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检索条件"任意字段=23rd International Conference on Computer Systems and Technologies, CompSysTech 2022"

共 801 条记录，以下是241-250 订阅

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Object-relational Mapping Using JPA, Hibernate and Spring Data JPA 23

Object-relational Mapping Using JPA, Hibernate and Spring Da...

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23rd international conference on Control systems and computer Science technologies, CSCS 2021

作者： Tudose, Catalin Odubasteanu, Carmen Luxoft Romania Learning Management Department Bucharest Romania 'Politehnica' University of Bucharest Department of Computer Science Bucharest Romania

ISBN: (纸本)9781665439398

Object persistence means that information from the objects in an application can survive after that application ended its execution;this information can be saved and retrieved. Persistence in Java will require object-relational mapping - mapping the object instances to the tables of a relational database. There are a few alternatives to make this object-relational mapping, from which we analyze, compare, and contrast Jakarta Persistence API (JPA), Hibernate, and Spring Data JPA. © 2021 IEEE.

关键词： Mapping

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Smart reactive grading system based on genetic algorithms and behavioral psychology 23

Smart reactive grading system based on genetic algorithms an...

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23rd international conference on Control systems and computer Science technologies, CSCS 2021

作者： Nutescu, Ciprian-Ionut Mocanu, Mariana University Politehnica Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania

ISBN: (纸本)9781665439398

In this paper, we propose an intelligent scoring system based on genetic algorithms to provide students with exams for their level of study. The system is reactive, adapting the exam to the student's level using genetic algorithms to generate exam questions, but it is also gamified using behavioral psychology to encourage students to get better results in exam. The reward model behind the generation of the exam is based on the behavioral psychology used in modern video games for a greater involvement of the student. © 2021 IEEE.

关键词： Genetic algorithms

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Prediction based Auto-Pilot Interface for Drone to Object Chasing using Historical TSPI Data 23

Prediction based Auto-Pilot Interface for Drone to Object Ch...

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23rd international conference on Control, Automation and systems, ICCAS 2023

作者： Jang, Shinhyoung Park, Byeonghwi Jeong, Juheon Mahedy, Jack Gebreslassie, Nebey Lee, Minji Matson, Eric T. Chungnam National University Department of Computer Science and Engineering Daejeon34134 Korea Republic of Purdue University Department of Computer and Information Technology West LafayetteIN47906 United States Purdue University Department of Technology West LafayetteIN47906 United States

ISBN: (纸本)9788993215274

Replacing military aircraft with Unmanned Aerial Vehicles (UAV) offers advantages such as reducing costs and risks associated with conventional military aircraft. To defend against enemy UAV attacks, anti-drone systems are used. While most technologies focus on drone detection, this paper proposes a process of tracking targets using arbitrary UAV to enhance camera data quality or directly engage and neutralize targets. The system comprises drone detection, movement prediction, and chasing. Devices such as radar are used to detect drones, and they provide Time-Space-Position Information (TSPI) using suitable data formats. Using Machine Learning (ML) techniques make it feasible to predict an object's future location, represented with equations. Utilizing trained machine learning models, the Auto-pilot Application Programming Interface (API) of the drone is employed to track based on the predicted results. Through experiments, the feasibility of predicting an object's future location using various ML techniques has been confirmed. Furthermore, verification has been conducted to establish the attainability of implementing the Auto-pilot API through the utilization of DJI's API. © 2023 ICROS.

关键词： Drones

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KylinArm: An Arm Gesture Recognition System for Mobile Devices 1

引用

23rd international conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Zhao, Shikun Hong, Jingxuan Zhang, Zixuan Wang, Xuqiang Zhang, Jin Gong, Xiaoli Nankai Univ Coll Comp Sci Tianjin Peoples R China Tianjin Key Lab Brain Sci & Intelligent Rehabil Tianjin Peoples R China State Key Lab Highend Server & Storage Technol Jinan Peoples R China State Grid Tianjin Informat & Commun Co Tianjin Peoples R China

ISBN: (数字)9789819708017

ISBN: (纸本)9789819708000;9789819708017

Gesture-based Human-computer Interaction (HCI) has become a primary means of device control due to its naturalness and humanized characteristics, making it applicable for tasks such as drone control and gaming. Gesture recognition using an inertial measurement unit (IMU) has emerged as a major trend in this field. However, due to the intricate nature of the arm structure and the diversity of gestures, relying on a single IMU system for gesture recognition results in limited accuracy. Modern mobile devices, such as smartphones and smartwatches, are equipped with IMUs that allow for convenient data acquisition methods and offer computing resources for deep learning model inference. In this paper, we propose a real-time arm gesture recognition method, called KylinArm, which achieves high-precision gesture recognition by coordinating 2 IMUs. The KylinArm method is optimized for mobile devices and based on a dual-branch 1D-CNN classifier. It supports the classification of 12 arm gestures with an optimized strategy for mobile devices that have limited computation resources and power supply. Additionally, we adopt an optimization method based on CORrelation ALignment (CORAL) to address the decreasing accuracy that occurs when new users are introduced. Finally, we evaluate KylinArm and test it in real scenes, achieving a recognition accuracy of over 98%.

