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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Lam, Nguyen Thanh Thao Le, Nu Uyen Phuong Islam, Md Rafiqul Sakib, Md KowsarHossain Prome, Shanjita Akter Sanin, Cesar Szczerbicki, Edward Zhou, Jianlong College of Business & Management VinUniversity Hanoi12426 Viet Nam Faculty of Engineering Architecture and Information Technology University of Queensland BrisbaneQLD4072 Australia Business Information Systems Australian Institute of Higher Education SydneyNSW Australia School of Computer Science Taylor's University Malaysia Faculty of Management and Economics Gdansk University of Technology Gdansk Poland School of Computer Science University of Technology Sydney Australia

In recent times, the number of spam reviews through various online platforms has emerged as a prime challenge, profoundly impacting businesses and consumers. These fake reviews not only distort clients' perceptions of products and services but also erode trust within the digital ecosystem. Despite the advent of machine learning (ML) techniques for identifying spam reviews, comparing text, and pinpointing groups of spammers, there remain notable gaps in both accuracy and the combination of interactive visualization for real-time decision-making. This paper presents SpamVis, a visual interactive system that leverages deep learning (DL) and ML blended with advanced visualization techniques for spam review detection, enabling analysts to conduct complicated analytical queries. The system allows users to input via click-on or touch to generate interactive charts and plots tailored for spam review analysis. The findings of the baseline test carried out on 67,395 review texts demonstrate that Bidirectional Encoder Representations from Transformers (BERT) carried out the best accuracy (86%) compared to other models. Our outcomes suggest that SpamVis can alleviate the gaps concerning accuracy and visualization needs in contemporary techniques, guiding analysts to make informed decisions for mitigating spam reviews and enhancing consumer trust. Furthermore, SpamVis empowers users to seamlessly discover the online reviews of various social media platforms in real-time, such as Facebook, Youtube, etc., giving them practical insights to navigate the online marketplace effectively. © 2024 The Authors.

关键词： Adversarial machine learning

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eagle: early approximated gradient based learning rate estimator

arXiv

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arXiv 2025年

作者： Fujimoto, Takumi Nishi, Hiroaki Faculty of Science and Technology Japan Center for Information and Computer Science School of Science for Open and Environmental Systems Keio University Japan

We propose EAGLE update rule, a novel optimization method that accelerates loss convergence during the early stages of training by leveraging both current and previous step parameter and gradient values. The update algorithm estimates optimal parameters by computing the changes in parameters and gradients between consecutive training steps and leveraging the local curvature of the loss landscape derived from these changes. However, this update rule has potential instability, and to address that, we introduce an adaptive switching mechanism that dynamically selects between Adam and EAGLE update rules to enhance training stability. Experiments on standard benchmark datasets demonstrate that EAGLE optimizer, which combines this novel update rule with the switching mechanism achieves rapid training loss convergence with fewer epochs, compared to conventional optimization methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Optimization algorithms

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Current Trends in Automated Test Case Generation 18

Current Trends in Automated Test Case Generation

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18th Conference on computer science and Intelligence systems, FedCSIS 2023

作者： Potuzak, Tomas Lipka, Richard Department of Computer Science and Engineering/ Ntis - New Technologies for Information Society Faculty of Applied Sciences University of West Bohemia Univerzitni 8 European Center of Excellence Plzen306 14 Czech Republic European Center of Excellence/ Department of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Univerzitni 8 Ntis - New Technologies for the Information Society Plzen306 14 Czech Republic

ISBN: (纸本)9788396744784

The testing is an integral part of the software development. At the same time, the manual creation of individu-al test cases is a lengthy and error-prone process. Hence, an intensive research on automated test generation methods is ongoing for more than twenty years. There are many vastly dif-ferent approaches, which can be considered automated test case generation. However, a common feature is the generation of the data for the test cases. Ultimately, the test data decide the prog-ram branching and can be used on any testing level, starting with the unit tests and ending with the tests focused on the behavior of the entire application. The test data are also mostly independent on any specific technology, such as programming language or paradigm. This paper is a survey of existing literature of the last two decades that deals with test data generation or with tests based on it. This survey is not a systematic literature review and it does not try to answer specific scientific questions formulated in advance. Its purpose is to map and categorize the existing methods and to summarize their common features. Such a survey can be helpful for any teams developing their methods for test data generation as it can be a starting point for the exploration of related work. © 2023 Polish information Processing Society.

关键词： Software testing

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Updating Top-k Dominate Individuals with Incomplete Data Addition

Updating Top-k Dominate Individuals with Incomplete Data Add...

