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arXiv 2022年

作者： Aqeel, Imra Majid, Abdul Biomedical Informatics Research Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan

SARS-COV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdown, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In this paper, we developed an in-silico study-based hybrid framework to repurpose existing therapeutic agents in finding drug-like bioactive molecules that would cure Covid-19. We employed the Lipinski rules on the retrieved molecules from the ChEMBL database and found 133 drug-likeness bioactive molecules against SARS coronavirus 3CL Protease. Based on standard IC50, the dataset was divided into three classes active, inactive, and intermediate. Our comparative analysis demonstrated that the proposed Extra Tree Regressor (ETR) based QSAR model has improved prediction results related to the bioactivity of chemical compounds as compared to Gradient Boosting, XGBoost, Support Vector, Decision Tree, and Random Forest based regressor models. ADMET analysis is carried out to identify thirteen bioactive molecules with ChEMBL IDs 187460, 190743, 222234, 222628, 222735, 222769, 222840, 222893, 225515, 358279, 363535, 365134 and 426898. These molecules are highly suitable drug candidates for SARS-COV-2 3CL Protease. In the next step, the efficacy of bioactive molecules is computed in terms of binding affinity using molecular docking and then shortlisted six bioactive molecules with ChEMBL IDs 187460, 222769, 225515, 358279, 363535, and 365134. These molecules can be suitable drug candidates for SARS-COV-2. It is anticipated that the pharmacologist/drug manufacturer would further investigate these six molecules to find suitable drug candidates for SARS-COV-2. They can adopt these promising compounds for their downstream drug development stages. © 2022, CC BY.

关键词： SARS

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A Critical Analysis of Deployed Use Cases for Quantum Key Distribution and Comparison with Post-Quantum Cryptography

arXiv

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arXiv 2025年

作者： Aquina, Nick Cimoli, Bruno Das, Soumya Hövelmanns, Kathrin Weber, Fiona Johanna Okonkwo, Chigo Rommel, Simon Škorić, Boris Monroy, Idelfonso Tafur Verschoor, Sebastian Department of Electrical Engineering Eindhoven University of Technology Flux Building Eindhoven5612 AZ Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology MetaForum Building Eindhoven5612 AZ Netherlands Eindhoven Quantum Hub Eindhoven University of Technology Qubit Building Eindhoven5612 AZ Netherlands Informatics Institute University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands

Quantum Key Distribution (QKD) is currently being discussed as a technology to safeguard communication in a future where quantum computers compromise traditional public-key cryptosystems. In this paper, we conduct a comprehensive security evaluation of QKD-based solutions, focusing on real-world use cases sourced from academic literature and industry reports. We analyze these use cases, assess their security and identify the possible advantages of deploying QKD-based solutions. We further compare QKD-based solutions with Post-Quantum Cryptography (PQC), the alternative approach to achieving security when quantum computers compromise traditional public-key cryptosystems, evaluating their respective suitability for each scenario. Based on this comparative analysis, we critically discuss and comment on which use cases QKD is suited for, considering factors such as implementation complexity, scalability, and long-term security. Our findings contribute to a better understanding of the role QKD could play in future cryptographic infrastructures and offer guidance to decision-makers considering the deployment of QKD. © 2025, CC BY-SA.

关键词： Qubits

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Correction to: Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

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Multimedia tools and applications 2022年第20期81卷 28799-28800页

作者： Amir Masoud Rahmani Elham Azhir Morteza Naserbakht Mokhtar Mohammadi Adil Hussein Mohammed Aldalwie Mohammed Kamal Majeed Sarkhel H Taher Karim Mehdi Hosseinzadeh Future Technology Research Center National Yunlin University of Science and Technology Douliu Yunlin Taiwan. Research and Development Center Mobile Telecommunication Company of Iran Tehran Iran. Mental Health Research Center Psychosocial Health Research Institute Iran University of Medical Sciences Tehran Iran. Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq. Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq. Information Technology Department Faculty of Applied Science Tishk International University Erbil Iraq. Computer Department College of Science University of Halabja Halabja Iraq. Computer Networks Department Sulaimani Polytechnic University Technical College of Informatics Sulaymaniyah Iraq. Computer Science University of Human Development Sulaymaniyah Iraq.

[This corrects the article DOI: 10.1007/s11042-022-12952-7.].