关键词： Arm Gesture Recognition Inertial Measurement Unit Human-computer Interaction Mobile Devices Deep Learning

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On developing deep learning models with particle swarm optimization in the presence of poisoning attacks 23

On developing deep learning models with particle swarm optim...

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23rd international Arab conference on Information Technology, ACIT 2022

作者： Maabreh, Majdi Darwish, Omar Karajeh, Ola Tashtoush, Yahya The Hashemite University Department of Computer Information Systems Zarqa Jordan Eastern Michigan University Information Security and Applied Computing Department MI United States Virginia Polytechnic Institute and State University Department of Computer Science VA United States Jordan University of Science and Technology Department of Computer Science Irbid Jordan

ISBN: (纸本)9798350320244

Deep learning (DL) has demonstrated several successes in a variety of fields, particularly in the era of big data. The process of training a deep learning model entails selecting the ideal learning parameters such as the number of hidden layers and neurons. Particle Swarm Optimization (PSO) is one useful nature-inspired algorithm to set those two influential learning parameters. In this study, two different datasets are used to study and evaluate the particle swarm optimizer with deep learning on datasets of different concentrations of poisoning attack, where some adversarial samples were crafted by attackers to ruin the learning process. The results showed that particle swarm optimization could find settings for deep learning with the existence of poisoned data that maximizes the model accuracy on the unseen testing dataset, and could also offer better recommendations compared to those recommended on all benign samples. This may introduce a concern that optimizers might conceal the existence of data poisoning, which may lead to unreliable learning in the advanced stages of upgrading the model on updated datasets. © 2022 IEEE.

关键词： Particle swarm optimization (PSO)

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Performance improvement of a hybrid energy system feeding an isolated load 23

Performance improvement of a hybrid energy system feeding an...

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23rd international Middle East Power systems conference, MEPCON 2022

作者： Mossa, Mahmoud A. Ouanjli, Najib El Gam, Olfa Kamel, Omar Makram Minia University Faculty of Engineering Electrical Engineering Department Minia61111 Egypt Hassan 1st University Faculty of Sciences and Technology Settat26000 Morocco Université Québéc en Abitibi Témiscamingue Departement Ecole de Genie Rouyn-NorandaQCJ9X 5E4 Canada Higher Institute of Engineering and Technology Electrical and Computer Department El-Minia Egypt

ISBN: (纸本)9781665463638

This article concerns with enhancing the dynamics of a hybrid wind/PV/battery system feeding an isolated load. The complete system components are firstly modeled and described in detail. The paper contribution is presented through designing an efficient control topology for managing the dynamics of the wind-driven generator and comparing its performance with one of the existent control algorithms to visualize its advantages. The study also adopts a power management procedure for achieving the optimal power flow in the system. An effective control procedure is also utilized to control the battery performance. The performance of PV system is optimally managed to maintain the maximum power extraction. The obtained results confirm the advantages of the designed controller used with the wind generation system in comparison with the existent control approach. It also approves the validity of the power management procedure in stabilizing the power transfer and achieving an optimal exploitation of wind and solar energies. © 2022 IEEE.

关键词： Power management

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Efficient Arabic Query Auto-Completion for Question Answering at a University 23

Efficient Arabic Query Auto-Completion for Question Answerin...

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23rd international Arab conference on Information Technology, ACIT 2022

作者： Abdelhafez, Momin Khateeb, Ghaydaa Yahya, Adnan Birzeit University Dept. Computer Engineering Birzeit Palestine

ISBN: (纸本)9798350320244

In this paper we describe an implementation of an Arabic query auto-completion system for student question-answering at a University. University students make many inquiries concerning academic life: about majors, concentrations, dates, instructors, courses, rooms, exams and more. Auto-completion (AC) has recently been part of many user interfaces, such as search bars on web pages, social media sites and mobile applications. We investigate multiple approaches to completion candidate generation and ranking and the role Arabic NLP may play in that. After experimenting with other options, we collected the data used in our system directly from students at the University. This data can be expanded to account for more types of queries and ways to express information needs. We describe our dataset, give an evaluation of individual system components and of the system results in general. The goal of this work is to improve the answer search experience by reducing the time of entering the query and biasing the completed query towards unambiguous and easily answerable questions. We divided the implementation into several stages and report on the results for each stage separately. Despite the difficulties of Arabic NLP, the results of our system were encouraging and compared well with other QAC systems described in the literature. Finally, we give an informal evaluation of overall results and the improvement resulting from using QAC for our QA system. © 2022 IEEE.

