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IEEE Open Journal of Nanotechnology 2023年 4卷 181-194页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering the Institute of Biomedical Engineering the Department of Electronics and Electrical Engineering the Institute of Pioneer Semiconductor Innovation and the Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

Machine learning (ML) is poised to play an important part in advancing the predicting capability in semiconductor device compact modeling domain. One major advantage of ML-based compact modeling is its ability to capture complex relationships and patterns in large datasets. Therefore, in this paper a novel design scheme based on dynamically adaptive neural network (DANN) is proposed to develop fast and accurate compact model (CM). This framework constitutes a powerful yet computationally efficient methodology and exhibits emergent dynamic behaviors. This paper demonstrates that the compact model based on ML can be designed to replicate the performance of conventional compact model for nanodevices. For this work, gate-all-around (GAA) nanosheet (NS) device characteristics are comprehensively analyzed for process variability sources using the proposed model. The device geometry parameters such as channel length, nanosheet width and nanosheet thickness are fed as input features to the DANN model. The adaptive neural network learns dynamically by updating weights of the model in accordance with the input features and achieves accurate neural weight convergence. The proposed model predicted the electrical characteristics of NS devices with less than 1% error rate. The model is also implemented and validated for the simulations of digital circuit designs such as inverter, and logic gates. ©;2023 The Authors.

关键词： Circuit simulation

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Automatic Partitioning of MPI Operations in MPI+OpenMP Applications

Automatic Partitioning of MPI Operations in MPI+OpenMP Appli...

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International Conference on High Performance computing, ISC High Performance 2021

作者： Jammer, Tim Bischof, Christian Darmstadt Germany Scientific Computing Group Department of Computer Science Technical University Darmstadt Darmstadt64283 Germany

ISBN: (纸本)9783030905385

The new MPI 4.0 standard includes a new chapter about partitioned point-to-point communication operations. These partitioned operations allow multiple actors of one MPI process (e.g. multiple threads) to contribute data to one communication operation. These operations are designed to mitigate current problems in multithreaded MPI programs, with some work suggesting a substantial performance benefit (up to 26%) when using these operations compared to their existing non-blocking counterparts. In this work, we explore the possibility for the compiler to automatically partition sending operations across multiple OpenMP threads. For this purpose, we developed an LLVM compiler pass that partitions MPI sending operations across the different iterations of OpenMP for loops. We demonstrate the feasibility of this approach by applying it to 2D stencil codes, observing very little overhead while the correctness of the codes is sustained. Therefore, this approach facilitates the usage of these new additions to the MPI standard for existing codes. Our code is available on github: https://***/tudasc/CommPart. © 2021, Springer Nature Switzerland AG.

关键词： Static analysis

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Deep Learning Based End-to-End Specular Reflection Removal for Medical Endoscopic Images 23

Deep Learning Based End-to-End Specular Reflection Removal f...

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2023 Research in Adaptive and Convergent Systems, RACS 2023

作者： Shih, Chi-Sheng Liao, Yu-Cheng Tan, Ching-Ting Graduate Institute of Networking and Multimedia Department of Computer Science and Information Engineering High Performance and Scientific Computing Center National Taiwan University Taipei Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Department of Otolaryngology National Taiwan University Hospital Taipei Taiwan

ISBN: (纸本)9798400702280

Endoscopic images usually have many overexposed regions due to strong and focused light sources and, consequently, physicians need to change the camera angle for a clear view from time to time. This work targets removing the specular reflections from the endoscopy images but maintaining the structural and semantic texture in the images. Unlike the state-of-the-art image inpainting methods, the proposed method treats the specular reflection removal problem as an image-to-image translation task by skipping the specular detection step. The technique uses Dense Multi-scale Fusion Network (DMFN), which aims for small size and clear images, as the base and revises the network to support the large size and noisy endoscopy images. Moreover, the method can recover structure from large overexposed regions. Experimental results show that under different glare ratios and granularity settings, it is more effective than the Partial Convolution Model when recovering the corrupted pixels: the mean average error of pixel values decreases 13.2%, and the PSNR value increases 2.62dB. Qualitative analysis shows that even in the worst case when the proposed model generates output images with MAE up to 4.30, its recoveries still possess good visual qualities. The proposed model can process a 480 × 768 image in 16.2ms on a computer equipped with 36 CPU cores and an RTX-3090 GPU. It usually takes at least 20 minutes for a human to recover such images. © 2023 ACM.

