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5th International Conference on Big data engineering, BDE 2023

作者： Luo, Zhen Zhang, Yingfang Hu, Chengxuan Xia, Yuxuan Zhu, Shengxin Department of Statistical Science University College London United Kingdom Department of Statistics and Actuarial Science University of Hong Kong Hong Kong Hong Kong Department of Industrial and Systems Engineering Polytechnic University of Hong Kong Hong Kong Hong Kong School of Mathematics University of Southampton United Kingdom Research Center for Mathematics Advanced Institute of Natural Science Beijing Normal University Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College China

ISBN: (纸本)9798400708695

Deep learning frameworks have achieved extraordinary results in popular technological frontiers, which also greatly inspires considerable researchers in recommender system. As one of the mainstream research frontiers, click-through rate (CTR) predictions have received increasing attention from both industrial and academic circles. Recently, Alibaba group has come up with novel idea of interest modeling that can effectively overcome the limitations brought by previous deep learning CTR prediction models, which is also the key to improve performance. For the past few years, more and more relevant works begin to explore approaches taking advantage of the learning, representation, and interpretation ability from neural networks. However, there are few comprehensive surveys targeted at this topic. In this paper, we systematically review and summarize recent CTR prediction models based on interest modeling. First, we elaborate their different design ideas and pay additional attention to their intrinsic relationship including improvements and extensions, similarities and differences, advantages and disadvantages and so on. Next, we deploy them separately according to official open-source code and compare their performance assessment in terms of accuracy and speed, looking for best optimizers and other hyperparameters. Furthermore, we briefly discuss some possible refinement directions and future research trends. © 2023 ACM.

关键词： Recommender systems

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A data-Driven Approach Towards Software Regression Testing Quality Optimization 25

A Data-Driven Approach Towards Software Regression Testing Q...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Moubayed, Abdallah Alhindawi, Nouh Alsakran, Jamal Injadat, MohammadNoor Kanan, Mohammad King Fahd University of Petroleum & Minerals Computer Engineering Department Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia Arizona State University School of Computing and Augmented Intelligence GilbertAZ United States Jadara University Department of Software Engineering Irbid Jordan Fairleigh Dickinson University Canada Zarqa University Data Science & Artificial Intelligence Department Zarqa Jordan University of Business and Technology Department of Industrial Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

Software testing is very important in software development to ensure its quality and reliability. As software systems have become more complex, the number of test cases has increased, which presents the challenge of executing all the tests in a limited time frame. Various test case prioritization techniques have been introduced to solve this problem. These methods aim to identify and implement the most critical tests first. In this paper, we propose an implementation of a dynamic test case prioritization strategy to improve software quality by increasing code coverage with special attention to edge case handling. Edge case test prioritization is a technique that improves test efficiency by selecting extreme case scenarios that can reveal critical bugs or unexpected behavior early in development, improving overall software reliability and dependability. In order to prioritize test cases, this paper presents a regression-based method that makes use of machine learning algorithms. The approach leverages previous performance data to optimize regression testing efficiency by examining variables like test time and execution status. Performance evaluations, when compared against industry standards and cutting-edge techniques, show how effective these algorithms are at correctly prioritizing test cases and identifying faults. This study offers simplified yet reliable solutions for regression testing optimization by shedding light on the efficacy of regression algorithms, such as Random Forest and decision trees. © 2024 IEEE.

关键词： Software testing

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Towards Cluster-Based Split Federated Learning Approach for Continuous User Authentication 7

Towards Cluster-Based Split Federated Learning Approach for ...

