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IEEE Conference on Wireless Communications and Networking

作者： Boyi Lei Jianhong Zhou Maode Ma Xianhua Niu School of Computer and Software Engineering Xihua University National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China College of Engineering Qatar University

Applying blockchain technology in the Internet of Things (IoT) systems to realize massive data storage is a promising technology, which can eliminate the dependence of IoT on central servers and protect data security effectively. However, in legacy IoT systems, it is hardly to store a complete blockchain at all nodes due to the limited storage space. In order to securely store massive data in the resource-constrained IoT systems, we design a blockchain-assisted distributed storage mechanism, by which multiple nodes collaboratively store a complete blockchain. Meanwhile, we design a deep reinforcement learning (DRL) based algorithm to obtain the optimal block allocation strategy for the collaborative storage. The optimal strategy can maximize the number of storage blocks so as to maximize the utilization of the limited storage space under the premise of meeting the requirements of user query delay and reliability. The simulation results show that compared with the random search algorithm and the policy gradients algorithm, the proposed Deep Q Network algorithm can achieve the maximum number of storage blocks in the same storage space, and the fastest storage speed.

关键词： Deep learning Simulation Memory Reinforcement learning Blockchains Resource management Internet of Things

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Cultivating software Quality Improvement in the Classroom: An Experience with ChatGPT

Cultivating Software Quality Improvement in the Classroom: A...

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Conference on software engineering Education & Training (CSEE&T)

作者： Eman Abdullah AlOmar Mohamed Wiem Mkaouer Software Engineering Department Stevens Institute of Technology Hoboken NJ USA Computer Science Department University of Michigan-Flint Flint MI USA

ISBN: (数字)9798350378979

ISBN: (纸本)9798350378986

Large Language Models (LLMs), like ChatGPT, have gained widespread popularity and usage in various software engineering tasks, including programming, testing, code review, and program comprehension. However, their effectiveness in improving software quality in the classroom remains uncertain. In this paper, our aim is to shed light on our experience in teaching the use of Programming Mistake Detector (PMD) to cultivate a bugfix culture and leverage LLMs to improve software quality in educational settings. This paper discusses the results of an experiment involving 102 submissions that carried out a code review activity of 1,230 rules. Our quantitative and qualitative analysis reveals that a set of PMD quality issues influences the acceptance or rejection of the issues, and design-related categories that take longer to resolve. Although students acknowledge the potential of using ChatGPT during code review, some skepticism persists. We envision our findings to enable educators to support students with code review strategies to raise students' awareness about LLMs and promote software quality in education.

关键词： Codes Reviews Large language models Education software quality Detectors Chatbots

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Discrimination and Diagnosis of Hypertrophic Cardiomyopathy and Cardiac Amyloidosis via Unimodal Supervision Joint Contrastive Learning

Discrimination and Diagnosis of Hypertrophic Cardiomyopathy ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Xiaofeng Zhang, Hongmei Luo, Xiaoxian Dang, Jiachen Yin, Lixue Peng, Bo Southwest Petroleum University School of Computer Science and Software Engineering Chengdu China Ultrasound and Noninvasive Cardiology Department of Cardiovascular China Key Laboratory of Sichuan Province Ultrasound in Cardiac Electrophysiology and Biomechanics China University of Electronic Science and Technology of China Sichuan Provincial People's Hospital Chengdu China Chengdu University of Traditional Chinese Medicine School of Medical and Life Sciences Chengdu China

