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32nd British Machine Vision Conference, BMVC 2021

作者： Wu, Kewei Wang, Tao Xie, Zhao Guo, Dan Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Human pose estimation is a challenging task that requires the comprehension of the pose structure. This work can refer to spatial relation inference in a pose structure model;how to model the dynamic spatial relation against various unreliable joints is critical. To this end, we propose a Distilling Dynamic Spatial Relation network (DDSR), which builds pose-based graph representation by exploiting the feature of spatial relation from the location distribution of joints. We use a dynamic message propagation mechanism to update the spatial relation on edges. Specifically, to filter out the noisy predictions, we select the joints with high confidence;to enhance the spatial relation in a large receptive field, we propagate multi-stage messages among joints. Besides, to reduce the computation cost of the multi-stage message propagation, we design a cross-resolution distillation framework. We use a new spatial distillation loss to verify the spatial relation between the teacher model and the student model. Experimental results on COCO and MPII datasets show that our method is superior to the state-of-the-art methods. The visualization results further verify the interpretability of our spatial relation. © 2021. The copyright of this document resides with its authors.

关键词： Distillation

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A Semantic Framework for Chinese Historical Events Based on Linked data and knowledge Graph 16th

A Semantic Framework for Chinese Historical Events Based on ...

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16th International Conference on Diversity, Divergence, Dialogue, iConference 2021

作者： Wang, Hao Li, Yueyan Deng, Sanhong School of Information Management Nanjing University Nanjing210023 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China

ISBN: (纸本)9783030712914

In the era of big data, data redundancy has become an obstacle to deep reading. The objective of linked data as a new data organization model is to transform data into structured data following unified standards. The lack of Chinese conceptual terms has seriously hindered the semantization and standardization of Chinese domain ontology. Taking Chinese historical events as an example, ontology technology is used in this paper to standardize the definition of concepts and semantic relations in domain knowledge. Moreover, concepts from text resources are extracted through a deep learning algorithm Bi-LSTM-CRFs and combined with an external knowledge base to realize the fusion of related data within various data sets. Ultimately, the knowledge ontology of historical events is displayed in the way of a knowledge graph to further explore the practical application. The results show that the accuracy of the terms extraction of historical events is about 80% indicating the good recognition performance and portability of the model. © 2021, Springer Nature Switzerland AG.

关键词： Linked data

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Marginal Value-Based Edge Resource Pricing and Allocation for Deadline-Sensitive Tasks

Marginal Value-Based Edge Resource Pricing and Allocation fo...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Puwei Wang Zhouxing Sun Ying Zhan Haoran Li Xiaoyong Du School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing China Guizhou University of Finance and Economics Guiyang City China

In edge computing (EC), resource allocation is to allocate computing, storage and networking resources on the edge nodes (ENs) efficiently and reasonably to tasks generated by users. Due to the resource-limitation of ENs, the tasks often need to compete for the resources. Pricing mechanisms are widely used to deal with the resource allocation problem, and the valuations of tasks play a critical role in the price mechanisms. However, users naturally are not willing to expose the valuations of their tasks due to conflicts of interests. Current research works usually adopt truthful auctions to motivate the users to report honestly the valuations of their tasks. In this paper, we introduce the marginal value to estimate the valuations of tasks, and propose a marginal value-based pricing mechanism using the incentive theory, which motivates the tasks with higher marginal values to actively request more resources. The EC platform sets the resource prices using the price mechanism, and then the users determine their resource requests relying on the resource prices and the valuations of their tasks. After receiving the deadline-sensitive tasks from the users, the resource allocation can be modeled as a knapsack problem with the deadline constraints. Extensive experimental results demonstrate that our approach is computationally efficient and is promising in enhancing the utility of the EC platform and the tasks.

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Electronic Medical Record Sharing System Based on Hyperledger Fabric and InterPlanetary File System 2021

Electronic Medical Record Sharing System Based on Hyperledge...

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5th International Conference on Compute and data Analysis, ICCDA 2021

作者： Li, Lei Yue, Zhengxiang Wu, Gongqing Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781450389112

Electronic Medical Records (EMR) and other medical data contain important and sensitive privacy information of patients, which provide important basis and reference for their doctors to diagnose and treat them. With the continuous development of data sharing, big data applications and other technologies, new requirements are proposed for the storage of medical privacy data. At present, medical data have the characteristics of obstructed sharing and exchange, difficult data tracing and data sensitivity, which increases the cost and difficulty of each diagnosis. Moreover, patients lack the right to know and control their own medical data, which is easy to cause data fraud or data leakage. As a decentralized, tamper proof and traceable distributed ledger system, blockchain brings new solutions to these problems. This paper designs a medical data fusion and distributed privacy management system based on the Hyperledger Fabric blockchain development platform and InterPlanetary File System. It can not only solve the problem of mutual distrust in the traditional system, but also realize the safe data storage and the safe sharing of medical record data between medical organizations which is a beneficial attempt to overcome the disadvantages of traditional hospital data separation. At the same time, according to the requirements of different levels and types of medical users or groups for data security and personal privacy protection, the access control technology of users is studied to ensure personal privacy data. © 2021 ACM.

