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6th IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics, UkrMiCo 2023

作者： Strelkovska, Iryna Onatskiy, Oleksiy Yona, Larysa International Humanitarian University Dept. Cybersecurity Software Engineering and Computer Science Odesa Ukraine

ISBN: (纸本)9798350348484

The paper proposes a cryptographic protocol two-factor authentication with the zero-knowledge over the extended field GF(2m) on elliptic curves using biometric data and private key of the user. The implementation of a cryptographic protocol with zero-knowledge proof based on elliptic curves allows significantly reducing the size of protocol parameters and increasing the cryptographic strength (computational complexity of the breaking). The cryptographic protocol was modeled in the High-Level Protocol Specification Language, the model validation and protocol verification was performed using the Security Protocol Animator tool for Automated Validation of Internet Security Protocols and Applications. The software verification of the cryptographic protocol was performed using the software modules On the Fly Model Checker and Constraint Logic based Attack Searcher. © 2023 IEEE.

关键词： Authentication

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Quantitative Trends Analysis on Russian Literature Research in Korean Academia: Insights from KCI Data 10

Quantitative Trends Analysis on Russian Literature Research ...

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10th International Conference on Platform Technology and Service, PlatCon 2024

作者： Iosifovich, Ten Alexander Yeo, Sang-Soo Jung, Young-Ae Dept. of Computer Science Sun Moon University Asan Korea Republic of Dept. of Computer Engineering Mokwon University Daejeon Korea Republic of Dept. of AI Software Technology Sun Moon University Asan Korea Republic of

ISBN: (纸本)9798350367874

This paper presents a quantitative analysis of trends in Russian literature and Korean-Russian translated literature within Korean academia from 1996 to 2024. By analyzing frequency themes and contexts, the study explores evolving research trends, revealing how sociocultural, political, and artistic exchanges between Korea and Russia have influenced each other. Future research using text mining promises to uncover more specific patterns, offering deeper insights into literary and cultural exchanges. This study contributes to understanding the role of literature in expressing and understanding sociopolitical and cultural values in both countries. © 2024 IEEE.

关键词： Korean Citation Index Korean-Russian literature Quantitative analysis Research trend analysis Text mining

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Suitable and Style-Consistent Multi-Texture Recommendation for Cartoon Illustrations

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ACM Transactions on Multimedia Computing, Communications and Applications 2024年第7期20卷 1-26页

作者： Wu, Huisi Wang, Zhaoze Li, Yifan Liu, Xueting Lee, Tong-Yee College of Computer Science and Software Engineering Shenzhen University No. 3688 Nanhai Road Nanshan District Guangdong Shenzhen518060 China Dept. of Computer Science and Information Engineering National Cheng-Kung University No. 1 University Road Tainan70101 Taiwan

Texture plays an important role in cartoon illustrations to display object materials and enrich visual experiences. Unfortunately, manually designing and drawing an appropriate texture is not easy even for proficient artists, let alone novice or amateur people. While there exist tons of textures on the Internet, it is not easy to pick an appropriate one using traditional text-based search engines. Although several texture pickers have been proposed, they still require the users to browse the textures by themselves, which is still labor-intensive and time-consuming. In this article, an automatic texture recommendation system is proposed for recommending multiple textures to replace a set of user-specified regions in a cartoon illustration with visually pleasant look. Two measurements, the suitability measurement and the style-consistency measurement, are proposed to make sure that the recommended textures are suitable for cartoon illustration and at the same time mutually consistent in style. The suitability is measured based on the synthesizability, cartoonity, and region fitness of textures. The style-consistency is predicted using a learning-based solution since it is subjective to judge whether two textures are consistent in style. An optimization problem is formulated and solved via the genetic algorithm. Our method is validated on various cartoon illustrations, and convincing results are obtained. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Textures

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Deep Learning-Based Energy Efficiency Maximization in Massive MIMO-NOMA Networks with Multiple RISs 6

Deep Learning-Based Energy Efficiency Maximization in Massiv...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Perdana, Ridho Hendra Yoga Nguyen, Toan-Van Pramitarini, Yushintia An, Beongku Hongik University Dept. of Software and Communications Engineering in Graduate School Korea Republic of San Diego State University Department of Electrical and Computer Engineering San DiegoCA United States Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798350344349

In this paper, we study a deep learning (DL)-based energy efficiency maximization (EEM) problem in massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) networks with multiple reconfigurable intelligent surfaces (RISs). These RISs are deployed randomly at the edge of the near radius to assist the base station (BS) in communicating with near and far users. We formulate the problem of jointly optimizing the precoding matrix and phase shift of the RISs to maximize the overall energy efficiency subject to the quality-of-service of each user, phase shift of RISs, and power budget of the BS constraints. To address this challenging non-convex problem with mixed-integer constraints, the original problem is decoupled into phase shift and beamforming sub-problems, then addressed them separately. We introduce the bisection search algorithm to address the challenge of the phase shifts optimization problem. For the beamforming optimization, it is transformed into an equivalent non-convex problem but with a more tractable form. Then, we propose an iterative algorithm based on the inner approximation method for its solution. To support real-Time optimization, we design a deep learning framework to predict optimal solutions of phase shifts at RISs and precoding matrix under different parameter settings. Simulation results show that the proposed DL-based approach can predict the optimal solution with high accuracy in a short time compared to the conventional approach. Additionally, the effect of the maximum power budget at the base station, the number of RISs, and BS's antennas are evaluated thoroughly. © 2024 IEEE.