关键词： Natural language processing systems

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Advanced Architecture Design of High-Radix Router Based on Chiplet Integration and IP Reusability 23

Advanced Architecture Design of High-Radix Router Based on C...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liang, Chongshan Dai, Yi Xu, Weixia Li, Qiong College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665494571

The performance and cost of large-scale interconnection networks are increasingly determined by the high-radix routers which can effectively reduce the hop count and provide path diversity. But with Moore's law approaching its physical limitations, the exponential growth of I/O pins density on an integrated circuit has stopped, so as the clock frequency. When the number of I/O pins integrated on a router chip remains stable, increasing the radix of the router will, in turn, reduce the single-port bandwidth. Therefore, it will become infeasible to increase the radix of the routers meanwhile improving per port bandwidth, which motivates us to propose a truly scalable router architecture to enable more flexible and spacious design alternatives, rather than constraining the design space to a single monolithic chip. Consequently, we leverage the 'chiplet' technology to integrate multiple switch dies to construct higher-radix routers. In this paper, by exploring different successful chip architectures based on chiplet in various fields, we show that chiplet, a heterogeneous integration technology based on IP reusability, is the promising way to overcome the slowdown of Moore's law and enable the routers continue scaling to higher radix. Then we illustrate that how to construct a chiplet-based higher-radix router architecture by efficiently integrating multiple switch dies with specific high-radix topologies. What's more, combining with the extremely high integration density and performance of 3D chiplet stacking advanced packaging technologies, we propose a chiplet architecture of high-radix router based on 3D stacking for future design. In addition, due to the transmission distance between switch die is significantly shorter than that between monolithic router chip interconnected on traditional PCB boards, we appropriately reduce the buffer resource of the switch dies without the degradation of performance according to the evaluation of buffer depth usage. Based on this

关键词： Semiconductor device modeling Three-dimensional displays Costs Moore's Law Stacking Switches Packaging

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Optimized Monocular Depth Estimation With Reparameterization on Embedded Devices 23

Optimized Monocular Depth Estimation With Reparameterization...

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作者： Liu, Siping Li, Renfa Tu, Xiaohan Xie, Guoqi Xu, Cheng College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Railway Police College Department of Image and Network Investigation Zhengzhou450053 China

ISBN: (纸本)9781665494571

Monocular depth estimation (MDE) has been a basic task in many fields. Currently, researchers usually improve the accuracy of MDE by designing deep neural networks, including complex branches or other components. Although complex networks achieve increased precision, they bring slow speed and high memory access cost (MAC) in embedded systems. To balance the speed, accuracy, and MAC for embedded systems, we propose a directly connected model (DCM), which relies on existing networks, for fast inference in embedded systems. DCM balances the speed, precision, and MAC with structural reparameterization that decouples its training and inference networks. To further reduce the MAC and latency of DCM for embedded systems, we construct a network optimization frame-work based on recent template-based optimization methods. In the framework, we respectively adopt the machine learning LightGBM and contextual simulated annealing algorithm as the cost model and optimizer to complete rapid optimization in embedded systems. Our comprehensive experiments confirm that our methods achieve a better trade-off among precision, latency, and MAC. The accuracy of our original DCM inference networks reaches at least 6.13% better than the state of the arts. The optimized DCM inference networks decrease latency, main MAC, and power consumption by 46.49%, 90.97%, and 65.31% on Jetson TX2 GPU, without unchanged accuracy. © 2021 IEEE.

关键词： Training Embedded systems Costs Power demand Smart cities Graphics processing units Estimation

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A critical analysis of bio-inspired classifiers and decision-making solutions 23

A critical analysis of bio-inspired classifiers and decision...

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23rd international conference on Control systems and computer Science technologies, CSCS 2021

作者： Sergiu, Manolache Popescu, Nirvana University Politehnica of Bucharest Computer Science Department Bucharest Romania

ISBN: (纸本)9781665439398

As society becomes more inter-dependent and inter-connected, the need of timely and efficient decision-making has become a necessity. Thus, the number of decision-making systems has steadily risen, allowing for an overview of their application relevant to their strengths and weaknesses. Concomitantly classifiers were developed, first to optimize performance, then as alternatives for decision-making tools. This paper presents a critical analysis of the bio-inspired classifiers and decision-making systems, showcasing their evolution in time, studying their application in several domains, emphasizing their advantages and shortcomings, while presenting performance data. © 2021 IEEE.

关键词： Decision making

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