检索结果-内蒙古大学图书馆

International Conference on Emerging Electronic and Automation technology 2022, ICEEAT 2022

作者： Abdalla, Ahmad N. Tao, Hai Shahzad Nazir, Muhammad Ju, Yongfeng Bin Ishak, Ruhaizas Faculty of Info. and Elect. Eng. Huaiyin Institute of Technology Huai'an223003 China School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou558000 China Faculty of Automation Engineering Huaiyin Institute of Technology Huai'an223003 China Faculty of Electr. and Elect. Eng. Universiti Malayisa Pahang Pekan Malaysia

Measurement and model uncertainties in power grids lead to the unpredictability of its power output, which brings great challenges to the safe and sustainable system operation. Furthermore, optimal placement strategies of measurement devices and new energy sources have a great impact to maintain the system operation. This study introduces and constructed an active distribution network (ADN) including an energy storage system (ESS) and is optimized by using a hybrid Ant Colony Optimization-Random Forest Regression (HACORFR). Briefly introduces the operation and combined system modelled of the ESS-RDS (ERDS). Then the HACORFR algorithm optimizes the operation status of the RDS and detects the abnormality distribution network. After applying the proposed approach, if the operation indicators of the distribution network, don't return to normal operation, the network topology will further reconstruct and optimize the ERDS. The IEEE-28 bus power distribution testing system was used to validate the proposed method. Finally, the results show that the proposed optimization method reduces the loss of the distribution network by 2.34%, and the voltage profile improved by 2.94%. The present method makes full use of the flexible load and realizes the interaction between supply and demand. It also greatly improves the reliability of the distribution network operation and economy. © Published under licence by IOP Publishing Ltd.

关键词： Electric power distribution

来源：评论

学校读者我要写书评

暂无评论

Adaptive information fusion network for multi-modal personality recognition

Adaptive information fusion network for multi-modal personal...

引用

作者： Bao, Yongtang Liu, Xiang Qi, Yue Liu, Ruijun Li, Haojie School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Virtual Reality Research Institute Beihang University Qingdao Research Institute Qingdao China School of Software Beihang University Beijing China

Personality recognition is of great significance in deepening the understanding of social relations. While personality recognition methods have made significant strides in recent years, the challenge of heterogeneity between modalities during feature fusion still needs to be solved. This paper introduces an adaptive multi-modal information fusion network (AMIF-Net) capable of concurrently processing video, audio, and text data. First, utilizing the AMIF-Net encoder, we process the extracted audio and video features separately, effectively capturing long-term data relationships. Then, adding adaptive elements in the fusion network can alleviate the problem of heterogeneity between modes. Lastly, we concatenate audio-video and text features into a regression network to obtain Big Five personality trait scores. Furthermore, we introduce a novel loss function to address the problem of training inaccuracies, taking advantage of its unique property of exhibiting a peak at the critical mean. Our tests on the ChaLearn First Impressions V2 multi-modal dataset show partial performance surpassing state-of-the-art networks. © 2024 John Wiley & Sons Ltd.

关键词： Information fusion

来源：评论

学校读者我要写书评

暂无评论

NUWADYNAMICS: DISCOVERING AND UPDATING IN CAUSAL SPATIO-TEMPORAL MODELING 12

NUWADYNAMICS: DISCOVERING AND UPDATING IN CAUSAL SPATIO-TEMP...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Wang, Kun Wu, Hao Duan, Yifan Zhang, Guibin Wang, Kai Peng, Xiaojiang Zheng, Yu Liang, Yuxuan Wang, Yang China Suzhou Institute for Advanced Research USTC China School of Software Engineering USTC China School of Computer Science USTC China Hong Kong Tongji University China Shenzhen Technology University China National University of Singapore Singapore JD iCity JD Technology

