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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Huangjian Yi Ruigang Yang Xuelei He Hongbo Guo Beilei Wang Yuqing Hou Xiaowei He Xi’an Key Laboratory of Radiomics and Intelligent Perception the School of Information Sciences and Technology Northwest University Xi’an China

Non-linear least square minimization algorithms are often employed to solve Diffuse Optical Tomography (DOT) inverse problem. However, it is time-consuming to calculate the Jacobian matrix. This work has proposed a data-driven neural network method to improve computational efficiency. The singular value decomposition is employed to compute the updated Jacobian and a mapping from boundary measurements to the singular values based on a convolutional neural network (CNN) is learned to obtain the singular values. The method is validated with 3D numerical simulation data. We have demonstrated that the approach can save computation time compared to Adjoint method, and reconstructed absorption coefficient close to Adjoint *** Relevance— These results are not focused on clinical relevance currently, but in the future may be helpful to accelerant DOT reconstruction in clinic.

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Task Priority Aware Incentive Mechanism with Reward Privacy-Preservation in Mobile Crowdsensing 25

Task Priority Aware Incentive Mechanism with Reward Privacy-...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Wang, Jiahu Li, Peng Huang, Weiyi Chen, Zeqiang Nie, Lei Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-Time Industrial System China

ISBN: (纸本)9781665405270

In mobile crowdsensing, there are generally two types of tasks, popular tasks, and unpopular tasks. For popular tasks, many people can perform that task, and the budget is overallocated. For unpopular tasks, fewer or no one is willing to complete them. How to motivate users to complete different popularity tasks during their work time is a challenging problem. In this paper, we design a task priority-aware incentive mechanism to solve this problem. First, we use hierarchical clustering to classify tasks into different priorities by considering their budgets, deadlines, and density distribution. The higher the task priority, the higher the extra rewards and credits the participating users get. To motivate more users to perform unpopular tasks, we give high priority to unpopular tasks. Then, we propose a greedy algorithm that allows more users to do high-priority tasks. However, too many budget adjustments can cause most users to do unpopular tasks as users are obsessed with their income. To prevent users from all selecting high-priority tasks, we further propose a differential privacy-based algorithm to protect task priority and reduce users' attention to their income. This algorithm protects users' income and allows users to focus more on task characteristics, such as task distribution and task deadline. Through many experiments in the reality Roma dataset, we evaluate two proposed algorithms compared with other solutions. © 2022 IEEE.

关键词： Budget control

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Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1801-1817页

作者： Ye Tian Haowen Chen Haiping Ma Xingyi Zhang Kay Chen Tan Yaochu Jin the Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China the Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230601China IEEE the Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China the Department of Computing The Hong Kong Polytechnic UniversityHong Kong SARChina the Faculty of Technology Bielefeld UniversityBielefeld 33619Germany

Large-scale multi-objective optimization problems(LSMOPs)pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search *** evolutionary algorithms are good at solving small-scale multi-objective optimization problems,they are criticized for low efficiency in converging to the optimums of *** contrast,mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems,but they have difficulties in finding diverse solutions for ***,how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains *** this paper,a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient *** the one hand,conjugate gradients and differential evolution are used to update different decision variables of a set of solutions,where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto *** the other hand,objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions,and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its *** comparison with state-of-the-art evolutionary algorithms,mathematical programming methods,and hybrid algorithms,the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.

关键词： Conjugate gradient differential evolution evolutionary computation large-scale multi-objective optimization mathematical programming

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Chinese image captioning via double decoding based on visual prompts 5

Chinese image captioning via double decoding based on visual...