关键词： Endoscopy

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Adaptive AI-Based Personalized Learning for Accelerated Vocabulary and Syntax Mastery in Young English Learners

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International Journal of Advanced computer science and Applications 2025年第4期16卷 678-687页

作者： Aravind, Angalakuduru Durairaj, M. Chitkara, Preeti Baker El-Ebiary, Yousef A. Muniyandy, Elangovan Ushasree, Linginedi Ammar, Mohamed Ben Kodad Telangana 508206 India Dept.of English Panimalar Engineering College Poonamallee Chennai600123 India Head PR & International Relations Department of Applied Sciences KIET Group of Institutions Delhi-NCR Ghaziabad India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur Dist. 522302 India Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

Language acquisition is an integral part of early schooling, but young English language learners struggle to learn vocabulary and syntax since they are not provided with specialized instruction. Conventional teaching may vary according to different learning speeds and it leads to unbalanced levels of proficiency among students and possibly leading to disengagement among slow learners. The present computer-assisted learning aids provide practice interactively but without real-time adaptation and personalized feedback, limiting their capacity to address learners’ unique problems. To overcome these constraints, this study suggests an Artificial Intelligence based personalized learning system that supports vocabulary and syntax learning via adaptive learning models, NLP-based chatbots and gamified interactive lessons. The system dynamically adapts content according to students’ most recent performance in real time to enable a personalized learning experience, which results in efficient Learning. The research has experimental study design, and two groups are considered, an AI-supported learning group and a traditional learning group. Pre-test and post-test design measures the effects of the system on vocabulary recall and syntax correctness. Other learner engagement rates like survey results and qualitative feedback inform learner experience and learning efficacy. Initial results indicate that learners working with the Artificial Intelligence powered learning system gained 25percent in recalling vocabulary and 30percent in syntax accuracy over the control group. Further, learner engagement rates are elevated because of real-time feedback and gamification components. These results emphasize the promise of AI-based personalized learning to boost language acquisition and lay the basis for further effective innovations in adaptive education technologies. © (2025), (science and Information Organization). All rights reserved.

关键词： Students

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Analytic weak-stable manifolds in unfoldings of saddle-nodes

arXiv

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arXiv 2024年

作者： Kristiansen, Kristian Uldall Szmolyan, Peter Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs Lyngby2800 Denmark Institute of Analysis and Scientific Computing Technical University of Vienna Austria

Any attracting, hyperbolic and proper node of a two-dimensional analytic vector-field has a unique strong-stable manifold. This manifold is analytic. The corresponding weak-stable manifolds are, on the other hand, not unique, but in the nonresonant case there is a unique weak-stable manifold that is analytic. As the system approaches a saddle-node (under parameter variation), a sequence of resonances (of increasing order) occur. In this paper, we give a detailed description of the analytic weak-stable manifolds during this process. In particular, we relate a "flapping-mechanism", corresponding to a dramatic change of the position of the analytic weak-stable manifold as the parameter passes through the infinitely many resonances, to the lack of analyticity of the center manifold at the saddle-node. Our work is motivated and inspired by the work of Merle, Raphaël, Rodnianski, and Szeftel, where this flapping mechanism is the crucial ingredient in the construction of C∞-smooth self-similar solutions of the compressible Euler *** Codes 34C23, 34C45, 37G05, 37G10 Copyright © 2024, The Authors. All rights reserved.

关键词： Euler equations

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RELS-DQN: A Robust and Efficient Local Search Framework for Combinatorial Optimization

arXiv

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arXiv 2023年

作者： Shao, Yuanhang Dey, Tonmoy Vuckovic, Nikola Van Popering, Luke Kuhnle, Alan Department of Computer Science Florida State University United States Department of Scientific Computing Florida State University United States Department of Computer Science Texas A&M University United States

Combinatorial optimization (CO) aims to efficiently find the best solution to NP-hard problems ranging from statistical physics to social media marketing. A wide range of CO applications can benefit from local search methods because they allow reversible action over greedy policies. Deep Q-learning (DQN) using message-passing neural networks (MPNN) has shown promise in replicating the local search behavior and obtaining comparable results to the local search algorithms. However, the over-smoothing and the information loss during the iterations of message passing limit its robustness across applications, and the large message vectors result in memory inefficiency. Our paper introduces RELS-DQN, a lightweight DQN framework that exhibits the local search behavior while providing practical scalability. Using the RELS-DQN model trained on one application, it can generalize to various applications by providing solution values higher than or equal to both the local search algorithms and the existing DQN models while remaining efficient in runtime and memory. © 2023, CC BY-NC-SA.

关键词： Combinatorial optimization

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AI-Safe Transportation: Real-Time Incident Detection and Alerting System in Smart Cities

AI-Safe Transportation: Real-Time Incident Detection and Ale...

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IEEE International Conference on Intelligent computing and Information Systems (ICICIS)

作者： Ziad Al-Agroudy Abdelrahman Mohamed Ziad Ashraf Salma Al-Sayed Walaa Gad Ziad Hassan Radwa Reda Information Systems Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt Scientific Computing Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt

Recently, with the increasing complexity of urban environments, the need for advanced technologies to ensure safe transportation in smart cities has become paramount. It has become necessary also to propose a model based on the latest technology, which can detect various road and environmental accidents in real time using live feed from street cameras. In this work, the proposed model can identify incidents such as car accidents, fires, floods, and other malfunctions in the road environment, and automatically generates two reports - one for the government to seek assistance and another for drivers to avoid the affected area and mitigate traffic congestion. The developed model utilizes state-of-the-art Deep learning techniques. It is implemented by convolutional neural networks (CNNs), for efficient processing and analysis of live video feeds. The model is trained on a comprehensive dataset of images, encompassing different types of road and environmental incidents. The trained model is subsequently integrated into a real-time monitoring system that continuously receives and analyzes live feeds from multiple cameras installed in the streets. The main features of the developed system include real-time detection and classification of road and environmental incidents, automatic generation of reports for government and users, and proactive traffic management to reduce traffic congestion in affected areas. The system is designed to work seamlessly with existing smart city infrastructure and can be easily integrated into transportation management systems. The experimental results proved that the deep learning YOLO model with a dataset licensed by MIT achieved an average accuracy of 91% in detecting various types of road and environmental incidents in real-time.

