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14th International Conference on Information and Communication Systems, ICICS 2023

作者： Al-Tawara, Feda'a Qasaimeh, Malik Jarad, Dania Al-Qassas, Raad S. Princess Sumaya University for Technology Department of Software Engineering Amman Jordan Jordan University of Science and Technology Department of Software Engineering Department of Cyber Security Irbid Jordan Princess Sumaya University for Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798350307870

The availability of important characteristics such as decentralization, permanence, anonymity, and audacity has driven interest in blockchain technology more recently than ever before. This technology has been employed in a variety of applications, including education. Therefore, this research is a systematic review of research that investigates the applications of blockchain technology in higher education institutions in addition to those that present the role of this technology in combating the Corona pandemic, as it focuses on the following main topics: (1) blockchain-based applications and systems that can be used in higher education institutions;and (2) the current state of blockchain challenges in this area that need to be addressed in the future. A detailed analysis of the results of each topic as well as an extensive discussion were conducted based on the results. This review also provides insight into other areas that could benefit from blockchain technology during and after the pandemic. © 2023 IEEE.

关键词： Blockchain

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Accurate Anomaly Detection in Medical Images using Transfer Learning and Data Optimization: MRI and CT as Case Studies 5

Accurate Anomaly Detection in Medical Images using Transfer ...

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5th International Conference on Neural Networks and Neurotechnologies, NeuroNT 2024

作者： Nizamli, Yasser Filatov, Anton Yu. Fadel, Weaam Shichkina, Yulia A. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications Saint Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Department of Computer Science Saint Petersburg Russia

ISBN: (纸本)9798350363739

Today, medical imaging techniques are widely used to detect a variety of human conditions and diseases. To speed up the diagnostic process, systems are often automated using deep learning methods, which have been proven to yield outstanding results. However, these models may be limited to a specific domain, prone to overfitting, and difficult to update. In this paper, we present a transfer learning approach that achieves high accuracy for detecting anomalies in both MRI and CT images. Our system utilizes the ConvNeXt network, inspired by the Swin Transformer architecture, as a high-level feature extractor from processed and augmented medical images. The resulting data is then optimized using the Edited Nearest Neighbors algorithm, which selects the most effective observations and discards the rest. Finally, the data is passed to the K-Nearest Neighbors classifier to detect anomalies in numerically represented images. Our model achieves high performance for two different types of medical images and can easily be updated without the need for retraining. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Interference cancellation in RIS-assisted FD relay transmission 14

Interference cancellation in RIS-assisted FD relay transmiss...

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14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Kim, Donghyeon Choi, Wooyeol Chun, Chanjun Kim, Yohan Kim, Yonggang Chosun University Department of Computer Engineering Korea Republic of Dongseo University Department of Software Korea Republic of Kongju National University Division of Computer Science and Engineering Korea Republic of

ISBN: (纸本)9798350335385

Reconfigurable intelligent surface (RIS) is one of the promising technology for the next-generation wireless networks. The RIS reflects the received signal with phase shift introduced by reflecting elements without analog to digital conversion. In relay transmission scenario, RIS could be exploited to enhance the network coverage and improve transmission performance. In this paper, we consider RIS-assisted full-duplex (FD) relay transmission scenario. In the considered scenario, RIS and FD relay node performs relay transmission. Although the beamformed signal could be generated by phase shift of RIS, the interference from RIS to FD relay node may degrade the throughput performance. The proposed method cancels both of interference from RIS and self-interference at the FD relay node by estimating interference channels in the preparation phase before data transmission. © 2023 IEEE.

