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International Workshop on Empirical software engineering in Practice (IWESEP)

作者： Ryo Ishizuka Hironori Washizaki Yoshiaki Fukazawa Shinobu Saito Saori Ouji Dept. of Computer Science and Engineering Waseda University Tokyo Japan Software Innovation Center NTT Corporation Tokyo Japan

ISBN: (数字)9781728155906

ISBN: (纸本)9781728155913

Acquiring knowledge and context of the ongoing project is one of the most challenging issues for new project members. New project members need to comprehend the contents of features already implemented in software systems. However, most software documents (e.g., flow chart and data-model) were initially created and not updated. Herein we focus on tickets issued during the project period because they are created based on stakeholders' requirements as a project evolves. We propose a novel approach to categorize and visualize tickets to better understand the features implemented in the existing software systems. Specifically, tickets are grouped by a clustering method and the number of clusters (i.e., ticket categories) is automatically estimated. Then the ticket categories are visualized by (i) creating a heatmap of the tickets lifetimes, (ii) extracting keywords of the ticket categories, and (iii) analyzing the relationships between ticket categories. We applied our approach to an NTT software development project. Additionally, we interviewed the project members and external experts to evaluate the effectiveness of our approach. Our approach helps comprehend the features of the software system.

关键词： Data visualization software computer science Text mining Machine learning Technological innovation Data collection

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Network2Vec: Learning node representation based on space mapping in networks

arXiv

引用

arXiv 2019年

作者： Huang, Zhenhua Wang, Zhenyu Zhang, Rui Zhao, Yangyang Xie, Xiaohui Mehrotra, Sharad School of Software Engineering South China University of Technology Dept. of Computer Science University of California Irvine United States

Complex networks represented as node adjacency matrices constrains the application of machine learning and parallel algorithms. To address this limitation, network embedding (i.e., graph representation) has been intensively studied to learn a fixed-length vector for each node in an embedding space, where the node properties in the original graph are preserved. Existing methods mainly focus on learning embedding vectors to preserve nodes proximity, i.e., nodes next to each other in the graph space should also be closed in the embedding space, but do not enforce algebraic statistical properties to be shared between the embedding space and graph space. In this work, we propose a lightweight model, entitled Network2Vec, to learn network embedding on the base of semantic distance mapping between the graph space and embedding space. The model builds a bridge between the two spaces leveraging the property of group homomorphism. Experiments on different learning tasks, including node classification, link prediction, and community visualization, demonstrate the effectiveness and efficiency of the new embedding method, which improves the state-of-the-art model by 19% in node classification and 7% in link prediction tasks at most. In addition, our method is significantly faster, consuming only a fraction of the time used by some famous methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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An IoT and blockchain-based multi-sensory in-home quality of life framework for cancer patients 15

An IoT and blockchain-based multi-sensory in-home quality of...

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15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019

作者： Rahman, Md. Abdur Rashid, Mamunur Barnes, Stuart Shamim Hossain, M. Hassanain, Elham Guizani, Mohsen Cyber Security and Forensic Computing Department University of Prince Mugrin Madinah Saudi Arabia Research Centre King's College London United Kingdom Department of Software Engineering King Saud University Riyadh11543 Saudi Arabia Dept. of Electrical and Computer Engineering University of Idaho MoscowID83844-1023 United States Dept. of Computer Science Umm Al-Qura University Makkah Saudi Arabia

ISBN: (纸本)9781538677476

Once a subject is diagnosed with cancer, a patient goes through a series of diagnosis and tests, referred to as after cancer treatment. Due to the nature of the treatment and side effects on regular lifestyles, maintaining quality of life in the home environment is a challenging task. Sometimes within a home environment, a cancer patient's situation changes abruptly, as the functionality of certain organs deteriorate, which affects their quality of life. In this paper, we propose a Blockchain and off-chain based framework which will allow multiple medical and ambient intelligent IoT sensors to capture quality of life information from one's home environment and securely share it with one's community of interest. Using our proposed framework, both transactional records and multimedia big data - consisting of a user's physiological as well as mental states - can be shared with an oncologist or palliative care unit for real-time decision support. We have also developed Blockchain-based data analytics, which will allow a clinician to visualize the immutable history of the patient's data available from an in-home secure monitoring system for a better understanding of a patient's current or historical states. We further designed a generic oncologist smart contract and digital wallet for different stakeholders to automate the treatment plan of a particular patient. Finally, we will present our current implementation status, which provides significant encouragement for further development. © 2019 IEEE.

