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Junior Conference on Lighting (Lighting)

作者： Plamen Stanchev Gergana Vacheva Nikolay Hinov Departement of Information Technology in industry Faculty of Computer Systems and Technologies Technical University of Sofia Sofia Bulgaria Departement of Power Electronics Faculty of Electronic Engineering and Technologies Technical University of Sofia Sofia Bulgaria

In the presented article, an assessment and measurements were realized to determine the energy efficiency of a medium-sized industrial enterprise. Real data from a three-shift plant was used. An assessment of the current electricity consumption was made and an energy audit was carried out to reduce electricity costs. The main goal of the current research is to ensure the basic functionality of the buildings, as well as the production-technological process, ensuring minimum electricity costs and maximum efficiency.

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Preliminary Study on the Effect of Traffic Representation on Accuracy Degradation in Machine Learning-based IoT Device Identification

Preliminary Study on the Effect of Traffic Representation on...

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Computing (ICOCO), IEEE International Conference on

作者： Nik Aqil Firdaus Afifi Faiz Zaki Nor Badrul Anuar Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Nerus Terengganu Malaysia

ISBN: (纸本)9781665489973

The Internet of Things (IoT) has gained attention for its rapid growth in the past few years. IoT devices such as temperature and humidity sensors and voice controllers are implemented widely, from household appliances to industrial machines. However, with the rapid growth and benefits IoT offers, we are exposed to various security vulnerabilities, such as data breaches and IoT-specific malware. Researchers are using IoT device identification as a solution for IoT security issues. IoT device identification helps network administrators identify network traffic into its originating devices. However, researchers often overlook an important issue in IoT device identification, which is accuracy degradation over time. Thus, this paper explores the severity of accuracy degradation in IoT device identification on different traffic representation approaches, which are flow, sub-flow, and packet. This paper utilizes a private, and the UNSW IoT Traffic Traces public dataset. Based on the experimental findings, the sub-flow-based approach recorded the best overall performance, with only 8% degradation in the private dataset and 1% degradation in the public dataset. Meanwhile, even though the packet-based approach only degraded 5% on the private dataset, it recorded up to an 11% accuracy decrease in the public dataset.

关键词： Degradation Temperature sensors Performance evaluation Training data Feature extraction Temperature control Internet of Things

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BookChain: A Secure Library Book Storing and Sharing in Academic Institutions using Blockchain technology

BookChain: A Secure Library Book Storing and Sharing in Acad...

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International Conference on computer Communication and Informatics (ICCCI)

作者： P. Chinnasamy Anupriya Elumalai Ramesh Kumar Ayyasamy S.P. Kavya S. Dhanasekaran Ajmeera Kiran Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India School of Computer Science and Engineering VIT – AP University Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Department of Information Technology Kalasalingam Academy of Research and Education Deemed to be University Srivilliputtur Tamilnadu India

By exploiting wireless operators, contemporary bookcrossing allows people to share books conveniently and hastens the spread of knowledge. Nevertheless, the widespread use of smartphone bookcrossing has been seriously hampered by the lack of tracking, resulting in missing and disappearance of textbooks, additionally, it leads to organizational inefficiency. Hence, we suggest using the emerging blockchain technology to exploit the advantages of its immutability and encrypted smart contract in order to overcome these difficulties. In this research, we propose a BookChain, a blockchain-based intra-campus book-sharing system that is verifiable and effective. BookChain securely records all of the book’s interacting entities on blockchain because every consumer could retrace the book’s renting activity, hence lowering the risk of a book being misplaced. Additionally, BookChain promotes the use of smart contracts to streamline the distribution of books with very little human interference, maximizing productivity. The experimental findings demonstrate the efficiency and affordability of the suggested system even with a large number of concurrent participants.

关键词： Wireless communication Productivity Smart contracts Force Interference Decentralized applications Libraries

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IoT-Based Contactless Body Temperature Scanning System for a Smart and Safe Campus 4

IoT-Based Contactless Body Temperature Scanning System for a...

