检索结果-内蒙古大学图书馆

2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Vijay, Margret Jayan, J.P. Department of Computer Science Tamil Nadu India Department of Software Engineering Tamil Nadu India

ISBN: (纸本)9798350333244

This study explores the evolving landscape of adaptive gamification-related research from 2014 to 2022. A comprehensive analysis of 1110 documents from journals and books was conducted to investigate publication trends, prominent countries, authors, and the development of knowledge in the field. The findings reveal a consistent growth in publications, with an annual growth rate of 26.89%. Gamification emerges as a dominant cluster, consistently exhibiting high centrality and density scores, indicating its strong interconnectedness and influence within the research network. The country-wise analysis demonstrates a diverse global research landscape, with the United States contributing the highest number of publications, followed by Spain and Germany. Prominent authors from universities and institutions worldwide have made significant contributions to the field. The thematic evolution demonstrates a shift from collaborative learning to gamification and online learning, followed by a focus on technology-driven themes and the emergence of machine learning as a prominent area of research. The increasing relevance of machine learning indicates its growing significance in the domain of adaptive gamification. These findings contribute to the knowledge base of adaptive gamification and offer insights for researchers in shaping the future of gamified learning environments. © 2023 IEEE.

关键词： Knowledge based systems

来源：评论

学校读者我要写书评

暂无评论

Performance Based Scheduling in Distributed Mixed Criticality Systems

引用

IEEE Access 2025年 13卷 95321-95340页

作者： Ali, Amjad Wasly, Saud Khattak, Asad Masood Ali, Ihsan Iqbal, Shahid Hayat, Bashir University of Swat Department of Computer and Software Technology Khyber Pakhtunkhwa Swat Pakistan King Abdul Aziz University Electrical and Computer Engineering Department Jaddah Saudi Arabia Zayed University College of Technological Innovation United Arab Emirates Institute of Management Sciences School of Computer Science and IT Peshawar Pakistan

With a focus on computationally intensive, distributed, and parallel workloads, scheduling in mixed-criticality distributed systems presents significant challenges due to shared memory and resources, as well as the diverse demands of tasks. The system’s efficiency is heavily dependent on the overall scheduling duration (make span), while individual task deadlines impose strict timing constraints. When the tasks need to simultaneously access the shared memory, then these tasks interfere the execution of one another. For managing the scheduling of interfering tasks in distributed mixed-criticality systems, a novel Interference-Aware Partitioning Fixed Priority (IAP-FP) approach is proposed, which effectively handles task partitioning among cores while considering interference, ensuring better performance and adherence to critical deadlines. This method reduces task waiting times, which minimizes the scheduling duration and improving the schedulability of the task set. The novel proposed approach is compared with Mixed-Criticality Multicore Compositional Earliest Deadline First (MMC-EDF), Global and Mixed Criticality Partitioning (MC-Partitioning) approaches to show the efficiency. The proposed approach schedules 75% tasks while the MMC-EDF, Global and MC-Partitioning approaches schedules 65%, 0% and 18% tasks respectively for target utilization U=0.8. As the utilization of mixed criticality (MC) workload increases, the schedulability of MC task sets decreases, but still the proposed approach performs better than the MMC-EDF, Global and MC-partitioning approaches. © 2013 IEEE.

关键词： Embedded systems

来源：评论

学校读者我要写书评

暂无评论

Evaluating Generative Language Models with Prompt engineering for Categorizing User Stories to its Sector Domains 9

Evaluating Generative Language Models with Prompt Engineerin...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Alawaji, Batool Hakami, Mona Alshemaimri, Bader Qassim University Department of Computer Qassim Saudi Arabia King Saud University Department of Software Engineering Riyadh Saudi Arabia

ISBN: (纸本)9798350394474

In Agile software development, user stories capture requirements through a concise, user-centric approach. Manual categorization of these stories is both labor-intensive and error-prone. This study addresses the gap in existing research by exploring the application of generative language models, specifically GPT-style models, with prompt engineering for user story *** research introduces experiments utilizing two generative language models, gpt-3.5-turbo and Llama-2-chat-hf, emphasizing innovative prompt engineering. The evaluation of gpt-3.5-turbo and Llama-2-chat-hf demonstrates commendable performance in zero-shot experiments. Notably, gpt-3.5-turbo outperforms in few-shot prompting with a higher F-score of 67.93%. The gpt-3.5-turbo showcased superior compliance with instructions compared to Llama-2-chat-hf. The study underscores the promising capability of generative language models, with prompt engineering, to automate user story categorization *** work contributes to the potential of automating user story categorization in low-resource settings, showcasing how leveraging LLMs can enhance accuracy and streamline the categorization process in software development, ultimately leading to more efficient outcomes. © 2024 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

