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

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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Primary cilia support cartilage regeneration after injury

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International Journal of Oral Science 2023年第2期15卷 291-303页

作者： Dike Tao Lei Zhang Yunpeng Ding Na Tang Xiaoqiao Xu Gongchen Li Pingping Niu Rui Yue Xiaogang Wang Yidong Shen Yao Sun Department of Implantology School&Hospital of StomatologyTongji UniversityShanghaiChina Shanghai Engineering Research Center of Tooth Restoration and Regeneration ShanghaiChina State Key Laboratory of Cell Biology Shanghai Institute of Biochemistry and Cell BiologyCenter for Excellence in Molecular Cell ScienceChinese Academy of Sciences University of Chinese Academy of Sciences ShanghaiChina Department of Oral and Maxillofacial Surgery School&Hospital of StomatologyTongji UniversityShanghaiChina Institute for Regenerative Medicine Shanghai East HospitalFrontier Science Center for Stem Cell ResearchShanghai Key Laboratory of Signaling and Disease ResearchSchool of Life Sciences and TechnologyTongji UniversityShanghaiChina Shanghai Institute of Stem Cell Research and Clinical Translation ShanghaiChin Key Laboratory of Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijingChina

In growing children,growth plate cartilage has limited self-repair ability upon fracture injury always leading to limb growth ***,one type of fracture injuries within the growth plate achieve amazing self-healing,however,the mechanism is *** this type of fracture mouse model,we discovered the activation of Hedgehog(Hh)signaling in the injured growth plate,which could activate chondrocytes in growth plate and promote cartilage repair.

关键词： fracture plate injuries

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An Efficient Reference-Point Based k Neighbors Algorithm for Imbalanced data 7

An Efficient Reference-Point Based k Neighbors Algorithm for...

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7th International Conference on Cloud Computing and big data Analytics, ICCCBDA 2022

作者： Wang, Junkuan Wu, Yanmin Qi, Jinli Chen, Zizhong Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China ChongQing College of Electronic Engineering Department of Artificial Intelligence and Big Data Chongqing China ChongQing College of Electronic Engineering Department of General Education and International Studies Chongqing China

ISBN: (纸本)9781665487115

The kNNs are very simple, effective and online mul-ticategory learning algorithms, which makes them widely applica-ble to various fields. However, almost all kNNs have the problem of low accuracy in imbalanced data. In this paper, an interesting point is observed that the nearest neighbors might not always be the best samples for prediction, and a reference-point based k Neighbors algorithm (RPkN) is proposed. The proposed algorithm uses some reference-points to measure the distance between two data points rather than directly computing the distance. This makes it able to find better neighbors than their nearest ones, and the problem of low accuracy in imbalanced data sets could be solved. Therefore, the proposed method can achieve higher aver-age accuracy than the existing exact kNNs. In addition, it avoids the direct computation of distances between each pair of data points without using any tree structure, and this makes the RPkN decrease the time complexity to O(nlogn). The time complexity to classify a new data point is O(logn). The time complexity is lower than almost all the existing exact kNNs. The low time complexity and not relying on tree structures makes it very suitable to be paralleled for large-scale data processing when compared with other fast exact kNNs using tree structure. © 2022 IEEE.

关键词： Training Cloud computing big data Prediction algorithms data processing Mathematical models Classification algorithms

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Discrete Jaya algorithm to solve the police resource deployment problem in emergency traffic accident 10

Discrete Jaya algorithm to solve the police resource deploym...

