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International Conference on Computer Supported Cooperative Work in Design

作者： Kecheng Tang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Fuzzing is increasingly being utilized as a method to test the reliability of Deep Learning (DL) systems. In order to detect more errors in DL systems, exploring the internal logic of more DNNs has become the main objective of fuzzing. Despite advancements in the seed selection aspect of fuzzing, considerable opportunities still exist for improving testing efficiency. Current research has issues with the repeated consideration of neurons in the model that will be covered in the future by other seeds, leading to redundant seeds and lower testing efficiency. Additionally, there is a lack of a method to measure the potential of seeds to increase coverage, making it difficult to select the most worthy seeds for mutation in each iteration. We propose an uncovered neurons information based (UNIB) fuzzing method for DNN. UNIB uses clustering methods to organize the seed queue based on initial seed data, aiming to enhance the coverage rate improved in each iteration. It also integrates coverage information from the testing phase to identify the seeds with the greatest potential. The experimental results show that UNIB achieved a higher NC than the second-best method by 1.1% and 3% in LetNet-4 and LetNet-5, respectively. UNIB consistently required the fewest number of iterations to reach the same NC as other methods. For both LetNet-4 and LetNet-5, the adversarial test case sets generated by UNIB exhibited the highest diversity.

关键词： Deep learning Federated learning Current measurement Neurons Neural networks Fuzzing Software

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture

A Graph Sequence Generator and Multi-head Self-attention Mec...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yuejia Wu Jian-Tao Zhou College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.

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Advancing biological fluorescence microscopy with deep learning:a bibliometric perspective

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Medical data Mining 2022年第4期5卷 42-55页

作者： Li Fu Cheng-Liang Yin Jia-Lei Wang Xiao-Zhu Liu College of Materials and Environmental Engineering Hangzhou Dianzi UniversityHangzhou 310018China Medical Big Data Research Center Medical Innovation Research Division of PLA General HospitalBeijing 100853China National Engineering Laboratory for Medical Big Data Application Technology Chinese PLA General HospitalBeijing 100853China Department of Algorithm Shining3D Technology Company LimitidHangzhou 311258China Department of Cardiology The Second Affiliated Hospital of Chongqing Medical UniversityChongqing 400010China

Background:Fluorescence microscopy has increasingly promising applications in life *** bibliometrics-based review focuses on deep learning assisted fluorescence microscopy imaging ***:Papers on this topic retrieved by Core Collection on Web of Science between 2017 and July 2022 were used for the *** addition to presenting the representative papers that have received the most attention,the process of development of the topic,the structure of authors and institutions,the selection of journals,and the keywords are analyzed in detail in this ***:The analysis found that this topic gained immediate popularity among scholars from its emergence in 2017,gaining explosive growth within three *** phenomenon is because deep learning techniques that have been well established in other fields can be migrated to the analysis of fluorescence *** 2020 onwards,this topic tapers off but has attracted a few stable research groups to tackle the remaining *** this topic has been very popular,it has not attracted scientists from all over the *** USA,China,Germany,and the UK are the key players in this *** analysis and clustering are applied to understand the different focuses on this ***:Based on the bibliometric analysis,the current state of this topic to date and future perspectives are summarized at the end.

关键词： deep learning fluorescence microscopy signal-to-noise ratio convolutional neural network cell segmentation

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Multi-Attention Fusion Modeling for Sentiment analysis of Educational big data

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big data Mining and Analytics 2020年第4期3卷 311-319页

作者： Guanlin Zhai Yan Yang Heng Wang Shengdong Du the School of Information Science and Technology National Engineering Laboratory of Integrated Transportation Big Data Application TechnologySouthwest Jiaotong UniversityChengdu 611756China

As an important branch of natural language processing,sentiment analysis has received increasing *** teaching evaluation,sentiment analysis can help educators discover the true feelings of students about the course in a timely manner and adjust the teaching plan accurately and timely to improve the quality of education and *** at the inefficiency and heavy workload of college curriculum evaluation methods,a Multi-Attention Fusion Modeling(Multi-AFM)is proposed,which integrates global attention and local attention through gating unit control to generate a reasonable contextual representation and achieve improved classification *** results show that the Multi-AFM model performs better than the existing methods in the application of education and other fields.

关键词： educational big data sentiment analysis aspect-level attention

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A Revised Model for Sea-Surface Wind Inversion data with Joint Interpolation Optimization and Residual Network

A Revised Model for Sea-Surface Wind Inversion Data with Joi...

