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A Secure Blockchain-based Authentication and Key Agreement Protocol for 5G Roaming

A Secure Blockchain-based Authentication and Key Agreement P...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Awaneesh Kumar Yadav Manoj Misra An Braeken Madhusanka Liyanage Dept. of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand India Department of Engineering Technology (INDI) Vrije Universiteit Brussel Belgium School of Computer Science University College Dublin Ireland

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The fifth generation (5G) is now widely used to access network services due to the emergence of the Internet of Things (IoT) and mobile devices. To secure 5G communication, the Third Generation Partnership Project (3GPP) organization created the 5G-Authentication and Key Agreement (AKA) protocol. Security evaluations have found a number of problems in the 5G-AKA, including a violation of perfect forward secrecy, a traceability attack, and denial of service (DoS) attacks. To address the shortcomings of 5G-AKA, several enhanced versions have been developed. However, it has been shown that either these versions are expensive or do not address security issues. Additionally, less effort is put into providing security when a user utilizes roaming mobile services while a malicious Serving Network (SN) is present. This paper introduces an authentication mechanism to handle the above issues. In addition to this, a handover mechanism is also designed for re-connection. The authentication and handover phase security assessment uses the mathematical model Real-Or-Random (ROR), AVISPA, and Scyther tool. Furthermore, the performance comparison depicts that the authentication and handover phase is more efficient than existing protocols. An assessment of the smart contract function’s cost and effectiveness is also provided.

关键词： Energy consumption Costs 5G mobile communication Smart contracts Authentication Handover Mathematical models

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A Comprehensive Study on Ethereum Blockchain-based Digital Marketplace using NFT Smart Contract Infrastructure

A Comprehensive Study on Ethereum Blockchain-based Digital M...

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International Conference on computer and Information technology (ICCIT)

作者： Md. Momenul Haque Subrata Kumer Paul Rakhi Rani Paul Md. Ekramul Hamid Sultan Fahim Shahinur Islam Dept. of Computer Science and Engineering Bangladesh Army University of Engineering & Technology (BAUET) Natore Bangladesh Dept. of Computer Science and Engineering University of Rajshahi Rajshahi Bangladesh

Every day thousands or millions of digital content or assets have been created and transected through the digital marketplace. Most of the cases after creating the content in the digital market a seller completely lose their content ownership and creative content. Not only that the entire system is centralized which means the whole transaction, information sharing, and database update are maintained by the third-party organization. And no contact verification and authentication happen between two parties. Also, generate the possibilities of data manipulation risk for a large amount of data. To solve this problem, we have introduced Ethereum blockchain-based digital marketplace where we use NFT-based smart contracts to verify and authenticate every transaction between buyer, seller, and marketplace owner. Our proposed system operated as a decentralized and distributed ledger. To develop the NFT-based smart contract we use the ERC721 token standard which provides us the non-fungible properties. We use two smart contracts to develop the whole infrastructure. One is for creating a market item and another one is for the marketplace. Also, develop the payment mechanism for our proposed structure and collect the listing fees. So, the proposed Ethereum blockchain-based digital marketplace is secure, trusted, immutable, and properly authenticated by the decentralized network.

关键词： Distributed ledger Databases Smart contracts Standards organizations Information sharing Authentication Organizations

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Optimizing Concrete Compressive Strength Prediction: A Comparative Study of Machine Learning Algorithms

Optimizing Concrete Compressive Strength Prediction: A Compa...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： H. P. Khandagale Mahesh S. Salunkhe Vaibhav K. Kamble Amrita A. Manjarekar P. P. Halkarnikar Suraj S. Redekar Department of Technology Shivaji University Kolhapur India Department of Computer Engineering D Y Patil Institute of Engineering Pune India Dept. of Computer Science and Engg. Ashokrao Mane Group of Institutions Kolhapur India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This study investigates the predictive capabilities of LBGM, CATB, GBR, ADAB, and XGB models for concrete compressive strength prediction. Through an evaluation of default hyperparameters and comprehensive metrics, including R2, MSE, RMSE, MAE, RMSLE, and MAPE, nuanced insights into each algorithm’s performance are obtained. ADAB emerges as a standout performer, displaying lower error rates across multiple metrics, suggesting AdaBoost’s suitability for concrete compressive strength prediction. The analysis of a self-prepared dataset reveals significant variations across different mixture combinations and curing times. This research not only establishes a benchmark for current practices but also provides avenues for future research, including hyperparameter tuning to optimize model performance further. The practical implications for concrete engineering are significant, guiding material composition decisions and contributing to the development of more durable and resilient structures. As the intersection of machine learning and concrete engineering progresses, this study lays a foundation for tailored approaches to address the specific challenges of predicting concrete compressive strength.

