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A Blockchain-based Trustworthy and Secure Review System for Decentralized e-Portfolio Platforms

A Blockchain-based Trustworthy and Secure Review System for ...

引用

International Conference on Information and Communication Technology Convergence (ICTC)

作者： Mpyana Mwamba Merlec Nday Kabulo Sinai Hoh Peter In Dept. of Computer Science and Eng. Korea University Seoul South Korea

E-portfolios have emerged as powerful tools in the education and professional development fields, enabling learners to showcase their skills, achievements, and credentials in a digital format. However, ensuring the security, authenticity and reliability of e-portfolio artifacts and credentials remains a challeng.. Existing review systems face challeng.s such as lack of transparency, vulnerability to manipulation, and reliance on centralized trust. Therefore, this paper proposes B-TrustReview, a trustworthy and secure review system for decentralized e-portfolio platforms that leverages blockchain and smart contracts. By leveraging blockchain’s decentralized and tamper-resistant nature, the system enhances the security, transparency, and reliability of reviews. Every review is securely recorded on the blockchain, creating an auditable and transparent record of evaluations. Smart contracts govern the execution of the review process, automating the validation and verification of reviews based on predefined criteria. This automation reduces reliance on intermediaries and minimizes the risk of biased or manipulated reviews fostering transparency, accountability, and credibility in evaluating e-portfolios. Moreover, it enables secure and efficient transactions, ensuring the confidentiality of user data and protecting against unauthorized access.

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Experiences and challeng.s on testing and validating Network Aware PPDR XR Applications for 5G Advanced networks

Experiences and challenges on testing and validating Network...

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IEEE Future Networks World Forum (FNWF)

作者： Panagiotis Papaioannou Manos Kamarianakis Antonis Protopsaltis Leonidas Perlepes Ioannis Chasiotis Evangelia Milolidaki George Margetis George Papagiannakis Constantine Stephanidis Christos Tranoris Electrical and Computer Eng. Dept. University of Patras Greece ORamaVR S.A. Switzerland & Greece Foundation for Research and Technology - Hellas (FORTH) Institute of Computer Science (ICS) Greece Computer Science Dept. University of Crete Greece Electrical and Computer Eng. Dept. University of Western Macedonia Greece Satways Ltd Greece p-Net Greece

ISBN: (数字)9798350379495

ISBN: (纸本)9798350379501

This paper presents initial findings from the FIDAL project, which tests and validates network-aware PPDR XR applications for 5G advanced networks. Using a 5G testbed, we evaluated use cases highlighting the crucial role of 5G technologies in enhancing emergency response through improved connectivity and low latency. Preliminary results show the potential of 5G features in revolutionizing PPDR services. Our testbed performance evaluation supports realistic, large scale trial preparations, promising improved situational awareness and operational efficiency in critical communications.

关键词： 5G mobile communication Extended reality Bandwidth Emergency services Resource management Low latency communication Testing

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IoT-based Monitoring of Refrigerated Vaccine Storage: A Literature Review and a Proposed Solution 3

IoT-based Monitoring of Refrigerated Vaccine Storage: A Lite...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Harrabi, Mokhtar Kechiche, Lilia Hamdi, Abdelaziz Ouni, Bouraoui Hadj Tahar, Jamal Bel Eniso ISITCom Hamam Dept. of Computer Eng Iset Sousse Nooccs Research Lab Sousse Tunisia University College of Ranyah Taif University Department of Science and Technology Taif21944 Saudi Arabia Eniso University of Sousse Dept. of Telecom Eng Isitcom Nooccs Research Lab Tunisia Eniso University of Sousse Dept. of Industrial Electronics Eniso Nooccs Research Lab Tunisia Eniso University of Sousse Dept. of Computer Eng. Eniso Nooccs Research Lab Tunisia

ISBN: (纸本)9798350321487

The storage of vaccines and other sensitive materials is a critical issue, as it requires proper temperature control to maintain their efficacy. IoT technology has emerged as a promising solution for monitoring and controlling the temperature of refrigerated vaccine storage, providing real-time temperature monitoring, data analysis, and alerts for any deviations from the desired temperature range. This paper reviews the existing literature on IoT-based solutions for monitoring the storage of vaccines and other sensitive materials. The authors survey various approaches and technologies used in these solutions, including microcontrollers, temperature sensors, wireless communication modules, cloud-based databases, and machine learning algorithms. In addition, the authors present their solution, which is based on an embedded system consisting of an ESP32 microcontroller, temperature sensors, and firebase services, along with a mobile application for remote monitoring and control. The authors identify the challeng.s and limitations of the existing solutions and suggest future directions for research and development. This paper provides a comprehensive overview of the state-of-the-art in IoT-based solutions for monitoring the storage of vaccines and other sensitive materials and is intended to inspire further research and innovation in this field. © 2023 IEEE.