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2023 IEEE International Symposium on information Theory, ISIT 2023

作者： Wu, Jimmy Ming-Tai Wang, Ke Yan, Huizhen Chen, Chao-Chun Chen, Pei-Wei Lin, Jerry Chun-Wei Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China National Cheng Kung University Institute of Manufacturing Information and Systems Tainan70101 Taiwan Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9781665475549

Top-k dominance (TKD) query is an extended query method of skyline query and top-k query, which reveals the top-k dominant individuals in an incomplete dataset by analyzing the dominance relationships between individuals and is a common decision tool in intelligent recommendation applications. This research proposes two parallel query algorithms based on Spark computing engine to address the shortcomings of the parallel top-k-dominated query algorithms for dynamic incomplete datasets. The designed model achieves good performance in terms of runtime performance compared to previous studies. © 2023 IEEE.

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An integrated framework based deep learning for cancer classification using microarray datasets

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2249-2260页

作者： Alrefai, Nashat Ibrahim, Othman Shehzad, Hafiz Muhammad Faisal Altigani, Abdelrahman Abu-ulbeh, Waheeb Alzaqebah, Malek Alsmadi, Mutasem K. Johor Johor Bahru81310 Malaysia Johor Skudai81310 Malaysia Department of Computer Science and IT University of Sargodha Sargodha40100 Pakistan Computer Science Department College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Faculty of Information Technology Al Istiqlal University Jericho4728 Palestine Department of Mathematics College of Science Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

Around the world, cancer is one of the leading reasons of mortality. The importance of earlier detection and prognosis of cancer types is highly significant for patients’ health. In recent research, deep neural networks were trained using gene expression microarray, to classify cancer. Biologists are able to monitor thousands of genes in one experiment using microarray technology. Microarray datasets are considered high-dimensional data, as they are cluttered with irrelevant, redundant, and noisy genes that contribute insignificantly to classification. The most informative genes contributing to cancer classification have been identified using computational intelligence algorithms. In this paper, we propose an integrated framework for cancer classification. This framework is divided into three tasks. Firstly, particle swarm optimization with ensemble learning (PSO-ensemble) reduces the microarray dataset's high dimensionality. Secondly, The Adaptive self-training method (ASTM) is used to solve low-size issues. Finally, a Convolutional Neural Network (CNN) was employed for classification. CNN has the ability to discover the complex non-linear relationships between features and select the most informative. Transfer learning was used sequentially with CNN to integrate the classification procedure because it can reduce the training time and computational complexity. Six microarray datasets are used, namely liver, breast, colon, prostate, central nervous system, and lung. The proposed CNN architecture with transfer learning provided 100% classification accuracy for colon, prostate, CNS and lung microarray datasets, and 97.62%, 95.45% accuracy for liver and breast cancer respectively. Experiments show that our proposed method delivers the highest classification accuracy and reduces training time with the smallest gene subset. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolutional neural networks

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Development of a Smart Technique for Mobile Web Services Discovery

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computers, Materials & Continua 2021年第11期69卷 1483-1501页

作者： Mohamed Eb-Saad Yunyoung Nam Hazem M.El-bakry Samir Abdelrazek Department of Information Systems Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Web service(WS)presents a good solution to the interoperability of different types of systems that aims to reduce the overhead of high processing in a resource-limited *** the increasing demand for mobile WS(MWS),the WS discovery process has become a significant challenging point in the WS lifecycle that aims to identify the relevant MWSs that best match the service *** discovery process is a resource-consuming task that cannot be performed efficiently in a mobile computing environment due to the limitations of mobile ***,a cloud computing can provide rich computing resources for mobile environments given its unlimited and easily scalable *** paper proposes a semantic WS discovery and invocation framework in mobile environments based on cloud and a relationship-aware matchmaking *** discovery algorithm enriches MWS and user requests semantically with the functional and non-functional properties of Ontology Web Language for Services,such as Quality of Web Service,device context,and user *** WS repository is filtered based on logical reasoning and a parameter-based matching algorithm to minimize the matching space and improve runtime *** cosine similarity between the user request and services repository is then assessed to generate the most relevant *** relationships among concepts in the ontology are considered to improve the recall and precision *** the WS discovery process,users can invoke and test these services in a mobile environment through a dynamic user *** interface of the invocation process is changed according to the WS description *** application prototype is also developed to evaluate the framework based on a Cordova cross-mobile development framework.

关键词： Cloud web service web service discovery semantic matching mobile web service discovery

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A Sentiment Analysis Study of Twitter Users' Reactions to the COVID-19 Vaccine 14

A Sentiment Analysis Study of Twitter Users' Reactions to th...

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14th International Conference on information and Communication systems, ICICS 2023

作者： Alkhushayni, Suboh Alomari, Zakaria Al-Zaleq, Du'a Yarmouk University Faculty of Information Technology and Computer Science Dept. Information Systems Irbid Jordan New York Institute of Technology Dept. Computer Science VancouverBC Canada Al-Ahliyya Amman University Dept. Robotics and Artificial Intelligence Engineering Amman Jordan

ISBN: (纸本)9798350307870

This study investigates public attitudes towards the COVID-19 vaccine through Twitter data analysis. Using the Twitter API, tweets were collected, preprocessed, and labeled. Features were extracted using the Bag of Words representation, and sentiment analysis was conducted using Text Blob and Vader. Machine learning and deep learning models were trained and tested, revealing that deep learning models achieved the highest accuracy and F1 score. The research underscores the efficacy of machine learning and deep learning in analyzing COVID-19 vaccine-related tweets, shedding light on factors influencing vaccine resistance. These insights are crucial for pharmaceutical companies and public health officials, enabling them to address barriers to vaccine acceptance and enhance the societal benefits of widespread vaccination, contributing to the pandemic's resolution. © 2023 IEEE.