An Efficient Dialogue Policy Agent with Model-Based Causal Reinforcement Learning 31

An Efficient Dialogue Policy Agent with Model-Based Causal R...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Xu, Kai Wang, Zhenyu Zhao, Yangyang Fang, Bopeng School of Software Engineering South China University of Technology Guangdong China Department of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798891761964

Dialogue policy trains an agent to select dialogue actions frequently implemented via deep reinforcement learning (DRL). The model-based reinforcement methods built a world model to generate simulated data to alleviate the sample inefficiency. However, traditional world model methods merely consider one-step dialogues, leading to an inaccurate environmental simulation. Furthermore, different users may have different intention preferences, while most existing studies lack consideration of the intention-preferences causal relationship. This paper proposes a novel framework for dialogue policy learning named MCA, implemented through model-based reinforcement learning with automatically constructed causal chains. The MCA model utilizes an autoregressive Transformer to model dialogue trajectories, enabling a more accurate simulation of the environment. Additionally, it constructs a causal chains module that outputs latent preference distributions for intention-action pairs, thereby elucidating the relationship between user intentions and agent actions. The experimental results show that MCA can achieve state-of-the-art performances on three dialogue datasets over the compared dialogue agents, highlighting its effectiveness and robustness. © 2025 Association for Computational Linguistics.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Optimization of Lane-changing Advisory in Mixed Traffic of Connected Vehicles and Human-driven Vehicles at Expressway Bottlenecks

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2023年 1-10页

作者： Lin, Jingmin Yu, Chunhui Wang, Ling Liu, Guanjun Wang, Jiacun Ma, Wanjing Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai P.R. China Department of Computer Science and Technology Tongji University Shanghai P.R. China Computer Science and Software Engineering Department Monmouth University USA

Lane closure due to events such as accidents creates bottlenecks on expressways. The mandatory lane changes of merging vehicles lead to congestion. As the cyber-physical system (CPS) develops, mixed traffic consisting of connected vehicles (CVs) and human-driven vehicles (HVs) has emerged. CVs can receive lane-changing (LC) advisories and complete merging to alleviate congestion. Most existing studies assume CVs can timely and exactly follow LC advisories, which is not realistic with human drivers. This study develops an LC advisory model for CVs, whose response time and compliance degrees are considered, at an expressway bottleneck with lane closure under mixed traffic of CVs and HVs. The LC strategies are optimized for CVs to minimize the total delay of CVs and HVs approaching the bottleneck. The constraints include the domains of decision variables, vehicle passing states at the bottleneck, vehicle kinematics, implementation of LC advisories, LC safety, the maximum number of LC manoeuvres, the minimum time interval between LC advisories, the potential LC CVs, and the evolution of vehicle states. The simulation-based method is applied to predict vehicle delay, which is formulated as an implicit function of the LC strategies for CVs. Genetic Algorithm (GA) is designed for solutions. The numerical studies validate the advantages of the proposed model. The sensitivity analysis shows that: 1) the critical CV penetration rate is 60%, below which the marginal benefits are significant with increasing CV penetration rates;and 2) the consideration of the response time and the compliance degree of CVs makes a great difference. IEEE

关键词： Vehicles

来源：评论

学校读者我要写书评

暂无评论

Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

引用

ACM Transactions on Intelligent Systems and Technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Fast Implementation of Eta-T pairing on FPGAs

Fast Implementation of Eta-T pairing on FPGAs

引用

2024 International Conference on Horizons of Information Technology and engineering, HITE 2024

作者： Aslam, Waseem Raza, Asim University of Central Punjab Department of Software Engineering Lahore Pakistan University of Central Punjab Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798331516055

Key distribution as a core feature of any security system is one of the challenging tasks in an online transaction. Pairing is used to share the key between the users as an answer to the underlying security problem. Diffie-Hellman key exchange protocol computes pairing in two rounds whereas Eta-T is the most efficient and widely used pairing algorithm in the field of elliptic curve cryptosystems. After careful analysis of existing Eta-T pairing techniques, this work proposes a modification from serial computation scheme to parallel for better overall performance. Parallel Karatsuba multiplier and Most Significant reduction algorithm is used to speed up and optimize the pairing algorithm. Consequently, proposed pairing accelerator calculates Eta-T pairing in just 36.4μs and takes only 3597 clock cycles with maximum clock frequency of 98.7 MHz on Virtex-II. Further, by increasing the number of coefficients processed per cycle up to 6, this work shows reduction in the calculation time and clock cycles of Eta-T pairing up to 26.16μs and 1997 respectively. It is also shown that calculation time starts increasing after reaching D = 14. Similarly, it is shown that the fastest calculation of Eta-T pairing in characteristics three over the field F397 uses significantly less clock cycles than previous known implementations at the cost of less clock frequency. © 2024 IEEE.

