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2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Zheng, Wei Han, Na Hu, Yan Kang, Peipei Zhang, Yue Yang, Aqing Zhang, Lei School of Computer Science Guangdong Polytechnic Normal University China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data China School of Computer Science and Technology Guangdong University of Technology China School of Information Engineering East University of Heilongjiang China

ISBN: (纸本)9781510680425

In the fields of computer vision and natural language processing, cross-modal retrieval is of great importance that cannot be ignored. In existing multi-granularity alignment methods, significant progress has been made by globally aligning images and sentences or locally aligning regions and words. However, most of these methods rely on attention mechanisms, which may be affected by noise and cannot balance the differences between modalities, resulting in suboptimal image-text alignment. In addition, training with attention mechanisms consumes a lot of computational resources, and the retrieval process is time-consuming. To address these challenges, we develop a novel multigranularity image-text alignment model, which we call DFVLM. First, we independently train the intra-modal encoder and cross-modal encoder to reduce interference between different modal encoders and better learn intra-modal and inter-modal information. Then, we propose a joint strategy to combine the intra-modal and inter-modal information to better capture the information between image and text. In addition, we introduce a hard negative pair-based method to train the performance of the intra-modal multi-granularity encoder without using attention mechanisms. Unlike existing methods, We treat image and text retrieval as a two-way process and delve into the intrinsic connections between the two matrices. We propose a cross-validation method to optimize the retrieval result based on common image-to-text alignment scenarios in daily life, i.e., during the image-to-text retrieval process, a text-to-image reverse verification is performed on the retrieved corresponding text. Through extensive qualitative experiments and analysis, our approach performs well on both Flickr30K and MSCOCO datasets and also significantly reduces the time required for testing. © 2024 SPIE.

关键词： Natural language processing systems

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Fusing PSA to Improve YOLOv5s Detection algorithm for Electric Power Operation Wearable devices 13th

Fusing PSA to Improve YOLOv5s Detection algorithm for Elec...

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13th EAI International Conference on Mobile Networks and Management, MONAMI 2023

作者： Liu, Qiuming Xu, Wei Zhou, Yang Li, Ruiqing Wu, Dong Luo, Yong Chen, Longping School of Software Engineering Jiangxi University of Science and Technology Nanchang330013 China Nanchang Key laboratory of Virtual Digital Factory and Cultural Communications Nanchang330013 China Information and Communication Branch State Grid Jiangxi Electric Power Co Nanchang330095 China School of Software Jiangxi Normal University Nanchang330022 China

ISBN: (纸本)9783031554704

In order to determine whether the electric power workers wear safety equipment such as safety helmet, insulation boots, insulation gloves, insulation clothes, etc., to ensure the safety of the electric power construction site. We propose a electric power operation safety equipment detection algorithm incorporating PSA to improve YOLOv5s algorithm, using polarized self-attention mechanism to improve the feature extraction end of YOLOv5s algorithm, improving the channel resolution and spatial resolution of safety equipment images of electric power operation scenes, and preserving the information of key nodes of small targets that are obscured;GSConv is used to replace the ordinary convolution to reduce the complexity of the model, improve the calculation speed of the algorithm and improve the detection accuracy. The experimental results show that the average accuracy mean (IoU = 0.5) of the proposed algorithm reaches 0.961, which is 1.58% higher than that of the original network detection performance, and the model parameters are reduced from 7.03 to 5.48 millions. It effectively improves the detection speed and accuracy of the algorithm, and can effectively monitor whether the operator wears the safety equipment correctly when there are occlusions and missing safety equipment in the electric power operation scene, which has a excellent application effect. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Signal detection

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Enhancing Cloud data Integrity Verification Scheme with User Legitimacy Check 13th

Enhancing Cloud Data Integrity Verification Scheme with User...