关键词： COVID-19

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An Enhanced Secure Authentication Scheme for the Internet of Things

An Enhanced Secure Authentication Scheme for the Internet of...

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2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology, ICSEIET 2023

作者： Neetu Srivastava, Animesh Barik, Lal Bihari Dhondiyal, Shiv Ashish College of Smart Computing Coer University Roorkee India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350329186

The Internet of Things (IoT) has led to the proliferation of interconnected devices, including smart appliances and industrial sensors. Nevertheless, the rapid expansion of the IoT ecosystem has given rise to apprehensions regarding security, as compromised devices have the potential to result in data breaches and cyber assaults. This paper presents an enhanced secure authentication scheme for the Io'T, which aims to tackle the issues above by building upon existing authentication methodologies. The multi-step system commences with an initial setup phase wherein each loT device is assigned a unique device ID and a confidential pre-shared key. A secure key exchange protocol generates a session key for each authenticated device. The authentication server generates a random attempt to join the network or execute specific tasks. The machine then signs the data with its private key to authenticate the challenge. The server verifies the answer by comparing it with the public key signature of the device. The proposed methodology enables devices to update their session keys periodically to safeguard against potential attacks. © 2023 IEEE.

关键词： Authentication

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An Attention Mechanism Based AVOD Network for 3D Vehicle Detection

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-13页

作者： Yuan, Di Zhang, Haiping Shu, Xiu Liu, Qiao Chang, Xiaojun He, Zhenyu Shi, Guangming Guangzhou Institute of Technology Xidian University Xian China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China National Center for Applied Mathematics Chongqing Normal University Chongqing China Faculty of Engineering and Information Technology University of Technology Sydney Broadway New South Wales Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

With the continuous advancement of autonomous driving technology, 3D vehicle detection has become of widespread interest. The traditional aggregate view object detection (AVOD) framework has achieved some good results in 3D vehicle detection tasks. However, the complexity of the 3D vehicle detection scenario makes the current detection methods still not meet the actual requirements. To enhance the detection accuracy of 3D vehicle targets, we propose to equip an attention mechanism to improve the representation capability of feature maps, thereby further increasing the precision of 3D vehicle detection. Specifically, we have added the channel attention ECANet, spatial attention SANet, and mixed attention ECANet+SANet respectively into the image-based feature pyramid network of the AVOD detection framework, which can enhance the feature maps representation and improve the detection accuracy observably. The improved AVOD network is verified using the KITTI dataset. By showing the detection results of these attention mechanisms, it is found that the feature pyramid networks (FPN) module in the AVOD network based on Image has the best performance when integrating a mixed attention mechanism. In comparison to the original AVOD network, the detection results on the average precision index of the proposed method have improved by 2.29%, 2.81%, and 1.32% in the three indexes of simple, medium, and difficult, respectively. Extensive experiments have confirmed the practicality and efficacy of the AVOD network to equip the attention mechanisms for 3D vehicle detection. IEEE

关键词： Semantics

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Towards a Test Framework for Reactive (Type 2) Asset Administration Shell Implementations 29

Towards a Test Framework for Reactive (Type 2) Asset Adminis...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Miny, Torben Heppner, Sebastian Garmaev, Igor Ristin, Marko Otto, Björn Braunisch, Nico Jacoby, Michael Kleinert, Tobias Van De Venn, Hans Wernher Wollschlaeger, Martin Information and Automation Systems Rwth Aachen University Aachen Germany Zurich Switzerland Institute for Automation and Communication Otto von Guericke University Magdeburg Germany Institute of Applied Computer Science Tu Dresden Dresden Germany Karlsruhe Germany

ISBN: (纸本)9798350361230

As Industrie 4.0 (I4.0) technologies continue to advance, ensuring the reliability and robustness of emerging standards like Asset Administration Shells (AAS) becomes paramount. This paper introduces a comprehensive testing approach for reactive (a.k.a. Type 2) AAS implementations, addressing the challenges posed by their intricate and dynamic nature. Our methodology adopts a design-by-contract approach to formalize API behavior. We design the client as a test oracle, where code contracts extensively validate the AAS operations, their interactions and effects. The related test cases cover various operation chains, including superpaths, service specifications, and serialization modifiers. The objective of this approach is to enhance the reliability and effectiveness of testing in the evolving landscape of AAS Type 2 implementations. © 2024 IEEE.

关键词： Software testing

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