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engineering 2023年第5期24卷 126-137,I0003,I0004页

作者： Can Leng Zhuo Tang Yi-Ge Zhou Zean Tian Wei-Qing Huang Jie Liu Keqin Li Kenli Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China National Supercomputing Center in Changsha Changsha 410082China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Institute of Chemical Biology and Nanomedicine State Key Laboratory of Chemo/Biosensing and ChemometricsCollege of Chemistry and Chemical EngineeringHunan UniversityChangsha 410082China Department of Applied Physics School of Physics and ElectronicsHunan UniversityChangsha 410082China Department of Computer Science State University of New YorkNew PaltzNY 12561USA

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地... 详细信息

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地阐明了第五范式的本质,旨在促进第五范式在其他领域的培养。第五范式平台主要包括模型自动构建(原始数据提取)、指纹自动构建(神经网络特征选择)以及跨学科知识串联的重复迭代(“火山图”)。与分解一起进行的是对迭代中实现的体系结构的性能评估。通过讨论,第五范式的智能驱动平台可以极大地简化和改进研究中极其繁琐和具有挑战性的工作,并通过补偿机器学习中缺少样本和替代一些由于计算资源不足而导致的数值计算来实现数值计算与机器学习之间的相互反馈,从而加速探索过程。在数据驱动的学科中,跨学科专家的协同作用和对动态数据需求的急剧增长仍然是一个挑战。我们相信,对第五范式平台的一瞥可以为其在其他领域的应用铺平道路。

关键词：机器学习自动构建天河一号数据驱动神经网络动态数据知识整合科学研究

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Groove Gap Waveguide Crossover for Butler Matrices and Beamforming in Millimeter-Wave Satellite Antenna Systems

Groove Gap Waveguide Crossover for Butler Matrices and Beamf...

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International Conference on Radioelektronika

作者： Mohammad Alibakhshikenari Peiman Parand Ali Zidour Bal S. Virdee Lida Kouhalvandi Patrick Longhi Takfarinas Saber Ernesto Limiti Electronics Engineering Department University of Rome “Tor Vergata” Rome Italy Department of Electrical Systems Engineering Faculty of Technology University of M'hamed Bougara Boumerdes Algeria Center for Communications Technology London Metropolitan University London UK Department of Electrical and Electronics Engineering Dogus University Istanbul Turkiye Lero Science Foundation Ireland Centre for Software School of Computer Science University of Galway Galway Ireland

ISBN: (数字)9798331544478

ISBN: (纸本)9798331544485

This paper presents an innovative H-plane crossover based on groove gap-waveguide (GGW) technology for high-performance millimeter-wave (mm-wave) circuits. The design facilitates the development of key transmission components, such as Butler matrices (BMs) and beamforming feeding networks (BFNs), for multi-beam antenna systems operating in the V-band spectrum (40–50 GHz). The proposed crossover is built by cascading two identical 3-dB/90° hybrid couplers. Each coupler is designed with GGW unit-cells constructed from metallic pins spaced less than a quarter-wavelength apart. This configuration creates a wide stopband of 20–57 GHz, ensuring minimal signal interference and strong impedance matching. The coupler achieves 90° phase shift, 50 dB isolation, and low insertion loss of 0.02 dB at 45 GHz, with a fractional bandwidth of 22.22%. The crossover demonstrates excellent performance over the entire V-band, making it suitable for advanced antenna systems in satellite communications and space applications. The design reduces complexity, cost, and losses typically associated with 3D and multilayer crossover technologies, providing a compact and efficient solution for mm-wave networks.