Spatio-temporal (ST) prediction plays a pivotal role in earth sciences, such as meteorological prediction, urban computing. Adequate high-quality data, coupled with deep models capable of inference, are both indispensable and prerequisite for achieving meaningful results. However, the sparsity of data and the high costs associated with deploying sensors lead to significant data imbalances. Models that are overly tailored and lack causal relationships further compromise the generalizabilities of inference methods. Towards this end, we first establish a causal concept for ST predictions, named NuwaDynamics, which targets to identify causal regions in data and endow model with causal reasoning ability in a two-stage process. Concretely, we initially leverage upstream self-supervision to discern causal important patches, imbuing the model with generalized information and conducting informed interventions on complementary trivial patches to extrapolate potential test distributions. This phase is referred to as the discovery step. Advancing beyond the discovery step, we transfer the data to downstream tasks for targeted ST objectives, aiding the model in recognizing a broader potential distribution and fostering its causal perceptual capabilities (denoted as Update step). Our concept aligns seamlessly with the contemporary backdoor adjustment mechanism in causality theory. Extensive experiments on six real-world ST benchmarks showcase that models can gain outcomes upon the integration of the NuwaDynamics concept. NuwaDynamics also can significantly benefit a wide range of changeable ST tasks like extreme weather and long temporal step super-resolution predictions. Our codes are available at https://***/easylearningscores/NuwaDynamics. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Computation theory

来源：评论

学校读者我要写书评

暂无评论

NLSP: A novel lattice-based secure primitive for privacy-preserving smart grid communications

NLSP: A novel lattice-based secure primitive for privacy-pre...

引用

作者： Bao, HaiYong Xu, Zheng Hong, HaiBo Kong, QingLei Qian, HaiFeng Software Engineering Institute East China Normal University Shanghai China School of Computer Science and Information Engineering Zhejiang Gongshang University Hangzhou China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China Institute of Space Science and Applied Technology Harbin Institute of Technology Shenzhen China

As the new generation of power scheme, smart grid is proposed to overcome the shortcomings of traditional systems, such as low efficiency and reliability. In this article, a novel lattice-based secure primitive for privacy-preserving smart grid communications is proposed, which has the remarkable characteristics, such as scalable multi-dimensional fine-grained power data structure and differential privacy security. First, combining with the lattice-based data encryption technology, while effectively resisting quantum attacks, the method of simultaneous processing of multi-dimensional data is innovated. Second, through combining the additive homomorphism of the lattice-based cryptosystem and the Chinese remainder theorem, the data aggregation mechanism that can directly perform homomorphic operations on compressed ciphertext is constructed. Thanks to the above innovative design ideas, the proposed scheme not only significantly improves the efficiency of data communication and processing, greatly reduces the computational cost of the intermediate entity, but also realizes the data confidentiality and information privacy. Finally, observing the decentralized topology of communication nodes in the typical cyber-physical system of smart grid, the localized differential privacy technology is leveraged to optimize and balance the utility, security, and efficiency of differential privacy. Extensive performance evaluations are conducted to illustrate that the proposed scheme outperforms the state-of-the-art similar schemes in terms of computation complexity and communication cost. © 2022 John Wiley & Sons, Ltd.

关键词： Smart power grids

来源：评论

学校读者我要写书评

暂无评论

Gaussian Differential Privacy Integrated Machine Learning Model for Industrial Internet of Things

引用

SN computer Science 2023年第5期4卷 454页

作者： Lazar, Arokia Jesu Prabhu Soundararaj, Sivaprakash Sonthi, Vijaya Krishna Palanisamy, Vishnu Raja Subramaniyan, Vanithamani Sengan, Sudhakar Department of Computer Science and Engineering AI & ML CMR Institute of Technology Telangana Hyderabad 501401 India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 522502 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai 638060 India Master of Computer Applications M. Kumarasamy College of Engineering Tamil Nadu Karur 638060 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Agriculture, energy, mining, healthcare, and transportation are a few of the top industries transformed by the industrial internet of things (IIoT). Industry 4.0 mainly relies on machine learning (ML) to use the vast interconnectedness and large amounts of IIoT data that IIoT primarily drives. ML approaches are trained on confidential data generated by the IIoT environment often exposes privacy to adversarial assaults. Blockchain-based ML is established in the proposed secured IIoT research work to safeguard and enhance privacy. This research proposes a Gaussian differential privacy-integrated machine learning model (GDPIMLM), created for a scalable and controlled IIoT system, fully connected (FC) layer, implemented blockchain benefits to create a privacy-preserving mechanism while considering other limitations and reasonable time. A probability test using two altered Gaussian distributions (GD) forms the basis for defining Gaussian differential privacy (GDP). Due to a central restriction theorem for differential privacy, GDP is the primary privacy term within the family of Federal differential privacy (f-DP) policies. Ethereum is used to implement experimental evaluations. Data collections show that the recommended method enhances digital data privacy with industry-leading security without reducing performance. The performance analysis of the proposed model of secured industrial internet of things (IIoT) is studied using the accuracy of comparison with existing methods and for diverse distributed entities (DE). The proposed secured IIoT attains a minimal time consumption (TC) of 790 Sec—the highest accuracy of 92%, outperforming the existing method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Industry Industry 4.0 Learning Privacy Security Smart objects

