overhead. To address these issues, model pruning is introduced as a strategy to streamline computations. However, existing pruning methods, when applied solely based on local data, often produce sub-models that inadequately reflect clients’ specific tasks due to data insufficiency. To overcome these challenges, this paper introduces SAFL (Structure-Aware Federated Learning), a novel framework that enhances personalized federated learning through client-specific clustering and Similar Client Structure Information (SCSI)-guided model pruning. SAFL employs a two-stage process: initially, it groups clients based on data similarities and uses aggregated pruning criteria to guide the pruning process, facilitating the identification of optimal sub-models. Subsequently, clients train these pruned models and engage in server-based aggregation, ensuring tailored and efficient models for each client. This method significantly reduces computational overhead while improving inference accuracy. Extensive experiments demonstrate that SAFL markedly diminishes model size and improves performance, making it highly effective in federated environments characterized by heterogeneous data. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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