关键词： Blockchain

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Detecting the research diversity of researchers in library and information science:An exploratory study

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data Science and Informetrics 2022年第3期2卷 22-36页

作者： Yuehua Zhao Sicheng Zhu Jie Wu Hao Wang Sanhong Deng Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing UniversityNanjingChina School of Information Management Nanjing UniversityNanjingChina

Metrics have emerged as an important tool for quantitatively evaluating researchers from a variety of perspectives,including research impact,research quality,interdisciplinarity,and *** in the field of library and information science,many previous studies have highlighted the characteristics of researchers in this ***,only a minority of the studies address the aspect of diversity in research *** purpose of this study is to(1)evaluate the topic diversity of researchers in library and information science and(2)examine the relationships between the researcher topic diversity and research *** propose an indicator to quantify author topic diversity,which we refer to as author topic diversity(ATD).Latent Dirichlet Allocation(LDA)is used to detect topics in the field,while cosine similarity is used to calculate the diversity of research topics in a given researcher’s *** results show that topic diversity in the field of library and information science varies greatly from author to *** addition,weak positive correlations are found between the ATD and citation indicators,suggesting that engaging in diversified topics may lead to higher research impact.

关键词： Research diversity Research evaluation Library and information science

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Uncovering the Impact of Chain-of-Thought Reasoning for Direct Preference Optimization: Lessons from Text-to-SQL

arXiv

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arXiv 2025年

作者： Liu, Hanbing Li, Haoyang Zhang, Xiaokang Chen, Ruotong Xu, Haiyong Tian, Tian Qi, Qi Zhang, Jing Gaoling School of Artificial Intelligence Renmin University of China Beijing China School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China Engineering Research Center of Database and Business Intelligence Beijing China China Mobile Information Technology Center China

Direct Preference Optimization (DPO) has proven effective in complex reasoning tasks like math word problems and code generation. However, when applied to Text-to-SQL datasets, it often fails to improve performance and can even degrade it. Our investigation reveals the root cause: unlike math and code tasks, which naturally integrate Chain-of-Thought (CoT) reasoning with DPO, Text-to-SQL datasets typically include only final answers (gold SQL queries) without detailed CoT solutions. By augmenting Text-to-SQL datasets with synthetic CoT solutions, we achieve, for the first time, consistent and significant performance improvements using DPO. Our analysis shows that CoT reasoning is crucial for unlocking DPO’s potential, as it mitigates reward hacking, strengthens discriminative capabilities, and improves scalability. These findings offer valuable insights for building more robust Text-to-SQL models. To support further research, we publicly release the code and CoT-enhanced datasets © 2025, CC BY.

关键词： Chains

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Sentiment Analysis of Semester Learning Essays in Design Education

Sentiment Analysis of Semester Learning Essays in Design Edu...

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2023 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2023

作者： Wang, Z.H. Ming, Z.J. Wang, G.X. Mistree, F. Allen, J.K. School of Mechanical Engineering Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China Ministry of Industry and Information Technology Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Beijing China The University of Oklahoma The Systems Realization Laboratory Norman United States

ISBN: (纸本)9798350323153

AME4163: Principles of engineering Design is a design, build and test course offered at the University of Oklahoma, Norman, USA. Throughout the semester students are expected to reflect on authentic and immersive experiences and write Take-aways and Learning Statements. At the end of the semester each student submits a summative Semester Learning Essay. Between 2019-2021 we collected about 10,000 Take-aways and Learning Statements from the Semester Learning Essays of nearly 400 students. In this paper, we attempt to help instructors gain insight of what students have learned through these materials and thence improve the delivery of the course in the future. We analyze their Take-aways from summative Semester Learning Essays by using dictionary-based sentiment analysis to assess students' subjective feelings toward what they have learned. The results prove that we gain insight into their learning thereby providing instructors with evidence-based guidance on modifying the course from different perspectives. The method proposed is generalizable to courses that involve authentic immersive experiences. © 2023 IEEE.