来源：评论

学校读者我要写书评

暂无评论

CHEMFUZZ: Large Language Models-Assisted Fuzzing for Quantum Chemistry software Bug Detection 23

CHEMFUZZ: Large Language Models-Assisted Fuzzing for Quantum...

引用

23rd IEEE International Conference on software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Qiu, Feng Ji, Pu Hua, Baojian Wang, Yang School of Software Engineering University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China The Johns Hopkins University Computer Science Department United States

ISBN: (纸本)9798350359398

Quantum chemistry software implements the first principle quantum computation and is indispensable in both scientific research and chemical industries. Any bugs in such software will lead to serious consequences, thus defeating its trustworthiness and reliability. However, bug detection techniques for such software have not been fully investigated. In this paper, to fill this gap, we propose a novel approach to fuzz quantum chemistry software with the aid of Large Language Models (LLMs). Our basic idea is utilize LLMs to mutate and generate syntactic and semantic valid input files from seed inputs, by proving valuable domain-specific knowledge of chemistry. With this basic idea, we have designed and implemented CHEMFuzz, a fully automatic fuzzing framework to fuzz quantum chemistry software for bugs. Our evaluation of CHEMFUZZ leverages popular LLMs including GPT3.5, Claude-2, and Bart as test oracles to generate parameters to mutate inputs and analyze computation results. CHEMFUZZ detected 40 unique bugs, which have been classified and reported to developers, with a code coverage of 17.4%. © 2023 IEEE.

关键词： Quantum chemistry

来源：评论

学校读者我要写书评

暂无评论

SCAF-DG: A Multi-site Medical Image Denoising with A Domain-Generalized Spatial-Channel Attention Fusion 6

SCAF-DG: A Multi-site Medical Image Denoising with A Domain-...

引用

6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Fiasam, Linda Delali Rao, Yunbo Sey, Collins Klugah-Brown, Benjamin Tettey, Obed Nartey Aggrey, Esther Stacy E.B. Agyemang, Isaac Osei Adjei-Mensah, Isaac Yang, Yang University of Electronic Science Technology of China School of Information and Software Engineering Chengdu China University of Electronic Science Technology of China School of Life Science and Technology Chengdu China University of Electronic Science Technology of China School of Information and Communication Engineering Chengdu China Sichuan Institute of Computer Sciences Chengdu China

ISBN: (纸本)9798350325485

Images from multiple medical sites usually contain varying noise levels that can affect the generalization performance of the denoising models. Domain generalization (DG), which seeks to learn a model that can generalize to an unseen test domain, has not been extensively explored in medical image denoising for multi-site images. We present an approach for multi-site medical image denoising, which addresses the issue of varying noise levels across different imaging sites. Our proposed method, called SCAF-DG (Spatial-Channel Attention Fusion for Domain Generalization), employs two attention units to extract both domain-specific channel features and mutually-invariant spatial features from the noisy images. These features are then combined to form a rich feature space. Additionally, we utilized skip connections to preserve structural information during the denoising process. We evaluate SCAF-DG on publicly available brain magnetic resonance image (MRI) datasets from three different imaging sites, and demonstrate an average increase of 1.01 in peak signal-To-noise ratio (PSNR) and 0.016 in structural similarity index measure (SSIM) for denoising images. Our results demonstrate that SCAF-DG has the potential to generalize well to new, unseen test domains, and outperforms existing state-of-The-Art denoising models on multi-site medical images. © 2023 IEEE.