关键词： Blockchain

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Dynamic Weight Allocation–Based Network Security and Anomaly Detection Model for Intelligent VANETs

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Transactions on Emerging Telecommunications Technologies 2025年第6期36卷

作者： Aadam Quraishi Rakeshnag Dasari Sushilkumar Dangiya Sateesh Kumar Nallamala Krishna Kanth Kondapaka Swaroop Reddy Gayam Isa Bayhan Uguloy Berdieva Rubal Jeet M.D. Research Cognitive and AI laboratory Interventional treatment institute Houston Texas USA Department of CSE Acharya Nagarjuna University Nagarjuna Nagar Guntur India Department of Electronics and Communication Engineering GTU – School of Engineering and Technology Ahmedabad India Independent Researcher USA Independent Researcher California USA Independent Researcher and Senior Software Engineer at TJMax USA Assistant Professor of Tourist Guiding Dept Bolu Abant Izzet Baysal University Golkoy Campus Bolu Turkey The Department of Tax and Taxation Tashkent State University of Economics Tashkent Uzbekistan Chandigarh Group of Colleges Jhanjeri Chandigarh Engineering College Department of Computer Science and Engineering Punjab India

Determining the weights of evaluation metrics is one of the key factors influencing the cybersecurity and anomaly detection of intelligent vehicular ad hoc networks (VANETs). To address the limitations of traditional weighting methods, which often overlook the impact of changes in metric attribute states on evaluation weights, this paper proposes a dynamic weight allocation–based network security and anomaly detection model. The model begins by decomposing and analyzing the security and anomaly detection objectives of VANETs, constructing a comprehensive evaluation metric system. The network security assessment model for VANETs presented in this research overcomes the drawbacks of conventional static models by utilizing a dynamic weight allocation technique. Based on current network conditions, a state variable weight method was created that dynamically computes security values by combining incentive and penalty mechanisms. A ranking-based weighting algorithm is employed to analyze the correlation between security and anomaly detection metrics. Subsequently, the proposed dynamic weight allocation algorithm calculates the dynamic weights of individual metrics within the system, enabling a robust assessment of network security and anomaly detection for intelligent VANETs. The evaluation results provide security level classifications and identify anomalies effectively. Experimental results demonstrate that the model significantly enhances the rationality and accuracy of intelligent VANET evaluations, contributing to improved cybersecurity and anomaly detection.

关键词： anomaly detection dynamic weight allocation intelligent vehicle network security VANETs

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An improved simulation model for pedestrian crowd evacuation

arXiv

引用

arXiv 2020年

作者： Muhammed, Danial A. Rashid, Tarik A. Alsadoon, Abeer Bacanin, Nebojsa Fattah, Polla Mohammadi, Mokhtar Banerjee, Indradip Computer Department College of Science University of Sulaimani SulaymaniyahKRG46001 Iraq Computer Science and Engineering Department University of Kurdistan Hewler ErbilKRG44001 Iraq Department of Information Technology School of Computing and Mathematics Charles Sturt University Sydney BathurstNSW2795 Australia Faculty of Informatics and Computing Vice-dean Singidunum University Belgrade11000 Serbia Software and Informatics Engineering Dept Engineering College Salahaddin University-Erbil ErbilKRG44001 Iraq Department of Information Technology Lebanese French University ErbilKRG44001 Iraq Department of Computer Science and Engineering University Institute of Technology The University of Burdwan BurdwanWest Bengal713101 India

This paper works on one of the most recent pedestrian crowd evacuation models, i.e., "a simulation model for pedestrian crowd evacuation based on various AI techniques", developed in late 2019. This study adds a new feature to the developed model by proposing a new method and integrating it with the model. This method enables the developed model to find a more appropriate evacuation area design, among others regarding safety due to selecting the best exit door location among many suggested locations. This method is completely dependent on the selected model's output, i.e., the evacuation time for each individual within the evacuation process. The new method finds an average of the evacuees’ evacuation times of each exit door location;then, based on the average evacuation time, it decides which exit door location would be the best exit door to be used for evacuation by the evacuees. To validate the method, various designs for the evacuation area with various written scenarios were used. The results showed that the model with this new method could predict a proper exit door location among many suggested locations. Lastly, from the results of this research using the integration of this newly proposed method, a new capability for the selected model in terms of safety allowed the right decision in selecting the finest design for the evacuation area among other designs. © 2020, CC BY.

关键词： Location

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An improved simulation model for pedestrian crowd evacuation

TechRxiv

引用

TechRxiv 2020年

作者： Muhammed, Danial A. Rashid, Tarik A. Alsadoon, Abeer Bacanin, Nebojsa Fattah, Polla Mohammadi, Mokhtar Banerjee, Indradip Computer Department College of Science University of Sulaimani KRG Sulaymaniyah46001 Iraq Computer Science and Engineering Department University of Kurdistan Hewler KRG Erbil44001 Iraq Department of Information Technology School of Computing and Mathematics Charles Sturt University Bathurst SydneyNSW2795 Australia Faculty of Informatics and Computing Vice-dean Singidunum University Belgrade11000 Serbia Software and Informatics Engineering Dept Engineering College Salahaddin University-Erbil KRG Erbil44001 Iraq Department of Information Technology Lebanese French University KRG Erbil44001 Iraq Department of Computer Science and Engineering University Institute of Technology The University of Burdwan West Bengal Burdwan713101 India

关键词： Location

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Improving Prediction Accuracy for Logistic Regression on Imbalanced Datasets

Improving Prediction Accuracy for Logistic Regression on Imb...