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4th International Symposium on Agents, Multi-Agents Systems and Robotics, ISAMSR 2021

作者： Onn, Thoong Chun Alduais, Nayef.A.M. Abdullah, Jiwa Saad, Abdul-Malik H.Y. Ramli, Azizul Azhar Mahdin, Hairulnizam B. Kasim, Shahreen Mustapha, Aida Mostafa, Salama A Jawad, M.Saeed. Ghanem, Waheed Ali H. M. Abdul-Qawy, Antar S.H. Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Malaysia School of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia Johor Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu21030 Malaysia Faculty of Science SUMAIT University Science and Information Technology Dept Zanzibar Tanzania United Republic of

ISBN: (纸本)9781728166544

Nowadays, everyone is required to check the body temperature and record personal details whenever he arrives at a different place. However, there are some problems when the actions are performed. Therefore, this paper proposed an Internet of Things (IoT)-based contactless body temperature scanning system. The software and hardware used in this study were Microsoft Visual Studio, ThingSpeak, Arduino IDE, Radio Frequency Identification (RFID), and contactless body temperature sensor. A parallel development model was used as the project management methodology. The proposed system was tested through system testing, user acceptance testing, and accuracy testing. Based on the testing results, the proposed system was successfully implemented and ready to be deployed. The average accuracy of the proposed system was 99.86% compared to a standard device. © 2021 IEEE.

关键词： Physiology

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Assessing Virtual Reality Sickness in Highly Immersive Virtual Laboratory Environments : Simulator Sickness Questionnaire and Mitigation Strategies

Assessing Virtual Reality Sickness in Highly Immersive Virtu...

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Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Ahmed Jamah Ahmed Alnagrat Abdulaziz E Salem Shagluf Rajaa. F. Mahmoud Sulieman Abubaker Almintisir Abubaker Akeel Rizalafande Che Ismail Syed Zulkarnain Syed Idrus Department of Computer Science and Information Technology Higher Institute of Science and Technology Wadi al-Shati Fezzan Libya Department of Information Technologyn Faculty of Technical Engineering Mesallata Mesallata Libya Department of Computer Science Faculty of Science and Arts Gaminis University of Benghazi Benghazi Libya Department of Mathematics and Computer Faculty of Education Wadi Alshatti University Wadi Alshatti Fezzan Libya Faculty of Electronic Engineering Technology (FTKEN) Universiti Malaysia Perlis (UniMAP) Arau Malaysia Department of Communication Faculty of Business & Communication (FPK) Universiti Malaysia Perlis (UniMAP) Arau Malaysia

Virtual Reality (VR) sickness, a common challenge among VR users, has increasingly gained attention in academic research due to its adverse side effects. The Simulator Sickness Questionnaire (SSQ) has been traditionally employed to measure simulator motion sickness; however, the extent to which it measures symptoms specifically attributable to VR sickness remains underexplored. This study investigates the applicability of SSQ in assessing VR sickness in a virtual semiconductor laboratory environment, with a focus on reducing motion sickness in VR devices. A total of 97 participants performed target selection tasks using HP Reverb G2 and Oculus Quest 2 headsets in a virtual laboratory setting. The original SSQ was revised to create a VR Sickness Questionnaire, which served as the measurement index for VR-induced symptoms. The primary objective of this research was to determine the prevalence of VR sickness and explore ways to mitigate motion sickness in VR devices. The Results indicate that the majority of VR-related side effects pertain to nausea dysfunction, with a limited number of symptoms linked to cybersickness arising from various factors. These findings hold significant implications for future studies aiming to measure and design interventions for simulator sickness in VR environments. By shedding light on the effectiveness of the SSQ for evaluating VR sickness, this study contributes to the development of improved VR experiences and technologies.