关键词： Butler matrices Satellite antennas Millimeter wave circuits Three-dimensional displays Array signal processing Impedance matching Transmitting antennas Gap waveguide Couplers Millimeter wave communication

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Multi-Modal Medical Vision-and-Language Learning for Retinal Vein Occlusion Classification

健康数据科学（英文）

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健康数据科学（英文） 2023年第4期 42-43页

作者： Weibin Liao Yanfeng Liao Zhimin Fan Jiexuan Zhang Shuochen Li Jiarui Yang Liantao Ma National Engineering Research Center for Software Engineering Peking UniversityBeijingChina Computer Science and Technology Beijing Institute of TechnologyBeijingChina Department of Ophthalmology Peking University Third HospitalBeijing Key Laboratory of Restoration of Damaged Ocular NervePeking University Third HospitalBeijingChina National Engineering Research Center for Software Engineering Peking UniversityBeijingChina

Background:Retinal vein occlusion(RVO)is one of the most common retinal vascular diseases leading to vision loss if not diagnosed and treated in *** diagnosis of central and branch RVO(CRVO and BRVO)can alleviate the workload of ophthalmologists while facilitating early detection and treat-ment of RVO and laying the foundation for subsequent symptom grading,treatment planning,and ***,the development of a fast,high-performance,and robust diagnostic model is a crucial step toward achieving quantitative and accu-rate assessment of RVO *** there is an extensive research using fundus visual images for clinical assisted diagno-sis with deep learning models,there is a lack of focus on incor-porating doctors'text reports to further enhance the models. Objectives:In this study,we propose a multi-modal medical visual-and-language learning model that utilizes fundus fluo-rescein angiography(FFA)images and physician analysis reports to classify BRVO and CRVO cases. Methods:As shown in Fig.1,the proposed model utilizes an advanced convolutional neural network to extract visual features from FFA images for patient visual representation *** language representation learning,the model first extracts basic patient features such as gender,age,vision,and blood pressure,and then uses regular expression matching to obtain typical patient symptoms from expert text ***,we cre-ated a sign vocabulary for RVO patients,including exudate,macular edema,and intraretinal hemorrhage,among *** on this vocabulary,the proposed model analyzes patient symptom manifestations and learns symptom presentation(such as exudate stage and location).Finally,based on the patient's visual and text representation,the proposed model uses a fully connected(FC)layer for classification tasks. Results:We evaluated the proposed model on a private dataset consisting of 101 patients and 1,265 FFA images,composed of 58 BRVO patients and 43 CRVO patie

关键词： language vision learning classification medical multi-modal occlusion retinal vein

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Online Dynamic Acknowledgement with Learned Predictions

Online Dynamic Acknowledgement with Learned Predictions

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Sungjin Im Benjamin Moseley Chenyang Xu Ruilong Zhang Electrical Engineering and Computer Science University of California at Merced Merced US Tepper School of Business Carnegie Mellon University Pittsburgh US Software Engineering Institute East China Normal University Shanghai China Department of Computer Science City University of Hong Kong Hong Kong China

We revisit the online dynamic acknowledgment problem. In the problem, a sequence of requests arrive over time to be acknowledged, and all outstanding requests can be satisfied simultaneously by one acknowledgement. The goal of the problem is to minimize the total request delay plus acknowledgement cost. This elegant model studies the tradeoff between acknowledgement cost and waiting experienced by requests. The problem has been well studied and the tight competitive ratios have been determined. For this well-studied problem, we focus on how to effectively use machine-learned predictions to have better *** develop algorithms that perform arbitrarily close to the optimum with accurate predictions while concurrently having the guarantees arbitrarily close to what the best online algorithms can offer without access to predictions, thereby achieving simultaneous optimum consistency and robustness. This new result is enabled by our novel prediction error measure. No error measure was defined for the problem prior to our work, and natural measures failed due to the challenge that requests with different arrival times have different effects on the objective. We hope our ideas can be used for other online problems with temporal aspects that have been resisting proper error measures.

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Visualizing Student Attention in Smart Classrooms: A Preliminary Study

Visualizing Student Attention in Smart Classrooms: A Prelimi...