关键词： Measurement Machine learning algorithms Error analysis Machine learning Predictive models Curing Benchmark testing Prediction algorithms Concrete Tuning

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Optimizing Convolutional Neural Network Hyperparameters for MNIST Image Classification: Insights and Implications

Optimizing Convolutional Neural Network Hyperparameters for ...

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Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： Eman Dar Khadija Rana Hoor Fatima Yousaf Talha Asif Muhammad Zunnurain Hussain Hussain Ijaz Muhammad Zulkifl Hasan Dept. of Computer Engineering Information Technology University Lahore Pakistan Bahria University Lahore Campus NCBA&E DHA Campus Dept. of Computer Science Bahria University Lahore Campus Cloud Engineer Information Technology University Researcher Cyber Security Center Universitie Putra Malaysia

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

The recognition of handwritten digits has been among the most enduring fundamental problems explored in the field of machine learning and computer vision. The objective of this work is to design a state-of-art Convolutional Neural Network (CNN) for the image classification on the MNIST dataset to resolve issues related to data pre-processing and parameter tuning effectively. The work starts with standardizing the image data and then using one-hot encoding for the models that will improve the generalization and representation. The CNN structure followed in this work includes the convolutional layer, batch normalization, pooling layers, and fully connected layers with an efficient integration of features. To measure such performance indicators as accuracy, the given dataset is divided for training and validation. The results are also preprocessed to control for overfitting and convergence using back propagation with early stop to stop training when significant accuracy improvement is not observed and learning rate decay to encourage the model to search for better weights in the final stages of training. The model’s performance is tested and evaluated by using different epoch intervals and learning rates, and it is shown how critical it is to select the correct default hyperparameters for achieving efficiency. This paper also presents the approach for the practical application of the preprocessing of images and the systematic assessment in enhancing CNN-based handwritten digit recognition.

关键词： Training Adaptation models Accuracy Image coding Computational modeling Machine learning Convolutional neural networks Batch normalization Testing Image classification

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Pattern Recognition in Analog Wafermaps with Multiple Ensemble Approaches 2

Pattern Recognition in Analog Wafermaps with Multiple Ensemb...

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2nd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2021

作者： Abdullah, Md. Rahman, Md. Habibur Akhter, Shamim International University of Business Agriculture and Technology Aisip Lab Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781665415767

A wafer is a thin slice or substrate of semiconductors used for the fabrication of microelectronics devices. Thus, a large scale of precision is needed to make the microdevices work properly and to meet the requirements. Wafer test is an important step in wafer manufacturing to validate the activity of the microdevices and produces pictures of each wafer based on the electrical measurement of each of the devices on the wafer. Wafer shows different patterns while testing based on the requirement gap present on some of the devices on the wafer. To detect this error pattern manually is almost an impossible and time-consuming act. Therefore, we are presenting a wafer map pattern recognition system based on some ensemble approaches. Three (3) different ensemble including bagging, boosting, and voting approaches are implemented. Bagging performs relatively better than the others. © 2021 IEEE.

关键词： Machine learning

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Exploiting Change Blindness for Video Coding: Perspectives from a Less Promising User Study

Exploiting Change Blindness for Video Coding: Perspectives f...

