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arXiv 2024年

作者： Gupta, Deepti Elluri, Lavanya Jain, Avi Moni, Shafika Showkat Aslan, Omer Dept. of Computer Information Systems Texas A&M University - Central Texas TX United States College of Business University of Tampa Florida United States Dept. of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Florida United States Department of Software Engineering Bandırma Onyedi Eylül University Bandırma Turkey

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamper-proof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls—such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture—this approach strengthens an organization’s defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth’s transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, enab

关键词： Blockchain

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Preprocessing of Pure Tone Audiometry Data and Design of Machine Learning Models for Hearing Loss Classification

Preprocessing of Pure Tone Audiometry Data and Design of Mac...

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International Conference on Imaging, Signal Processing and Communication (ICISPC)

作者： Jaesung Shin Jun Ma Seong Jun Choi Min Hong dept. Department of Software Convergence Soonchunhyang University Asan Republic of Korea Department of Otorhinolaryngology-Head and Neck Surgery College of Medicine Soonchunhyang University (Cheonan Hospital) Asan Republic of Korea Department of Computer Software Engineering Soonchunhyang University Asan Republic of Korea

ISBN: (数字)9798350367157

ISBN: (纸本)9798350367164

In this research, we conducted a preprocessing step on pure-tone audiometry image data to determine the presence or absence of hearing loss in patients, and designed machine learning models for hearing loss classification using the preprocessed data. The dataset utilized consisted of a total of $\mathbf{1 8, 5 3 0}$ data $(\mathbf{9, 2 6 5}$ normal, 9,265 hearing loss), and detailed parameter configuration was performed using randomly extracted training and validation data. This research involved converting image data into a CSV file format during the preprocessing stage, and the preprocessed data was then utilized to design and construct the Logistic Regression and Decision Tree Classifier machine learning models for patient classification. These models achieved an accuracy of $85.61 \%$ and $\mathbf{8 5. 2 5 \%}$, respectively, in automatically determining the presence or absence of hearing loss.

关键词： Training Analytical models Accuracy Signal processing algorithms Machine learning Auditory system Signal processing

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Cybersecurity and Privacy Attacks Detection in IoT Networks with Improved Data engineering and Machine Learning Methods

Cybersecurity and Privacy Attacks Detection in IoT Networks ...

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IEEE International Conference on Big Data Computing Service and Applications (BigDataService)

作者： Donald Elmazi Dimitrios A. Karras Abdulsalam Alkholidi Klea Çapari Dept. of Electronics and Telecommunications Faculty of Engineering Canadian Institute of Technology (CIT) Tirana Albania Dept. S/W Engineering Engineering Faculty Canadian Institute of Technology (CIT) Tirana Albania Dept. General School of Science National and Kapodistrian University of Athens (NKUA) Athens Greece Canadian Institute of Technology (CIT) Tirana Albania Faculty of Engineering - Sana’a University Yemen Dept. of Computer Engineering Faculty of Engineering University of Rijeka Croatia Dept. of Software Engineering Faculty of Engineering CIT Tirana Albania

Presently, the global proliferation of Internet of Things (IoT) devices and applications has accelerated owing to their advantages in enhancing the business and industrial environment, as well as the daily lives of individuals. Nevertheless, IoT devices are vulnerable to malevolent network traffic, which can lead to undesirable outcomes and disrupt the operation of IoT devices. Consequently, it is imperative to devise a screening method for network traffic to identify and categorize malicious activity and reduce its harmful effects. In this regard, the present study proposes a framework and thoroughly compares single and ensemble machine learning models that are using predictive analytics to detect and classify network activity in an IoT network. Specifically, our framework and models distinguish between normal and anomalous network activity. Furthermore, network traffic is classified into eight categories, normal, denial of service (DoS) attack, Scan attack, Data Type Probing, Malicious Operation, Malicious Control, Spying attack and Wrong Setup based attack. The special focus of this paper is in identifying privacy related attacks in comparison with main cybersecurity related ones. Several supervised machine learning models have been herein implemented to characterize their performance in detecting and classifying network activities for IoT Networks based on the ensemble learning framework as well as on the single learning machines framework. Moreover, classifiers based on the statistical learning theory framework are herein involved too, including hybrid Bayesian theory models, decision trees (DT) and the ordinal Learning Modelling approach. The statistical pattern recognition based models as well as all supervised learning machines models have been evaluated on a broad benchmark dataset for IoT attacks. Besides, an improved data engineering feature processing model has been applied to the dataset under consideration to improve the accuracy of all models under co

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AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogeneous Knowledge Representations

AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogene...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Alain Bertrand Bomgni Ribot Fleury Téné Ceskoutsé Kevin Jordan Njike Njingang Thomas Bouetou Bouetou Venkataramana Gadhamshetty Etienne Gnimpieba Z. Math Science and Technology Oglala Lakota College Kyle SD USA Ecole Nationale Supérieure Polytechnique University of Yaounde 1 Yaounde Cameroon Dept. of Computer Science University of Dschang Dschang Cameroon Dept. of Civil and Environmental Engineering South Dakota School of Mines and Technology Rapid City SD USA Dept. of Biomedical Engineering University of South Dakota Vermillion SD USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The synthesis of small molecules is a crucial task across multiple scientific domains, including drug discovery, materials science, and sustainable chemistry. As AI and Machine Learning (ML) continue to advance, these technologies offer transformative potential in molecular synthesis, enhancing efficiency and expanding synthetic possibilities. In particular, small molecule synthesis has profound implications for creating novel therapeutic agents, high-performance materials, and greener chemical processes, but a major challenge remains in designing efficient synthetic routes for target molecules. Retrosynthesis, the process of mapping out synthetic pathways by working backwards from a target molecule, represents a vital step; however, traditional retrosynthesis methods often struggle to predict complex or novel transformations. To address this limitation, we introduce AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogeneous Knowledge Representations, a model that leverages graph neural network techniques to enhance retrosynthetic predictions. AIM-HKR integrates information from heterogeneous knowledge graphs, capturing the intricate relationships and analogical reasoning required for retrosynthesis. This model generates type-specific embeddings that reflect both network topology and semantic connections across different entity types, such as molecules, reactions, and functional groups. AIM-HKR’s unique capacity to leverage heterogeneous graphs allows it to propose synthetic pathways that extend beyond established precedents, enabling predictions of chemically feasible but previously uncharted transformations. We believe AIM-HKR has the potential to significantly advance molecular synthesis through AI-driven retrosynthesis, establishing a new paradigm in AI-assisted chemistry.

关键词： Technological innovation Chemistry Accuracy Scalability Semantics Machine learning Predictive models Reliability engineering Drug discovery Chemicals

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Optimization-Based Comparative System Evaluation of Single and Dual Traction Inverters with Focus on Partial Load Efficiency and Chip Area

Optimization-Based Comparative System Evaluation of Single a...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Christoph Sachs Fabian Stamer Jan Allgeier Duleepa Thrimawithana Martin Neuburger Faculty of Mobility and Technology Esslingen University Germany Institute for System Dynamics University of Stuttgart Germany Robert Bosch GmbH Germany Dept. of Electrical Computer and Software Engineering The University of Auckland New Zealand

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

The transition to electric transportation demands efficient and cost-effective powertrains. Optimizing energy use is crucial for extending range and reducing expenses. However, comparing inverter and motor efficiency based on inverter topologies is challenging due to biased methodologies that favor certain designs over others. This document introduces a novel optimization-based approach for enhancing partial load efficiency and minimizing chip area of single and dual traction inverters, indicating potential energy savings and cost reduction. Recent publications of both industry [1], [2] and academia [3] underscore the importance of these design goals achieved by either novel inverter topologies or enhanced control methods. Two promising topologies with the inherent capability of partial load optimization are evaluated regarding chip area and system efficiency to find the most suitable concept for future electric vehicle power trains.

关键词： MOSFET Costs Silicon carbide Modulation Mechanical power transmission Motors Electric vehicles Inverters Topology Optimization

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Hyper-IIoT: A Smart Contract-inspired Access Control Scheme for Resource-constrained Industrial Internet of Things

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年

作者： Li, Dun Li, Hongzhi Crespi, Noel Minerva, Roberto Li, Ming Liang, Wei Li, Kuan-Ching Tsinghua University Department of Industrial Engineering China Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau91120 France Chizhou University College of Big Data and Artificial Intelligence Chizhou247100 China The Hong Kong Polytechnic University Research Institute for Advanced Manufacturing Hung Hom Hong Kong Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Sanya Research Institute Hunan Key Laboratory for Service Computing and Novel Software Technology China Providence University Dept. of Computer Science and Information Engineering Taiwan

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data in Industrial Internet of Things (IIoT) devices have highlighted the need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. Besides, we defined a storage model and data format for private data by designing and deploying smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in real-world simulations, Hyper-IIoT demonstrates excellent contract execution time control, stable system throughput, and rapid consensus processes that are capable of handling high throughput and efficient consensus in distributed systems, even in large-scale request scenarios. © 2016 IEEE.

关键词： Smart contract

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Improving the SNBWAN Efficiency based on the Improvement of Signal-Code Constructions 13

Improving the SNBWAN Efficiency based on the Improvement of ...