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IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： Hao Zhang Zhuolin Li Hossain Shahriar Lixin Tao Prabir Bhattacharya Ying Qian College of CSE Kennesaw State University Dept. of Software Engineering Pace University Dept. of Computer Science SUNY at Albany Albany NY USA Dept. of Computer Science East China Normal University

An imbalanced dataset is a dataset that has a majority class which is a class has far more example distributions than other classes. It is difficult to deal with unbalanced datasets in classification problems, and many classification algorithms do not perform well in unbalanced datasets. In this paper, we present our logistic regression analysis with Python on imbalanced datasets and determine different thresholds for classification according to the data proportion of imbalanced datasets.

关键词： Logistics Classification algorithms Credit cards Genetic algorithms Machine learning Companies Data mining

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Synthesizing Wider WiFi Bandwidth for Respiration Rate Monitoring in Dynamic Environments

Synthesizing Wider WiFi Bandwidth for Respiration Rate Monit...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Shuyu Shi Yaxiong Xie Mo Li Alex X. Liu Jun Zhao State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China School of Computer Science and Engineering Nanyang Technological University (NTU) Singapore Dept. of Computer Science and Engineering Michigan State University USA

Respiration rate monitoring is beneficial for the diagnosis of a variety of diseases, such as heart failure and sleep disorders. Radio Frequency (RF) based respiration rate monitoring systems, namely ultra-wideband radar and COTS device, have been proposed without requiring any direct contact with the detected person. However, existing RF based systems either require expensive UWB radio (radar based) or work only in stationary environments (COTS device based). To address the limitations of both radar based and COTS device based systems, in this paper, we propose RespiRadio, a system that can detect a person's respiration rate in dynamic ambient environments via a single TX-RX pair of WiFi cards. The key novelty of RespiRadio is that it overcomes the limit of existing COTS device based respiration rate systems by synthesizing a wider-bandwidth WiFi radio. With the synthesized WiFi radio, we can identify the path reflected by the breathing person and then analyze the periodicity of the signal power measurements only from this path to infer the respiration rate. We experimentally evaluate the performance of RespiRadio in non-static indoor environments and the results demonstrate that the overall estimation error is 0.152 breaths per minute (bpm).

关键词： Wireless fidelity Monitoring Bandwidth Radio frequency Delays Ultra wideband radar

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Safety and Security Analysis of Control Chains in SCADA Using the NFR Approach

引用

IFAC-PapersOnLine 2018年第6期51卷 214-219页

作者： Subramanian, Nary Zalewski, Janusz Dept. of Computer Science University of Texas at Tyler TylerTX75799 United States Dept. of Software Engineering Florida Gulf Coast University Fort MyersFL33965 United States

The objective of this research is to shed light on the applicability of the Non-Functional Requirements (NFR) Approach to the concept of control chains for the combined analysis of safety and security of SCADA systems and, more generally, to computer systems used in automation, including functional safety and security standards. The paper discusses the concept of control chains, presents an overview of the NFR Approach, and applies this approach to the safety and security analysis of control chains in a case study of the automatic control of the hydrocarbon tank pressure control system. © 2018

关键词： computer control systems Automation Chains Embedded systems Network security SCADA systems Security of data Security systems ANSI/ISA 62443 Automation systems Cyber physical systems (CPSs) Cyber security IEC 61508 IEC 61511 NFR Approach SCADA

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Detecting Default Payment Fraud in Credit Cards

Detecting Default Payment Fraud in Credit Cards

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IEEE International Conference on Intelligent Systems and Green Technology (ICISGT)

作者： S. S. Harshini Padmanabhuni Aditya Sai Kandukuri Debachudamani Prusti Santanu Kumar Rath Dept. of Computer Science of Engineering National Institute of Technology Rourkela Rourkela India Associate Software Engineer Micro Focus Bangalore India

ISBN: (数字)9781728114231

ISBN: (纸本)9781728114248

A credit card account is said to be in default if the payment into the credit card account is not done in due time because of credit card fraud. In many cases the default account credit card holders are found to be fraudsters. It is necessary for financial Institutions to determine which accounts may go into default so that they can take necessary preventive steps to curb any fraudulent transactions. The transactions of the clients can be observed and using this information if any account resembles a fraud, the financial institutions can take necessary actions like blocking the account. We have considered some standard machine learning algorithms for the classification of the default accounts. Later, we have proposed a model by combining the classification algorithms and critically examined it with other machine learning and deep learning models.

关键词： Credit cards Hidden Markov models Machine learning algorithms Biological neural networks Machine learning Forestry Decision trees

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