关键词： Energy efficiency

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IoT Security: A Deep Learning-Based Approach for Intrusion Detection and Prevention

IoT Security: A Deep Learning-Based Approach for Intrusion D...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kauhsik, Bhanu Nandanwar, Himanshu Katarya, Rahul Delhi Technological University Dept. of Software Engineering Delhi India Delhi Technological University Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350313413

The size and market worth of the Internet of Things (IoT) have expanded, but unfortunately, the likelihood of user data being compromised has also risen. This presents a notable danger that has the potential to create disorder. Consequently, the main aim of this paper is to recognize any weaknesses within existing security solutions and suggest an innovative approach to address them. In order to accomplish this, the paper puts forth four research inquiries concerning IoT security that leverage Machine Learning and Deep Learning techniques. Additionally, it conducts an extensive review of the latest publications and concludes that a combination of specific techniques can provide a potential solution to the security problems in IoT. Overall, the findings of this study suggest that this systematic literature review could help in significantly enhancing the security of IoT devices and applications, safeguarding user data from unauthorized access and potential misuse. © 2023 IEEE.

关键词： Cyberattack Deep Learning Internet of Things (IOT) Intrusion Detection System (IDS) Network Security

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Medicinal Plants Recognition Using Deep Learning

Medicinal Plants Recognition Using Deep Learning

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2023 International Conference on Multimedia Computing, Networking and Applications, MCNA 2023

作者： Sharrab, Yousef Al-Fraihat, Dimah Tarawneh, Monther Sharieh, Ahmad Isra University Dept. of Computer Science Amman Jordan Isra University Dept. of Software Engineering Amman Jordan The University of Jordan Dept. of Computer Science Amman Jordan

ISBN: (纸本)9798350339277

The use of medicinal plants has been a longstanding practice in traditional medicine worldwide. Accurately identifying medicinal plants is crucial for determining their medicinal properties and potential applications. However, it can be a challenging task due to the complexity of their appearance. Variations in growth stage, lighting, and imaging conditions can make classification challenging, which limits the application of traditional methods for plant identification. This paper proposes a deep learning-based approach that uses a convolutional neural network (CNN) based on the VGG-16 model. With a dataset of 25,686 images, the CNN is capable of learning and representing complex features in images, enabling it to recognize and classify medicinal plants with high accuracy. The proposed approach can efficiently classify plants with different growth stages, lighting conditions, and imaging settings, providing a reliable tool for plant identification. We achieved an impressive recognition rate of 98%, demonstrating the feasibility of using deep learning techniques for accurate plant classification. The proposed approach has enormous potential for providing healthcare professionals and herbal medicine researchers with a reliable tool for identifying herbal plants. the study represents an essential advancement in the use of deep learning techniques for medicinal plant recognition, overcoming the challenges posed by their complex appearance. The proposed approach has farreaching implications and can significantly impact the field of herbal medicine research, enabling researchers and healthcare professionals to identify and classify medicinal plants more accurately © 2023 IEEE.

关键词： Classification (of information)

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A Systematic Review on Pattern-based GUI Testing of Android and Web Apps: State-of-the-Art, Taxonomy, Challenges and Future Directions 25

A Systematic Review on Pattern-based GUI Testing of Android ...

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25th International Multi Topic Conference, INMIC 2023

作者： Kousar, Ambreen Khan, Saif Ur Rehman Hussain, Shahid Abdul Basit Ur Rahim, M. Wang, Wen-Li Ibrahim, Naseem Comsats University Islamabad Dept of Computer Science Islamabad Pakistan Shifa Tameer-e-Millat University Dept. of Software Engineering Islamabad Pakistan Penn State University Behrend School of Computer Science and Software Engineering Pennsylvania16563 United States California State University Dept. of Computer Engineering and Computer Science Long Beach United States Penn State Erie Dept. of Computer Science and Software Engineering United States