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13th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops, ICUMT 2021

作者： Bobrovsky, Vadim Sartakov, Anatoly Kirichek, Ruslan Pham, Van Dai Semakov, Leonid Zhuravel, Evgeny University of Telecommunications Bonch-Bruevich Saint Petersburg State Dept. of Software Engineering and Computer Science Saint-Petersburg Russia KB 'Mars' Novosibirsk Russia

ISBN: (纸本)9781665402194

The paper discusses the issues of increasing the efficiency of an IoT network - SNBWAN based on improving signal-code constructions. Two ways to improve efficiency are considered: based on the use of complementary code keying and advanced signal construction (SC) with quadrature amplitude modulation (QAM). The results of analytical calculations and computer simulation are presented. and copy;2021 IEEE. © 2021 IEEE computer Society. All rights reserved.

关键词： Internet of things

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"MealShare: Using Blockchain Technology and a Reward-Based System to Combat Global Food Waste"

TechRxiv

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TechRxiv 2023年

作者： Kumara, H.A.P. manjula Jayakodi, J.K.R.P. Gamage, I.D. Wickramasinghe, R.G.M.N. De Zoysa, R.R.P. De Sliva, D.I. Dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology New Kandy RD Malabe Sri Lanka

"MealShare" is an upcoming internet application that aims to reduce food waste and promote sustainable dietary habits while supporting a more sustainable global food system. It uses blockchain technology and a rewards-based system to incentivize companies to donate their excess food. This innovative approach ensures transparency, traceability, security, and decentralization throughout the entire food donation process. The use of blockchain technology enables the monitoring and recording of each contribution of surplus food, providing an immutable record of the entire supply chain, increasing accountability, and reducing the possibility of food waste due to negligence or mismanagement. Furthermore, "MealShare" uses blockchain smart contracts to automate verification and identification, speeding up the processing of food donations while saving money. The automatic and open incentive structure offered by smart contracts encourages companies to take part in the food donation program and make frequent contributions, compensating them based on specified criteria such as the quantity or quality of leftover food supplied. This unique approach makes use of blockchain technology and a rewards-based system to promote sustainable food practices, reduce food waste, and motivate companies to donate extra food. It is a potential tool in the worldwide effort to reduce food waste due to its openness, traceability, security, and automated incentive structure. © 2023, CC BY.

关键词： Blockchain

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Distress Study Through Machine-Learning Techniques

Distress Study Through Machine-Learning Techniques

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International Conference on Biomedical Innovations and Applications (BIA)

作者： Ginka Marinova Neli Kalcheva Dept. of Computer Science and Engineering Technical University of Varna Varna Bulgaria Dept. of Software and Internet Technologies Technical University of Varna Varna Bulgaria

ISBN: (纸本)9781665445825

Machine learning as a contemporary approach in research provides an opportunity to cover and process a large amount of data. This is a condition for the synthesis of reliable characteristic descriptors of distress. The process of model selection is a factor in achieving the necessary adequacy and improving the quality of results. Age is a risky reason to take into account the development of cancer. This paper analyzed classification models for predicting distress in patients with the oncological disease, segmented into three age groups. Experimental results of a study conducted on the age of the patient and assessment of distress, using three methods of machine-learning are presented.

关键词： Support vector machines Technological innovation Analytical models Biological system modeling Machine learning Predictive models Reliability

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Methodology for the Study of Defects in Technical Structures at Construction Sites

Methodology for the Study of Defects in Technical Structures...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Liliya R. Snitsar Kazizat T. Iskakov Makpal G. Zhartybaeva Bakhytzhan K. Suleimenov Samat A. Boranbaev dept. of Computer and Software Engineering L.N. Gumilyov Eurasian National University Nur-Sultan Kazakhstan

ISBN: (数字)9781665498043

ISBN: (纸本)9781665498050

The article deals with the problem of analyzing existing approaches to the survey of construction sites. It is given an explanation of the relevance of the study of objects by non-destructive diagnostic methods. In practice, the use of one of the methods of non-destructive testing of construction objects - the ultrasonic method is shown. The mathematical approach to solving the problems is also described in the article, which presents the basic formulas used in solving the problem. Authors also characterized the available non-destructive testing devices and their technical characteristics. The results and algorithms of various methods for solving the problem are presented. An experimental study is described using one of the considered devices. The process of preparation for measurements and installation of sensors is described in detail. There were given the results of experimental studies in laboratory conditions and on the construction site. According to the results of the experiments, conclusions were made for each of them.

关键词： Space technology Propagation Sensor phenomena and characterization Acoustics Electron devices Testing

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