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International Conference on Intelligent System and Knowledge engineering, ISKE

作者： Weihong Lin Lijun You Tianqiang Huang Liqing Huang Haifeng Luo Chao Xu College of Computer and Cyber Security Fujian Normal University Fujian Provincal Engineering Research Center of Big Data Analysis and Application Digital Fujian Institute of Big Data Security Technology Fuzhou China Fujian Meteorological Information Center Fujian Key Laboratory of Severe Weather Fuzhou China

The sea-surface wind fields are pivotal for studies in both oceanography and meteorology. However, the acquisition of ocean wind data is often challenged by practical constraints, especially near coasts, leading to significant data gaps. This paper introduces a two-stage approach that combines interpolation optimization with a residual network to improve satellite-derived sea-surface wind fields data. Initially, the Kriging interpolation method is employed to fill missing regions. Subsequently, a network structure similar to ResNet-18 is tailored to revise the interpolated data using buoy anemometer observations. By establishing a correlation model between the interpolated results and ground truth through supervised training, the approach ensures the interpolated results gravitate towards the actual measurements. The efficacy of the proposed model is corroborated through experimental visual analysis and error cure assessments.

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Translational analysis of data science and causal learning in real-world clinical evaluation of traditional Chinese medicine

Science of Traditional Chinese Medicine

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Science of Traditional Chinese Medicine 2024年第1期2卷 57-65页

作者： Wei Yang Danhui Yi XiaoHua Zhou Yuanming Leng Institute of Basic Research in Clinical Medicine China Academy of Chinese Medical SciencesBeijingChina School of Statistics Renmin University of ChinaBeijingChina Beijing International Center for Mathematical Research Peking UniversityBeijingChina Department of Biostatistics School of Public HealthPeking UniversityBeijingChina National Engineering Laboratory of Big Data Analysis and Applied Technology Peking UniversityBeijingChina Department of Biostatistics School of Public HealthBoston UniversityBostonMAUSA

Real-world clinical evaluation of traditional Chinese medicine(RWCE-TCM)is a method for comprehensively evaluating the clinical effects of TCM,with the aim of delving into the causality between TCM intervention and clinical *** study explored data science and causal learning methods to transform RWD into reliable real-world evidence,aiming to provide an innovative approach for *** study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in *** methodology involves several key steps,including data integration and warehouse building,high-dimensional feature selection,the use of interpretable statistical machine learning algorithms,complex networks,and graph network analysis,knowledge mining techniques such as natural language processing and machine learning,observational study design,and the application of artificial intelligence tools to build an intelligent engine for translational *** goal is to establish a method for clinical positioning,applicable population screening,and mining the structural association of TCM characteristic *** addition,the study adopts the principle of real-world research and a causal learning method for TCM clinical *** constructed a multidimensional clinical knowledge map of“disease-syndrome-symptom-prescription-medicine”to enhance our understanding of the diagnosis and treatment laws of TCM,clarify the unique therapies,and explore information conducive to individualized *** causal inference process of observational data can address confounding bias and reduce individual heterogeneity,promoting the transformation of TCM RWD into reliable clinical *** data science improves efficiency and accuracy for implementing *** proposed data science methodology for TCM can handle complex data,ensure high-quality RWD acquisition and analysis,and provide in-depth insights into clinical benefits of *** method supp

关键词： Traditional Chinese medicine Real-world study data science Causal learning Health information system Machine learning Artificial intelligence Treatment effects

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Vision-Amplified Semantic Entropy for Hallucination Detection in Medical Visual Question Answering

arXiv

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arXiv 2025年

作者： Liao, Zehui Hu, Shishuai Zou, Ke Fu, Huazhu Zhen, Liangli Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore National University of Singapore Singapore119077 Singapore

Multimodal large language models (MLLMs) have demonstrated significant potential in medical Visual Question Answering (VQA). Yet, they remain prone to hallucinations—incorrect responses that contradict input images, posing substantial risks in clinical decision-making. Detecting these hallucinations is essential for establishing trust in MLLMs among clinicians and patients, thereby enabling their real-world adoption. Current hallucination detection methods, especially semantic entropy (SE), have demonstrated promising hallucination detection capacity for LLMs. However, adapting SE to medical MLLMs by incorporating visual perturbations presents a dilemma. Weak perturbations preserve image content and ensure clinical validity, but may be overlooked by medical MLLMs, which tend to over rely on language priors. In contrast, strong perturbations can distort essential diagnostic features, compromising clinical interpretation. To address this issue, we propose Vision Amplified Semantic Entropy (VASE), which incorporates weak image transformations and amplifies the impact of visual input, to improve hallucination detection in medical VQA. We first estimate the semantic predictive distribution under weak visual transformations to preserve clinical validity, and then amplify visual influence by contrasting this distribution with that derived from a distorted image. The entropy of the resulting distribution is estimated as VASE. Experiments on two medical open-ended VQA datasets demonstrate that VASE consistently outperforms existing hallucination detection methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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applications of molten salt and progress of molten salt electrolysis in secondary metal resource recovery

