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2023 IEEE Conference on computer Applications, ICCA 2023

作者： Aung, Myint Myitzu Win, Kay Thi University of Computer Studies Software Engineering Lab Mandalay Mandalay Myanmar University of Computer Studies Faculty of Information Science Mandalay Mandalay Myanmar

ISBN: (纸本)9781665435994

Structural testing is one of the testing techniques in software testing. It is the testing process of internal structure in the given source code by comparing the expected result and real results and finding out faults. To complete the structural testing, it takes a long time and needs to improve code coverage. Thus, this paper focus on improving code coverage of structural testing by using parametric inter-procedural slicing with backward slicing technique. In program slicing, there are two types of slicing: forward and backward slicing depending on the slicing direction. In this paper, backward slicing is used and it starts at the criteria for the slice consideration. The parametric inter-procedural slicing is one of the program slicing techniques which is an algorithm that takes the slice criteria as a parameter and work to handle inter-procedural data flow by considering many possible dependence relations. By slicing the original input program before measuring code coverage, the input program is simplified and excluded unexecutable statements. Therefore, the proposed system in this paper can reduce the complexity of the source code and improve code coverage for structural testing. Moreover, the used dataset in experiment is benchmark dataset in C and it is converted to Java for the improvement of language oriented. © 2023 IEEE.

关键词： software testing

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Automatic identification of secondary narratives in videogames 2

Automatic identification of secondary narratives in videogam...

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2nd Congreso Espanol de Videojuegos, CEV 2023 - 2nd Spanish Video Games Congress

作者： Marín, Alejandro Ruiz, Alejandro León, Carlos Department of Software Engineering and Artificial Intelligence Computer Science Faculty Universidad Complutense de Madrid Madrid28040 Spain

Computational narrative is a complex field. While the computational processing of narratives has been tackled from different perspectives, the literature has focused on the analysis of a main plot, even if it is composed of different narrative sub-plots. However, modern narrative often involves additional threads that complement, justify or enhance the main narrative. This is commonly observed in narrative in video games. In this paper, we present a computational engine that can automatically differentiate between primary and secondary stories. This differentiation is based on the identification of a number of features that are believed to be key factors in the classification. We have analyzed a number of stories by using a simple game prototype with several stories, and a prototype assessment of the effectiveness of the system with human players has been carried out. Results indicate the hypothesized features can certainly be used to identify or produce secondary stories linked to a main plot. © 2023 CEUR-WS. All rights reserved.

关键词： Automation

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Relationship between factors influencing the software development process and software defects 13

Relationship between factors influencing the software develo...

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13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Gordieiev, Oleksandr Gordieieva, Daria Rainer, Austen Pishchukhina, Olga Lutsk National Technical University Department of Software engineering Lutsk Ukraine Queen's University Belfast School of Electronics Electrical Engineering and Computer Science Belfast United Kingdom Lutsk National Technical University Department of Computer Engineering and Cybersecurity Lutsk Ukraine

ISBN: (纸本)9798350396119

Poor software quality can lead to crashes, failures or downtime, the consequences of which may be lost profits, financial costs, leakage or loss of data, accidents, human casualties, material losses or environmental disasters. The total cost of poor software quality for IT companies in the USA is estimated at $2.41 trillion per year and tends to increase. Among the identified costs, the cost of unsuccessful projects is estimated at $260 billion, and the total cost of operational failures caused by poor quality software is estimated at $1.81 trillion. Late finding and fixing of software defects directly affects the success of the project as a whole, since it significantly increases the cost and development time, and affects its quality. This paper discussed the relationship between factors affecting the software development process and various groups of defects. It has been found that the leading groups of defects affecting the software quality are the defects in software requirements and defects in the design of software interfaces for human-computer interaction with the total percentage of influence of 33.3%. © 2023 IEEE.

关键词： Defects

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An evaluation on the effectiveness of virtual tutorials for training emergency professionals 2

An evaluation on the effectiveness of virtual tutorials for ...

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2nd Congreso Espanol de Videojuegos, CEV 2023 - 2nd Spanish Video Games Congress

作者： Villar, Alejandro León, Carlos Department of Software Engineering and Artificial Intelligence Computer Science Faculty Universidad Complutense de Madrid Madrid28040 Spain

The use of virtual reality as a tool for professional training enables access to knowledge in a more active way. Applying it in high-risk situations training encourages finding the most effective way to share knowledge. With this objective in mind, video game tutorials as a method of learning in virtual environments are becoming increasingly popular. This study proposes a methodology in which virtual reality tutoring techniques are applied to enhance learning in training for radiation emergencies. A virtual reality experience involving a high-risk situation has been developed. In this experience two groups of professionals involved in such emergencies participated, one group were trained with a tutorial, and the other group faced the simulation without it. The results show that participants who received tutoring were able to solve the test correctly in a more effective way. © 2023 CEUR-WS. All rights reserved.

关键词： Virtual reality

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Imitation Learning from Purified Demonstrations 41

Imitation Learning from Purified Demonstrations

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Yunke Dong, Minjing Zhao, Yukun Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Accurate Real-World Co-ordinate Finding and the Object Classification for the Robot Manipulator 3

Accurate Real-World Co-ordinate Finding and the Object Class...

