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4th World Symposium on software engineering, WSSE 2022

作者： Matcha, Wyao Toure, Fadel Badri, Mourad Badri, Linda Department of Mathematics and Computer Science Lab of Software Engineering University of Quebec at Trois-Rivieres Canada

ISBN: (纸本)9781450396950

This paper aims at investigating the use of deep learning to suggest (prioritize) classes to be tested rigorously during unit testing of object-oriented systems. We relied on software unit testing information history and source code metrics. We conducted an empirical study using data collected from two Apache open-source Java software systems (POI and ANT). For each software system, we extracted the source code of five different versions. For each version, we collected various metrics from the source code of the Java classes. Then, for all software classes, we extracted testing coverage measures at instruction and method levels of granularity. We used the existing JUnit test cases developed for these systems. Based on the different datasets we collected, we trained several deep neural network models. We validated the obtained classifiers using four validation techniques: (1) CV: Cross Version validation, (2) CPV: Combined Previous Version validation, (3) CSPV: Combined System and Previous Version validation, and (4) LOSO: Leave One System Out validation. The obtained results in terms of classifiers' performance vary between 70% and 80% of accuracy and strongly support the viability of our approach. © 2022 ACM.

关键词： Deep neural networks

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Optimizing Depression Prediction in Older Adults: A Comparative Study of Feature Extraction and Machine Learning Models

Optimizing Depression Prediction in Older Adults: A Comparat...

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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Javeed, Ashir Anderberg, Peter Ghazi, Ahmad Nauman Javeed, Asim Dallora, Ana Luiza Berglund, Johan Sanmartin Blekinge Institute of Technology Department of Health Blekinge Karlskrona37179 Sweden Blekinge Institute of Technology Department of Software Engineering Blekinge Karlskrona37179 Sweden Times Institute Department of Computer Science Multan60650 Pakistan

ISBN: (纸本)9798350361025

Depression emerged as a major public health concern in older adults, and timely prediction of depression has become a difficult problem in medical informatics. The latest studies have attentiveed on feature transformation and selection for better depression prediction. In this study, we assess the performance of various feature extraction algorithms, including principal component analysis (PCA), independent component analysis (ICA), locally linear Embedding (LLE), and t-distributed stochastic neighbor embedding (TSNE). These algorithms are combined with machine learning (ML) classifier algorithms such as Gaussian Naive Bayes (GNB), Logistic Regression (LR), K-nearest-neighbor (KNN), and Decision Tree (DT) to enhance depression prediction. In total, sixteen automated integrated systems are constructed based on the above-mentioned feature extraction methods and ML classifiers. The performance of all of these integrated models is assessed using data from the Swedish National Study on Aging and Care (SNAC). According to the experimental results, the PCA algorithm combined with the Logistic Regression (LR) model provides 89.04% depression classification accuracy. As a result, it is demonstrated that the PCA is a more suitable feature extraction method for depression data than ICA, LLE, and TSNE. © 2024 IEEE.

关键词： Feature extraction

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AffordAD: A User Friendly Tool for Estimating Housing Affordability in Abu Dhabi 14

AffordAD: A User Friendly Tool for Estimating Housing Afford...

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14th Annual Undergraduate Research Conference on "ICT for Resilient and Sustainable Infrastructure", URC 2022

作者： Almusalami, Aisha Habuza, Tetiana Singh, Harsh Zaki, Nazar United Arab Emirates University Department of Computer Science & Software Engineering United Arab Emirates

ISBN: (纸本)9798350346800

Improving housing affordability is one of the United nations' sustainable development goals (SDGs). It is also highlighted in Abu Dhabi Community Facility Planning Standards (PS5 affordable housing planning standard). The proposed work presents a user-friendly tool to estimate affordability. First, a user selects a location of interest. The tool automatically finds the district where the selected geocoordinate is located. Then, it counts different facilities within 1 km of range and number of past accidents happened in the district corresponding to selected point. A user can manually select the type of the unit (studio, villa, and apartment), number of rooms and alter the auto-filled fields to see their effect on housing affordability. It provides flexibility in city planning. All facilities within 1km are displayed on the map for more informative and data-driven decisions. Then a user can click the predict button to infer through a trained machine learning model. This model is developed using data from 42 heterogeneous datasets retrieved from Abu Dhabi open data platform. Data from different sources such as GIS, facilities, and accidents were pre-processed and integrated to develop the solution. A user can estimate the affordability of a particular location based on its surrounding area. Additionally, detailed reports about the district and nearby facilities can be downloaded for further analysis. Our solution will help users to find the best location to live in based on their budget. It will help decisionmakers to evaluate housing affordability in the future. Our machine learning model (Gradient Boosting) that is behind the tool presents a high performance in terms of Mean Absolute Error (MAE) and Coefficient of Determination (R-Squared). © 2022 IEEE.