ISBN: (纸本)9798350317701

Graphical User Interface (GUI) designing and testing of different applications is significant from a user point of view. GUI testing ensures that the user interface of apps is visually appealing, user-friendly, and enhances user satisfaction. Pattern-Based GUI Testing (PBGT) is a new model-based testing approach that aims to enhance reusability while reducing the workload associated with modeling user interfaces and testing applications. The objective of conducting a Systematic Literature Review (SLR) on pattern-based GUI testing is to consolidate existing knowledge and provide insights for improving the effectiveness of PBGT methods. The objectives of conducting an SLR are the identification of current state-of-the-art approaches and tools in the targeted research context. To accomplish the mentioned objectives, A hybrid methodology combining Kitchenham's SLR and PRISMA guidelines was used to conduct this SLR. We performed a keyword-based search on well-known databases IEEE, ACM, Science Direct, Wiley, and Springer Link to determine the potential studies published in the last 10 years. Following a comprehensive systematic analysis, we identified a total of twenty-four studies employing varied approaches for pattern-based GUI testing. Additionally, we identified 11 tools that have been used and proposed in PBGT for Android and web apps and made a taxonomy of tools. We also identified challenges and research directions. The implications of this study are significant. The current study is helpful for researchers intending to work in pattern-based GUI testing. The comprehensive knowledge about the current state-of-the-art Tools, techniques, and approaches will help practitioners to effectively apply them in industry and enable researchers to extend their work in this context in future research. © 2023 IEEE.

关键词： Graphical user interfaces

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A Reinforcement-Learning Self-Healing Approach for Virtual Network Function Placement 36

A Reinforcement-Learning Self-Healing Approach for Virtual N...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Avgeris, Marios Leivadeas, Aris Lambadaris, Ioannis Carleton University Dept. of Systems and Computer Eng. Ottawa Canada Dept. of Software Engineering and It Montréal Canada

ISBN: (纸本)9781665477161

Modern networking paradigms like Service Function Chaining (SFC) allow for services to be broken down to a series of ordered and interconnected Virtualized Network Functions (VNFs) that can be hosted in generic servers in EdgeCloud datacenters. Nonetheless, a critical issue arises, when a hardware or software failure occurs and the VNFs of an SFC need to be repositioned, allowing to autonomously bring the system back to its normal operation, a process called self-healing. In this paper, a distributed methodology is proposed that aims to address this challenge, considering the requirements of all involved actors. Specifically, a Reinforcement Learning (RL) based algorithm is proposed that allows to iteratively optimize and determine an SFC healing solution upon a datacenter failure. As a second stage, a revenue-driven resource allocation mechanism is integrated, to resolve the contention for resources in an already functional datacenter that potentially occurs due to the repositioning. Various simulation scenarios prove the efficiency of our proposed resilient healing mechanism. © 2023 IEEE.

关键词： Resource allocation

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Analyzing Different Protocols of Information Granularity Distribution to Improve Consistency of Fuzzy Preference Relations in Decision-Making

Analyzing Different Protocols of Information Granularity Dis...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Gonzalez-Quesada, Juan Carlos Perez, Ignacio Javier Morente-Molinera, Juan Antonio Alonso, Sergio Herrera-Viedma, Enrique Cabrerizo, Francisco Javier University of Granada Dept. of Computer Science and Ai Granada Spain University of Granada Dept. of Software Engineering Granada Spain

ISBN: (纸本)9781665430654

A fundamental principle of Granular Computing is that of an information granularity distribution and its optimization process. It has been used in system modelling to elevate a numerical model to its granular counterpart, which is more in rapport with reality. For example, in decision-making with fuzzy preference relations, it has been applied to enhance the existing numerical consistency improvement procedures. However, even though different protocols of information granularity distribution have been proposed, only the one based on a uniform and symmetric distribution and the one based on a symmetric but non-uniform distribution have been considered. Given that there exist others, this study aims to analyze how we can take advantage of all of them to improve the consistency of the fuzzy preference relations. Some numerical experiments are also completed to show the performance of these protocols. © 2023 IEEE.

关键词： consistency decision-making fuzzy preference relation information granularity

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Mitigating YouTube Recommendation Polarity using BERT and K-Means Clustering 10

Mitigating YouTube Recommendation Polarity using BERT and K-...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Ahmad, Zoheb Quasim, Danish Garg, Shreeya Kumar, Vinod Delhi Technological University Dept. of Software Engineering New Delhi India Delhi Technological University Dept. of Computer Engineering New Delhi India Iiit Delhi Dept. of Computer Engineering and Bioscience New Delhi India

ISBN: (纸本)9789380544472

YouTube's recommendation system is famous for its success in maintaining high retention rates. The cause of its success is its ability to learn and predict an individual user's preferences appropriately. An unintended consequence, however, is that users get stuck in what is known as their own 'echo chambers' when dealing with and feeding users back their preferences. These echo chambers can cause increasing perspective bias within users, making it difficult for users to understand differing opinions. This work aims to prepare a model that counteracts YouTube's recommendation system by forcefully exposing users to content from varying viewpoints. The SSKA pipeline (Suno Sabki, Karo Apni) is a complementary deep learning model that involves Natural Language Processing (NLP) and K-Means clustering. It utilizes modern software libraries such as the YouTube API (Application Programming Interface) for data collection and was trained and tested on a varied set of users. The results prove that the model is successful in decreasing the bias recommendation by exposing users to the content of varying opinions and helping them break away from their echo chambers. The proposed methodology of explicitly exposing users to the content of varying opinions can positively impact local societies and the global community. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： K-means clustering

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