关键词： Analog to digital conversion

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Fully Distributed Multi-Agent RL Framework for QoS Routing 6

Fully Distributed Multi-Agent RL Framework for QoS Routing

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6th IEEE Future Networks World Forum, FNWF 2023

作者： Duong, Quang Huy Janulewicz, Emil Jaumard, Brigitte Bentaleb, Abdelhak Slobodrian, Sergio Concordia University Department of Computer Science & Software Engineering MontréalQC Canada Ciena OttawaON Canada

ISBN: (纸本)9798350324587

Classical routing heuristics, e.g., Open Shortest Path First, have several significant issues, such as they are not able to generalize or adapt to heterogeneous environments including dynamics of topology, traffic patterns, and Quality of Service (QoS) requirements. To generalize solutions, network operators recently utilized machine learning algorithms at centralized controllers. However, centralized machine learning solutions are not scalable due to many reasons, such as slow data transfer to the central controller in a large network. Distributed multi-agent systems do not require a tedious and complex central controller while reducing data storage and computation burden as tasks are divided and handled at local servers/computers. In this paper, we present a fully distributed multi-agent system named MADQN, addressing the request provisioning problem, i.e., provisioning a request on a dedicated path satisfying latency and bandwidth requirements. MADQN applies a Deep Q-Network reinforcement learning algorithm to train the agents. Although each agent has its data and policy locally, they can still cooperate with other agents to finish a common routing task that maximizes the total reward. We evaluate the effectiveness of the MADQN with a benchmark network that consists of 100 nodes and 432 directed links, and a dynamic set of thousands of requests. The results show that the agents in MADQN can learn to cooperate and provision 99% of the requests, which is about a 9% improvement against the centralized single agent scheme. © 2023 IEEE.

关键词： Data transfer

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Performance Comparison of Deep and Machine Learning Approaches Toward COVID-19 Detection

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Intelligent Automation & Soft Computing 2023年第8期37卷 2247-2261页

作者： Amani Yahyaoui Jawad Rasheed Shtwai Alsubai Raed M.Shubair Abdullah Alqahtani Buket Isler Rana Zeeshan Haider Department of Software Engineering Istanbul Sabahattin Zaim UniversityIstanbul34303Turkey Department of Software Engineering Nisantasi UniversityIstanbul34398Turkey Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Electrical and Computer Engineering New York University(NYU)Abu Dhabi129188United Arab Emirates College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Engineering Istanbul Topkapi UniversityIstanbul34087Turkey Baqai Institute of Hematology Baqai Medical UniversityKarachi75340Pakistan

The coronavirus(COVID-19)is a disease declared a global pan-demic that threatens the whole *** then,research has accelerated and varied to find practical solutions for the early detection and correct identification of this *** researchers have focused on using the potential of Artificial Intelligence(AI)techniques in disease diagnosis to diagnose and detect the *** paper developed deep learning(DL)and machine learning(ML)-based models using laboratory findings to diagnose *** different methods are used in this study:K-nearest neighbor(KNN),Decision Tree(DT)and Naive Bayes(NB)as a machine learning method,and Deep Neural Network(DNN),Convolutional Neural Network(CNN),and Long-term memory(LSTM)as DL *** approaches are evaluated using a dataset obtained from the Israelita Albert Einstein Hospital in Sao Paulo,*** data consists of 5644 laboratory results from different patients,with 10%being Covid-19 positive *** dataset includes 18 attributes that characterize *** used accuracy,f1-score,recall and precision to evaluate the different developed *** obtained results confirmed these approaches’effectiveness in identifying COVID-19,However,ML-based classifiers couldn’t perform up to the standards achieved by DL-based *** all,NB performed worst by hardly achieving accuracy above 76%,Whereas KNN and DT compete by securing 84.56%and 85%accuracies,*** these,DL models attained better performance as CNN,DNN and LSTM secured more than 90%*** LTSM outperformed all by achieving an accuracy of 96.78%and an F1-score of 96.58%.

关键词： Artificial intelligence COVID-19 deep learning diagnosis machine learning

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Adaptive Climbing Strategy of Snake Robot Based on Compliance Control 4

Adaptive Climbing Strategy of Snake Robot Based on Complianc...