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Location Functions for Self-stabilizing Byzantine Tolerant Swarms 1

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23rd International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2021

作者： Ashkenazi, Yotam Dolev, Shlomi Kamei, Sayaka Katayama, Yoshiaki Ooshita, Fukuhito Wada, Koichi Department of Computer Science Ben-Gurion University of the Negev Beersheba Israel Graduate School of Advanced Science and Engineering Hiroshima University Higashihiroshima Japan Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan Department of Applied Informatics Faculty of Science and Engineering Hosei University Koganei Japan

ISBN: (数字)9783030910815

ISBN: (纸本)9783030910808

This paper proposes a novel framework to realize self-stabilizing Byzantine tolerant swarms. In this framework, non-Byzantine robots execute tasks while satisfying location functions, that is, the robots use a policy for their location choice, which restricts their location to satisfy the functions. We give a general Byzantine-resilient self-stabilizing algorithm based on the location function, and then provide an efficient implementation of the self-stabilizing algorithms for special classes of tasks, called polynomial-based tasks and shape-based tasks. We also demonstrate the usefulness of the proposed framework by implementing typical tasks of robots. © 2021, Springer Nature Switzerland AG.

关键词： Location

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A Detailed Investigation of Implementation of Internet of Things (IOT) in Cyber Security in Healthcare Sector 2

A Detailed Investigation of Implementation of Internet of Th...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Ganai, Prince Tehseen Bag, Akash Sable, Anita Abdullah, Khairul Hafezad Bhatia, Sandeep Pant, Bhasker Shri Jagdishprasad Jhabarmal Tibrewala University Department of Information Technology Rajasthan India Amity School of Law Amity University Chhattisgarh Raipur India University Maharashtra Pune India Universiti Teknologi MARA Perlis Branch Science Laboratory Unit Department of Academic Affairs Arau Campus Malaysia Electronics and Communication Engineering Raj Kumar Goel Institute of Technology U.P. Ghaziabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

The use of 'electronic healthcare technology' is widespread, and it has huge potential to improve 'clinical outcomes and transform' how therapy is administered. On the other hand, medical device and information security is becoming a significant problem. Security breaches were, obviously, an issue even before the advent of digital clinical information. The interdependency of today's documentation, on the other hand, offers various prospective entry points: the ability to obtain from anywhere (whereas earlier, hard copies would have been kept in healthcare institutions and only available through personal breaches);the capacity for security breaches to go unobserved;and access towards a more comprehensive medical record, offering a more precious asset for possible threats (whereas traditionally, medical files may have been divided between many various providers). Previously, a data leak might have affected hundreds or even thousands of people due to missing paper documents or a stolen computer;but, now that this content is digitised and accessible across several networks, a cyber-attack has the capability to harm millions of patients. To demonstrate the concept, celebrity healthcare information has long been a target for cybercriminals. Prior to the introduction of electronic medical records, meanwhile, these vulnerabilities were restricted to hospital workers who had direct access to the documentation. Celebrity health information may now be viewed remotely, thus raising the risk of a data leak. For mitigating this issue, IoT or Internet of Things is widely used in healthcare industry to increase service security. This research has considered secondary qualitative method to gather wide range of information related to the study. © 2022 IEEE.

关键词： Cybersecurity

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Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis

arXiv

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arXiv 2023年

作者： Bera, Asish Nasipuri, Mita Krejcar, Ondrej Bhattacharjee, Debotosh The Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Campus Rajasthan Pilani333031 India The Department of Computer Science and Engineering Jadavpur University Kolkata32 India The Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic

Human body-pose estimation is a complex problem in computer vision. Recent research interests have been widened specifically on the Sports, Yoga, and Dance (SYD) postures for maintaining health conditions. The SYD pose categories are regarded as a fine-grained image classification task due to the complex movement of body parts. Deep Convolutional Neural Networks (CNNs) have attained significantly improved performance in solving various human body-pose estimation problems. Though decent progress has been achieved in yoga postures recognition using deep learning techniques, fine-grained sports, and dance recognition necessitates ample research attention. However, no benchmark public image dataset with sufficient inter-class and intra-class variations is available yet to address sports and dance postures classification. To solve this limitation, we have proposed two image datasets, one for 102 sport categories and another for 12 dance styles. Two public datasets, Yoga-82 which contains 82 classes and Yoga-107 represents 107 classes are collected for yoga postures. These four SYD datasets are experimented with the proposed deep model, SYD-Net, which integrates a patch-based attention (PbA) mechanism on top of standard backbone CNNs. The PbA module leverages the self-attention mechanism that learns contextual information from a set of uniform and multi-scale patches and emphasizes discriminative features to understand the semantic correlation among patches. Moreover, random erasing data augmentation is applied to improve performance. The proposed SYD-Net has achieved state-of-the-art accuracy on Yoga-82 using five base CNNs. SYD-Net’s accuracy on other datasets is remarkable, implying its efficiency. Our Sports-102 and Dance-12 datasets are publicly available at https://***/view/syd-net/home. © 2023, CC BY.

