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6th World Symposium on Communication engineering, WSCE 2023

作者： Lioupa, Anna Memos, Vasileios A. Stergiou, Christos L. Ishibashi, Yutaka Psannis, Konstantinos E. School of Information Sciences University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

ISBN: (纸本)9798350339505

The technological advances impact the future classrooms and the whole education system. The evolution of 6G promises faster speeds based on terahertz, wireless communication, the leverage of artificial intelligence (AL), virtually reality (VR) and generally a fully connected world. The use of applications of IoT and AIoT improves the education environment, but the intelligent network services undoubtedly gather, store and analyze big data which are vulnerable to attackers. As a solution we may consider the technology of blockchain. The trust and security of next generation intelligent networks are essential part of their design. In this article we suggest a data security system based on blockchain for AL applications in 6G networks. First, we present two Al-enabled applications, indoor positioning and mobile payment within the framework of 6G in the university area. We present the efficiency of blockchain in data security for Al-enabled systems using two case studies of indoor positioning system and mobile payment. The 6G system which is fully supported by Al and the integration of this in technology of blockchain, is ensured the security in future wireless communication. © 2023 IEEE.

关键词： Blockchain

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Occupancy-level-aware Indoor Terahertz Channel Prediction: A Robust Deep Learning Approach 100

Occupancy-level-aware Indoor Terahertz Channel Prediction: A...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Hasan, Eslam Mahalal, Elmahedi Ismail, Muhammad Wu, Zi-Yang Fouda, Mostafa M. Kato, Nei Tennessee Technological University Department of Computer Science CookevilleTN United States Northeastern University College of Information Science and Engineering Shenyang China Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Idaho FallsID United States Tohoku University Graduate School of Information Sciences Sendai Japan

ISBN: (纸本)9798331517786

Accurate channel prediction using deep learning (DL) algorithms can address the challenges of terahertz (THz) propagation, such as atmospheric absorption and object scattering, by enabling proactive handover and beamforming. However, indoor environments are inherently dynamic, with factors like occupancy level variations causing the channel characteristics to change over time. This phenomenon, known as concept drift, can severely degrade the DL model performance used in channel prediction. This paper investigates the impact of indoor occupancy level variations on the generalization ability of state-of-the-art DL models for THz channel prediction. We identify three distinct occupancy levels (low, medium, and high) within the THz indoor channel. Our results demonstrate that the state-of-the-art DL models exhibit limited generalization capabilities, with performance deterioration in prediction accuracy ranging from 4-62%. We propose a robust two-stage framework to mitigate concept drift in THz channel prediction. The first stage predicts the indoor occupancy level from the THz wireless signal, which is a multi-class classification problem. Due to the reoccurring concept of occupancy levels, the second stage contains a pool of models in a sleeping mode based on a hybrid convolutional neural network (CNN) long-short-term memory (LSTM) architecture. One of these DL expert models is activated for channel prediction based on the occupancy level predicted from the previous stage. Our framework demonstrates superior generalization by limiting the performance deterioration from 62% due to concept drift to ≤ 9%. This represents an 85% reduction in performance deterioration compared to the existing state-of-the-art DL models. © 2024 IEEE.

关键词： Convolutional neural networks

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Determinants of Tobacco Cultivation and the Associated Impacts of Adoption in Hurungwe District, Zimbabwe

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Agricultural sciences 2024年第4期15卷 505-531页

作者： Spencer Sibanda Satoshi Tsuyuki Department of Information and Computer Sciences Graduate School of Science and Engineering Saitama University Saitama Japan Department of Global Agricultural Sciences Graduate School of Agricultural and Life Sciences The University of Tokyo Tokyo Japan

Tobacco is an essential cash crop in Zimbabwe and a strategic livelihood option for hundreds of thousands of rural households. However, the crop is linked to negative environmental, economic, and social impacts. The existing studies on tobacco cultivation in Zimbabwe present contradictory findings on the determinants and impacts of adoption, leaving unanswered questions about the crop’s sustainability impact in the country. This article investigates the determinants of smallholder farmers’ decisions to grow tobacco and the associated impacts of adoption. Random and purposive sampling were used to select 273 household surveys, including tobacco and non-tobacco smallholder farmers, and 56 expert interviews to answer the research questions. We employed regression models alongside expert interviews and document analysis to identify the determinants influencing the decision-making process of smallholder farmers in Zimbabwe regarding tobacco cultivation. Additionally, our investigation aimed to elucidate the perceived impacts associated with the adoption of this agricultural practice. The regression analysis indicated that the farmer’s age, education level, farming experience, family size, household income, and perceived high farm profitability are significant drivers of tobacco adoption. We also discovered divergent and convergent perceptions of the critical impacts of tobacco cultivation. The study highlights the need for proactive multi-stakeholder collaboration and sustainable financial arrangements to address the negative impacts of tobacco production. As the primary stakeholder responsible for regulating and promoting agricultural activities, the Zimbabwean government should provide meaningful financial support, increase access to credit, and ensure better market facilities for alternative crops to reduce the over-dependence on tobacco.