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4th International Symposium on Intelligent Robotics and Systems, ISoIRS 2024

作者： Yang, Huizhuo Dong, Jiyuan Wang, Meng Meng, Shiyong Yang, Lei Xue, Jiashuo Tiangong University School of Electronic and Information Engineering Tianjin China Tiangong University Department of Computer Science and Technology Tianjin China Tiangong University Department of Software Engineering Tianjin China

ISBN: (纸本)9798350364798

Snake robots, exhibiting a high degree of redundancy, are a class of biomimetic robots capable of forming a helical rolling gait for climbing trees and pipes. The surface structure of naturally grown trees is irregular, which may hinder the snake robots from achieving optimal surface conformation. Additionally, the presence of numerous branching structures in pipes introduces unpredictability in pipe climbing. To address these challenges, we propose an adaptive climbing strategy for snake robots based on compliant control. By adjusting a pre-set elasticity coefficient, the snake robot modulates the output torque of each joint, thereby altering the target angle of the joint. This adjustment enables the snake robot to adapt to unstructured environments. We conducted an experiment on climbing curved pipes to verify the effectiveness of this method. The result demonstrate that under unknown conditions, the robustness of the snake robot's climbing motion is enhanced by modifying the pre-set elasticity coefficient based on compliant control. This finding underscores the potential of compliant control in improving the adaptability and performance of Snake robots in complex environments. © 2024 IEEE.

关键词： Microrobots

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A New Method of Search Engine Optimization Based on Semantic Kernel Idea

A New Method of Search Engine Optimization Based on Semantic...

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作者： Orekhov, Sergey Godlevsky, Michail Malyhon, Hennadiy Goncharenko, Tetiana Computer Science and Software Engineering Department National Technical University Kharkiv Polytechnical Institute Kharkiv61002 Ukraine

The results of more than 30 projects in the field of search engine optimization on the Internet show that the effectiveness of existing approaches is catastrophically reduced. Paid promotion methods no longer guarantee a banal increase in the traffic of a given web resource, and even more so the number of leads. The reason is that competition has moved to the Internet as much as possible. So, we need to look for new alternative methods. Therefore, the paper proposes to consider a new alternative way to promote goods on the Internet. This approach is a new information technology – virtual promotion. It is based on the creation of a virtual image of a product on the Internet. We called this virtual image the semantic kernel. Such a kernel is placed on the Internet in order to attract a potential buyer. It is placed in the Internet nodes according to a special online sales scheme, which is based on a map of the user's travels on the Internet. This scheme is called the virtual promotion map. The map must guarantee maximum traffic to a given web resource. However, it will not be possible to immediately build a map. Therefore, its creation is iterative. We are, as it were, setting ourselves up to find effective ways between the semantic core and the user who is ready to buy our product online. The article offers a description of the virtual promotion technology. The main idea of virtual promotion is that promotion is a logistical channel between a company and a client. We put a special message (semantic core) into the channel. He moves through the logistics channel and tries to attract as many new buyers of the goods as possible. This article presents the results of the work of special software that implements the synthesis, quality assessment and management of the semantic kernel of web content. The program generates and manages two channels: the first is intended for the distribution of the semantic kernel, the second is for assessing the quality of such distribution. T

关键词： Semantics

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Reliable Service Recommendation: A Multi-modal Adversarial Method for Personalized Recommendation under Uncertain Missing Modalities

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IEEE Transactions on Services Computing 2025年第03期18卷 1724-1738页

作者： Chen, Junyang Yang, Ruohan Guo, Jingcai Wang, Huan Wu, Kaishun Zhang, Liangjie Shenzhen University College of Computer Science and Software Engineering China Huazhong Agricultural University College of Informatics Wuhan China The Hong Kong Polytechnic University Department of Computing Hong Kong DSA Thrust and IoT Thrust Center China