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3rd International Conference on Embedded Systems and Artificial Intelligence, ESAI 2024

作者： Sharan, Sudeep Thanh, Hung Do Nauth, Peter Dominguez-Jimenez, Juan Jose Frankfurt University of Applied Sciences Faculty of Computer Science and Engineering Frankfurt am Main Germany University of Cádiz UCASE Software Engineering Group School of Engineering Cádiz Spain

ISBN: (纸本)9798331522056

This paper presents an object detection and classification of the objects using Deep Learning (DL). The integration of object detection algorithms and depth camera developed is capable of providing robots, such as self developed assistive intelligent robot called 'ROSWITHA' (RObot System WITH Autonomy) with the ability to detect an object and interpret their surrounding environment. It also provides the real-world coordinates/position of the object in that environment, which helps the robot to perform some applications, such as grabbing and serving, etc. You Only Look Once (YOLO) is one of the most popular object detection algorithms which is treated as a single regression problem and is processed by a single neural network. In this work, the YOLOv5 (YOLO version 5) network is implemented on a depth camera 'RealSense' for processing object detection and estimating the position of the objects. The model is trained on reduced/filtered application-required objects (e.g. bottle and cup) from the available COCO dataset, and the training outcome alongside the performance of the overall system was evaluated. The results exhibited a satisfactory performance of the model in accurately detecting and classifying the objects with a high confidence score ranging from the least to7 2% to the highest of9 0%. The confidence score could also be adjusted by the user on the developed GUI to attain the most certain predictions. © 2024 IEEE.

关键词： Bottles

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A New cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

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computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security Information and Event Management Systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

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The Emergence of IIoT and its Cyber Security Issues in Critical Information Infrastructure 21

The Emergence of IIoT and its Cyber Security Issues in Criti...

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21st European Conference on Cyber Warfare and Security, ECCWS 2022

作者： Bhaiyat, Humairaa Yacoob Sithungu, Siphesihle Philezwini Academy of Computer Science and Software Engineering Faculty of Science University of Johannesburg South Africa

ISBN: (纸本)9781914587405

The emergence of the Industrial Internet of Things (IIoT) can transform and improve industrial domain processes. This is achieved by IIoT’s ability to collect and process vast amounts of data using technology such as sensors. IIoT capabilities can improve the manufacturing processes of these sectors and contribute to the improved functioning of critical information infrastructure. In addition, current trends - such as the Fourth Industrial Revolution (4IR) - use IIoT to realise specific goals. While the emergence of IIoT systems does introduce many benefits, such as improved efficiency and sustainability, it can also introduce security concerns. These security concerns pose a significant threat to the industrial domain, including critical information infrastructures. The resulting threats emphasise the need to implement solutions to secure IIoT systems. The paper aims to discuss the emergence of IIoT and its cyber security issues within the context of critical information infrastructure. The research paper follows a theoretical research methodology to provide an improved understanding of the emergence of IIoT and its cyber security issues in critical information infrastructure. The paper contains an exhaustive discussion of what is IIoT. A discussion on where IIoT fits within the context of critical information infrastructure and its impact on 4IR is also highlighted in the paper. Due to the many vulnerabilities that IIoT systems can contain, the paper also discusses security concerns surrounding the emergence of IIoT. The security concerns make IIoT systems attractive targets for cyberattacks. Therefore, different approaches that can be applied to secure IIoT systems is also provided. Since IIoT capabilities can impact the critical information infrastructure of businesses and nations, the authors’ stance on how IIoT systems could transform the current understanding of critical information infrastructure is also discussed. © 2022 Curran Associates Inc.. All rights r

关键词： Internet of things

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A Low Complexity ML-Based Methods for Malware Classification

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computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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A Cross Attention Transformer-Mixed Feedback Video Recommendation Algorithm Based on DIEN

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computers, Materials & Continua 2025年第1期82卷 977-996页

作者： Jianwei Zhang Zhishang Zhao Zengyu Cai Yuan Feng Liang Zhu Yahui Sun College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450000China Faculty of Information Engineering Xuchang Vocational Technical CollegeXuchang461000China College of Computer Science and Technology Zhengzhou University of Light IndustryZhengzhou450000China College of Electronics and Information Zhengzhou University of Light IndustryZhengzhou450000China

The rapid development of short video platforms poses new challenges for traditional recommendation *** systems typically depend on two types of user behavior feedback to construct user interest profiles:explicit feedback(interactive behavior),which significantly influences users’short-term interests,and implicit feedback(viewing time),which substantially affects their long-term ***,the previous model fails to distinguish between these two feedback methods,leading it to predict only the overall preferences of users based on extensive historical behavior ***,it cannot differentiate between users’long-term and shortterm interests,resulting in low accuracy in describing users’interest states and predicting the evolution of their *** paper introduces a video recommendationmodel calledCAT-MFRec(CrossAttention Transformer-Mixed Feedback Recommendation)designed to differentiate between explicit and implicit user feedback within the DIEN(Deep Interest Evolution Network)*** study emphasizes the separate learning of the two types of behavioral feedback,effectively integrating them through the cross-attention ***,it leverages the long sequence dependence capabilities of Transformer technology to accurately construct user interest profiles and predict the evolution of user *** results indicate that CAT-MF Rec significantly outperforms existing recommendation methods across various performance *** advancement offers new theoretical and practical insights for the development of video recommendations,particularly in addressing complex and dynamic user behavior patterns.

关键词： Video recommendation user interest cross-attention transformer

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