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Jing, Zhang Weijie, Ding Zhejiang Police College Department of Computer and Information Security Hangzhou310053 China Big-data and Network Security Research Institute Zhejiang Police College Hangzhou310053 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Hangzhou310053 China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Aiming at the high frequency of urban traffic accidents and the relative shortage of police resources, a multi-objective discrete Jaya algorithm is proposed to solve the problem of police resource deployment in emergency traffic accident handling. The purpose is to make timely response and handling of traffic accidents through the rational allocation of police deployment points in the jurisdiction. Taking the coverage of accident points and the average response time of accidents as the optimization objectives, the discrete Jaya algorithm is used as the global search strategy to approach the Pareto front by constantly approaching the optimal solution and far away from the worst solution, and then the neighborhood search strategy is used to improve the local search ability of the algorithm. Finally, a practical example is given to verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Law enforcement

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A High-Speed Stable Polarization Encoder for Quantum key Distribution 16

A High-Speed Stable Polarization Encoder for Quantum Key Dis...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Li, Ju Wang, Zexu Wang, Changlei Yin, Feifei Xu, Kun Xu, Huaxing Dai, Yitang Beijing University of Posts and Telecommunications State Key Laboratory of Information Photonics and Optical Communications Beijing100876 China National Engineering Research Center for Public Safety Risk Perception and Control by Big Data China Academy of Electronics and Information Technology Beijing100041 China Cetc Academy of Electronics and Information Technology Group Co. Ltd. Beijing100041 China

We report a high-speed polarization encoder which can be applied for quantum key distribution, resulting in an average bit error rate (BER) below 0.1% at a repetition frequency of 10 GHz under interference-free measur... 详细信息

ISBN: (纸本)9798350372076

关键词： Quantum cryptography

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Learning Relation-Specific Representations for Few-shot knowledge Graph Completion

arXiv

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arXiv 2022年

作者： Li, Yuling Yu, Kui Zhang, Yuhong Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China

Recent years have witnessed increasing interest in few-shot knowledge graph completion (FKGC), which aims to infer unseen query triples for a few-shot relation using a few reference triples about the relation. The primary focus of existing FKGC methods lies in learning relation representations that can reflect the common information shared by the query and reference triples. To this end, these methods learn entity-pair representations from the direct neighbors of head and tail entities, and then aggregate the representations of reference entity pairs. However, the entity-pair representations learned only from direct neighbors may have low expressiveness when the involved entities have sparse direct neighbors or share a common local neighborhood with other entities. Moreover, merely modeling the semantic information of head and tail entities is insufficient to accurately infer their relational information especially when they have multiple relations. To address these issues, we propose a Relation-Specific Context Learning (RSCL) framework, which exploits graph contexts of triples to learn global and local relation-specific representations for few-shot relations. Specifically, we first extract graph contexts for each triple, which can provide long-term entity-relation dependencies. To encode the extracted graph contexts, we then present a hierarchical attention network to capture contextualized information of triples and highlight valuable local neighborhood information of entities. Finally, we design a hybrid attention aggregator to evaluate the likelihood of the query triples at the global and local levels. Experimental results on two public datasets demonstrate that RSCL outperforms state-of-the-art FKGC methods. © 2022, CC BY.

关键词： knowledge graph

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Tibetan Literature Recommendation Based on Vague Similarity of Cited Number and Downloads 8th

Tibetan Literature Recommendation Based on Vague Similarity ...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Liu, Yongzhi Wu, Gang Cai, Zangtai College of Alibaba Big Data Fuzhou Polytechnic Fuzhou350108 China College of Information Engineering Tarim University Alar843300 China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Xining810008 China Key Laboratory of Tibetan Information Processing Ministry of Education Xining810008 China

ISBN: (纸本)9783031067600

In scientific research, it is very important to quickly and accurately retrieve relevant important documents. This paper sorts the keywords according to their importance and calculates the fuzzy membership degree of keywords;The literatures with the same keywords enter the recommendation set, and two kinds of experiments are carried out on the literatures entering the recommendation set: one is to use vague similarity technology to recommend documents according to keywords;Second, on the basis of the first experiment, vague similarity technology is used to fuse the cited numbers and downloads for literature recommendation. Experiments show that the recommendation algorithm proposed in this paper is reasonable and effective, and can give a reasonable ranking, which meets the needs of researchers to retrieve literatures. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Academic resources Recommend Tibetan Vague technology

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SwimVG: Step-wise Multimodal Fusion and Adaption for Visual Grounding

arXiv

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arXiv 2025年

作者： Shi, Liangtao Liu, Ting Hu, Xiantao Hu, Yue Yin, Quanjun Hong, Richang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei230009 China Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China School of systems engineering National University of Defense Technology Hunan Province Changsha410073 China Department of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210014 China