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International Conference on Innovations in Information Technology

作者： Medha Mohan Ambali Parambil Amna Mohammed Abdulla Alhammadi Fady Alnajjar Zouheir Trabelsi Luqman Ali Muhammed Swavaf Department of Information Systems and Security College of IT United Arab Emirate University Al Ain UAE Department of Computer Science and Software Engineering College of IT United Arab Emirate University Al Ain UAE

In recent years, there has been a swift progression in employing novel methods in classrooms to enhance students’ academic achievements, especially in line with the growing digitization of education. Such methods often encompass systems like facial recognition to monitor various aspects of students, including attendance, emotional states, and attention. These tools are capable of evaluating students’ presence and engagement in class, offering quantifiable metrics regarding their concentration and emotions. However, a prominent challenge has been the translation of this data into an accessible form that enables educators to assess and enhance their teaching techniques swiftly. Our suggested solution tackles this issue by offering a real-time visual depiction of students’ classroom status through different visualization techniques. These visual aids allow teachers to promptly recognize trends in student focus, thus aiding in the strategic alteration of teaching styles. Furthermore, these visual representations can be tailored to display various metrics and applied to tasks beyond monitoring attention, like overseeing attendance or assessing student progress. By integrating these advanced visualizations into the educational process, both teaching efficacy and the learning experience for students and teachers alike can be substantially elevated.

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Llm-Based Automated Modeling in Symbolic Execution for Securing Medical software

SSRN

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SSRN 2024年

作者： Chen, Jingxue Deng, Liangjun Qiu, Yao Zhao, Pengbiao Lei, Hang Song, Jingcheng Wang, Xiaopei School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China Institute of Building Information Modeling Ningbo Polytechnic College Zhejiang Ningbo315800 China College of Information Science and Engineering Linyi University Shandong Linyi276000 China Department of Computer Science and Engineering University of California RiversideCA92521 United States

There are lots of sensitive information in medical consumer devices. Since Consumer electronics products in the healthcare mainly focus on health monitoring and personal health management, such as wearable ECG monitoring devices, smart weight scales and so on. It is paramount to ensure the security and reliability of software in healthcare delivery, especially for sensitive medical data of integration with artificial intelligence (AI). Therefore, it is necessitating strict security verification and continuous vigilance against potential software vulnerabilities. WebAssembly, known for its security, portability, and speed, has emerged as a crucial technology for medical device software, significantly impacting the healthcare electronics sector, particularly in AI-generated content. There is a novel machine learning-based approach to address the challenges of symbolic execution in WebAssembly by automatically modeling unknown functions using a fine-tuned large language model (LLM) with 20 billion parameters, enhancing the execution's integrity and efficiency. The method, applicable across multiple programming languages and extendable to WebAssembly-compiled programs, shows promising experimental results in model performance and efficiency. This ensures enhanced security and reliability of software for CE, setting the stage for more flexible WebAssembly standards in medical CE and AI-driven healthcare devices. © 2024, The Authors. All rights reserved.

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Enhancing the Sum Rate in Massive MIMO Optical Wireless Communication System by Using MMSE Precoding

Enhancing the Sum Rate in Massive MIMO Optical Wireless Comm...

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International Conference on Communication, Computing and Digital Systems (C-CODE)

作者： Zakir Ali Muhammad Furqan Qasim Jan National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Computer Sciences Bahria University Lahore Campus Pakistan Department of Software Engineering Capital University of Science and Technology (CUST) Islamabad Pakistan Department of Computer Science COMSATS University Islamabad Attock Campus Attock Pakistan

We investigate an optical wireless communications system based on massive multiple-input multiple-output (MIMO). Through a transmit lens, the optical base station with its huge optical transmitters communicates with the user terminals (UTs). We create a channel model for optical massive MIMO transmissions and examine light refraction caused by lenses, with an emphasis on LED transmitters. We formulate the sum rate maximization problem and convert it into a convex optimization problem to get a closed-form solution. MMSE is used to increase the sum rate and Karush-Kuhn-Tucker (KKT) conditions were used to solve the aforementioned problem. We study traditional precoding techniques, maximum ratio transmission (MRT), and regularized zero-forcing precoding (RZF) for an optical massive MIMO system. The proposed technique is ajoint precoder design to enhance the sum rate. The simulation results show that the proposed MMSE precoder design outperforms MRT and RZF precoders in a massive MIMO optical wireless communication system.

