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18th International Conference on Computational Intelligence and Security, CIS 2022

作者： Wu, Kangjian Li, Yuan Song, Xiaobo Wang, Xiaoli School of Computer Science and Technology Xidian University Shaanxi Xi'an710071 China Xi' An Vocational and Technical College Basic Course Teaching Department Shaanxi Xi'an710077 China The 20th Research Institute of China Electronics Technology Group Corporation Shaanxi Xi'an710068 China

ISBN: (纸本)9798350346275

The large-scale arbitrarily divisible load scheduling has been solved effectively by periodic multi-installment scheduling (PMIS), but there are relatively few studies on coarse-grained divisible loads. Therefore, we propose a coarse-grained multi-installment scheduling model in this paper, named CMIS. Two crucial issues were solved: First, an optimal load partition was derived through a strict mathematical derivation;Second, an optimal number of installments was obtained between the subtle upper and lower bounds we found. Finally, we compare the proposed model with an up-to-date model and the experimental results show that the proposed model outperforms the existing model in obtaining a shorter makespan. © 2022 IEEE.

关键词： Coarse-grained modeling

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Prediction of daily patient visits in respiratory department using deep learning 18

Prediction of daily patient visits in respiratory department...

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18th International Conference on Computational Intelligence and Security, CIS 2022

作者： Song, Xiaobo Zhang, Xinyi Wang, Xiaoli Li, Yuan The 20th Research Institute of China Electronics Technology Group Corporation Shaanxi Xi'an710068 China School of Computer Science and Technology Xidian University Shaanxi Xi'an710071 China Xi'An Vocational and Technical College Basic Course Teaching Department Shaanxi Xi'an710077 China

ISBN: (纸本)9798350346275

Since climate changes and air pollution have a direct impact on the number of emergency respiratory patients visited, in this paper, we study predicting methods of daily patient visits in respiratory department based on deep learning. We validate our methods via experiments on the actual data of the First Affiliated Hospital of Xi'an Medical University. To improve the accuracy of prediction, we propose four data preprocessing methods, including meteorological lag and time sensitive method, sliding window method, seasonal classification method, as well as correlation analysis method. Experimental results show that the prediction accuracy of the deep learning model is improved from 76.28% to 86.37% after adopting our four preprocessing methods. The maximum error between predicted and actual visits is less than 20. © 2022 IEEE.

关键词： Forecasting

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Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks

arXiv

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arXiv 2025年

作者： Cui, Shuang Li, Yi Li, Jiangmeng Tang, Xiongxin Su, Bing Xu, Fanjiang Xiong, Hui National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China China Department of Computer Science & Engineering the Hong Kong University of Science and Technology Hong Kong

Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation of existing methods during out-of-distribution inferences. In this work, we gauge the intrinsic reason behind the performance degradation, which is identified as the heterogeneity of lens-specific point spread functions. Empirical evidence supports this finding, motivating us to employ a continual test-time adaptation (CTTA) paradigm for SIDD. However, traditional CTTA methods, which primarily rely on entropy minimization, cannot sufficiently explore task-dependent information for pixel-level regression tasks like SIDD. To address this issue, we propose a novel Siamese networks-based continual test-time adaptation framework, which adapts source models to continuously changing target domains only requiring unlabeled target data in an online manner. To further mitigate semantically erroneous textures introduced by source SIDD models under severe degradation, we revisit the learning paradigm through a structural causal model and propose Causal Siamese networks (CauSiam). Our method leverages large-scale pre-trained vision-language models to derive discriminative universal semantic priors and integrates these priors into Siamese networks, ensuring causal identifiability between blurry inputs and restored images. Extensive experiments demonstrate that CauSiam effectively improves the generalization performance of existing SIDD methods in continuously changing domains. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Optimizing resource allocation in flex-grid entanglement distribution networks

Optimizing resource allocation in flex-grid entanglement dis...

