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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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Joint-Motion Mutual Learning for Pose Estimation in Videos

arXiv

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arXiv 2024年

作者： Wu, Sifan Chen, Haipeng Yin, Yifang Hu, Sihao Feng, Runyang Jiao, Yingying Yang, Ziqi Liu, Zhenguang College of Computer Science and Technology Jilin University Changchun China A*STAR Singapore Georgia Institute of Technology Atlanta United States School of Artificial Intelligence Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Institute of Blockchain and Data Security Hangzhou China

Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Nevertheless, this task remains difficult because of the complex video scenes, such as video defocus and self-occlusion. Recent methods strive to integrate multi-frame visual features generated by a backbone network for pose estimation. However, they often ignore the useful joint information encoded in the initial heatmap, which is a byproduct of the backbone generation. Comparatively, methods that attempt to refine the initial heatmap fail to consider any spatiotemporal motion features. As a result, the performance of existing methods for pose estimation falls short due to the lack of ability to leverage both local joint (heatmap) information and global motion (feature) dynamics. To address this problem, we propose a novel joint-motion mutual learning framework for pose estimation, which effectively concentrates on both local joint dependency and global pixel-level motion dynamics. Specifically, we introduce a context-aware joint learner that adaptively leverages initial heatmaps and motion flow to retrieve robust local joint feature. Given that local joint feature and global motion flow are complementary, we further propose a progressive joint-motion mutual learning that synergistically exchanges information and interactively learns between joint feature and motion flow to improve the capability of the model. More importantly, to capture more diverse joint and motion cues, we theoretically analyze and propose an information orthogonality objective to avoid learning redundant information from multi-cues. Empirical experiments show our method outperforms prior arts on three challenging benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion estimation

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Hybrid Local Causal Discovery

arXiv

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arXiv 2024年

作者： Ling, Zhaolong Peng, Honghui Zhang, Yiwen Zhou, Peng Wu, Xingyu Yu, Kui Wu, Xindong School of Computer Science and Technology Anhui University Anhui Hefei230601 China School of Hong Kong Polytechnic University Department of Computing 999077 Hong Kong Key Laboratory of Knowledge Engineering with Big Data the Ministry of Education of China China School of Computer Science and Information Technology Hefei University of Technology Hefei230009 China

Local causal discovery aims to learn and distinguish the direct causes and effects of a target variable from observed data. Existing constraint-based local causal discovery methods use AND or OR rules in constructing the local causal skeleton, but using either rule alone is prone to produce cascading errors in the learned local causal skeleton, and thus impacting the inference of local causal relationships. On the other hand, directly applying score-based global causal discovery methods to local causal discovery may randomly return incorrect results due to the existence of local equivalence classes. To address the above issues, we propose a Hybrid Local Causal Discovery algorithm, called HLCD. Specifically, HLCD initially utilizes a constraint-based approach combined with the OR rule to obtain a candidate skeleton and then employs a score-based method to eliminate redundant portions in the candidate skeleton. Furthermore, during the local causal orientation phase, HLCD distinguishes between V-structures and equivalence classes by comparing the local structure scores between the two, thereby avoiding orientation interference caused by local equivalence classes. We conducted extensive experiments with seven state-of-the-art competitors on 14 benchmark Bayesian network datasets, and the experimental results demonstrate that HLCD significantly outperforms existing local causal discovery algorithms. Copyright © 2024, The Authors. All rights reserved.

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Theoretical data Science: bridging the gap between domain-general and domain-specific studies

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data Science and Informetrics 2021年第1期1卷 1-28页

作者： Chaolemen Borjigin Chen Zhang Zhizong Sun Ni Yi Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of ChinaBeijingChina School of Information Resource Management Renmin University of ChinaBeijingChina