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Investigating self-supervised, weakly supervised and fully supervised training approaches for multi-domain automatic speech recognition: A study on Bangladeshi Bangla

arXiv

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arXiv 2022年

作者： Samin, Ahnaf Mozib Kobir, M. Humayan Rafee, Md. Mushtaq Shahriyar Ahmed, M. Firoz Hasan, Mehedi Ghosh, Partha Kibria, Shafkat Rahman, M. Shahidur Department of Computer Science and Engineering Shahjalal University of Science and Technology Bangladesh Faculty of Information & Communication Technology University of Malta Malta Department of Computer Science and Engineering Leading University Bangladesh

Despite huge improvements in automatic speech recognition (ASR) employing neural networks, ASR systems still suffer from a lack of robustness and generalizability issues due to domain shifting. This is mainly because principal corpus design criteria are often not identified and examined adequately while compiling ASR datasets. In this study, we investigate the robustness of the state-of-the-art transfer learning approaches such as self-supervised wav2vec 2.0 and weakly supervised Whisper as well as fully supervised convolutional neural networks (CNNs) for multi-domain ASR. We also demonstrate the significance of domain selection while building a corpus by assessing these models on a novel multi-domain Bangladeshi Bangla ASR evaluation benchmark - BanSpeech, which contains approximately 6.52 hours of human-annotated speech and 8085 utterances from 13 distinct domains. ***, a mostly read speech corpus for the morphologically rich language Bangla, has been used to train the ASR systems. Experimental evaluation reveals that self-supervised cross-lingual pre-training is the best strategy compared to weak supervision and full supervision to tackle the multi-domain ASR task. Moreover, the ASR models trained on *** face difficulty recognizing speech from domains with mostly spontaneous speech. The BanSpeech will be publicly available to meet the need for a challenging evaluation benchmark for Bangla ASR. Copyright © 2022, The Authors. All rights reserved.

关键词： Speech recognition

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RPF-ELD: Regional Prior Fusion using Early and Late Distillation for Breast Cancer Recognition in Ultrasound Images

RPF-ELD: Regional Prior Fusion using Early and Late Distilla...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haosen Wang Gengyuan Zhang Yingnan Zhao Fang Lai Wenwei Cui Jiexiao Xue Qihang Wang Hao Zhang Yi Lin School of Computer Science and Information Technology Beijing Jiaotong University Beijing China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China School of Computer Science and Technology Harbin Engineering University Harbin China Department of Computer Science University of Denver Denver USA Wentworth College University of York York UK The Second Surveying and Mapping Institute of Heilongjiang Harbin China School of Information Science and Engineering Southeast University Nanjing China Faculty of Computing Harbin Institute of Technology Harbin China School of Interdisciplinary Medicine and Engineering Harbin Medical University Harbin China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Breast cancer is one of the main factors responsible for the deaths of women worldwide. Ultrasound imaging is a key method for early detection of breast cancer, which can help patients gain valuable treatment time and improve their chances of survival. The computer-aided system of breast cancer recognition has started to receive attention due to the lack of experienced sonographers. Presently, most breast cancer recognition methods typically suffer from uncertain locations and proportions of tumor regions in ultrasound images. In this paper, we propose a novel Regional Prior Fusion framework using Early and Late Distillation (RPF-ELD), inspired by the knowledge distillation of the teacher-student framework, for breast cancer recognition in ultrasound images. Firstly, to enhance the concentration of the tumor regions, a high-performing prior-fused model is trained as the teacher model using ultrasound images with the corresponding regional prior information. Next, a diagnostic model is trained as the student model under the prior-fused model distillation using early and late features to implicitly obtain the regional prior knowledge. Finally, the diagnostic model recognizes the categories of breast cancer from only ultrasound images using the experience from distilled prior knowledge. Two publicly released datasets are used to evaluate the proposed RPF-ELD framework. Experimental results demonstrate that the proposed RPF-ELD surpasses current state-of-the-art methods.

