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HighTech and Innovation Journal 2023年第4期4卷 749-760页

作者： Rexha, Blerim Neziri, Vehbi Dervishi, Ramadan Faculty of Electrical and Computer Engineering University of Prishtina Prishtina Kosovo10000 Department of Computer Science and Engineering UBT Higher Education Institution Prishtina Kosovo10000 Department of Computer Science Universum Higher Education Institution Prishtina Kosovo10000

Decentralized applications leveraging blockchain technology are gaining widespread adoption within the decentralized applications ecosystem. Interoperability, a fundamental concept facilitating seamless data and processing power exchange across diverse blockchain networks, is paramount in this context. The primary objective of this paper is to explore the transformative potential of "blockchain bridges" in facilitating secure and transparent electronic voting processes across multiple blockchain networks. The study employs a comprehensive analysis of various approaches, including atomic exchanges, sidechains, cross-chain bridges, token wrappers, and interledger protocols. The selection of a specific method is guided by the unique requirements and privacy considerations of the electronic voting use case. The application of two distinct blockchains serves as a practical demonstration, illustrating the principles of blockchain bridges in real-world scenarios. The research reveals that blockchain bridges not only streamline the exchange of data between diverse blockchain networks but also establish a dual decentralization paradigm. This paradigm enables the creation of openly maintained, purpose-specific, decentralized ledgers for electronic voting. The integration of blockchain bridges significantly reduces the risk of fraud, instilling greater confidence in the accuracy of election results. Thus, by presenting a comprehensive array of approaches and emphasizing their practical application, this research contributes to advancing the understanding and implementation of blockchain technology in the critical domain of electronic voting. © 2023, Ital Publication. All rights reserved.

关键词： Blockchain

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Misuse of Wi-Fi Data to Analyze User Behaviour

Misuse of Wi-Fi Data to Analyze User Behaviour

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ELEKTRO

作者： Lukas Kapicak Jiri Stipal Kamil Trubak Petr Machnik Patrik Burdiak Jan Rozhon Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic

ISBN: (数字)9798350372359

ISBN: (纸本)9798350372366

There has been a huge increase in the number of mobile devices over the past few years. Many mobile users own more than one mobile device. Compared to conventional laptops, mobile devices are constantly active and their users use them outdoors more often than personal computers. Compared to desktop computers, mobile devices accompany their owners on their travels and therefore there is a significant risk of tracking and eavesdropping on user data without a user being aware of it. This paper describes techniques how the attacker could track users by using the Wireless Fidelity (Wi-Fi) network. Every mobile device generates traffic that can be divided into “Automatic” and “User”. When” Automatic” traffic is generated, the device can be found because of its pattern. The paper also describes how the attacker can get into the victim's Wi-Fi network without being in its range. When breaching the Wi-Fi network security, the attacker can connect to the victim's network during his first appearance in range of this network. Security pre-cautions and possible directions of future work are discussed at the end of this paper.

关键词： Wireless communication Portable computers Social networking (online) Telecommunication traffic Mobile handsets Virtual private networks Communication system security

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A deep audio-visual model for efficient dynamic video summarization

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Journal of Visual Communication and Image Representation 2024年 100卷

作者： El-Nagar, Gamal El-Sawy, Ahmed Rashad, Metwally Department of Computer Science Faculty of Computers & Artificial Intelligence Benha University Benha Egypt Department of Computer Engineering and Information College of Engineering Wadi Ad Dwaser Prince Sattam Bin Abdulaziz University Al-Kharj16273 Saudi Arabia

The adage "a picture is worth a thousand words" resonates in the digital video domain, suggesting that a video could be seen as a composition of millions of these words. Videos are composed of countless frames. Video summarization creates cohesive visual units in scenes by condensing shots from segments. Video summarization gains prominence by condensing lengthy videos while retaining crucial content. Despite effective techniques using keyframes or keyshots in video summarization, integrating audio components is imperative. This paper focuses on integrating deep learning techniques to generate dynamic summaries enriched with audio. To address that gap, an efficient model employs audio-visual features, enriching summarization for more robust and informative video summaries. The model selects keyshots based on their significance scores, safeguarding essential content. Assigning these scores to specific video shots is a pivotal yet demanding task for video summarization. The model's evaluation occurs on benchmark datasets, TVSum and SumMe. Experimental outcomes reveal its efficacy, showcasing considerable performance enhancements. On the TVSum, SumMe datasets, an F-Score metric of 79.33% and 66.78%, respectively, is achieved, surpassing previous state-of-the-art techniques. © 2024 Elsevier Inc.