关键词： Tobacco Cultivation Determinants of Adoption Sustainability Impacts Economic Livelihood Stakeholder Perceptions

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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

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IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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Dynamic Spectrum Allocation for Bursty Traffic Using Quantum Annealing 9

Dynamic Spectrum Allocation for Bursty Traffic Using Quantum...

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9th International Conference on Platform Technology and Service, PlatCon 2023

作者： Inoue, Shuta Kawakita, Yuusuke Tobe, Yoshito Graduate School of Science and Engineering Aoyama Gakuin University Sagamihara Japan Kanagawa Institute of Technology Department of Information and Computer Sciences Atsugi Japan Aoyama Gakuin University Department of Integrated Information Sagamihara Japan

ISBN: (纸本)9798350305999

As the demand for wireless communications continues to grow, securing limited spectrum resources has become an issue. Under such circumstances, Dynamic Spectrum Allocation (DSA), in which existing frequency bands are jointly used and allocated to carriers, has been attracting attention. However, the problem is that a large number of base stations and interferers causes a combinatorial explosion, which requires an enormous amount of computation time. In a previous study, this problem was viewed as a combinatorial optimization problem and formulated to be solved by quantum annealing for the time slot-specific requirements of each base station. However, each base station uses the frequency band differently, and the timeslot length requirement must also be considered. In this paper, we focus on the DSA problem of maximizing the satisfaction of the time-based requirements of each base station and describe a formulation for solving the problem using quantum annealing. These time-based requirements can accommodate the demand for bursty traffic, which was not the case in previous papers. We also present implementation results using Fixstars Amplify and discuss future prospects. © 2023 IEEE.

关键词： Base stations

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Research on Image Generation Optimization based Deep Learning 24

Research on Image Generation Optimization based Deep Learnin...

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2024 International Conference on Machine Learning, Pattern Recognition and Automation engineering, MLPRAE 2024

作者： Yan, Hao Wang, Zixiang Bo, Shi Zhao, Yi Zhang, Yang Lyu, Ranran College of Engineering and Computer Science Syracuse University United States Graduate School of Arts and Sciences Boston University United States United States Khoury College of Computer and Information Sciences Northeastern University United States Amazon Sagemaker GoundTruth Amazon Web Services United States

ISBN: (纸本)9798400709876

Image generation optimization is an important research direction in the field of deep learning, which aims to improve the performance of image generation models and the quality of generated images. In recent years, researchers have made significant progress in image generation optimization with the development of deep generative models such as generative adversarial networks (GANs) and variational autoencoders (VAEs). These models are able to generate high-quality, realistic images by learning the distribution of image data. In this study, a deep learning-based image generation optimization model was adopted, which combined the advantages of GAN and VAE. The model architecture consists of a generator and a discriminator, where the generator is responsible for generating the image and the discriminator is used to judge the authenticity of the image. In addition, the model also introduces attention mechanism and self-supervised learning strategy to further improve the quality and diversity of generated images. In the training process, a large-scale image dataset is used, and a variety of optimization algorithms are used to improve the stability and efficiency of the model. By evaluating various indicators of the generative model, including image quality, generation speed and model convergence, it was found that the introduced attention mechanism and self-supervised learning strategy significantly improved the performance of the model. © 2024 Owner/Author.

关键词： Generative adversarial networks

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Implementation and Performance Evaluation of Collective Communications Using CIRCUS on Multiple FPGAs

Implementation and Performance Evaluation of Collective Comm...

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2023 International Conference on High Performance Computing in Asia-Pacific Region Workshops, HPC Asia 2023

作者： Kikuchi, Kohei Fujita, Norihisa Kobayashi, Ryohei Boku, Taisuke Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9781450399890

In the high-performance computing domain, Field Programmable Gate Array (FPGA) is a novel accelerator that exhibits high flexibility and performance characteristics distinct from other accelerators such as the Graphics Processing Unit (GPU). Recent advanced high-end FPGA is equipped with multiple channels of high speed optical link up to 100Gbps performance for each. This is a crucial feature when we construct PC clusters with FPGAs as accelerators, however it is not easy to utilize from user kernels because this feature is implemented in low level and simple direct communication between neighboring FPGAs. In order to provide the communication feature between FPGAs for accelerated PC clusters, we developed a communication system named CIRCUS which implies a user-friendly API from OpenCL and is equipped with routing function over multi-hop communication on multi-dimensional torus network of FPGAs. However, current CIRCUS only provides a point-to-point communication between source and destination FPGAs. In ordinary parallel processing environment such as MPI, the user program the message passing with various collective communication functions for parallel algorithm, for instance Allreduce, Allgather, etc. In this paper, we implement the collective communication function over CIRCUS for user-friendly programming of ordinary parallel algorithms on FPGAs. As the first target, we implement Allreduce function which is the most essential and important function. The paper describes the CIRCUS system briefly followed by the design, implementation and preliminary performance evaluation on Intel Stratix10 FPGAs. © 2023 ACM.