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International Journal of Minerals,Metallurgy and Materials 2020年第12期27卷 1599-1617页

作者： Xiao-li Ming Feng Li-wen Zhang Zuo-ren Nie College of Materials Science and Engineering Key Laboratory of Advanced Functional MaterialsEducation Ministry of ChinaBeijing University of TechnologyBeijing 100124China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of TechnologyBeijing 100124China

Molten salt is an excellent medium for chemical reaction,energy transfer,and *** salt innovative technologies should be developed to recover metals from secondary resources and reserve metals from primary natural *** these technologies,molten salt electrolysis is an economic and environment-friendly method to extract metals from waste *** the perspective of molten salt characteristics,the application of molten salts in chemistry,electrochemistry,energy,and thermal storage should be comprehensively *** review discusses further directions for the research and development of molten salt electrolysis and their use for metal recovery from various metal wastes,such as magnet scrap,nuclear waste,and cemented carbide *** is placed on the development of various electrolysis methods for different metal containing wastes,overcoming some problems in electrolytes,electrodes,and electrolytic *** focus is given to future development directions for current associated processing obstacles.

关键词： molten salt application molten salt electrochemistry resource recycling cemented carbide scrap

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Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios

arXiv

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arXiv 2024年

作者： Chen, Geng Wang, Qingyue Rekik, Islem National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University China BASIRA Lab Imperial-X Department of Computing Imperial College London United Kingdom

A connectional brain template (CBT) is a holistic representation of a population of multi-view brain connectivity graphs, encoding shared patterns and normalizing typical variations across individuals. The federation of CBT learning allows for an inclusive estimation of representative center of multi-domain brain connectivity datasets in a fully data-preserving manner. However, existing methods overlook the non-independent and identically distributed (non-IDD) issue stemming from multidomain brain connectivity heterogeneity, in which data domains are drawn from different hospitals and imaging modalities. This non-IDD issue degrades the centrality of locally learned CBT from multiple decentralized datasets (i.e., domains/hospitals), eventually leading to a limited representation ability. To overcome this limitation, we unprecedentedly propose a metadata-driven federated learning framework, called MetaFedCBT, for cross-domain CBT learning. Given the data drawn from a specific domain (i.e., hospital), our model aims to learn a metadata in a fully supervised manner by introducing a local client-based regressor network. The generated meta-data is forced to meet the statistical attributes (e.g., mean) of other domains, while preserving their privacy. Our supervised meta-data generation approach boosts the unsupervised learning of a more centered and representative and holistic CBT of a particular brain state across diverse domains. As the federated learning progresses over multiple rounds, the learned metadata and associated generated connectivities are are continuously updated to better approximate the target domain information. MetaFedCBT overcomes the non-IID issue of existing methods by generating informative brain connectivities for privacy-preserving holistic CBT learning with the guidance using metadata. Extensive experiments on multi-view morphological brain networks of normal and patient subjects demonstrate that our MetaFedCBT is a superior federated CBT learnin

关键词： Metadata

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Service Decomposition method for multimodal data exchange scenarios 3

Service Decomposition method for multimodal data exchange sc...

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3rd International Conference on Computer Science and Management technology, ICCSMT 2022

作者： Jiang, Deyou Huang, Lei Tian, Hanyu Xie, Yujie Li, Xiaoming Beijing Jiaotong University School of Economics and Management Beijing China Beijing Jingwei Information Technologies Limited Company National Engineering Laboratory of Comprehensive Transportation Big Data Application Technology Beijing China Institute of Computing Technologies China Academy of Railway Sciences Beijing China

ISBN: (纸本)9798350320404

Influenced by the complexity of multimodal data exchange scenarios and their differences in data flow graph construction, the objective basis of existing service decomposition methods is not sufficient, and the granularity of the results is too large and difficult to be decomposed. To address this problem, this paper presents a service decomposition method for multimodal data exchange scenarios. This method reconstructs the differentially constructed data flow diagram by unified rules at the stage of requirement analysis and design, and then completes the fine-grained division of microservices around documents based on service-decomposed rules. The case study shows that the method is more effective than the existing method in the multimodal data exchange scenario. © 2022 IEEE.

关键词： Electronic data interchange

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