关键词： Deep learning

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Generic Hardware Realization of K Nearest Neighbors on FPGA

Generic Hardware Realization of K Nearest Neighbors on FPGA

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2022 International Conference on Microelectronics, ICM 2022

作者： Yacoub, Mohammed H. Ismail, Samar M. Said, Lobna A. Madian, Ahmed H. Radwan, Ahmed G. Giza Egypt School of Engineering and Computer Science Egypt National Centre for Radiation Research and Technology Radiation Engineering Department Cairo11787 Egypt Cairo University Faculty of Engineering Engineering Mathematics and Physics Department Giza12613 Egypt Nile University School of Engineering and Applied Sciences Giza Egypt

ISBN: (纸本)9781665493246

K Nearest Neighbors (KNN) algorithm is a straight-forward yet powerful Machine Learning (ML) tool widely used in classification, clustering, and regression applications. In this work, KNN is applied, with three distance metrics, to classify different datasets, experimentally testing each distance metric effect on the classification performance. A static K is applied for the whole dataset optimally chosen based on a 5-fold cross-validation. A reconfigurable hardware realization on field programmable gate array (FPGA) of each distance metric applying selection sort algorithm is proposed. The FPGA realization reaches a throughput up to 4.44 Gbit/sec while only occupying 1% of the Genesys 2 Kintex-7 board area. The algorithm managed to classify all the tested datasets with above 90% accuracy. © 2022 IEEE.

关键词： Machine learning

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Sentiment Analysis of Local Tourism in Thailand from YouTube Comments Using BiLSTM 1

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15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

作者： Khruahong, Sanya Surinta, Olarik Lam, Sinh Cong Department of Computer Science and Information Technology Faculty of Science Naresuan University Phitsanulok Thailand Faculty of Informatics Mahasarakham University Mahasarakham Thailand Faculty of Electronics and Telecommunication VNU - University of Engineering and Technology Hanoi Viet Nam

ISBN: (数字)9783031209925

ISBN: (纸本)9783031209918

Currently, social networks, where people can express their opinion through content and comments, are fast developing and affect various areas of daily life;Particularly, some research on YouTube travel channels found that almost tourists and audiences leave comments about their attitudes to that place. Thus, mining the emotional recognition of comments through artificial intelligence can bring knowledge about the tourists’ general view. This article analyzes the relationship(s) between social media use and its effect on community-based tourism in Thailand using the Social Media Sensing framework (S-Sense) as sentiment analysis and the Bidirectional Long Short-Term Memory (BiLSTM) methods to analyze the text comments. This research collected 51,280 comments on 114 Youtube Videos, which are tourist attractions in various provinces in Thailand. The approach categorizes attractions based on sentiment analysis of 60% or more, including restaurants, markets, historical sites, temples, or natural attractions. The results show that 67.51% of the 19,391 clean-processed comments were satisfied with those attraction places. Therefore S-Sense and BiLSTM models can be sufficient to analyze the attitude of comments about attraction places with from 43 to remain 33 keywords of 1,603 comments. Furthermore, the offered sentiment analysis method has higher precision, recall, and F1 scores. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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Mitigating Knowledge Discrepancies among Multiple Datasets for Task-agnostic Unified Face Alignment

arXiv

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arXiv 2025年

作者： Xia, Jiahao Xu, Min Huang, Wenjian Zhang, Jianguo Zhang, Haimin Xiao, Chunxia Faculty of Engineering and IT University of Technology Sydney UltimoNSW2007 Australia Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China School of Computer Science Wuhan University Hubei Wuhan430072 China

Despite the similar structures of human faces, existing face alignment methods cannot learn unified knowledge from multiple datasets with different landmark annotations. The limited training samples in a single dataset commonly result in fragile robustness in this field. To mitigate knowledge discrepancies among different datasets and train a task-agnostic unified face alignment (TUFA) framework, this paper presents a strategy to unify knowledge from multiple datasets. Specifically, we calculate a mean face shape for each dataset. To explicitly align these mean shapes on an interpretable plane based on their semantics, each shape is then incorporated with a group of semantic alignment embeddings. The 2D coordinates of these aligned shapes can be viewed as the anchors of the plane. By encoding them into structure prompts and further regressing the corresponding facial landmarks using image features, a mapping from the plane to the target faces is finally established, which unifies the learning target of different datasets. Consequently, multiple datasets can be utilized to boost the generalization ability of the model. The successful mitigation of discrepancies also enhances the efficiency of knowledge transferring to a novel dataset, significantly boosts the performance of few-shot face alignment. Additionally, the interpretable plane endows TUFA with a task-agnostic characteristic, enabling it to locate landmarks unseen during training in a zero-shot manner. Extensive experiments are carried on seven benchmarks and the results demonstrate an impressive improvement in face alignment brought by knowledge discrepancies mitigation. Copyright © 2025, The Authors. All rights reserved.

关键词： Zero-shot learning

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Enhanced Vision Transformer with Lion Optimizer for Accurate Classification of Monkeypox Skin Images 4

Enhanced Vision Transformer with Lion Optimizer for Accurate...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Bhatnagar, Roheet Hassan, Esraa Saber, Abeer Shams, Mahmoud Y. Elbedwehy, Samar Manipal University Jaipur Department of Computer Science and Engineering Rajasthan India Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial Intelligence Information Technology Dept. Damietta34517 Egypt Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Data Science Kafrelsheikh33511 Egypt

ISBN: (纸本)9798350387490

Monkeypox is a contagious viral infection caused by the monkeypox virus (Mpoxvirus). Characterized by skin lesions, fever, respiratory difficulties, swollen lymph nodes, and neurological complications, MPX can be fatal. Recent outbreaks have spread the disease across Europe, Australia, the United States, and Africa. Traditionally diagnosed through PCR testing of skin lesions, this process poses significant risks to medical personnel due to potential exposure to the highly contagious virus. However, the usage artificial intelligence (AI) is revolutionizing diagnostics, providing a safer and more efficient approach to MPX detection. In this paper, a new AI technique based on vision transformer and lion optimizer for classifying Monkeypox Skin Images. The presented model used for classifying the dataset and results reached accuracy of 99.6%. © 2024 IEEE.