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International Workshop on Quality of Multimedia Experience, QoMEx

作者： Mitra Amiri Steven Le Moan Christian Herglotz Dept. of Computer Science ColorLab Norwegian University of Science and Technology (NTNU) Gjøvik Norway Chair of Computer Engineering Brandenburgisch-Technische Universität Cottbus-Senftenberg Cottbus Germany

ISBN: (数字)9798350361582

ISBN: (纸本)9798350361599

What the human visual system can perceive is strongly limited by the capacity of our working memory and attention. Such limitations result in the human observer’s inability to perceive large-scale changes in a stimulus, a phenomenon known as change blindness. In this paper, we started with the premise that this phenomenon can be exploited in video coding, especially HDR-video compression where the bitrate is high. We designed an HDR-video encoding approach that relies on spatially and temporally varying quantization parameters within the framework of HEVC video encoding. In the absence of a reliable change blindness prediction model, to extract compression candidate regions (CCR) we used an existing saliency prediction algorithm. We explored different configurations and carried out a subjective study to test our hypothesis. While our methodology did not lead to significantly superior performance in terms of the ratio between perceived quality and bitrate, we were able to determine potential flaws in our methodology, such as the employed saliency model for CCR prediction (chosen for computational efficiency, but eventually not sufficiently accurate), as well as a very strong subjective bias due to observers priming themselves early on in the experiment about the type of artifacts they should look for, thus creating a scenario with little ecological validity.

关键词： Accuracy Biological system modeling Semantics Bit rate Blindness Predictive models Observers

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Fine-grained Infectious Disease Surveillance Data Aggregation Scheme in Cloud Computing 4

Fine-grained Infectious Disease Surveillance Data Aggregatio...

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4th Asia Conference on Algorithms, Computing and Machine Learning, CACML 2025

作者： Yang, Xiaodong Wang, Yaqi Lian, Shuqian Chen, Chen Dai, Xuexia Northwest Normal University Dept. of Computer Science and Engineering Lanzhou China Zhongbo Information Technology Research Institute Co. Ltd. Nanjing China Yinchuan University of Energy Dept. of Personnel Ningxia China

ISBN: (纸本)9798331543143

In the cloud computing environment, a fine-grained infectious disease monitoring data aggregation scheme is crucial for formulating effective epidemic prevention and control strategies. Developing a powerful data aggregation scheme for infectious disease monitoring requires careful consideration of several factors, including data integrity, fine-grained analysis, privacy protection, and fault tolerance. However, existing data aggregation schemes often fail to simultaneously address all of these issues. To address these challenges, we propose a cloud computing-based fine-grained infectious disease monitoring data aggregation scheme, which combines an improved Paillier encryption algorithm with Keyed-hash Message Authentication Code (HMAC) and superincreasing sequences. This approach not only addresses the concerns mentioned above, but also enhances resilience against malicious data mining attacks. Meanwhile, the proposed scheme satisfies the desired security properties of the infectious disease surveillance system. © 2025 IEEE.

关键词： Fault tolerant computer systems

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Instructors' Perspectives on Capstone Courses in Computing Fields: A Mixed-Methods Study 2024

Instructors' Perspectives on Capstone Courses in Computing F...

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29th Working Group Reports on Innovation and technology in computer Science Education, ITiCSE WGR 2024

作者： Hooshangi, Sara Shakil, Asma Dasgupta, Subhasish Davis, Karen C.C. Farghally, Mohammed Fitzpatrick, KellyAnn Gutica, Mirela Hardt, Ryan Riddle, Steve Seyam, Mohammed Department of Computer Science Virginia Tech BlacksburgVA United States School of Computer Science University of Auckland Auckland New Zealand Info Systems and Tech Mgmt Dept George Washington University WashingtonDC United States Computer Science and Software Engineering Dept. Miami University OxfordOH United States School of Computing Instruction Georgia Institute of Technology AtlantaGA United States School of Computing & Academic Studies British Columbia Institute of Technology BurnabyBC Canada Computer & Information Science University of St. Thomas Saint PaulVA United States School of Computing Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

Team-based capstone courses are integral to many undergraduate and postgraduate degree programs in the computing field. They are designed to help students gain hands-on experience and practice professional skills such as communication, teamwork, and self-reflection as they transition into the real world. Prior research on capstone courses has focused primarily on the experiences of students. The perspectives of instructors who teach capstone courses have not been explored comprehensively. However, an instructor's experience, motivation, and expectancy can have a significant impact on the quality of a capstone course. In this working group, we used a mixed methods approach to understand the experiences of capstone instructors. Issues such as class size, industry partnerships, managing student conflicts, and factors influencing instructor motivation were examined using a quantitative survey and semi-structured interviews with capstone teaching staff from multiple institutions across different continents. Our findings show that there are more similarities than differences across various capstone course structures. Similarities include team size, team formation methodologies, duration of the capstone course, and project sourcing. Differences in capstone courses include class sizes and institutional support. Some instructors felt that capstone courses require more time and effort than regular lecture-based courses. These instructors cited that the additional time and effort is related to class size and liaising with external stakeholders, including industry partners. Some instructors felt that their contributions were not recognized enough by the leadership at their institutions. Others acknowledged institutional support and the value that the capstone brought to their department. Overall, we found that capstone instructors were highly intrinsically motivated and enjoyed teaching the capstone course. Most of them agree that the course contributes to their professional de