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5th International Conference on Computer Vision and Data Mining, ICCVDM 2024

作者： Tan, Weiguo Xiao, Yannan Wen, Yayuan Deng, Zhenrong Huang, Wenming School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guangxi Guilin China College of Electronic Engineering Guangxi Normal University Guangxi Guilin China

ISBN: (纸本)9781510682863

The significant progress of vision-language pre-trained models (VLMs) and large language models (LLMs) have provided a feasible new mode for image captioning, which relies on VLMs to process images and then utilizes LLMs to generate captions, simplifying the caption generation process and making it lighter. Based on this mode, to address the issues of deviation between the generated captions and the content of image expressions, incomplete descriptive information, and resource hungry in previous Chinese image captioning models, we propose a Chinese image captioning model via double decoding based on Visual Prompts, DDVP. This model employs the CLIP model as the encoder, the GPT2 model as the decoder, and introduces Visual Prompts, which are keywords related to image content. The model adopts a double decoding approach, first decoding to generate Visual Prompts and then decoding to generate the final captions based on the Visual Prompts. Through evaluation, we have demonstrated that our model achieves competitive results on the AIC-ICC dataset, and while maintaining fluency, the generated captions of DDVP can also cover the information in the image more comprehensively and accurately. © 2024 SPIE.

关键词： Visual languages

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Fusing Latent Dirichlet Allocation with Fuzzy Matching for Improved Topic Expressiveness in Text Mining 3

Fusing Latent Dirichlet Allocation with Fuzzy Matching for I...

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3rd International Conference on Digital Data processing, DDP 2023

作者： Yu, Jiahua Fong, Simon Song, Qun Tang, Lianggui Millham, Richard C. University of Macau Taipa Department of Computer and Information Science Macau China Chongqing Technology and Business University Chongqing Key Laboratory of Intelligent Perception and Blockchain Department of Artificial Intelligence Chongqing China Durban University of Technology Faculty of Accounting and Informatics Durban South Africa

ISBN: (纸本)9798350329018

This paper introduces an innovative fusion of Latent Dirichlet Allocation (LDA) with fuzzy matching, aiming to elevate the expressiveness of topics in text mining. Traditional LDA techniques, while effective, often fall short in capturing the intricate semantic nuances and contextual variations of keywords within a given document. In this enhanced version, we propose a methodology that seamlessly integrates LDA with fuzzy matching algorithms, offering a more refined approach to topic modeling. The key distinction lies in the enhanced model's ability to not only identify keywords but also accommodate their diverse forms and variations, thus creating a more comprehensive and expressive representation of topics. The integration of fuzzy matching enriches the association between topics and the underlying text by considering partial matches and accounting for variations in spelling, structure, and context. This enhancement ensures a more accurate reflection of the intricate relationships between topics and the surrounding text, fostering a deeper understanding of the document's content. To validate the effectiveness of our proposed approach, we conducted extensive experiments on diverse textual datasets. Results demonstrate that the enhanced model consistently outperforms traditional LDA in terms of capturing topic expressiveness. By revealing the subtle nuances and contextual richness within documents, our methodology contributes to a more nuanced and insightful interpretation of textual data. This paper provides a significant advancement in the field of text mining, offering researchers and practitioners an enhanced tool for uncovering the hidden layers of meaning embedded in large corpora of text. © 2023 IEEE.

关键词： Semantics

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Time Series Data Association Mining With High-Frequency Data Interference 24

Time Series Data Association Mining With High-Frequency Data...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhu, Rongjing Li, Peng Liu, Yu Deng, Miao School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350319934

Time series data mining is an important problem in data mining. It discovers the intrinsic correlation rules by analyzing a large amount of time series data. However, high-frequency data may exist in the dataset. High-frequency data is the data that occurs frequently in time series data, which will affect the mining of association rules. Traditional time series association mining tends to classify high-frequency data as correlated data because they co-occur too many times. In this paper, we propose the list mining framework to mine the association data by list co-occurrence to achieve the mining of massive data. We divide it into three phases, list generation phase, matrix generation phase, and rule generation phase. First, we create data lists based on time windows. Second, we create the initial count matrix, update the count matrix based on the list, and propose a GCPM algorithm to generate a conditional probability matrix based on the count matrix. Third, we propose a List Mining (LM) algorithm to generate the initial rules based on the conditional probability matrix and optimize the initial rules to get the final association rules. In addition, we weigh the rules according to the list mining method and propose a Weighted List Mining (WLM) algorithm to filter rules by reducing the weight of frequent item interference. The experimental results show that LM and WLM can effectively improve the accuracy of mining results. © 2022 IEEE.