关键词： Classification Deep Learning Diagnosis Monkeypox

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AGV indoor localization: a high fidelity positioning and map building solution based on drawstring displacement sensors

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2277-2293页

作者： Wang, Shih-Yuan Li, Che-Ming Liong, Sze-Teng Sheng, Yu-Ting Huang, Yen-Chang Liong, Gen-Bing Gan, Yee-Siang Graduate Institute of Architecture National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronic Engineering Feng Chia University Taichung40724 Taiwan School of Architecture Feng Chia University Taichung40724 Taiwan Department of Applied Mathematics National University of Tainan Tainan Taiwan Faculty of Computer Science & amp Information Technology University of Malaya Kuala Lumpur50603 Malaysia

The rapid evolution of artificial intelligence (AI) and robotics technologies are bringing drastic changes to society and industry in these recent years. The impressive progress in facilitating smart manufacturing in this era of Industry 4.0 has made our lives much more convenient than ever before. Despite the greater reliability and stability of the robotic system, there are several challenges to overcome such as the restrictions of scenes, obstacles, and hardware specifications. Since a high precision positioning algorithm is of paramount importance in devising a mobile robot, the research in developing simultaneous localization and mapping has been garnered immerse attention especially from domains of the computer vision and autonomous robots. In this paper, a novel method is presented to significantly enhance the positioning precision of indoor unmanned guided vehicles. The approach involves several steps, including setting up hardware configurations and collecting relevant data by installing necessary devices and system packages within the robot operating system (ROS). Trilateration is employed to determine the relative position of the mobile robot using distance measurements. Coordinate transformation is then conducted to convert the collected input data of relative distances and orientations. Trajectory paths are obtained, and occupancy maps are constructed to estimate the resulting trajectory and generate a 2D grid map. Indoor localization and mapping are achieved using three drawstring displacement sensors along with orientation information from an Inertial Measurement Unit (IMU). The proposed method is extensively evaluated through experimentation on predefined navigation paths, and its performance is compared to state-of-the-art methods such as RealSense T265, Hector SLAM, and wheel odometry. The results show that the proposed method exhibits compelling performance in both mean error and occupancy map construction. Ultimately, the findings reported herein

关键词： Mobile robots

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Diabetes Prediction Model for Better Clarification by using Machine Learning 6

Diabetes Prediction Model for Better Clarification by using ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Eben, J. Lysa Jayasudha, R. Ramya, S. Kaliappan, S. Aswal, Shobha Al-Salehi, Khalid Ali Salem Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Division of Research and Development Lovely Professional University Punjab Phagwara144411 India School of Computing Graphic Era Hill University Dehradun India Biomedical Engineering Study Program Faculty of Science and Technology Airlangga University Surabaya Indonesia

ISBN: (纸本)9798350398496

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. The newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy. © 2023 IEEE.

关键词： Forecasting

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Brain Sight: A Novel system for substituting Vision Sensor 3

Brain Sight: A Novel system for substituting Vision Sensor

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3rd International Conference on Distributed Sensing and Intelligent Systems, ICDSIS 2022

作者： Elsaeed, Nadine Y. Alshaarawy, Mariam M. Esmaiel, Abd Elrhman S. El-Deen Elmahdy, Seif Abdelhameed, Omar Fakhry, Mariam M. Ogabih, Naglaa A. Ali, Nouran M. El-Deeb, Reem Software Engineering Department Faculty of Computers and Information Mansoura University Mansoura35516 Egypt Computer Science Department Faculty of Computers and Information Mansoura University Mansoura35516 Egypt The Department of Computer Science Faculty of Computers and Information Mansoura University P.O. Box: 35516 Mansoura Egypt

ISBN: (纸本)9781839538186

In a world where nearly everything we do depends on sight;it is ever more challenging for the unsighted to cope with it and lead a normal life without being reliant on the presence of a companion. Finding a mechanism to convey visual information to the brain is the most difficult part of the research as well as the development of efficient sensory replacement and augmentation systems. In this paper, we aim to propose Brain Sight as an innovative system to help blind people identifying and discovering things in front of them using vibro-tactile display for vision sensory substitution. Brain Sight is a wearable vest-like fabric containing small vibration motors that displays pulses to map the seen images on the Lower-back of the visually impaired. A deep labeling segmentation and edge detection techniques are applied to the captured image after preprocessing. A simple prototype is implemented and tested with volunteers. Thanks to neuroplasticity and the cognitive cortex inside humans' brain, the implemented vest showed promising results. Without the need for intensive training, Brain Sight enables blind to recognize different shapes and numbers. © IET Conference Proceedings 2022.

关键词： Semantics

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