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13th EAI International Conference on Mobile Networks and Management, MONAMI 2023

作者： Wu, Dong Peng, Chao Zheng, Meilan Fu, Chen Wang, Hua Zhong, Hua Liu, Qiuming Xiao, He Dong, Siwen Information and Communication Branch of Jiangxi Electric Power Co. Ltd. Nanchang China Nanchang Key Laboratory of Virtual Digital Factory and Cultural Communications Nanchang330013 China School of Software Engineering Jiangxi University of Science and Technology Ganzhou China

ISBN: (纸本)9783031554704

Users employ cloud servers to store data and depend on third-party audits to guarantee data integrity. However, this auditing system poses certain risks, as it may have vulnerabilities that attackers can exploit for intrusion. To address these concerns and achieve decentralization, a cloud data integrity verification scheme is proposed. This scheme is based on a verifiable random function and aims to eliminate the need for third-party auditing. Before performing data integrity verification, a blockchain smart contract is employed to calculate bilinear pairs, serving the purpose of verifying the user’s legitimacy. If the user successfully completes this verification, the integrity of the cloud data is then verified using the verifiable random function. The simulation results demonstrate that this scheme is effective in detecting the legitimacy of users and significantly reduces the computational and communication overhead associated with verifying data integrity. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Smart contract

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Roles of concentration-dependent Cu doping behaviors on the thermoelectric properties of n-type Mg_(3)Sb_(1.5)Bi_(0.5)

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Journal of Materiomics 2024年第1期10卷 154-162页

作者： Xinxin Yang Heng Ni Xiaotong Yu Biao Cao Juanjuan Xing Qiyong Chen Lili Xi Jiandang Liu Jiye Zhang Kai Guo Jing-Tai Zhao School of Materials Science and Engineering Shanghai University99 Shangda RoadShanghai200444China Materials Genome Institute Shanghai University99 Shangda RoadShanghai200444China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefeiAnhui230026China School of Physics and Materials Science Guangzhou UniversityGuangzhou510006China Research Center for Advanced Information Materials(CAIM) Huangpu Research&Graduate School of Guangzhou UniversitySino-Singapore Guangzhou Knowledge CityHuangpu DistrictGuangzhou510555China Key Lab of Si-based Information Materials&Devices and Integrated Circuits Design Department of Education of Guangdong ProvinceChina School of Materials Science and Engineering Guilin University of Electronic TechnologyGuilin541004China Guangxi Key Laboratory of Information Materials Guilin University of Electronic TechnologyGuilin541004China

Large Seebeck coefficients induced by high degeneracy of conduction band minimum,and low intrinsic lattice thermal conductivity originated from large lattice vibrational anharmonicity render Mg_(3)Sb_(2)as a promising n-type thermoelectric ***,we demonstrated unique concentration-dependent occupation behaviors of Cu in Mg_(3.4)Sb_(1.5)Bi_(0.49)Te_(0.01)matrix,evidenced by structural characterization and transport property *** is found that Cu atoms prefer to enter the interstitial lattice sites in Mg_(3)Sb_(2)host with low doping level(Mg_(3.4)Sb_(1.5)Bi_(0.49)Te_(0.01)+x%Cu,x<0.3%),acting as donors for providing additional electrons without deteriorating the carrier *** x is larger than 0.3%,the excessive Cu atoms are inclined to substitute Mg atoms,yielding acceptors to decrease the electron *** a result,the electrical conductivity of the Mg_(3.4)Sb_(1.5)Bi_(0.49)Te_(0.01)+0.3%Cu sample reaches 2.3×10^(4)S/m at 300 K,increasing by 300%compared with that of the pristine *** figure of merit zT values in the whole measured temperature range are significantly improved by the synergetic improvement of power factor and reduction of thermal *** average zT∼1.07 from 323 K to 773 K has been achieved for the Mg_(3.4)Sb_(1.5)Bi_(0.49)Te_(0.01)+0.3%Cu sample,which is about 30%higher than that of the Mg_(3.4)Sb_(1.5)Bi_(0.49)Te_(0.01) sample.

关键词： Mg_(3)Sb_(2) Cu doping Concentration-dependent occupation Thermoelectric properties Average zT

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Don’t Ignore the Drive of Curiosity: Rethinking Subtleties Between Universality of Commonsense knowledge and Excellence of Large Language Models

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SN Computer Science 2024年第6期5卷 798页

作者： Wang, Chao Chen, Tao Liu, Jingping School of Future Technology Shanghai University Shanghai China Institute of Artificial Intelligence Shanghai University Shanghai China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China School of Information Science and Engineering East China University of Science and Technology Shanghai China