关键词： Temperature sensors

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An Automated Data Cleaning Framework for Improving Facial Expression Classification

An Automated Data Cleaning Framework for Improving Facial Ex...

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International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)

作者： Anis Elebiary Saandeep Aathreya Shaun Canavan Dept. Computer Science and Eng. University of South Florida Tampa USA

The use of deep learning in Facial Expression Recognition (FER) has improved the prediction accuracy, where often large-scale datasets are necessary. Considering this, correct annotations are important for consistent, accurate assessment of deep models. Often, annotating datasets is a tedious process that can be error-prone. Moreover, previous work has shown that existing datasets are vulnerable to mislabeling as expression annotation is a subjective task that can be influenced by various cultural and contextual circumstances. Yet, little work has explored how to efficiently and objectively relabel existing datasets. In this paper, we propose a framework that uses zero-shot learning to discover images of similar learned features. Using this information, we proceed to relabel incorrectly annotated instances from the testing sets of two widely used datasets, AffectNet and FERPlus. Our approach achieves state-of-the-art accuracy of 78% and 90.8% on the relabeled test sets of AffectNet and FERPlus, respectively.

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Predicting DDoS Attacks: A Machine Learning Approach using ALDDoS Dataset

Predicting DDoS Attacks: A Machine Learning Approach using A...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Shantanu Shookdeb Debendra Muduli Sandesh Bhatta Aayush Adhikari Aaron Sapkota Prabhat Chaturvedi Dept. of computer Science and Eng. C V Raman Global University Bhubaneswar India Dept. of computer Science and Engineering C V Raman Global University Bhubaneswar India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The DDoS attack stands as Distributed Denial of Service attack refers a harmful effort to intercept on the regular traffic of a targeted web service or server. DDoS attack interferes with the normal network flow of the targeted server, potentially causing significant disruption to its *** send an incoming packet to every node except the arrived node,it used a non-adaptive routing algorithm. DDoS attacks are executed by using multiple compromised systems, which included combination of multiple computers system and other IoT device resources to make a large traffic on targeted network, rendering it inaccessible to legitimate users. In our study we experimented with various machine learning model like as RandomForest, DecisionTree, Xgbbost etc. with a high accuarcy of 99.99% to predict the DDoS attack with publicly available ALDDoS (Application-Layer DDoS) Dataset by Warda (Owner).

关键词： Accuracy Web services Computational modeling Prevention and mitigation Machine learning Denial-of-service attack Routing Security Servers computer crime

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Work in Progress: AI-Powered eng.neering-Bridging Theory and Practice

Work in Progress: AI-Powered Engineering-Bridging Theory and...

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IEEE World eng.neering Education Conference (EDUNINE)

作者： Oz Levy Ilya Dikman Natan Levy Michael Winokur Faculty of Industrial Eng. and Technology Management HIT Holon Israel Dept. Computer Science HUJI Jerusalem Israel

ISBN: (数字)9798331542788

ISBN: (纸本)9798331542795

This paper explores how generative AI can help automate and improve key steps in systems eng.neering. It examines AI's ability to analyze system requirements based on INCOSE’s "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced eng.neers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI’s ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI’s potential to streamline eng.neering processes and improve learning outcomes. It also highlights the challeng.s and limitations of AI, ensuring its safe and ethical use in professional and academic settings.

关键词： Ethics Educational programs Generative AI Reliability eng.neering Safety Test pattern generators Requirements eng.neering Artificial intelligence eng.neering education Standards

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Adaptive Traffic Signal Timing: Leveraging YOLOv10 and computer Vision for Real-Time Optimization

Adaptive Traffic Signal Timing: Leveraging YOLOv10 and Compu...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Suhas Papanashi Manya Chadaga Kshithi R Santosh S Huddar K Sreelakshmi Ramakanth Kumar P Dept. of Electronics and Telecomm RV College of Engineering Bengaluru Dept. of Computer Science & Eng. RV College of Engineering Bengaluru