关键词： Data mining

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Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 74-87页

作者： Bogang Qu Zidong Wang Bo Shen Hongli Dong Hongjian Liu the College of Automation Engineering Shanghai University of Electric PowerShanghai 200090China IEEE the Department of Computer Science Brunel University LondonUxbridgeMiddiesex UB83PHUK the College of Information Science and Technology Donghua UniversityShanghai 200051 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the Artificial Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast PetroleumDaqing 163318 the Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of EducationAnhui Polytechnic UniversityWuhu 2410000 the School of Mathematics and Physics Anhui Polytechnic UniversityWuhu 241000China

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

关键词： Decentralized state estimation(SE) measurements with anomalies minimum error entropy unscented Kalman filter wide-area power systems

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A Tolerance Classes Partition-Based Re-Definition of the Rough Approximations for Incomplete information System

A Tolerance Classes Partition-Based Re-Definition of the Rou...

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International Conference on Image, Vision and intelligent Systems, ICIVIS 2021

作者： Wang, Lei Liu, Bin Cai, Xiangxiang Wang, Chong Jiangxi Provincial Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang330099 China College of Information Engineering Nanchang Institute of Technology Nanchang330099 China

ISBN: (纸本)9789811669620

The information with missing attribute value is known as the incomplete information system. In an incomplete information system, there exist tolerance relations satisfying reflexivity and symmetry among objects in the universe of discourse and the tolerance classes for each object can be derived from the tolerance relation directly. Generally speaking, the tolerance classes of all objects can form some coverages of the universe. The research of this paper is based on the tolerance classes of each object in the incomplete information system. Firstly, a method is proposed in which a partition of the universe is constructed by using tolerance classes of objects. Then, the upper and lower approximations of concept, i.e., a subset of universe, is re-defined on the basis of acquisition of the partition of the universe using proposed method and this new definition can improve the approximation accuracy of concept effectively compared to the traditional definition. Finally, an illustrated example is exhibited for demonstrating the method to obtain a partition to universe by tolerance class as well as the calculation of the rough approximations of concept based on the partition of universe by tolerance class. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： information use

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Software Defect Prediction Based on SMOTE-Tomek and XGBoost 16th

Software Defect Prediction Based on SMOTE-Tomek and XGBoost

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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Yang, Haotian Li, Min School of Information Engineering Nanchang Institute of Technology Jiangxi330099 China Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Jiangxi330099 China

ISBN: (纸本)9789811912528

The use of machine learning techniques to predict software defects has received extensive attention over the years. In practice, there are far fewer defective software samples than non-defective software samples, resulting in a high imbalance of sample categories. The problem of imbalanced data classification is a hotspot and difficulty in machine learning and data mining. In order to effectively solve the problem of software defect prediction, we introduce a classification model that combines SMOTE-Tomek sampling and ensemble learning algorithm XGBoost. Our initial experimental results on ten NASA software defect datasets indicate a clear superiority of the proposed techniques when compared to other hybrid model regarding various assessment metrics, such Accuracy, F-Measure, and AUC. © 2022, Springer Nature Singapore Pte Ltd.

关键词： NASA

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study 9

Evaluating the Performance to Detecting Topic Shift of Large...

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9th International Conference on intelligent Computing and Signal processing, ICSP 2024

作者： Tian, Pingfang Liu, Deting Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Institute of Scientific and Technical Information of China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Beijing100038 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan430065 China

ISBN: (纸本)9798350376548

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor. © 2024 IEEE.

关键词： adjacent utterances level ChatGPT contextual level single utterances level topic shift detection

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