Commonsense reasoning is one of the abilities necessary for artificial intelligence to be as intelligent as humans. However, how to make AI understand commonsense has been a problem that has plagued artificial intelligence for more than 60 years. Existing efforts focus more on the means of knowledge acquisition and strive to enrich the capacity of commonsense knowledge (CSK) bases and dimensions of CSK through advanced methods. Unfortunately, this exuberance has obscured a general consideration of CSK, such as how to follow human habits to obtain the most representative knowledge we need to understand the world. In this paper, this representative knowledge is referred to as core CSK. The influence of core CSK is extensive, and it constitutes almost the fundamental element of human life and the most fundamental cognition of the world. Harnessing human curiosity to find solutions to the above problems is an effective and straightforward route. Specifically, we focus on a special corpus to mine core CSK, namely, why-questions. For example, we can harvest “the sky is blue” from “why is the sky blue?”. To this end, we propose a novel method to extract CSK from why-questions, which mainly consist of two modules. The first is a question classification module used to determine whether a question contains CSK. In this module, we propose a classifier based on a one-sided bootstrapping method and design several informative features for the classifier. The second is a crowdsourcing module used to improve the quality of the extracted commonsense. We conduct extensive experiments, and the experimental results show that our method effectively mines CSK from question corpora. Furthermore, statistical analysis demonstrates the feasibility of this curiosity-driven approach, implying that we provide a basic idea for collecting core CSK. Remarkably, today’s outstanding large language models do not have such simple knowledge summarization capabilities, demonstrating the barrier between

关键词： Classification Core Commonsense knowledge Crowdsourcing Curiosity Psychologically

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Understanding the spatio-temporal dynamics of ecosystem services under multiple future scenarios to assess the progress of Sustainable Development Goals implementation

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Journal of Geographical Sciences 2025年第4期35卷 745-762页

作者： ZHANG Ze JIANG Weiguo LING Ziyan PENG Kaifeng WU Zhifeng LI Zhuo State Key Laboratory of Remote Sensing and Digital Earth Science Faculty of Geographical SciencesBeijing Normal UniversityBeijing 100875China School of Information and Control Engineering Qingdao University of TechnologyQingdao 266520ShandongChina College of Geographic and Environmental Sciences Tianjin Normal UniversityTianjin 300387China School of Geography and Remote Sensing Guangzhou UniversityGuangzhou 510006China National Marine Data and Information Service Tianjin 300171China

Ecosystem services in urban agglomerations are the environmental conditions under which human survival and development are *** assessment of ecosystem services and complex interactions can contribute positively to the achievement of the Sustainable Development Goals(SDGs)for urban ***,studies on the future contribution of multi-scenario ecosystem services to the SDGS are *** pronovel integrated modeling framework that integrates the CLUES,InVEST,SOM,and GWR approaches to address the complex relationship between ecosystem services over a long“past-present-future”time *** construct a novel ecosystem service bundle-based approach for measuring urban agglomerations progress towards achieving ecologically relevant sustainable development goals at multiple *** the future scenario,the water yield(WY),habitat quality(HQ),and soil conservation(SC)show similar spatial patterns,with comparable spatial grids,while carbon stock(CS)remains predominantly unchanged and the ecological protection scenario(EPS)improves more *** high-synergy regions are mainly distributed in bundle 4,and most of the trade-off regions appear in bundles 1 and *** the last 30 years,all but the water-related SDGs are declining in bundle 1 of the two urban agglomerations,which are 15%higher in the Guangxi Beibu Gulf(GBG)than in the Guangdong-Hong Kong-Macao Greater Bay Area(GBA).From 2020 to 2035,the three scenarios demonstrate that the optimization of the SDGs progresses most effectively under the future ecological protection scenario(EPS).In particular,bundles 3 and 4 are significantly *** critical new knowledge can be used in sustainable ecosystem management and decision-making in urban agglomerations.

关键词： Sustainable Development Goals(SDGs) ecosystem service bundle trade-offs and synergies InVEST model self-organizing mapping(SOM)algorithm urban agglomeration in south China

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Sparrow Search Algorithm with Adaptive Collaborative Updating

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1706-1724页

作者： Hong, Chuanbo Liang, Quan Liang, Qiaoxin Yu, Wenjie Yu, Wenze Xiong, Neng School of Mechanical and Electrical Engineering Quanzhou University of Information Engineering Quanzhou362000 China College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China Quanzhou Management Branch Fujian Expressway Group Cp. Ltd Quanzhou362000 China