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7th Cyber Security in Networking Conference, CSNet 2023

作者： Wazzeh, Mohamad Arafeh, Mohamad Ould-Slimane, Hakima Talhi, Chamseddine Mourad, Azzam Otrok, Hadi Department of Software and It Engineering Montreal Canada Department of Mathematics and Computer Science Trois-Rivières Canada Cyber Security Systems and Applied Ai Research Center Lebanese American University Department of Csm Beirut Lebanon New York University Division of Science Abu Dhabi United Arab Emirates Khalifa University Department of Eecs Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350342871

In today's rapidly evolving technological landscape, ensuring the security of systems requires continuous authentication over sessions and comprehensive access management during user interaction with a device. With the increasing use of smartphones and Internet of Things (IoT) devices, Split Learning (SL) and Federated Learning (FL) have emerged as promising technologies that can tackle the authentication problem while protecting the user's private data. The SL distributed technology enables users with limited resources to complete neural network model training with server assistance, lessening the computational burden from the client side. In addition, FL aims to combine knowledge between different nodes collaboratively. The privacy and security of the user's data are ensured in both approaches, as only the models' weights are shared with a server. This study employs a cluster-based approach using split learning and federated learning techniques to improve the efficiency and robustness of training Machine Learning (ML) models. We compare the approaches' performance to baseline methods and demonstrate their advantages using the UMDAA-02-FD face detection and MNIST datasets. Our findings show that combining both technologies achieves high accuracy in continuous authentication scenarios while maintaining user privacy. These results highlight the importance of SL and FL in cybersecurity, enabling continuous authentication and demonstrating their potential to revolutionize how we address security. © 2023 IEEE.

关键词： Internet of things

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Detection of Malicious Node in VANETs Using Digital Twin

Detection of Malicious Node in VANETs Using Digital Twin

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International Conference on Big data Intelligence and Computing, dataCom 2022

作者： Arya, Varsha Gaurav, Akshat Gupta, Brij B. Hsu, Ching-Hsien Baghban, Hojjat Insights2Techinfo Jaipur India Ronin Institute MontclairNJ07043 United States International Center for AI and Cyber Security Research and Innovation and Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Lebanese American University Beirut1102 Lebanon Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan

ISBN: (纸本)9789819922321

Vehicular ad-hoc network (VANET) plays an essential role in helping the development of smart cars and intelligent transportation systems. By easing congestion and speeding up data transfer, VANET makes it possible for connected cars to function more smoothly. However, VANETs are affected by different types of cyber attacks, such as distributed denial of services (DDoS) attacks. A digital twin (DT) is a replica of a physical system that operates in tandem with the actual thing, allowing for continuous monitoring and management. The DT prepares the way for the monitoring of a physical entity on a regular basis and for its automated management. The improved efficiency in keeping tabs on the physical world is largely attributable to DT. For this reason, academics are advocating for its use in a variety of settings. In this research, we use DT to solve the problem of identifying and stopping malignant nodes on a VANET infrastructure. In this paper, we proposed a framework that uses the concepts of DT for the identification of malignant nodes. Our suggested approach employs machine learning to distinguish between regular traffic and attack traffic. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vehicular ad hoc networks

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A High-capacity Information Hiding Scheme Based on Blakley Secret Sharing

A High-capacity Information Hiding Scheme Based on Blakley S...

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Artificial Intelligence and Intelligent Manufacturing (aiIM), International Symposium on

作者： Jincheng Zhou Yue Li Yujie Shen Dan Tang Hongliang Cai Jing Gong Ningchao Bai School of Software Engineering Chengdu University of Information Technology Chengdu China Sichuan Province Engineering Technology Research Center of Support Software of lnformatization Application Chengdu China School of Computer Science and Engineering Sichuan University of Science & Engineering Yibin China Sichuan Provincial Big Data Technical Service Center Chengdu China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

A high-capacity reversible information hiding scheme based on Blakley secret sharing is proposed to address the issues of low embedding rate and weak disaster recovery performance of images when using reversible information hiding to protect data in scenarios with large data transmission. The scheme employs Euclidean division and a two-dimensional modulus function to establish a robust mapping relationship between the secret information and the pixels of the cover image. Subsequently, the values derived from the preprocessing of the secret information are incorporated into the secret sharing method proposed by Blakley as the elements of the secret vector. The proposed scheme exploits the disaster-tolerance property of the secret sharing method and enhances the redundancy of the secret-carrying encrypted image, thereby improving the embedding rate and security of the algorithm while maintaining the security of the secret sharing.