关键词： Accidents

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Towards Prevention of Sportsmen Burnout:Formal Analysis of Sub-Optimal Tournament Scheduling

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computers, Materials & Continua 2022年第1期70卷 1509-1526页

作者： Syed Rameez Naqvi Adnan Ahmad S.M.Riazul Islam Tallha Akram M.Abdullah-Al-Wadud Atif Alamri Department of Electrical and Computer Engineering COMSATS University IslamabadWah CampusWah47040Pakistan Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Research Chair of Pervasive and Mobile Computing King Saud UniversityRiyadh11543Saudi Arabia

Scheduling a sports tournament is a complex optimization problem,which requires a large number of hard constraints to *** the availability of several such constraints in the literature,there remains a gap sincemost of the new sports events pose their own unique set of requirements,and demand novel *** talking of the strictly time bound events,ensuring fairness between the different teams in terms of their rest days,traveling,and the number of successive games they play,becomes a difficult task to resolve,and demands *** this work,we present a similar situation with a recently played sports event,where a suboptimal schedule favored some of the sides more than the *** introduce various competitive parameters to draw a fairness comparison between the sides and propose a weighting criterion to point out the sides that enjoyed this schedule more than the ***,we use root mean squared error between an ideal schedule and the actual ones for each side to determine unfairness in the distribution of rest days across their entire *** latter is crucial,since successively playing a large number of games may lead to sportsmen burnout,which must be prevented.

关键词： Sports scheduling optimization constraint programming fatigue analysis

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Harnessing the meteorological effect for predicting the retail price of rice in Bangladesh

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International Journal of Business Intelligence and Data Mining 2022年第4期20卷 440-455页

作者： Imran, Abdullah Al Wahid, Zaman Prova, Alpana Akhi Hannan, Md. Department of Computer Science and Engineering American International University-Bangladesh 408/1 Kuratoli Dhaka1229 Bangladesh Department of Software Engineering Daffodil International University Daffodil Road Ashulia Dhaka Bangladesh Department of Computer Science Islamic University of Technology K B Bazar Rd Gazipur1704 Bangladesh

Bangladesh has seen an absurd, steeper prize-hike for the last couple of years in one of the most consumed foods taken by millions of people every single day: rice. The impact of this phenomenon, however, is indispensably critical, especially to the one striving for daily meals. Thus, understanding the latent facts is vital to policymakers for better strategic measures and decision-making. In this paper, we have applied five different machine learning algorithms to predict the retail price of rice, find out the top-most factors responsible for the price hike, and determine the best model that produces higher prediction results. Leveraging six evaluation metrics, we found that random forest produces the best result with an explain variance score of 0.87 and an R2 score of 0.86 whereas gradient boosting produces the least, meanwhile discovering that average wind speed is the topmost reason for rice price hike in retail markets. © 2022 Inderscience Enterprises Ltd.

关键词： Forecasting

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MKGL: Mastery of a Three-Word Language 38

MKGL: Mastery of a Three-Word Language

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Guo, Lingbing Bo, Zhongpu Chen, Zhuo Zhang, Yichi Chen, Jiaoyan Lan, Yarong Sun, Mengshu Zhang, Zhiqiang Luo, Yangyifei Li, Qian Zhang, Qiang Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Ant Group Joint Lab of Knowledge Graph China Ant Group China Department of Computer Science The University of Manchester United Kingdom School of Computer Science and Engineering Beihang University China School of Computer Science Beijing University of Posts and Telecommunications China School of Software Technology Zhejiang University China

Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks. Yet, their application to knowledge graphs (KGs), which describe facts in the form of triplets and allow minimal hallucinations, remains an underexplored frontier. In this paper, we investigate the integration of LLMs with KGs by introducing a specialized KG Language (KGL), where a sentence precisely consists of an entity noun, a relation verb, and ends with another entity noun. Despite KGL's unfamiliar vocabulary to the LLM, we facilitate its learning through a tailored dictionary and illustrative sentences, and enhance context understanding via real-time KG context retrieval and KGL token embedding augmentation. Our results reveal that LLMs can achieve fluency in KGL, drastically reducing errors compared to conventional KG embedding methods on KG completion. Furthermore, our enhanced LLM shows exceptional competence in generating accurate three-word sentences from an initial entity and interpreting new unseen terms out of KGs. © 2024 Neural information processing systems foundation. All rights reserved.