The research of swarm intelligence shows a rapid development trend. People have studied a lot of swarm intelligence algorithms inspired by biology. Sparrow search algorithm is concerned by many scholars because of its powerful search performance. However, in the case of high-dimension optimization problems, if only a single sparrow search algorithm is used to solve optimization problems, the optimal solution often cannot be found, and the phenomenon of dimension disaster will occur or it is easy to fall into the local optimal solution. In order to make sparrow search algorithm have better performance in solving high-dimensional complex problems, In this paper, we propose a new Sparrow Search Algorithm with Adaptive Collaborative Updating (SSA-ACU). The SSA-ACU algorithm initializes the population through the chaotic search strategy to make the population distribution more diverse. Then, adaptive parameters are introduced to update the position of the leader to provide a more reliable direction for particle search. Finally, in order to enhance the development ability of the population, the position of the adjoint is updated through the pseudo-affine transformation mechanism to change the mode of its position update. Then CEC2013 was used to test and compare SSA-ACU algorithm with other sparrow search algorithm variants and classical algorithms, and it was verified that SSA-ACU algorithm has good performance in convergence speed and optimization ability. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Swarm intelligence

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CoT-based Synthesizer: Enhancing LLM Performance through Answer Synthesis

arXiv

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arXiv 2025年

作者： Zhang, Bohan Zhang, Xiaokang Zhang, Jing Yu, Jifan Luo, Sijia Tang, Jie School of Information Renmin University of China China Tsinghua University China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China

Current inference scaling methods, such as Self-consistency and Best-of-N, have proven effective in improving the accuracy of LLMs on complex reasoning tasks. However, these methods rely heavily on the quality of candidate responses and are unable to produce correct answers when all candidates are incorrect. In this paper, we propose a novel inference scaling strategy, CoT-based Synthesizer, which leverages CoT reasoning to synthesize superior answers by analyzing complementary information from multiple candidate responses, even when all candidate responses are flawed. To enable a lightweight and cost-effective implementation, we introduce an automated data generation pipeline that creates diverse training data. This allows smaller LLMs trained on this data to improve the inference accuracy of larger models, including API-based LLMs. Experimental results across four benchmark datasets with seven policy models demonstrate that our method significantly enhances performance, with gains of 11.8% for Llama3-8B and 10.3% for GPT-4o on the MATH dataset. The corresponding training data and code are publicly available on the repository. Copyright © 2025, The Authors. All rights reserved.

关键词： data accuracy

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Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization

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Complex System Modeling and Simulation 2023年第3期3卷 202-219页

作者： Xiaoning Shen Hongli Pan Zhongpei Ge Wenyan Chen Liyan Song Shuo Wang Collaborative Innovation Center of Atmospheric Environment and Equipment Technology and also with the Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation the Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China School of Computer Science University of BirminghamBirminghamB152TTUK

Waste collection is an important part of waste management *** costs and carbon emissions can be greatly reduced by proper vehicle ***,each vehicle can work again after achieving its capacity limit and unloading the *** this,an energy-efficient multi-trip vehicle routing model is established for municipal solid waste collection,which incorporates practical factors like the limited capacity,maximum working hours,and multiple trips of each *** both economy and environment,fixed costs,fuel costs,and carbon emission costs are minimized *** solve the formulated model effectively,contribution-based adaptive particle swarm optimization is *** strategies named greedy learning,multi-operator learning,exploring learning,and exploiting learning are specifically designed with their own searching *** assessing the contribution of each learning strategy during the process of evolution,an appropriate one is selected and assigned to each individual adaptively to improve the searching efficiency of the ***,an improved local search operator is performed on the trips with the largest number of waste sites so that both the exploiting ability and the convergence accuracy of the algorithm are *** of the proposed algorithm is tested on ten waste collection instances,which include one real-world case derived from the Green Ring Company of Jiangbei New District,Nanjing,China,and nine synthetic instances with increasing scales generated from the commonly-used capacitated vehicle routing problem benchmark *** with five state-of-the-art algorithms show that the proposed algorithm can obtain a solution with a higher accuracy for the constructed model.

关键词： municipal solid waste collection energy conservation multi-trip contribution particle swarm optimization

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A method for correcting characteristic X-ray net peak count from drifted shadow peak

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Nuclear Science and Techniques 2023年第11期34卷 155-167页

作者： Lin Tang Xing‑Ke Ma Kai‑Bo Shi Yeng‑Chai Soh Hong‑Tao Shen College of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei 230039China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore College of Nuclear Technology and Automation Engineering Chengdu University of TechnologyChengdu 610059China

To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse *** trained model was validated using simulated *** relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM *** a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping *** slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping *** model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,*** measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.

关键词： Peak correction Triangular shaping Deep learning Long short-term memory Convolutional neural network X-ray fluorescence spectroscopy Silicon drift detector

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