Personalized recommendation is of paramount importance in online content platforms like Kuai and Tencent. To ensure accurate recommendations, it is crucial to consider multi-modal information in both items and user-user/item interactions. While existing works on multimedia recommendation have made strides in leveraging multi-modal contents to enrich item representations, many of them overlook the practical scenario of multiple modality missing. As a result, the performance of recommendation systems can be significantly compromised in such cases. In this paper, we introduce a novel multi-modal adversarial method called MMAM, which aims to provide reliable personalized recommendation services even in the presence of uncertain missing modalities. The core idea behind MMAM is to design a generator that can effectively encode both user-user/item interactions and multi-modal contents, taking into account various missing cases. The generator is trained to learn transferable features from different combinations of missing modalities in order to deceive a discriminative classifier. Additionally, we propose a modal discriminator that can classify the missing cases of multi-modalities, further enhancing the capability of the model. Moreover, a well-equipped predictor utilizes the transferable features to predict potential user interests. To improve the prediction accuracy, we design a type discriminator that enhances the classification of link types. By employing a mini-max game between the generator and the discriminators, MMAM successfully obtains transferable features that encompass multi-modal contents, even when facing uncertain missing modalities. We conduct extensive experiments on industrial datasets, including Kuai and Tencent. Comparing with state-of-the-art approaches, MMAM achieves improvements in personalized recommendation tasks under uncertain missing modalities. MMAM holds promise for enhancing multi-modal personalized recommendations in real-world applications

关键词： Discriminators

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Unveiling Interpretability: Analyzing Transfer Learning in Deep Learning Models for Traffic Sign Recognition

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SN computer science 2024年第6期5卷 682页

作者： Waziry, Sadaf Rasheed, Jawad Ghabban, Fahad Mahmoud Alsubai, Shtwai Elkiran, Harun Alqahtani, Abdullah Department of Software Engineering Istanbul Aydin University Isanbul 34295 Turkey Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul 34303 Turkey Faculty of Computer Science and Engineering Taibah University Medina Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al Kharj 11942 Saudi Arabia

Since the advent of automobiles and driver assistance technologies, traffic sign recognition has been of the utmost importance for Industry 4.0. In the driving system, good data pre-processing is critical. For such objectives, sophisticated transformations or fundamentally computational image processing approaches are out of the question. Convolutional Neural Networks (CNN) have been used to perform more object identification challenges, thus, improving most computer vision applications, both existing and new, because of their excellent recognition rate and rapid execution. This study introduces a method for recognizing traffic signs by utilizing a CNN-based model and the transfer learning concept. The TensorFlow library is used for training the underlying neural network model. The offered approach makes use of the German Traffic Sign Recognition Benchmark (GTSRB) and images from the Traffic Sign Images from Turkey (TSIT) databases. These datasets are dependable and vibrant, and they have been used to train many algorithms. Furthermore, after training the model, the proposed scheme acquired a testing accuracy is 99.44%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CNN German traffic sign Image processing Transfer learning

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Automated Analysis of Halo2 Circuits 21

Automated Analysis of Halo2 Circuits

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21st International Workshop on Satisfiability Modulo Theories, SMT 2023

作者： Soureshjani, Fatemeh Heidari Hall-Andersen, Mathias Jahanara, MohammadMahdi Kam, Jeffrey Gorzny, Jan Ahmadvand, Mohsen Computer Engineering and Software Engineering Department Polytechnique Montréal MontrealQC Canada Quantstamp Inc. United States Department of Computer Science Aarhus University Denmark

Zero-knowledge proof systems are becoming increasingly prevalent and being widely used to secure decentralized financial systems and protect the privacy of users. Given the sensitivity of these applications, zero-knowledge proof systems are a natural target for formal verification methods. We describe methods for checking one such proof system: Halo2. We use abstract interpretation and an SMT solver to check various properties of Halo2 circuits. Using abstract interpretation, we can detect unused gates, unconstrained cells, and unused columns. Using an SMT solver, we can detect under-constrained circuits (in the sense that for the same public input they have two efficiently computable satisfying assignments). This is the first work we are aware of that applies lightweight formal methods to PLONKish arithmetization and Halo2 circuits. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Timing circuits

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