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Artificial Intelligence-Based Centralized Resource Management Application for Distributed Systems 13

Artificial Intelligence-Based Centralized Resource Managemen...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Hettiarachchi, Lasal Sandeepa Jayadeva, Senura Vihan Bandara, Rusiru Abhisheak Vikum Palliyaguruge, Dilmi Arachchillage, Udara Srimath S. Samaratunge Kasthurirathna, Dharshana Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Due to the decentralized nature and emergence of new practices, tools, and platforms, microservices have become one of the most widely spread software architectures in the modern software industry. Furthermore, the advancement of software packaging tools like Docker and orchestration platforms such as Kubernetes enable developers and operation engineers to deploy and manage microservice applications more effectively and efficiently. However, establishing and managing microservice applications are still cumbersome due to the infrastructure configuration and array of disjoint tools that fail to understand the application's dynamic behavior. As a result, developers need to configure multiple tools and platforms to automate the deployment and monitoring process to provide the optimal deployment strategy for microservices. Even though many tools are available in the industry, the fully automated product which comprises deployment, monitoring, resiliency evaluation and optimization were not developed yet. In response to this issue, we propose an artificial intelligence (AI)-based centralized resource management tool, that provides an automated low latency container management, cluster metrics gathering, resiliency evaluation and optimal deployment strategy behave in dynamic nature. © 2022 IEEE.

关键词： Machine learning

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A Case Study on the Multi-Vector Data Breach on Astoria 4

A Case Study on the Multi-Vector Data Breach on Astoria

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4th International Conference on computer Communication and the Internet, ICCCI 2022

作者： Nadjar, Jorge Liu, Yuxuan Salinas, Jose Bhunia, Suman Miami University Department of Computer Science and Software Engineering OxfordOH45056 United States

ISBN: (数字)9781665469920

ISBN: (纸本)9781665469920

Managing the security of big data has become increasingly more complex as threat actors continue to maliciously exploit legacy and outdated systems of smaller and medium-sized firms. In this paper, we explore Astoria Company's data breach, whereby the attacker exfiltrated vast amounts of confidential user data by exploiting MySQL and PHP-based vulnerabilities in a popular database management tool. Furthermore, we analyze the attack methodology of the perpetrator and the range of impacts incurred by the breach. The novelty of this paper is centered primarily on the range of defensive solutions we propose for the firm and for similar companies to deter and mitigate such attacks. These defensive solutions and risk mitigation strategies range from relatively low-cost software updates to more wide-ranging IT governance changes. The aim of this paper is to highlight the increasing need to safeguard the security of the big data held by smaller businesses, which have become prime targets for such type of attacks. © 2022 IEEE.

关键词： Legacy systems

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Analyzing SocialArks Data Leak - A Brute Force Web Login Attack 4

Analyzing SocialArks Data Leak - A Brute Force Web Login Att...

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4th International Conference on computer Communication and the Internet, ICCCI 2022

作者： Qian, Jun Gan, Zijie Zhang, Jie Bhunia, Suman Miami University Department of Computer Science and Software Engineering OxfordOH45056 United States

ISBN: (数字)9781665469920

ISBN: (纸本)9781665469920

In this work, we discuss data breaches based on the '2012 SocialArks data breach' case study. Data leakage refers to the security violations of unauthorized individuals copying, transmitting, viewing, stealing, or using sensitive, protected, or confidential data. Data leakage is becoming more and more serious, for those traditional information security protection methods like anti-virus software, intrusion detection, and firewalls have been becoming more and more challenging to deal with independently. Nevertheless, fortunately, new IT technologies are rapidly changing and challenging traditional security laws and provide new opportunities to develop the information security market. The SocialArks data breach was caused by a misconfiguration of ElasticSearch Database owned by SocialArks, owned by 'Tencent.' The attack methodology is classic, and five common Elasticsearch mistakes discussed the possibilities of those leakages. The defense solution focuses on how to optimize the Elasticsearch server. Furthermore, the ElasticSearch database's open-source identity also causes many ethical problems, which means that anyone can download and install it for free, and they can install it almost anywhere. Some companies download it and install it on their internal servers, while others download and install it in the cloud (on any provider they want). There are also cloud service companies that provide hosted versions of Elasticsearch, which means they host and manage Elasticsearch clusters for their customers, such as Company Tencent. © 2022 IEEE.

关键词： Intrusion detection

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A Systematic Review of 2021 Microsoft Exchange Data Breach Exploiting Multiple Vulnerabilities 7

A Systematic Review of 2021 Microsoft Exchange Data Breach E...

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7th International Conference on Smart and Sustainable Technologies, SpliTech 2022

作者： Pitney, Alexis M. Penrod, Spencer Foraker, Molly Bhunia, Suman Miami University Department of Computer Science and Software Engineering OxfordOH45056 United States

ISBN: (纸本)9789532901160

At the beginning of 2021 a massive amount of servers using Microsoft's Exchange program were breached by a foreign hacker group called HAFNIUM. This group discovered and exploited 4 different zero-day vulnerabilities which sent the entire cybersecurity community into a panic. Immediately after data breach was discovered, Microsoft and other governmental security agencies alerted all the users. Microsoft released multiple patches to safeguard the attack surface. This paper provides an in-depth analysis of the attack methodology, impacts and possible defense solutions. An estimated 400,000 Exchange Servers were affected by this attack, and a large portion of servers are still vulnerable today. Microsoft has released an effective security patch to stop the exploitation of the vulnerabilities. © 2022 University of Split, FESB.

关键词： Personal computing

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