关键词： Image denoising

来源：评论

学校读者我要写书评

暂无评论

Fast Second-order Method for Neural Networks under Small Treewidth Setting

Fast Second-order Method for Neural Networks under Small Tre...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Li, Xiaoyu Long, Jiangxuan Song, Zhao Zhou, Tianyi Stevens Institute of Technology Department of Computer Science HobokenNJ United States South China University of Technology School of Software Engineering Guangzhou China University of California Berkeley The Simons Institute for the Theory of Computing BerkeleyCA United States University of Southern California Department of Computer Science Los AngelesCA United States

ISBN: (纸本)9798350362480

Training neural networks is a fundamental problem in theoretical machine learning. Second-order methods are rarely used in practice due to their high computational cost, even they converge much faster than first-order methods. The state-of-the-art result for the second-order method to train an over-parameterized neural network can run in O(log(1/ϵ)) iterations and each iteration has O(mnd + n3) running time, where n represent the number of data points, d is the dimension of the feature space of each data and m is the width of the neural network [Brand, Peng, Song and Weinstein ITCS 2021]. In this work, we further improve the convergence rate and the running time for each iteration to O(loglog(1/ϵ)) and O(mdτ2) respectively, where m = Ω(n4) and τ is the treewidth of the data matrix and usually very small. Our algorithm has a quadratic convergence rate, so it can be regarded as a truly second-order algorithm. To the best of our knowledge, our algorithm is the first to achieve a quadratic convergence rate for neural network training, with a per-iteration running time of O(mdτ2). © 2024 IEEE.

关键词： Trees (mathematics)

来源：评论

学校读者我要写书评

暂无评论

Blockchain Development 7th

Blockchain Development

引用

7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Xie, Siqi Cai, Jiahong Zhu, Hangyu Yang, Ce Chen, Lin Xiao, Weidong School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Software Engineering Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9783031281235

In recent years, blockchain research has set off an upsurge in academia, and it is called the next generation of value Internet. Because of its decentralization, anonymity, security, immutability, traceability and other characteristics, blockchain is gradually accepted and developed by people. With the deepening of research and the integration of technologies such as deep learning, blockchain has gradually been applied to various fields such as credit reporting, government, medical care, and industrial Internet of Things, not just the initial virtual currency field. This article mainly discusses the three important stages of blockchain public chain development, namely Bitcoin, Ethereum, and meta-verse, and introduces some basic supporting technologies of blockchain, as well as the research status and future trends of blockchain. Simple Analysis. By vertically introducing the development history of the blockchain, researchers can have a more concrete understanding of the status quo of the blockchain, and provide ideas for blockchain-related research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

A Method for Extracting Small Water Areas Based on an Improved FPN Network 19

A Method for Extracting Small Water Areas Based on an Improv...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Yu, Yufeng Wang, Meixiang Zhao, Qun Wang, Yong Chen, Qingqing Department of Computer and Software Hohai University Jiangsu Nanjing211108 China School of Computer Engineering Nanjing Institute of Technology Jiangsu Nanjing211167 China Bank of Nanjing Co. Nanjing Jiangsu211800 China Aerosun Corporation Information Center Jiangsu Nanjing China

ISBN: (纸本)9798331507817

Small water areas such as river branches, lakes, and ponds are vital components of aquatic ecosystems closely linked to human activities. Extracting small water areas from remote sensing images holds significant importance in water resource planning, investigation, sustainable water resource management, and environmental protection. However, the current small water area extraction methods based on remote sensing often encounter the problems of small target, few features, difficult localization and easy loss of edge features. Therefore, this paper proposes a small water area extraction model based on IFPN_BEM network. The model leverages a Transformer Encoder module with deformable attention mechanisms to globally model deep features of the FPN network and optimizes small water area edges using the BEM module, which further improves the extraction accuracy of small water areas. Experimental results on the GID dataset demonstrate that this model provides more complete and accurate extraction of small water areas, achieving precision, F1 score, and IoU accuracy metrics of 81.3%, 84.4%, and 73.4% respectively. These metrics show improvements of 9.5%, 8.2%, and 8.5% over the original FPN network, outperforming UNet, DeepLabV3+, and the improved FPN + PointRend networks across all metrics. © 2025 IEEE.

关键词： Lakes

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：