关键词： Assessment of learning learning by reflecting on doing sentiment analysis summative assessment

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Article reranking by memory-enhanced key sentence matching for detecting previously fact-checked claims 59

Article reranking by memory-enhanced key sentence matching f...

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Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Sheng, Qiang Cao, Juan Zhang, Xueyao Li, Xirong Zhong, Lei Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China

ISBN: (纸本)9781954085527

False claims that have been previously fact-checked can still spread on social media. To mitigate their continual spread, detecting previously fact-checked claims is indispensable. Given a claim, existing works retrieve fact-checking articles (FC-articles) for detection and focus on reranking candidate articles in the typical two-stage retrieval framework. However, their performance may be limited as they ignore the following characteristics of FC-articles: (1) claims are often quoted to describe the checked events, providing lexical information besides semantics;and (2) sentence templates to introduce or debunk claims are common across articles, providing pattern information. In this paper, we propose a novel reranker, MTM (Memoryenhanced Transformers for Matching), to rank FC-articles using key sentences selected using event (lexical and semantic) and pattern information. For event information, we propose to finetune the Transformer with regression of ROUGE. For pattern information, we generate pattern vectors as a memory bank to match with the parts containing patterns. By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns. Experiments on two real-world datasets show that MTM outperforms existing methods. Human evaluation proves that MTM can capture key sentences for explanations. The code and the dataset are at https://***/ICTMCG/MTM. © 2021 Association for Computational Linguistics

关键词： Semantics

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Reference-Based Iterative Interaction With P2-Matching for Stereo Image Super-Resolution

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 34卷 3779-3789页

作者： Runmin Cong Rongxin Liao Feng Li Ronghui Sheng Huihui Bai Renjie Wan Sam Kwong Wei Zhang School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education of China Hefei China School of Computer Science and Engineering Hefei University of Technology Hefei China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China Institute of Information Science Beijing Jiaotong University Beijing China Department of Computer Science Hong Kong Baptist University Hong Kong China School of Data Science Lingnan University Hong Kong China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China

Stereo Image Super-Resolution (SSR) holds great promise in improving the quality of stereo images by exploiting the complementary information between left and right views. Most SSR methods primarily focus on the inter-view correspondences in low-resolution (LR) space. The potential of referencing a high-quality SR image of one view benefits the SR for the other is often overlooked, while those with abundant textures contribute to accurate correspondences. Therefore, we propose Reference-based Iterative Interaction (RIISSR), which utilizes reference-based iterative pixel-wise and patch-wise matching, dubbed $P^{2}$ -Matching, to establish cross-view and cross-resolution correspondences for SSR. Specifically, we first design the information perception block (IPB) cascaded in parallel to extract hierarchical contextualized features for different views. Pixel-wise matching is embedded between two parallel IPBs to exploit cross-view interaction in LR space. Iterative patch-wise matching is then executed by utilizing the SR stereo pair as another mutual reference, capitalizing on the cross-scale patch recurrence property to learn high-resolution (HR) correspondences for SSR performance. Moreover, we introduce the supervised side-out modulator (SSOM) to re-weight local intra-view features and produce intermediate SR images, which seamlessly bridge two matching mechanisms. Experimental results demonstrate the superiority of RIISSR against existing state-of-the-art methods.

关键词： Feature extraction Iterative methods Superresolution Electronic mail Image resolution Image reconstruction Modulation Estimation data mining Merging

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An Intelligent Design Method Based on Case-based Reasoning and Reinforcement Learning

An Intelligent Design Method Based on Case-based Reasoning a...

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IEEE International Conference on Industrial engineering and engineering Management

作者： Yu Huang Ru Wang Zhuqin Wei Guoxin Wang Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology School of Machanical Engineering Beijing Institute of Technology Beijing China

Enterprises currently face the challenge of reducing production cycles and costs and utilizing existing cases for making changes and iterations has emerged as a viable solution. However, the acquisition and modification of historical cases present their challenges. To address this, the present paper proposes an intelligent design method based on reinforcement learning that aims to meet the demand for efficient and high-quality design solutions in the field of engineering design. This method comprises four key steps: case characterization, matching, retrieval, and selection. By employing case characterization and matching, users can acquire sets of similar cases that align closely with their specific requirements. Building upon this foundation incorporates a combination of reinforcement learning and weight order cross-reconstruction to generate more proposals. Subsequently, the multi-attribute decision-making method is utilized to select the extended set of design schemes. The effectiveness of the proposed method is demonstrated through its successful application to a radar design case.

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