关键词： Sports

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Enhancing Acceptance of Automated Vehicles Through Gamification: Insights from a Driving Simulator Study

SSRN

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SSRN 2025年

作者： Li, Chenchang Yin, Rao Yang, Bo Guan, Muhua Xie, Kun Nakano, Kimihiko Institute of Industrial Science University of Tokyo Tokyo153-8505 Japan School of Design & Innovation China Academy of Art Hangzhou311100 China School of Computer Science & System Engineering Kyushu Institute of Technology Fukuoka820-8502 Japan Department of Civil and Environmental Engineering Transportation Informatics Lab Old Dominion University NorfolkVA23529 United States

Automated vehicles hold great potential to revolutionize transportation, but subjective user acceptance remains a major obstacle to their widespread adoption and the realization of anticipated safety benefits. Recognizing this challenge, this study explores the role of gamification in enhancing user acceptance of automated vehicles and promoting safer driver engagement. By designing a gamified interaction framework and implementing it in a driving simulator experiment, the research addresses three core questions: Does gamification work? Why does it work? How can it be effectively implemented? In the experiment, participants experienced automated driving scenarios with integrated gamified interactions during overtaking tasks. Data on users' attitudes, behaviors, and eye movements were collected and analyzed through qualitative and quantitative methods. Furthermore, the study incorporated design research techniques, including interviews and user journey mapping, into the scientific framework to derive meaningful and actionable insights. The results reveal that gamification significantly improves user acceptance by enhancing Perceived Ease of Use and strengthening Behavioral Intentions. At a deeper mechanistic level, gamification enhances users' situational awareness and trust by increasing their perception efficiency during interactions and raising their risk tolerance, thereby shifting the balance between safety and reward. Based on these mechanisms and further findings, the study proposes three design strategies to maximize the benefits of gamification: designing system transparency, tailoring experiences to individual user characteristics, and leveraging incentive mechanisms to engage and motivate users effectively. This study's findings provide theoretical foundations and practical design directions for leveraging gamification to enhance human-vehicle interactions, utimately contributing to the development of safer and more efficient automated driving systems. © 2

关键词： Eye movements

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Tabular Q-learning Based Reinforcement Learning Agent for Autonomous Vehicle Drift Initiation and Stabilization

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IFAC-PapersOnLine 2023年第2期56卷 4896-4903页

作者： Szilárd H. Tóth Ádám Bárdos Zsolt J. Viharos Department of Automotive Technologies Budapest University of Technology and Economics Budapest Hungary Research Laboratory on Engineering and Management Intelligence Intelligent Processes Research Group Institute of Computer Science and Control Centre of Excellence in Production Informatics and Control Eötvös Loránd Research Network (ELKH) Centre of Excellence of the Hungarian Academy of Sciences (MTA) Budapest Hungary Department of Management and Business Law Faculty of Economics John von Neumann University Kecskemét Hungary

This paper aims to report on novel research results about developing a reinforcement learning agent for steady-state vehicle drift motion control. Based on the previous results of this research, the primary goal was to eliminate the problems causing learning instability experienced with the Soft Actor-Critic (SAC) algorithm applying Tabular Q-learning in this work. Trained in a MATLAB/Simulink-based simulation environment, the resulting agent succeeded in this task while being able to smoothly operate the vehicle to achieve and retain the desired target drift state, regardless of the discreet nature of the algorithm used for solving an inherently continuous task.

关键词： Motion Control Autonomous Vehicles Vehicle Dynamics Control Reinforcement Learning Vehicle Drifting Handling Limits

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High-Resolution Optical Imaging Incorporated Non-Destructive Morphological Boundary Detection of Fruit Disorders: A Pilot Study

High-Resolution Optical Imaging Incorporated Non-Destructive...