关键词： Electric clocks

来源：评论

学校读者我要写书评

暂无评论

A ShuffleNet Architecture-Based Multi-Layer Feature Scaling and Fusion Approach for Underwater Net Cage Damage Detection in Aquaculture

A ShuffleNet Architecture-Based Multi-Layer Feature Scaling ...

引用

2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Huang, Xiang-Rui Chen, Yi-Cheng Chen, Liang-Bi Hu, Wu-Chih National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

ISBN: (纸本)9798331530839

In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consuming and complex. To overcome this problem, this paper proposes a computer vision solution for identifying damage in underwater net cages to address the inefficiencies and challenges of traditional manual inspections. The proposed scheme utilizes a high-performance multi-branch computational architecture designed based on ShuffleNet architecture to detect net cage damage more efficiently. Experimental results demonstrate that this work performs well on the ImageNet ILSVRC-2010 dataset and achieves an accuracy of 88.54% in underwater net damage detection. © 2024 IEEE.

关键词： Aquaculture

来源：评论

学校读者我要写书评

暂无评论

Tomato Leaf Diseases Prediction Using Deep Learning Algorithms

Tomato Leaf Diseases Prediction Using Deep Learning Algorith...

引用

2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Ananthi, P. Devi, K. Nirmala Gopinath, D. Shanmugapriya, P. Gowtham, S. Kongu Engineering College Department Of Computer Technology Tamilnadu Perundurai India Bangaluru India Kristu Jayanti College Department Of Computer Science Bangaluru India Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798350364828

In the realm of agriculture, infections on tomato leaves pose a worldwide danger to established tomato production, impacting a large number of farmers worldwide. To ensure healthy tomato plant growth and food security for the world's expanding population, early disease detection, treatment, and solution identification are essential. The use of computer-assisted technologies for disease detection in plant leaf is prevalent in modern agriculture. The goal was to develop an effective deep learning model for the detection of diseases in tomato plant leaf. To address memory and computational resource constraints, the proposed model incorporates a lightweight 'Region Convolutional Neural Network (R-CNN)' head and conducted experiments with various neural network architectures, including Densenet-169, optimized Mobilenet-V2 and Resnet-50, to enhance detection accuracy and computational efficiency. The research extends the capabilities of these neural network architectures by optimizing anchor proportions in the Conventional Neural Network (CNN) and modifying feature extraction topologies. These optimizations resulted in improved detection accuracy and computational efficiency and compared proposed technique against cutting-edge models to evaluate its viability and robustness. The outcomes of the research were promising. Impressive outcomes were obtained by the proposed approach regarding accuracy, F1-score, recall, and precision. © 2024 IEEE.

关键词： Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

An Ingenious IoT Based Crop Prediction System Using ML and EL

引用

computers, Materials & Continua 2024年第4期79卷 183-199页

作者： Shabana Ramzan Yazeed Yasin Ghadi Hanan Aljuaid Aqsa Mahmood Basharat Ali Department of Computer Science&IT Government Sadiq CollegeWomen UniversityBahawalpur63100Pakistan Department of Computer Science/Software Engineering Al Ain UniversityAl Ain64141UAE Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)Riyadh11671Saudi Arabia Agronomic Research Station Bahawalpur63100Pakistan

Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers haveno proper knowledge to select which crop is suitable to grow according to the environmental factors and soilcharacteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sectorof the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning,and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC),and environmental factors like temperature to improve crop yield. These parameters play a vital role in suggestinga suitable crop to cope the food scarcity. This paper proposes a systemcomprised of twomodules, first module usesstatic data and the second module takes hybrid data collection (IoT-based real-time data and manual data) withmachine learning and ensemble learning algorithms to suggest the suitable crop in the farm to maximize the *** is used to train the model that predicts the crop. This system proposed an intelligent and low-cost solutionfor the farmers to process the data and predict the suitable *** implemented the proposed system in the *** efficiency and accuracy of the proposed system are confirmed by the generated results to predict the crop.

关键词： Machine learning Internet of Things sensors ensemble learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：