ISBN: (纸本)9798350386226

Hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) are both heart diseases. Their echocardiographic presentations are similar, whereas the latter is rare in clinical practice, making them susceptible to misdiagnosis. Recently, deep learning-based echocardiographic diagnostic methods have been proposed. These data-driven methods require a large amount of data for training to improve diagnostic accuracy. Therefore, these methods do not apply to conditions with sparse clinical data, such as CA. To address the problem of poor model performance due to few training data, we propose a multimodal model (USCL) combining multiview and text to differentiate between HCM and CA. Specifically, the USCL consists of two main parts: one part employs unimodal supervision (UMS) to obtain echocardiogram and text respectively, the relevant unimodal representations and prediction results. The other part uses a designed multimodal contrast learning (MMCL) approach to tune the representations of the two modalities, thus exploring more reliable multimodal representations in few-shot learning. We build a dataset for training and evaluation which includes 209 patients with HCM and 203 patients with CA, as well as 200 patients with normal cardiac function. Experiments show that USCL achieves promising results. © 2024 IEEE.

关键词： Contrastive Learning

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Blockchain-Enabled EHR Framework for Internet of Medical Things

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computers, Materials & Continua 2021年第4期67卷 211-221页

作者： Lewis Nkenyereye S.M.Riazul Islam Mahmud Hossain M.Abdullah-Al-Wadud Atif Alamri Department of Computer and Information Security Sejong UniversitySeoul05006Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Computer Science University of Alabama at BirminghamAlabama35294USA Department of Software Engineering King Saud UniversityRiyadh11543Saudi Arabia

The Internet of Medical Things(IoMT)offers an infrastructure made of smart medical equipment and software applications for healthcare *** the internet,the IoMT is capable of providing remote medical diagnosis and timely health *** patients can use their smart devices to create,store and share their electronic health records(EHR)with a variety of medical personnel including medical doctors and ***,unless the underlying commination within IoMT is secured,malicious users can intercept,modify and even delete the sensitive EHR data of *** also lose full control of their EHR since most healthcare services within IoMT are constructed under a centralized platform outsourced in the ***,it is appealing to design a decentralized,auditable and secure EHR system that guarantees absolute access control for the patients while ensuring privacy and *** the features of blockchain including decentralization,auditability and immutability,we propose a secure EHR framework which is mainly maintained by the medical *** this framework,the patients’EHR data are encrypted and stored in the servers of medical institutions while the corresponding hash values are kept on the *** make use of security primitives to offer authentication,integrity and confidentiality of EHR data while access control and immutability is guaranteed by the blockchain *** security analysis and performance evaluation of the proposed framework confirms its efficiency.

关键词： Internet of Medical Things security privacy preservation blockchain access control

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Ultrahigh-Resolution Corrugated Silicon Nitride Masks for Direct Patterning of OLED Microdisplays

Ultrahigh-Resolution Corrugated Silicon Nitride Masks for Di...

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SID International Symposium Digest of Technical Papers, 2023

作者： Dong, Shou-Cheng Yang, Jiye Deng, Sunbin Ting Tam, Bryan Siu Yao, Jingwen Tang, Ching W. State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Institute for Advanced Study Hong Kong University of Science and Technology Hong Kong

Corrugated silicon nitride masks (SiNMs) of 2000 ppi are developed to reduce the shadowing effect in direct patterning of high-resolution, RGB side-by-side OLED microdisplays. Periodic corrugations in the free-standing silicon nitride membrane elevate the through-apertures and reduce the gap between the mask and the substrate. © 2023, John Wiley and Sons Inc. All rights reserved.

关键词： Silicon nitride

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Effectiveness of Using Deep Learning for Blister Blight Identification in Sri Lankan Tea

Effectiveness of Using Deep Learning for Blister Blight Iden...