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A new transformation approach for complex systems modelling and simulation: Application to industrial control system

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International Journal of Simulation and Process Modelling 2021年第1期16卷 34-48页

作者： Seddari, Noureddine Boukelkoul, Sofiane Bouras, Abdelghani Belaoued, Mohamed Redjimi, Mohammed LICUS Laboratory Department of Computer Science University of 20 August 1955-Skikda Skikda21000 Algeria LIRE Laboratory Department of Software Technologies and Information Systems University of Abdelhamid Mehri-Constantine 2 Constantine25000 Algeria Department of Industrial Engineering Alfaisal University Riyadh12714 Saudi Arabia

Due to the increasing importance of complex systems, the problems of modelling and simulation (M&S) of these systems, their approaches and solutions have been studied extensively these last years. In this paper, we present a novel approach to modelling and simulation of discrete event dynamic systems (DEDS), based on the combination of discrete event systems specification (DEVS) and agent group role (AGR). In this study, the DEVS atomic models are transformed into agents and the DEVS coupled models into groups of agents. Thus, we provide a set of procedures and functions that allow the systematic transformation from DEVS into AGR models in order to be implemented in multi-agent platforms. The proposed approach was validated by a case study, an application in the water supply used in natural gas liquefaction process. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Water supply

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A Virtual Farm Tour for Public Education about Dairy Industry

A Virtual Farm Tour for Public Education about Dairy Industr...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Anh Nguyen Emma Windfeld Michael Francis Guillaume Lhermie Kangsoo Kim Department of Computer Science University of Calgary The Simpson Centre The School of Public Policy University of Calgary Department of Electrical and Software Engineering University of Calgary The Simpson Centre Faculty of Veterinary Medicine University of Calgary

In this paper, we present an immersive virtual farm simulation developed to provide realistic on-farm experiences to the public. Users could visit the virtual farm, walk through various sites where dairy cows are raised, and learn how the dairy products are produced through the virtual experience. Public users' responses about the virtual experience were collected in various measures, e.g., user experience and learning efficacy, via showcases at local libraries. We present preliminary results regarding the potential of the simulation as an effective agricultural education, and discuss our future plans.

关键词： Industries Solid modeling Three-dimensional displays Dairy products Conferences Virtual reality User interfaces

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Sinhala Named Entity Recognition Model: Domain-Specific Classes in Sports

Sinhala Named Entity Recognition Model: Domain-Specific Clas...

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Advancements in Computing (ICAC), International Conference on

作者： W.M.S.K. Wijesinghe Muditha Tissera Department of Statistics and Computer Science Faculty of Science University of Kelaniya Sri Lanka Department of Software Engineering Faculty of Computing & Technology University of Kelaniya Sri Lanka

ISBN: (纸本)9798350398106

Named Entity Recognition (NER) is one of the crucial and vital subtasks that must be solved in most Natural Language Processing (NLP) tasks. However, constructing a NER system for the Sinhala Language is challenging. Because it comes under the category of low-resource languages. Therefore, the proposed approach attempted designing a mechanism to identify specific named entities in the sports domain. Firstly, a domain-specific corpus was built using Sinhala sport e-News articles. Then a semi-automated, rule-based component named as “Class_Label_Suggester” was built to annotate pre-defined named entities. After auto annotation, the outcome was further validated manually with a little effort. Finally, it was trained using the annotated data. Linear Perceptron, Stochastic Gradient Descent (SGD), Multinomial Naive Bayes (MNB), and Passive Aggressive classifiers were used to train the NER model. Though, the above Machine Learning (ML) algorithms showed approximately 98% accuracy, the MNB model demonstrated highest accuracy for the identified class labels of which, 99.76% for ‘Ground’, 99.53% for ‘School’, 98.55% for ‘Tournament’, and 97.87% for ‘Other’ classes. Additionally, high precision values of the above classes were 81%, 72%, 62%, and 98% respectively. An accurately annotated Sinhala dataset and the trained Sinhala NER model are main contributions of the study.

关键词： Machine learning algorithms Annotations Computational modeling Stochastic processes Machine learning Approximation algorithms Natural language processing

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