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2022 IEEE Photonics Conference, IPC 2022

作者： Alnas, Jude Alshowkan, Muneer Nageswara Rao, S.V. Peters, Nicholas A. Lukens, Joseph M. University of Alabama Department of Electrical and Computer Engineering TuscaloosaAL35401 United States Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States Advanced Computing Methods Section Oak Ridge National Laboratory Oak RidgeTN37831 United States

We introduce a model for the distribution of frequency-polarization hyper-entangled photon pairs in a flexible-grid optical network. In order to optimize entanglement fidelity and entangled bit rate, we apply a geneti... 详细信息

ISBN: (数字)9781665434874

ISBN: (纸本)9781665434874

关键词： Genetic algorithms

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Algorithm for Solving Inverse Problems of Geoelectrical Prospecting for Vertical Electrical Sounding by the Indirect Near-Boundary Element Method 19

Algorithm for Solving Inverse Problems of Geoelectrical Pros...

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19th IEEE International Conference on computer science and Information Technologies, CSIT 2024

作者： Zhuravchak, Liubov Zabrodska, Nataliya Lviv Polytechnic National University Software Department Institute of Computer Science and Information Technologies Bandery street 12 Lviv Ukraine National Academy of Sciences Department of Geoelectromagnetic Methods Carpathian Branch of S.I. Subbotin Institute of Geophysics Naukova street 3-b Lviv Ukraine

ISBN: (纸本)9798331542627

using the indirect near-boundary element method, an algorithm for recognizing the physical and geometric parameters of the inclusion in a piecewise homogeneous half-plane, whose components are in ideal contact, based on the potential field data measured at its boundary, is constructed. The direct and inverse two-dimensional problems of the potential theory in relation to electrical prospecting, in particular, the method of vertical electrical soundings, have been solved. A computational experiment was conducted in which initial approximations for the electrical conductivity of the inclusion, its center of mass, orientation, and dimensions were determined by the nature of the change in apparent resistivity. The inverse problem is solved using excess boundary conditions by a number of iterations while minimizing the functional. © 2024 IEEE.

关键词： Inverse problems

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Transformative Applications and Key Challenges of Generative AI

Transformative Applications and Key Challenges of Generative...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power Engineering (CANDO-EPE)

作者： Attila Kovari Jozsef Katona Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Department of Institute of Engineering University of Dunaújváros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The paper discusses the integration and use of generative artificial intelligence technologies in education. Generative AI, such as OpenAI, in particular the GPT model, has fundamentally changed the way humans interact with machines. Advanced technologies in the field of generative AI in education are mostly used to personalize learning experiences by dynamically customizing learning materials according to the needs of learners, thus enabling the improvement of outcomes. This article explores the use of generative AI tools with the aim of assessing how useful students perceive these tools to be. The findings show mixed receptivity from users, highlighting the critical balance in using innovative AI capabilities through AI.

关键词： Power engineering Accuracy Generative AI Education Chatbots Market research Problem-solving Reliability

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Rl-Gcn: Trafffc Flow Prediction Based on Graph Convolution and Reinforcement Learning for Smart Cities

SSRN

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SSRN 2023年

作者： Xing, Hang Chen, An Zhang, Xuan College of Engineering South China Agricultural University Guangdong Guangzhou510642 China Department of Experiment Teaching Guangdong University of Technology Guangzhou510006 China Department of computer science University of Pennsylvania 19104-6106 United States

The trafffc ffow problem has become essential in urban planning and management in today’s increasingly urbanized world. Traditional trafffc ffow prediction models cannot fully consider urban trafffc networks’ complex and dynamic characteristics. To this end, this paper proposes a trafffc ffow prediction method for smart cities (RL-GCN) based on graph convolution, LSTM network and reinforcement learning, aiming to solve the problem of urban trafffc ffow prediction . Firstly, we use the graph convolutional neural network to process the urban trafffc network data features, then use the LSTM network model to learn the temporal information, and then combine the reinforcement learning algorithm to develop the optimal trafffc control strategy based on which the future trafffc ffow is predicted. Our experiments on several datasets show that the model developed in this paper has outstanding performance for urban trafffc ffow prediction . Compared with the traditional trafffc ffow prediction methods, the method in this paper has signiffcantly improved prediction accuracy . Our research can provide valuable references and inspiration in urban planning and trafffc management. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Trends and Challenges of Sustainable Agriculture in the Iraqi Environment

Trends and Challenges of Sustainable Agriculture in the Iraq...