关键词： Ultrasonic imaging Image recognition Fuses Biological system modeling Computational modeling Breast cancer computer aided diagnosis Bioinformatics Tumors Biomedical imaging

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Intelligent Tunicate Swarm-Optimization-Algorithm-Based Lightweight Security Mechanism in Internet of Health Things

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computers, Materials & Continua 2021年第1期66卷 551-562页

作者： Gia Nhu Nguyen Nin Ho Le Viet Gyanendra Prasad Joshi Bhanu Shrestha Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Graduate School Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science and Engineering Sejong UniversitySeoul05006South Korea Department of Electronic Engineering Kwangwoon UniversitySeoul01897South Korea

Fog computing in the Internet of Health Things(IoHT)is promising owing to the increasing need for energy-and latency-optimized health sector ***,clinical data(particularly,medical image data)are a delicate,highly protected resource that should be utilized in an effective and responsible manner to fulfil consumer ***,we propose an energy-efficient fog-based IoHT with a tunicate swarm-optimization-(TSO)-based lightweight Simon cipher to enhance the energy efficiency at the fog layer and the security of data stored at the cloud *** proposed Simon cipher uses the TSO algorithm to select the optimal keys that will minimize the deterioration of quality between the original and reconstructed(decrypted)*** this study,the decrypted image quality is preserved by the peak signal-to-noise ratio(PSNR)such that consumers can generate precise medical reports from IoHT devices at the application ***,a lightweight encryption step is implemented in the fog to improve energy efficiency and reduce additional computations at the cloud *** results indicate that the TSO-Simon model achieved a high PSNR of 61.37 dB and a pixel change rate of 95.31.

关键词： Internet of Health Things healthcare Simon cipher tunicate swarm optimization

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ReviveDiff: A Universal Diffusion Model for Restoring Images in Adverse Weather Conditions

arXiv

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arXiv 2024年

作者： Huang, Wenfeng Xu, Guoan Jia, Wenjing Perry, Stuart Gao, Guangwei The Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai200240 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China

Images captured in challenging environments–such as nighttime, foggy, rainy weather, and underwater–often suffer from significant degradation, resulting in a substantial loss of visual quality. Effective restoration of these degraded images is critical for the subsequent vision tasks. While many existing approaches have successfully incorporated specific priors for individual tasks, these tailored solutions limit their applicability to other degradations. In this work, we propose a universal network architecture, dubbed "ReviveDiff", which can address a wide range of degradations and bring images back to life by enhancing and restoring their quality. Our approach is inspired by the observation that, unlike degradation caused by movement or electronic issues, quality degradation under adverse conditions primarily stems from natural media (such as fog, water, and low luminance), which generally preserves the original structures of objects. To restore the quality of such images, we leveraged the latest advancements in diffusion models and developed ReviveDiff to restore image quality from both macro and micro levels across some key factors determining image quality, such as sharpness, distortion, noise level, dynamic range, and color accuracy. We rigorously evaluated ReviveDiff on seven benchmark datasets covering five types of degrading conditions: Rainy, Underwater, Low-light, Smoke, and Nighttime Hazy. Our experimental results demonstrate that ReviveDiff outperforms the state-of-the-art methods both quantitatively and visually. Copyright © 2024, The Authors. All rights reserved.

关键词： Restoration

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Comparative Analysis of Word vs. Character Embedding for Machine Learning Based Detection of Malicious URLs and DGA-Generated Domains

Comparative Analysis of Word vs. Character Embedding for Mac...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Oludayo C. Ayodele Suleiman Y. Yerima School of Computer Science and Informatics De Montfort University Leicester United Kingdom Faculty of Engineering and Information Technology British University in Dubai Dubai United Arab Emirates

This study presents a comprehensive comparative analysis of the effectiveness of word-level and character-level embeddings in the context of machine learning-based detection of malicious URLs and DGA-generated domains. Utilizing distinct datasets comprising DGA-generated domains and Spam URLs, we systematically evaluate various machine learning models coupled with word-level and character-level tokenization techniques. Our findings indicate that character-level tokenization yields superior results in identifying DGA-generated domains, particularly due to the random character composition of these URLs. Conversely, both word-level and character-level embeddings exhibit comparable success rates in classifying Spam URLs, owing to the non-random nature of their URL structures. The study sheds light on the importance of tailoring tokenization strategies based on the unique characteristics of the data. We recommend character-level embeddings for detecting DGA-generated domains characterized by random characters. In contrast, the choice between word-level and character-level embeddings is less critical when dealing with Spam URLs, as both approaches yield effective results.

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