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Advancements in Computing (ICAC), International Conference on

作者： Nethmi Tharushi Hakmana Nipun Shantha Kahatapitiya Sasmitha Weerasinghe Deshan Kalupahana Bhagya Nathali Silva Naresh Kumar Ravichandran Daewoon Seong Sangyeob Han Seung Yeol Lee Hee-Young June Mansik Jeon Jeehyun Kim Udaya Wijenayake Ruchire Eranga Wijesinghe Department of Mechanical Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Engineering Faculty of Engineering University of Sri Jayewardenepura Nugegoda Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Excellence in Informatics Electronics & Transmission (CIET) Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Scientific Instrumentation Korea Basic Science Institute Daejeon Republic of Korea School of Electronic and Electrical Engineering College of IT Engineering Kyungpook National University Daegu Republic of Korea ICT Convergence Research Center Kyungpook National University Daegu Republic of Korea Department of Plant Medicine Kyungpook National University Daegu Republic of Korea Institute of Plant Medicine Kyungpook National University Daegu Republic of Korea Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Plant diseases caused by various pathogens, such as fungi, bacteria, and viruses, lead to significant reductions in crop yield and quality. Early detection of these diseases is critical for preventing their spread and optimizing crop yield. Traditional methods of disease detection, while effective, often involve invasive or time-consuming procedures that may not provide real-time insights. This study investigates the use of Swept-Source Optical Coherence Tomography (SS-OCT) for non-destructive, high-resolution imaging to identify morphological abnormalities in fruit specimens affected by diseases. The research focuses on differentiating healthy regions, healthy boundaries, infected boundaries, and fully infected regions in apples. Using a 1310 nm center wavelength laser, the SS-OCT system captured detailed cross-sectional images of fruit tissue, revealing microstructural changes associated with disease progression. A qualitative assessment of the two-dimensional (2D) OCT images highlighted distinct layers in healthy tissues, noticeable structural deformities, and the merging of microstructural layers in the boundary and infected regions. Quantitative depth-profile analysis of axial-direction A-scan profiles further demonstrated the reduction and merging of intensity peaks correlating with tissue abnormalities in infected fruit specimens. These findings underscore the potential of SS-OCT as an effective tool for early disease detection in agricultural products, providing a means for timely intervention and optimized postharvest management.

关键词： Plant diseases Pathogens Statistical analysis Two-dimensional displays Merging High-resolution imaging Optical imaging Real-time systems Crop yield Diseases

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A Novel Deep Learning based IoT Enabled computerized Vehicle Number Plate Recognition System using OCR Principles

A Novel Deep Learning based IoT Enabled Computerized Vehicle...

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Advances in Computing, Communication and Applied informatics (ACCAI), International Conference on

作者： J. Bhuvaneswari N. Gireesh G. Srimathy Subrahmanyam Nandigam V. Nanammal Department of AI & DS R.M.K. College of Engineering and Technology Puduvoyal Chennai Department of ECE Mohan Babu University (Erstwhile Sree Vidya Nikethan Engineering College Tirupati Electrical and Electronics Engineering S.A Engineering College Chennai Department of Computer Science and Engineering Dr.M.G.R Educational and Research Institute Chennai Department of ECE Jeppiaar Engineering College Chennai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

A kind of intelligent transportation and detecting method is the license plate recognition system. The system can automatically recognize and read the license id of a vehicle’s number plate from digitally taken photographs thanks to this mix of technologies. The term “automatically capturing the license plate” refers to a technique that detects license plates and converts their pixel data into plain text or ASCII text. Modified Learning based Character Recognition (MLCR) is an analytical operation-based approach to vehicle number plate recognition that is part of this system. For the purpose of evaluating the suggested scheme’s efficiency, it is cross-validated with the traditional learning model known as Convolution Neural Network (CNN). The primary goal is to properly recognize and translate a specific vehicle’s license plate by using and combining several morphological techniques. Image enhancement, grayscale transformation, filtering, edge detection, and number plate extraction from vehicle images are some of the operations upon which this is built. Following the completion of the aforementioned processes, the segmentation process is being utilized to detect the text on the number plate. This is achieved by utilizing template matching and optical character recognition. From a photograph of the car, our method can swiftly and reliably deduce the license plate.

关键词： Vibrations Statistical analysis Neural networks Transportation Cameras Character recognition Reliability

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