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Traffic congestion poses a significant challeng. in modern urban environments, particularly in densely populated cities. Traditional traffic signals, which operate on fixed timings, often require manual adjustments to respond to varying traffic densities, leading to inefficient traffic flow. This issue arises because fixed intervals do not account for the dynamic nature of traffic patterns throughout the day. While developing intelligent systems that adjust signal durations dynamically offers a potential solution, such systems involve substantial financial investment and extended implementation timelines. This paper proposes a method to optimize green light durations by analyzing footage collected from a traffic junction in Beng.luru. This analysis can be performed using existing CCTV footage or manually gathered data. Even limited sample sizes can provide valuable insights for preliminary adjustments. The approach utilizes YOLOv10, a state-of-the-art computer vision algorithm, to extract relevant traffic data. The data is processed by an algorithm designed to reallocate green light durations, which are then compared with the original timings. An experimental setup involved recording one-minute videos over three days at the selec.ed junction and applying the proposed algorithm to assess and refine signal durations. The results demonstrate the feasibility of using such algorithms to improve traffic signal management effectively.

关键词： computer vision Urban areas Green products Traffic control Real-time systems Timing Data mining Junctions Vehicle dynamics Videos

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Elasticity Measurements of Expanded Foams Using a Collaborative Robotic Arm

Elasticity Measurements of Expanded Foams Using a Collaborat...

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IEEE Instrumentation and Measurement Technology Conference

作者： Luca Beber Edoardo Lamon Luigi Palopoli Luca Fambri Matteo Saveriano Daniele Fontanelli Dept. of Eng. and Computer Science University of Trento Trento Italy Dept. of Industrial Engineering University of Trento Trento Italy

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Medical applications of robots are increasingly popular to objectivise and speed up the execution of several types of diagnostic and therapeutic interventions. Particularly important is a class of diagnostic activities that require physical contact between the robotic tool and the human body, such as palpation examinations and ultrasound scans. The practical application of these techniques can greatly benefit from an accurate estimation of the biomechanical properties of the patient's tissues. In this paper, we evaluate the accuracy and precision of a robotic device used for medical purposes in estimating the elastic parameters of different materials. The measurements are evaluated against a ground truth consisting of a set of expanded foam specimens with different elasticity that are characterised using a high-precision device. The experimental results in terms of precision are comparable with the ground truth and suggest future ambitious developments.

关键词： Biomedical equipment Accuracy Ultrasonic imaging Ultrasonic variables measurement Shape Force Estimation

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Exploring Hybrid Quantum-Classical Machine Learning for Respiratory Sound Analysis

Exploring Hybrid Quantum-Classical Machine Learning for Resp...

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Mediterranean elec.rotechnical Conference (Melec.N)

作者： Diego García-Vega Samuel González-Castillo Francisco D. González-Martínez Jaime García-Martínez Elías F. Combarro Francisco J. Cañadas-Quesada José Ranilla Dept. of Computer Science University of Oviedo Gijón Spain Dept. of Computer Science University of Oviedo Oviedo Spain Dept. of Telecomm. Eng. University of Jáen Jaen Spain

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

Respiratory sound analysis plays a crucial role in the detection and diagnosis of diseases. With advances in digital signal processing and artificial intelligence techniques, the automated classification of these sounds has become an active area of research. The objective of this work is to analyze the behavior of emerging hybrid quantum-classical machine learning models, which combine the robust capabilities of classical learning algorithms with the potential of quantum variational circuits. To accomplish this, we undertake a comparative analysis between Quantum Support Vector Machines and Quantum Neural Networks, juxtaposed with the equivalent classical AI models, employing various signal representation techniques. Based on the experimental results, integrating quantum techniques into the analysis of respiratory sounds enhances the effectiveness of classical methods, particularly in scenarios with limited examples. This is a common scenario in this context, where data availability is typically constrained.

关键词： Support vector machines Training Analytical models Qubit Signal processing algorithms Machine learning Signal representation

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Single-round Lattice-based Multisignatures 9

Single-round Lattice-based Multisignatures

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9th International Symposium on Computing and Networking Workshops, CANDARW 2021

作者： Phalakarn, Kittiphon Suppakitpaisarn, Vorapong Hasan, M. Anwar University of Waterloo Dept. of Electrical And Computer Eng. Canada University of Tokyo Dept. of Computer Science Japan

ISBN: (纸本)9781665428354

This work presents a provably-secure lattice-based multisignature scheme which requires only a single round of communication, whereas the existing works need two or three rounds. The reduction in the number of rounds for the proposed scheme is achieved by utilizing lattice trapdoors. In order to generate multisignatures securely, our scheme however requires an honest centralized server that maintains the trapdoor of a shared matrix used in the scheme. © 2021 IEEE.

关键词： Quantum cryptography

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