关键词： Correlation Disasters Redundancy Reconstruction algorithms Vectors Manufacturing Cryptography data mining data communication Computational complexity

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Bioinformatic Analysis Reveals both Oversampled and Underexplored Biosynthetic Diversity in Nonribosomal Peptides

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ACS Chemical Biology 2023年第3期18卷 476-483页

作者： Jian, Bo-Siyuan Chiou, Shao-Lun Hsu, Chun-Chia Ho, Josh Wu, Yu-Wei Chu, John Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan Department of Chemistry National Taiwan University Taipei 10617 Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei 10675 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei 10675 Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei 10675 Taiwan

The traditional natural product discovery approach has accessed only a fraction of the chemical diversity in nature. The use of bioinformatic tools to interpret the instructions encoded in microbial biosynthetic genes has the potential to circumvent the existing methodological bottlenecks and greatly expand the scope of discovery. Structural prediction algorithms for nonribosomal peptides (NRPs), the largest family of microbial natural products, lie at the heart of this new approach. To understand the scope and limitation of the existing prediction algorithms, we evaluated their performances on NRP synthetase biosynthetic gene clusters. Our systematic analysis shows that the NRP biosynthetic landscape is uneven. Phenylglycine and its derivatives as a group of NRP building blocks (BBs), for example, have been oversampled, reflecting an extensive historical interest in the glycopeptide antibiotics family. In contrast, the benzoyl BB, including 2,3-dihydroxybenzoate (DHB), has been the most underexplored, hinting at the possibility of a reservoir of as yet unknown DHB containing NRPs with functional roles other than a siderophore. Our results also suggest that there is still vast unexplored biosynthetic diversity in nature, and the analysis presented herein shall help guide and strategize future natural product discovery campaigns. We also discuss possible ways bioinformaticians and biochemists could work together to improve the existing prediction algorithms. © 2023 The Authors. Published by American Chemical Society.

关键词： Algorithms Bacteria Bioinformatics and computational biology Peptides and proteins Pharmaceuticals

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A Bioinformatics Analysis and Homology 3D Modeling of Dihydroxy-orotate-dehydrogenase of Plasmodium ovale

A Bioinformatics Analysis and Homology 3D Modeling of Dihydr...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Rudi Nirwantono Khoirunnisa Assidqi Alam Ahmad Hidayat Joko Pebrianto Trinugroho Bens Pardamean Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Food Biotechnology Research Center Bina Nusantara University Jakarta Indonesia Mathematics Departement School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the liver. Thus, the research to study the drug to treat Plasmodium vivax is essential, where the enzyme dihydroorotate dehydrogenase (DHODH) has recently become a new drug target. However, the drug-enzyme interaction study has only recently been conducted in Plasmodium falciparum (pfDHODH) due to the lack of the 3D structure of enzyme DHODH from Plasmodium vivax that is crucial for the study. Therefore, this study aimed to perform the modelling study of Plasmodium vivax DHODH (PvDHODH) to create a 3D structure as a basis for drug-protein interaction study in upcoming studies. Sequence pvDHODH (Accession: SCO68359.1) was used in homology modelling using Modeller 10.2 with the crystal structure of DHODH from DHODH (Accession: 7KZY) as a template. Overall, the model generated from the homology modelling was considered a good model, and the 3D structure was close to the native state according to several parameters, including DOPE score, GA341 score, QMEAN4 value, 3D-1D score, ERRAT2 score, and ProSA score. Ramachandran plot also revealed that almost all amino acids were distributed in the desirable and allowed area (99.1%), and only 0.9% were in generously allowed regions. Superimposition of the model with the template also indicated that the model has an almost similar structure and amino acids positioning, including in the binding pocket and active site. Therefore, the model can be used for downstream analysis in drug-protein interaction studies. Nevertheless, some improvements can still be performed to upgrade the quality of the model by deleting unaligned residues on the C-terminal and realigning the residues at the doubting site.