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2023 International Research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Hewawitharana, G.H.A.U. Nawarathne, U.M.L.A. Hassan, A.S.F. Wijerathna, Lochana M. Sinniah, G.D. Vidhanaarachchi, Samitha P. Wickramarathne, Jagath Wijekoon, Janaka L. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Tea Research Institute of Sri Lanka Plant Pathology Division Talawakelle Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Military Technological College Systems Engineering Department Muscat Oman

ISBN: (纸本)9798350341454

Ceylon tea industry faces a major challenge in the form of pathogen-induced crop loss, with Blister Blight (BB) caused by Exobasidium vexans posing the greatest threat, leading to harvest losses of over 30%. This fungus attacks the tender tea shoots, resulting in a direct negative impact on the tea harvest. This paper presents a system to identify the suspicious tea leaves and BB disease at its early stages along with an assessment of severity, offering a potential solution to this critical issue. By utilizing real-time object detection, the system filters out non-tea leaves from the captured initial image of a segment of a tea plant. The identified tea leaves are then subjected to BB identification and severity assessment based on differing visual symptoms of the BB stages. This approach enables the system to accurately identify BB in the initial stage and severity stage, allowing for timely and targeted intervention to minimize crop losses. The YOLOv8 model has been able to correctly identify 98% of the objects it has detected as relevant (precision), and it has been able to correctly identify 96% of all the relevant objects present in the scene (recall). The Residual Network 50 (Resnet50) convolutional neural network (CNN) model was selected as the final model, achieving an accuracy of 89.90% during the training phase and an accuracy of 88.26% during the testing phase. © 2023 IEEE.

关键词： Convolutional neural networks

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Effects of laser conditioning with different pulse widths on picosecond laser-induced damage resistance in HfO2/SiO2 high-reflectivity coatings

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Optical Materials Express 2025年第7期15卷 1467-1482页

作者： Zhou, Yan Shuai, Kun Tao, Chunxian Li, Dawei Xu, Guang Li, Dawei Zhang, Weili Liu, Yuru Lian, Yafei Liu, Xiaofeng Zhao, Yuanan Dai, Yaping Shao, Jianda School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Laboratory of Thin Film Optics Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Department of Advanced Optical and Microelectronic Equipment Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China National Laboratory on High Power Laser and Physics Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Shanghai Institute of Laser Plasma China Academy of Engineering Physics Shanghai201800 China Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou310024 China

Current optimizations in the fabrication processes of multilayer dielectric (MLD) high-reflectivity (HR) coatings struggle to meet the demand for picosecond (ps) laser damage resistance in high-energy petawatt laser systems. This study investigates laser conditioning (LC) effects of 250 ps, 500 ps, 800 ps, and 10 nanosecond (ns) laser pulses on the ps laser-induced damage resistance of electron beam (e-beam) HfO2/SiO2 HR coatings. Results demonstrate a 12% enhancement in the laser-induced damage threshold (LIDT) under ∼10 ps laser irradiation for 500 ps conditioned samples compared to unconditioned ones, while negligible LIDT variations are observed for other pulse widths. The defect density and damage types in unconditioned and conditioned samples exhibit no statistical alteration. This indicates that LC does not eliminate nanoscale defects but only increases their LIDTs for 500 ps conditioned samples. To reveal the improvement mechanism of 500 ps LC on the ps laser-induced damage resistance, a simulated model of the interaction between sub-ns laser pulses and localized defect states is proposed, combining ionization rate equations with the two-temperature model (TTM). For 500 ps LC, although the initial electron number density of the defect energy level is increased, the defect energy level gap is also widened due to the appropriate lattice temperature. The effect of this widening gap surpasses that of the aforementioned increased initial electron density, making electron ionization more difficult under ∼10 ps laser irradiation. This work provides new strategies and approaches for enhancing the ps laser-induced damage resistance of MLD HR coatings. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Laser damage

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A Belief Rule Based Expert System to Diagnose Schizophrenia Using Whole Blood DNA Methylation Data 1st

A Belief Rule Based Expert System to Diagnose Schizophrenia...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Hossain, Mohammad Shahadat Ahmed, Mumtahina Raihan, S. M. Shafkat Sharma, Angel Islam, Raihan Ul Andersson, Karl Department of Computer Science and Engineering University of Chittagong Chittagong4331 Bangladesh Port City International University Dhaka Bangladesh Pervasive and Mobile Computing Laboratory Lulea University of Technology Lulea Sweden