Visual grounding aims to ground an image region through natural language, which heavily relies on cross-modal alignment. Most existing methods transfer visual/linguistic knowledge separately by fully fine-tuning uni-modal pre-trained models, followed by a simple stack of visual-language transformers for multimodal fusion. However, these approaches not only limit adequate interaction between visual and linguistic contexts, but also incur significant computational costs. Therefore, to address these issues, we explore a step-wise multimodal fusion and adaption framework, namely SwimVG. Specifically, SwimVG proposes step-wise multimodal prompts (Swip) and cross-modal interactive adapters (CIA) for visual grounding, replacing the cumbersome transformer stacks for multimodal fusion. Swip can improve the alignment between the vision and language representations step by step, in a token-level fusion manner. In addition, weight-level CIA further promotes multimodal fusion by cross-modal interaction. Swip and CIA are both parameter-efficient paradigms, and they fuse the cross-modal features from shallow to deep layers gradually. Experimental results on four widely-used benchmarks demonstrate that SwimVG achieves remarkable abilities and considerable benefits in terms of efficiency. Our code is available at https://***/liuting20/SwimVG. Copyright © 2025, The Authors. All rights reserved.

关键词： Visual languages

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Multimodal Identity Recognition of Spiking Neural Network Based on Brain-Inspired Reward Propagation

Multimodal Identity Recognition of Spiking Neural Network Ba...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Yang Liu Hongxia Zhang Yahui Li Quanqiang Wang Cheng Liu Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Department of Clinical Laboratory Huaihe Hospital of Henan University Kaifeng China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Deep learning has strongly promoted the research progress of unimodal feature learning such as face recognition and speech recognition, but it still needs to be strengthened in multimodal data representation learning, especially the problem of high power consumption, which is not conducive to the real-time application of mobile devices under limited resources. In order to realize multimodal identity recognition for low-energy edge smart devices, this paper proposes a multimodal identity recognition method of spiking neural network based on brain-like mechanism (SNN-MIR-BiM). Simulating the attention mechanism of the cognitive system to specific information improves the response of multimodal features, mimics the regulation mechanism of the neurotransmitter dopamine in the nervous system, and releases reward signals from the recognition results, which affects the neuronal state and regulates the synaptic weights through global propagation, and then optimizes the network structure. Experimental results on large-scale audio and video datasets show that the proposed method can reach 98.94% recognition accuracy, and the energy consumption of the model is about 1/10 of that of CNN under the same architecture, which verifies the effectiveness of the attention mechanism and reward propagation in network design. This research is of great practical significance in the industry applications of unmanned autonomous systems such as human-computer interaction of intelligent machines and intelligent security.

关键词： Energy consumption Neurotransmitters Attention mechanisms Accuracy Spiking neural networks Speech recognition Brain modeling Feature extraction Regulation Biological neural networks

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Overview of Essential Components in deep learning reference-based super resolution methods 11

Overview of Essential Components in deep learning reference-...

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Xue, Jiayu Liu, Junjie Shi, Yong School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Sino-Danish College University of Chinese Academy of Sciences Beijing100049 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States

Reference-based super resolution (RefSR) aims to recover the lost details in a low-resolution image and generate a high-resolution result, guided by a high-resolution reference image with similar contents or textures. In contrast to the traditional single-image super-resolution, which focuses on the intrinsic properties of the single low-resolution image, the challenge of RefSR lies in matching and aggregating highly-related but misaligned reference images with low-resolution images. Several effective but complex designs have been proposed to address this challenge, which poses difficulties in implementing RefSR in real-world applications. In order to better understand the working mechanism of RefSR and design a more efficient and lightweight architecture, we provide a review about the essential components of the existing deep learning-based RefSR. We decompose and classify the common pipeline into four submodules according to the functionalities. Then, we summarize and describe the implementation details of the commonly-adopted approaches in each submodule. Finally, we discuss the challenges and promising research directions. © 2024 The Authors.

关键词： Deep learning

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