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Social engineering attack types and prevention techniques- A survey

Social engineering attack types and prevention techniques- A...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Ramiz Salama Fadi Al-Turjman Shruti Bhatla Satya Prakash Yadav Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Nicosia Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Turkey Department of Computer Science and Engineering Graphic Era Hill University Dehradun Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management (GLBITM) Greater Noida India

The term "Cyber Security" is getting more and more popular and important over the last few years. Since computers and most of the devices are connected to the internet, they are likely to be hacked by the hackers. In the past, this issue was not a big problem, because not every device is required to be connected to the internet or the internet was not popular as today. But the case has changed over the years. All the changes in the technology area also changed the cyber-attack models as well. With the development of technology and the change of usage of the internet over the last years cyber-attacks have become more common and popular by the hackers. Hackers have discovered that they have an opportunity to steal or earn money in a short time without having to spend too much effort. These days, the type of cyber-attacks is not the same as the ones in the past. As time passes, the cyber-attack methods are also changing and evolving. Today, hackers are using more advanced and effective cyber-attack methods compared to past years. There are many methods that are impossible to cover all of them in this article. Our main focus will be on social engineering attacks in this article. Social engineering attacks use different approaches to cyber attacking. Unlike trying to explode a victim’s social media password etc. using advanced exporting programs, algorithms or techniques, social engineering attacks focus on fooling victims into providing their data to hackers by themselves without using or implementing any password cracking, exploiting techniques etc. We will go over what social media engineering is, type of social engineering methods, what countermeasures can be used to protect from social engineering and more in this article.

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applications of Transformer Attention Mechanisms in Information Security: Current Trends and Prospects 2

Applications of Transformer Attention Mechanisms in Informat...

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2nd IEEE International Conference on Artificial Intelligence of Things and Crowdsensing, aioTCs 2022

作者： Vubangsi, M. Abidemi, Sarumi Usman Akanni, Olukayode Mubarak, Auwalu Saleh Al-Turjman, Fadi Artificial Intelligence Engineering Dept. Ai and Robotics Institute Near East University Mersin 10 Nicosia Cyprus Research Center for Ai and IoT Faculty of Engineering University of Kyrenia Mersin 10 Kyrenia Cyprus Computational Materials Science Lab Department of Computer Science Htttc Bambili University of Bamenda NWR Cameroon

ISBN: (纸本)9798350334104

In this work, we present a comprehensive survey on applications of the most recent transformer architecture based on attention in information security. Our review reveals three primary areas of application: Intrusion detection, Anomaly Detection and Malware Detection. We have presented an overview of attention-based mechanisms and their application in each cybersecurity use case, and discussed open grounds for future trends in Artificial Intelligence enabled information security. © 2022 IEEE.

关键词： Anomaly detection

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A Detailed Structured Monitoting System for ICU Patient Mortality rate by using ai Algorithm

A Detailed Structured Monitoting System for ICU Patient Mort...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Shivani Joshi Rajiv Kumar Vipin Rai Avinash Dwivedi Vikash Chandra Tripathi Department of Computer Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida India Department of Applied Computational Science and Engineering NVIDIA AI and Learning & Research Center G L Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science & Engineering Galgotias University Greater Noida India Department of Computer Science & Engineering Galgotias University Greater Noida

The complexity and seriousness of diseases in ICU patients, though, make it difficult for medical practitioners to identify high-risk patients who may face irreversible harm. With ANN (Artificial Neural Network), this study will provide an ICU admission prediction model to help identify high-risk patients at an earlier stage. After collecting and analyzing extensive data from various perspectives including demographics, medical history, vital signs, test results and treatment modalities, exploratory data analysis has been conducted. These are used as markers on how statistical techniques and machine learning algorithms can be used to predict mortality among critically ill patients. Consequently, the parameters are designed within the ANN model to create a predictive model that will be able to effectively classify those at higher risk. Advanced machine learning algorithms together with modern tools such as deep learning techniques will aid in the prompt analysis of complex information for accurate forecasts by this predictive model. The ANN model’s training set will include a large dataset of ICU patients so that it can become reliable and valid. This makes the predictive model developed capable of accurately identifying high risk cases expeditiously

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