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A hybrid blockchain and AI-based approach for attack protection to secure internet of medical things networks

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Alexandria Engineering Journal 2025年 128卷 223-230页

作者： Hadeel Alsolai Fahd N. Al-Wesabi Ali M. Al-Sharafi Asma Alshuhail Abdulbasit A. Darem Bandar M. Alghamdi Fouad Shoie Alallah Abdulrahman Alzahrani Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information Technology University of Bisha Bisha 67714 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia

The Internet of Medical Things (IoMT) has revolutionized healthcare by enabling real-time monitoring, remote diagnosis, and seamless data exchange among medical devices and providers. However, the interconnected and distributed nature of IoMT systems exposes them to significant cybersecurity threats, including data breaches and unauthorized access. This study aims to develop a secure and scalable framework to safeguard IoMT environments using blockchain technology, federated learning, and dynamic consensus algorithms. The proposed approach integrates artificial intelligence to detect threats and employs federated learning to maintain patient privacy while training models collaboratively. Experimental evaluation of the framework shows improved data security, reduced response times, and enhanced system trustworthiness. The results suggest that the integration of blockchain and AI-driven mechanisms offers a robust solution to IoMT cybersecurity challenges, paving the way for more reliable and efficient healthcare systems.

关键词： Internet of medical things (IoMT) Blockchain security Federated learning AI-driven anomaly detection Cyber threat mitigation

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Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models

arXiv

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arXiv 2024年

作者： Bhandari, Shailendra Lencastre, Pedro Lind, Pedro Department of Computer Science OsloMet – Oslo Metropolitan University OsloN-0130 Norway Simula Research Laboratory Numerical Analysis and Scientific Computing Oslo0164 Norway

We explore the use of quantum generative adversarial networks QGANs for modeling eye movement velocity data. We assess whether the advanced computational capabilities of QGANs can enhance the modeling of complex stochastic distribution beyond the traditional mathematical models, particularly the Markov model. The findings indicate that while QGANs demonstrate potential in approximating complex distributions, the Markov model consistently outperforms in accurately replicating the real data distribution. This comparison underlines the challenges and avenues for refinement in time series data generation using quantum computing techniques. It emphasizes the need for further optimization of quantum models to better align with real-world data characteristics. © 2024, CC BY.

关键词： Generative adversarial networks

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Determinants of Cybercrime Victimization: Experiences and Multi-stage Recommendations from a Survey in Cameroon 14th

Determinants of Cybercrime Victimization: Experiences and Mu...

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14th EAI International Conference on Towards new e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2022

作者： Ntsama, Jean Emmanuel Tchakounte, Franklin Tchakounte Tchuimi, Dimitri Faissal, Ahmadou Fotso Kuate, Franck Arnaud Effa, Joseph Yves Udagepola, Kalum Priyanath Atemkeng, Marcellin Department of Mathematics and Computer Science Faculty of Science University of Ngaoundéré Ngaoundéré Cameroon Yaoundé Cameroon Department of Human Resource Economics Faculty of Economics and Management University of Yaoundé II SOA Yaoundé Cameroon Department of Physics Faculty of Science University of Ngaoundéré Ngaoundéré Cameroon Department of Information and Computing Sciences Scientific Research Development Institute of Technology Loganlea Australia Department of Mathematics Rhodes University Makhanda South Africa

ISBN: (纸本)9783031348952

Cybercrimes are multiplying and spreading at an elusive speed commensurate with the emerging technologies of the fourth revolution. Their sophistication and user’s vulnerability to attacks catalyze their success. Several surveys have been conducted to determine the factors favoring victimization. However, they can only be applied within a contextual framework since each ecosystem has particularities. Attempts in this direction are unavailable in Cameroon where cybercrimes cost 12.2 billion CFA in 2021. This work consists of a semi-direct survey conducted in Cameroon in 2021 to provide the determinants explaining the most frequent cybercriminal techniques, the vulnerabilities left by the users, the most targeted population segments, and the socio-demographic and economic factors justifying this security fragility. The results relate to 370 questionnaires collected throughout the territory. According to descriptive statistics and the chi-square test, the explanatory variables of cybercrime victimization are gender, age, intellectual level, level of digital knowledge, level of Information and Communication Technology (ICT) proficiency, type of equipment used, the mobile and desktop operating system used, the possession of an anti-virus/anti-spam and the marital status. Within this work, we have identified threats and their drivers and a theoretical framework has been provided with several stages that could be followed to contain cybercrime. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Statistical tests

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