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2022 IEEE International Conference on Blockchain, Smart Healthcare and Emerging Technologies, SmartBlock4Health 2022

作者： Al-Asadi, Hanan Hussein Goga, Maria Ali, Haider Abdullah Popa, Ramona Cristina Oludele, Awodele University POLITEHNICA of Bucharest Faculty of Automatic Control and Computers Bucharest Romania University of Technology-Iraq Baghdad Iraq Technical University of Civil Engineering of Bucharest UTCB Department for Training of Teaching Personnel Bucharest Romania Madenat Alelem University College Baghdad Iraq Babcock University Computer Science Department Ogun State Nigeria

ISBN: (纸本)9781665460149

The use of Agriculture Intelligent Systems (AISs) in Iraq has become popular among farmers. The greenhouse is a solution for plant growth, which uses an effective microclimate to simulate seasons and to use alternative energy sources like solar, thermal, and photovoltaic systems for microclimate control. Greenhouses are considered a successful solution with developments and achievements in the technology of this field. However, problems like high costs, difficulties in installation and maintenance, or the need for some procedures should be considered. Because Iraq is close to the equator (3669 km), it receives sufficient solar power. It also has large areas available for the building of solar greenhouse systems. The farmers need an affordable and functional smart greenhouse to enable and encourage them to use the environment efficiently. We intend to develop low-cost, accurate, and easy-to-use greenhouse systems controlled by artificial intelligence algorithms. Farmers will be able to control the greenhouse by using simple commands. Machine learning algorithms provide real-time and effective analysis that could support farmers in decision-making and provide relevant data for researchers and policymakers. AISs algorithms can lead to a sustainable and productive agriculture system. The system can be used in Iraq with sufficient government support and subsidy. © 2022 IEEE.

关键词： Intelligent systems

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VideoCutTool - Online Video Editor Tool for Wikimedia Commons 9

VideoCutTool - Online Video Editor Tool for Wikimedia Common...

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9th IEEE International Conference on Communication and Signal Processing, ICCSP 2020

作者： Vasanth, Gopa Heilman, James Shetty, Pratik Amin, Hassan Geetha, M. Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Department of Emergency Medicine Cranbrook Canada Founder of VideoWiki Mumbai India Teaching Assistance of Computer Science Faculty of Science Helwan University Egypt Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9781728149882

The image CropTool allows users to crop images present on Wikimedia Commons without leaving the Wikimedia family of sites within a web environment. We implemented the same workflow for videos with the VideoCutTool. Previously, a video on Wikimedia Commons had to be downloaded, changed, and later re-uploaded to Commons. The previous process took a significant amount of time and requires the use of outside software, while the VideoCutTool allows authorized users to work up-to 10x faster. This paper talks about the features of VideoCutTool and its implementation. The tool is currently live on https://***/video-cut-tool/ and published under the Creative Commons Attribution-ShareAlike 4.0 licence. © 2020 IEEE.

关键词： Crops

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Mixture of Knowledge Minigraph Agents for Literature Review Generation

arXiv

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arXiv 2024年

作者： Zhang, Zhi Liu, Yan Zhong, Sheng-Hua Chen, Gong Yang, Yu Cao, Jiannong The Hong Kong Polytechnic University the Department of Computing 999077 Hong Kong Shenzhen University the College of Computer Science and Software Engineering Guangdong Shenzhen518052 China The Education University of Hong Kong Centre for Learning Teaching and Technology 999077 Hong Kong

Literature reviews play a crucial role in scientific research for understanding the current state of research, identifying gaps, and guiding future studies on specific topics. However, the process of conducting a comprehensive literature review is yet time-consuming. This paper proposes a novel framework, collaborative knowledge minigraph agents (CKMAs), to automate scholarly literature reviews. A novel prompt-based algorithm, the knowledge minigraph construction agent (KMCA), is designed to identify relations between concepts from academic literature and automatically constructs knowledge minigraphs. By leveraging the capabilities of large language models on constructed knowledge minigraphs, the multiple path summarization agent (MPSA) efficiently organizes concepts and relations from different viewpoints to generate literature review paragraphs. We evaluate CKMAs on three benchmark datasets. Experimental results show the effectiveness of the proposed method, further revealing promising applications of LLMs in scientific research. © 2024, CC BY.

关键词： Reviews

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