关键词： Students

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Live Demonstration: Optical Communications using Solar Cells

Live Demonstration: Optical Communications using Solar Cells

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Walter D. Leon-Salas Diana Narvaez-Bernal Rodrigo Esparza Gabriel Baquero School of Engineering Technology Purdue University West Lafayette USA School of Engineering and Sciences Tecnologico de Monterrey Monterrey Mexico Electrical and Computer Engineering Dept. University of Houston Houston USA

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

This live demonstration showcases an optical communication system dubbed Optical Frequency Identification (OFID) that can be used to identify and track objects or animals as well as to sense environmental variables. The system consists of one or more optical tags and an optical reader. Each optical tag includes a GaAs solar cell, a low-power FPGA, an energy harvesting circuit and two sensors (light and temperature). GaAs solar cells are more efficient than their Si counterparts due to their direct bandgap electronic structure, which also makes them good emitters of infrared (IR) light when stimulated with light or with an electric current. This property, called luminescence, is exploited here to design electronic tags that use their on-board GaAs solar cells for three functions: 1) to transmit data (by modulating their IR luminescent emissions), 2) to harvest energy and 3) to receive information (by virtue of their photo-transduction property) [1] , [2] . The tags’ transmissions are received and decoded by the optical reader, which consists of a focusing lens, a photodiode, a trans-impedance amplifier followed by a gain stage and a digital decoder (implemented on an FPGA). Fig. 1 shows the live demonstration setup. It includes the optical tags, an optical reader, an oscilloscope to visualize received waveforms and a laptop to display received data packets.

关键词： Temperature sensors Temperature Stimulated emission Photovoltaic cells Gallium arsenide Silicon Optical sensors

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Explainable AI-Driven Vision Transformers for Assessing Fruit Freshness via Transfer Learning

Explainable AI-Driven Vision Transformers for Assessing Frui...

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International Conference on computer and Information technology (ICCIT)

作者： Aritra Das Hadiur Rahman Nabil Fahad Pathan Momotaz Rahman Ouishy M. F. Mridha Jungpil Shin Dept. of Computer Science American International University-Bangladesh Dhaka Bangladesh Institute of Information and Communication Technology Shahjalal University of Science and Technology Sylhet Bangladesh School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

The assessment of fruit freshness is a critical factor in ensuring the quality and safety of food products, impacting both consumer satisfaction and market value. This paper presents a comparative evaluation of Vision Transformer (ViT) models for assessing fruit freshness using transfer learning techniques. Three pretrained ViT models—ViT model A (vit_base_patch16_clip_384), ViT model B (vit_betwixt_patch16_reg4_gap_384), and ViT model C (vit_mediumd_patch16_reg4_gap_384)—were employed on two publicly available datasets: Dataset A from the Mendeley Data Repository and Dataset B from the Kaggle Repository. Data augmentation techniques were applied to enhance the generalization capabilities of the models. Superior performance was exhibited by ViT model B on Dataset A, with an accuracy of 98.44%, precision of 98.46%, an F1-score of 98.44%, and a recall of 98.44%. Again, exceptional results were achieved by ViT model B on Dataset B, reaching an accuracy of 99.57%, precision of 99.56%, an F1-score of 99.56%, and a recall of 99.56%. The effectiveness of ViTs in accurately classifying fruit freshness is underscored, demonstrating their potential for practical applications in food quality assessment. Later, Local Interpretable Model-agnostic Explanation (LIME) was incorporated to improve the interpretability of the model.

关键词： computer vision Accuracy Computational modeling Transfer learning Customer satisfaction Transformers Data augmentation Data models Real-time systems Quality assessment

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