ISBN: (纸本)9783031346217

Schizophrenia is a severe neurological disease where a patient’s perceptions of reality are disrupted. Its symptoms include hallucinations, delusions, and profoundly strange thinking and behavior, which make the patient’s daily functions difficult. Despite identifying genetic variations linked to Schizophrenia, causative genes involved in pathogenesis and expression regulations remain unknown. There is no particular way in life sciences for diagnosing Schizophrenia. Commonly used machine learning and deep learning are data-oriented. They lack the ability to deal with uncertainty in data. Belief Rule Based Expert System (BRBES) methodology addresses various categories of uncertainty in data with evidential reasoning. Previous researches showed the association of DNA methylation (DNAm) with risk of Schizophrenia. Whole blood DNAm data, hence, is useful for smart diagnosis of Scizophrenia. However, to our knowledge, no previous studies have investigated the performance of BRBES to diagnose Schizophrenia. Therefore, in this study, we explore BRBES’ performance in diagnosing Schizophrenia using whole blood DNAm data. BRBES was optimized by gradient-free algorithms due to the limitations of gradient-based optimization. Classification thresholds were optimized to yield better results. Finally, we compared performance to two machine learning models after 5-fold cross-validation where our model achieved the highest average sensitivity (76.8%) among the three. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Diagnosis

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Expedited Block Transmission in Blockchain Network by using Clusters

Expedited Block Transmission in Blockchain Network by using ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hua, Xiaoqi Chen, Jian Zhang, Peiyun Fu, Zhangjie Zhu, Haibin Lu, Kezhong Ren, Jigang School of Software Nanjing University of Information Science and Technology Nanjing210044 China Engineering Research Center of Digital Forensics of Ministry of Education The School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China Nipissing University Department of Computer Science and Mathematics North BayONP1B 8L7 Canada School of Big Data and Artificial Intelligence Chizhou University Chizhou247000 China

ISBN: (纸本)9781665410205

Blockchain technology has garnered increasing attention from researchers. Because blockchain systems may contain malicious or spatially limited nodes that may delay block verification and reduce block transmission rate, this work proposes a block transmission model by designing and using special clusters. This work proposes the cluster formation and selection mechanisms. Nodes are grouped into clusters in a blockchain, and clusters with high fitness values are chosen to transmit blocks by calculating their trust values and block transmission rates. The proposed method is compared with the peers: Layer-Chain, BlockP2P-EP and RNS. According to experimental findings, the proposed method is superior to its peers regarding the time needed for block synchronization and transmission, block occupation storage ratio, transaction throughput, and block transmission success ratio. © 2024 IEEE.

关键词： Blockchain

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A Survey on Scheduling the Task in Fog Computing Environment

arXiv

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arXiv 2023年

作者： Ishaq, Faiza Ashraf, Humaira Jhanjhi, Nz Department of computer and software Engineering International Islamic University Pakistan School of Computer Science SCS Taylors University Malaysia

With the rapid increase in the Internet of Things (IoT), the amount of data produced and *** processed is also increased. Cloud Computing facilitates to handle storage, processing, and analysis of data as needed. However cloud computing devices are located far away from the IoT devices. Fog computing has emerged as a small cloud computing paradigm that is near to the edge devices and handles the task very efficiently. Fog nodes have a small storage capability than the cloud node but it is designed and deployed near to the edge device so that request must be accessed efficiently and executes in time. In this survey paper we have investigated and analysed the main challenges and issues raised in scheduling the task in fog computing environment. To the best of our knowledge there is no comprehensive survey paper on challenges in task scheduling of fog computing paradigm. In this survey paper research is conducted from 2018 to 2021 and most of the paper selection is done from 2020-2021. Moreover this survey paper organizes the task scheduling approaches and technically plans the identified challenges and issues. Based on the identified issues, we have highlighted the future work directions in the field of task scheduling in fog computing environment. © 2023, CC BY.

关键词： Fog computing

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