关键词： Field programmable gate arrays (FPGA)

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Empirical Analysis of Software Success Rate Forecasting During Requirement engineering Processes

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computers, Materials & Continua 2023年第1期74卷 783-799页

作者： Muhammad Hasnain Imran Ghani Seung Ryul Jeong Muhammad Fermi Pasha Sardar Usman Anjum Abbas Lahore Leads University Lahore42000Pakistan Computer and Information Sciences Virginia Military InstituteLexingtonVA24450USA Graduate School of Business IT Kookmin UniversitySeoul136Korea School of Information Technology Monash UniversityBandar Sunway47500Malaysia Department of Computer Science and Software Engineering Grand Asian University Sialkot51040Pakistan Government Sarwar Shaheed Associate College Gujjar KhanRawalpindi46000Pakistan

Forecasting on success or failure of software has become an interesting and,in fact,an essential task in the software development *** order to explore the latest data on successes and failures,this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework?What human factors contribute to success or failure of a software?What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work?In order to conduct this empirical analysis a total of 104 practitioners were recruited to determine how human factors,misinterpretation,and miscommunication of requirements and decision-making processes play their roles in software success *** discussed a potential relationship between forecasting of software success or failure and the development *** noticed that experienced participants had more confidence in their practices and responded to the questionnaire in this empirical study,and they were more likely to rate software success forecasting linking to the development *** analysis also shows that cognitive bias is the central human factor that negatively affects forecasting of software success *** results of this empirical study also validated that requirements’misinterpretation and miscommunication were themain causes behind software systems’*** has been seen that reliable,relevant,and trustworthy sources of information help in decision-making to predict software systems’success in the software *** empirical study highlights a need for other software practitioners to avoid such bias while working on software *** investigation can be performed to identify the other human factors that may impact software systems’success.

关键词： Cognitive bias misinterpretation of requirements miscommunication software success and failure prediction decision making

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Research on the LLM-driven Vulnerability Detection System Using LProtector 4

Research on the LLM-driven Vulnerability Detection System Us...

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4th IEEE International Conference on Data science and computer Application, ICDSCA 2024

作者： Sheng, Ze Wu, Fenghua Zuo, Xiangwu Li, Chao Qiao, Yuxin Lei, Hang Computer and Information Science University of Pennsylvania Philadelphia United States Integrated Circuit Science & Engineering University of Electronic Science & Technology of China Chengdu China Computer Science & Engineering Texas A&M University College Station United States Graduate School of Arts and Sciences Georgetown University WashingtonDC United States Computer Information Technology Northern Arizona University Flagstaff United States Co. Ltd Nanjing China

ISBN: (纸本)9798350368239

The security issues of large-scale software systems and frameworks have become increasingly severe with the development of technology. As complexity of software grows, vulnerabilities are becoming more challenging to detect. Although traditional machine learning methods have been applied in cybersecurity for a long time, there has been no significant breakthrough until now. With the recent rise of large language models (LLMs), a turning point seems to have arrived. The powerful code comprehension and generation capabilities of LLMs make fully automated vulnerability detection systems a possibility. This paper presents LProtector, an automated vulnerability detection system for C/C++ codebases based on GPT-4o and Retrieval-Augmented Generation (RAG). LProtector performs binary classification to identify vulnerabilities in target codebases. To evaluate its effectiveness, we conducted experiments on the Big-Vul dataset. Results show that LProtector outperforms two state-of-the-art baselines in terms of F1 score, demonstrating the potential of integrating LLMs with vulnerability detection. ©2024 IEEE.

关键词： C++ (programming language)

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Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images

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computer Modeling in engineering & sciences 2024年第4期139卷 725-739页

作者： Puja S.Prasad Adepu Sree Lakshmi Sandeep Kautish Simar Preet Singh Rajesh Kumar Shrivastava Abdulaziz S.Almazyad Hossam M.Zawbaa Ali Wagdy Mohamed CSE Department Geethanjali College of EngineeringHyderabadTelangana501301India Department of Computer Science LBEF CampusKathmandu44600Nepal School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia CeADAR Ireland’s Centre for AI Technological University DublinDublinD07 EWV4Ireland Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

Pupil dynamics are the important characteristics of face spoofing *** face recognition system is one of the most used biometrics for authenticating individual *** main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,*** proposed model uses pupil characteristics for liveness detection during the authentication *** pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light *** proposed framework consists of two-phase *** the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video *** above measurement is converted into feature space using Kohn and Clynes model-defined *** Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the *** the second phase,we perform the facial recognition ***-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional *** features are scale,rotation,and orientation invariant and are used for recognizing facial *** brute force matching algorithm is used for matching features of two different *** threshold value we considered is 0.08 for good *** analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Err

关键词： SIFT pupil CASIA-SURF pupillary light reflex replay attack dataset brute force

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