The entering into big data era gives rise to a novel discipline called data *** Science is interdisciplinary in its nature,and the existing relevant studies can be categorized into domain-independent studies and domain-dependent *** domain-dependent studies and domain-independent ones are evolving into Domain-general data Science and Domain-specific data ***-general data Science emphasizes data Science in a general sense,involving concepts,theories,methods,technologies,and ***-specific data Science is a variant of Domain-general data Science and varies from one domain to *** most popular Domain-specific data Science includes data journalism,Industrial data Science,Business data Science,Health data Science,Biological data Science,Social data Science,and Agile data *** difference between Domain-general data Science and Domain-specific data Science roots in their thinking paradigms:DGDS conforms to data-centered thinking,while DSDS is in line with knowledge-centered *** a result,DGDS focuses on the theoretical studies,while DSDS is centered on applied ***,DSDS and DGDS possess complementary *** data Science(TDS)is a new branch of data Science that employs mathematical models and abstractions of data objects and systems to rationalize,explain and predict big data *** will bridge the gap between DGDS and *** contrasts with DSDS,which uses casual analysis,as well as DGDS,which employs data-centered thinking to deal with big data problems in that it balances the usability and the interpretability of data Science *** main concerns of TDS are concentrated on integrating the data-centered thinking with the knowledge-centered thinking as well as transforming a correlation analysis into the casual ***,TDS can bridge the gaps between DGDS and DSDS,and balance the usability and the interpretability of big data *** studies of TDS should be focused

关键词： data Science Big data Theoretical data Science Domain-general data Science Domain-specific data Science

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A Coffee-Ring Effect-Based Paper Sensor Chip for the Determination of Β-Lactoglobulin in Foods Via a Smartphone

SSRN

引用

SSRN 2022年

作者： Lu, Yang Yang, Jingyi Zhang, Yong Wu, Lin State Key Laboratory for Food Nutrition and Safety College of Food Science and Engineering Tianjin University of Science and Technology Tianjin300457 China State Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

To determine allergen β-lactoglobulin (β-LG) in foods, a method by a coffee-ring effect (CRE)-based paper sensor chip combined with a smartphone has been developed. The strategy was based on the principle of fluorescence quenching by the quantum dot-aptamer-graphene oxide (QD-Apt-GO) and where the degree of quenching was inversely proportional to the concentration of β-LG. First, the prepared β-LG solution was added to the QD-aptamer fluorescent probe solution followed by an appropriate amount of GO. After the reaction, a certain amount of the mixture was dispensed onto the detection zone of the sensor chip, resulting in the spread of the analyte solution by evaporation-driven flow. The QD-aptamer and the QD-Apt-β-LG in the mixed solution formed a visible fluorescent coffee-ring. For signal quantitation, an image of the coffee-ring was taken by a smartphone and imported into self-programmed software for analysis. The quantitative results for β-LG were acquired rapidly based on the magnitude of the generated fluorescent coffee-ring. The limit of detection (LOD) for β-LG was 0.048 mg L -1 and the linear range for the optimized assay was 0.39–1000 mg L -1 . The method was validated for analysis of real food samples and the results were consistent with those of a commercial ELISA assay kit. The smartphone-based assay provides a simple, economic, compact, and visual detection platform for the determination of β-LG in foods. © 2022, The Authors. All rights reserved.

关键词： Graphene

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An infrastructure with user-centered presentation data model for integrated management of materials data and services

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npj Computational Materials 2021年第1期7卷 779-786页

作者： Shilong Liu Yanjing Su Haiqing Yin Dawei Zhang Jie He Haiyou Huang Xue Jiang Xuan Wang Haiyan Gong Zhuang Li Hao Xiu Jiawang Wan Xiaotong Zhang Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijingChina School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina Beijing Key Laboratory of Knowledge Engineering for Materials Science BeijingChina Corrosion and Protection Center University of Science and Technology BeijingBeijingChina Collaborative Innovation Center of Steel Technology University of Science and Technology BeijingBeijingChina National Materials Corrosion and Protection Data Center Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijingChina Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijingChina Beijing Key Laboratory of Materials Genome Engineering University of Science and Technology BeijingBeijingChina

With scientific research in materials science becoming more data intensive and collaborative after the announcement of the Materials Genome Initiative,the need for modern data infrastructures that facilitate the sharing of materials data and analysis tools is compelling in the materials *** this paper,we describe the challenges of developing such infrastructure and introduce an emerging architecture with high *** call this architecture the Materials Genome engineering databases(MGED).MGED provides cloud-hosted services with features to simplify the process of collecting datasets from diverse data providers,unify data representation forms with user-centered presentation data model,and accelerate data discovery with advanced search *** also provides a standard service management framework to enable finding and sharing of tools for analyzing and processing *** describe MGED’s design,current status,and how MGED supports integrated management of shared data and services.

关键词： services service centered

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Hierarchical features-based targeted aspect extraction from online reviews

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Intelligent data Analysis 2021年第1期25卷 205-223页

作者： He, Jin Li, Lei Wang, Yan Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei Anhui China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Department of Computing Macquarie University Sydney Australia MiningLamp Academy of Sciences Mininglamp Techonologies Beijing China

With the prevalence of online review websites, large-scale data promote the necessity of focused analysis. This task aims to capture the information that is highly relevant to a specific aspect. However, the broad scope of the aspects of the various products makes this task overarching but challenging. A commonly used solution is to modify the topic models with additional information to capture the features for a specific aspect (referred to as a targeted aspect). However, the existing topic models, either perform the full analysis to capture features as many as possible or estimate the similarity to capture features as coherent as possible, overlook the fine-grained semantic relations between the features, resulting in the captured features coarse and confusing. In this paper, we propose a novel Hierarchical Features-based Topic Model (HFTM) to extract targeted aspects from online reviews, then to capture the aspect-specific features. Specifically, our model can not only capture the direct features posing target-to-feature semantics but also capture the latent features posing feature-to-feature semantics. The experiments conducted on real-world datasets demonstrate that HFTMl outperforms the state-of-the-art baselines in terms of both aspect extraction and document classification. © 2021 - IOS Press. All rights reserved.

关键词： Aspect extraction Focused analysis Online reviews Text mining Topic modeling

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ConsistencyDet: A Robust Object Detector with a Denoising Paradigm of Consistency Model

arXiv

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arXiv 2024年

作者： Jiang, Lifan Wang, Zhihui Wang, Changmiao Li, Ming Leng, Jiaxu Wu, Xindong the College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266510 China Shenzhen Research Institute of Big Data Shenzhen518172 China Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China the Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China the Key Laboratory of Knowledge Engineering with Big Data the Ministry of Education of China China School of Computer Science and Information Technology Hefei University of Technology Hefei230009 China

Object detection, a quintessential task in the realm of perceptual computing, can be tackled using a generative methodology. In the present study, we introduce a novel framework designed to articulate object detection as a denoising diffusion process, which operates on the perturbed bounding boxes of annotated entities. This framework, termed ConsistencyDet, leverages an innovative denoising concept known as the Consistency Model. The hallmark of this model is its self-consistency feature, which empowers the model to map distorted information from any temporal stage back to its pristine state, thereby realizing a "one-step denoising" mechanism. Such an attribute markedly elevates the operational efficiency of the model, setting it apart from the conventional Diffusion Model. Throughout the training phase, ConsistencyDet initiates the diffusion sequence with noise-infused boxes derived from the ground-truth annotations and conditions the model to perform the denoising task. Subsequently, in the inference stage, the model employs a denoising sampling strategy that commences with bounding boxes randomly sampled from a normal distribution. Through iterative refinement, the model transforms an assortment of arbitrarily generated boxes into definitive detections. Comprehensive evaluations employing standard benchmarks, such as MS-COCO and LVIS, corroborate that ConsistencyDet surpasses other leading-edge detectors in performance metrics. Our code is available at https://***/Tankowa/ConsistencyDet. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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CLDG: Contrastive Learning on Dynamic Graphs

arXiv

引用

arXiv 2024年

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2024, CC BY.

关键词： Contrastive Learning

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Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction

arXiv

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arXiv 2023年

作者： Wan, Qizhi Wan, Changxuan Xiao, Keli Xiong, Hui Liu, Dexi Liu, Xiping Jiangxi Key Laboratory of Data and Knowledge Engineering China School of Information Management Jiangxi University of Finance and Economics Jiangxi Nanchang China College of Business Stony Brook University Stony BrookNY United States Guangdong Guangzhou China

Document-level event extraction is a long-standing challenging information retrieval problem involving a sequence of sub-tasks: entity extraction, event type judgment, and event type-specific multi-event extraction. However, addressing the problem as multiple learning tasks leads to increased model complexity. Also, existing methods insufficiently utilize the correlation of entities crossing different events, resulting in limited event extraction performance. This paper introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role and a multi-channel argument role prediction module. The proposed data structure enables our model to uncover the primary role of tokens in multiple events, facilitating a more comprehensive understanding of event relationships. By leveraging the multichannel prediction module, we transform entity and multi-event extraction into a single task of predicting token-event pairs, thereby reducing the overall parameter size and enhancing model efficiency. The results demonstrate that our approach outperforms the state-of-the-art method by 9.5 percentage points in terms of the F1 score, highlighting its superior performance in event extraction. Furthermore, an ablation study confirms the significant value of the proposed data structure in improving event extraction tasks, further validating its importance in enhancing the overall performance of the framework. Copyright © 2023, The Authors. All rights reserved.

关键词： data structures

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