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A Survey on Blocking technology of Entity Resolution

Journal of computer science & technology 2020年第4期35卷 769-793页

作者： Bo-Han Li Yi Liu An-Man Zhang Wen-Huan Wang Shuo Wan College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Safety-Critical Software Ministry of Industry and Information TechnologyNanjing 211106China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210000China

Entity resolution(ER)is a significant task in data integration,which aims to detect all entity profiles that correspond to the same real-world *** to its inherently quadratic complexity,blocking was proposed to ameliorate ER,and it offers an approximate solution which clusters similar entity profiles into blocks so that it suffices to perform pair-wise comparisons inside each block in order to reduce the computational cost of *** paper presents a comprehensive survey on existing blocking *** summarize and analyze all classic blocking methods with emphasis on different blocking construction and optimization *** find that traditional blocking ER methods which depend on the fixed schema may not work in the context of highly heterogeneous information *** to use schema information flexibly is of great significance to efficiently process data with the new features of this *** learning is an important tool for ER,but end-to-end and efficient machine learning methods still need to be *** also sum up and provide the most promising trend for future work from the directions of real-time blocking ER,incremental blocking ER,deep learning with ER,etc.

关键词： blocking construction blocking optimization data linkage entity resolution

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Filtering Balun Design using Hairpin-Ring Resonator and SSPP Metamaterial with Wide Notched band and Wide Stopband

Filtering Balun Design using Hairpin-Ring Resonator and SSPP...

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2023 IEEE MTT-S International Wireless Symposium, IWS 2023

作者： Qi, Huanhuan Liu, Haiwen Huang, Mo School of Information and Communications Engineering Xi' An Jiaotong University Xi'an China Faculty of Science and Technology State Key Laboratory of Analog and Mixed-Signal Vlsi China Institute of Microelectronics University of Macau Department of Electrical and Computer Engineering China

ISBN: (纸本)9798350336535

In this paper, a novel filtering balun based on microstrip-to-slotline transition is proposed. Utilizing the inherent out-of-phase electric field distributions on the adjacent grounds of slotline, the balun function is realized. In order to realize the wide notched-band and stopband performance for radio astronomical applications, spoof surface plasmon polariton (SSPP) metamaterial and multi-mode hairpin ring resonators (HRRs) are adopted. With these novel structures loaded, the proposed filtering balun exhibits a wide band from 1GHz to 6 GHz. The notched band is from 3.2 GHz to 4.4 GHz, within the notched band, the rejection level is better than 15 dB. Besides, the out-of-band suppression is below 35 dB in the wide frequency range from 6 GHz to 8 GHz. © 2023 IEEE.

关键词： Metamaterials

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Efficient and Scalable Dynamic Graph Replication for Cloud Computing Services

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IEEE Transactions on Services Computing 2025年

作者： Javadpour, Amir Ja'fari, Forough Zhang, Weizhe Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518055 China Peng Cheng Laboratory Department of New Networks Guangdong Shenzhen518055 China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Guangdong518055 China

The cloud environment has garnered significant attention due to its crucial role as a supportive framework in computer science and engineering activities. This ever-growing adoption has increased among users, necessitating ongoing efforts to maintain and enhance cloud performance. A widely recognised approach to improving the user experience in cloud environments is data replication. This article proposes a Locality-Aware Dynamic Data Replication Algorithm (LADRE) designed to balance network load and enhance performance. The proposed model identifies the most appropriate data files for replication and selects optimal storage nodes for placing replicas. Simulations conducted using the CloudSim library demonstrate that LADRE improves cloud network load balancing by 5% compared to prior methods. Furthermore, LADRE outperforms existing approaches regarding network response time, storage efficiency, and the frequency of data removal operations. © 2008-2012 IEEE.

关键词： Cloud environment Data locality Dynamic data replication

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Can We Improve Meta-Learning Model in Few-Shot Learning by Aligning Data Distributions?

SSRN

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SSRN 2023年

作者： Tian, Pinzhuo Li, Wenbin Qi, Lei Yu, Hang Gao, Yang Xie, Shaorong School of Computer Engineering and Science Shanghai University Shanghai200444 China The State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing210023 China School of Computer Science and Engineering Southeast University Jiangsu Nanjing211189 China

Meta-learning becomes a promising way to solve the few-shot learning problem in recent research. This paradigm mainly relies on hierarchical architecture and episodic training to achieve good generalization on the new learning task. However, as we know, support and query sets in each training task in episodic training only contain a few data points. Too limited training data can lead to data discrepancy between the training and test data which further hurts the performance of learning models in classical supervised and semisupervised learning. Hence, there is an interesting question: Can we also improve the meta-learning model in few-shot learning by aligning the data distributions of support and query sets? In this work, we try to answer this question. Firstly, we give a theoretical analysis of the generalization ability of meta-learning to show that eliminating the data-distribution discrepancy between the support and query sets in each training task can improve the meta-learning model in few-shot learning. Next, we design an experiment to further verify our analysis. The experimental results show that aligning the Preprint submitted to Pattern Recognition December 27, 2022 data distributions is crucial and even can help the learning model trained by the classical batch-based training achieve better performance on unseen few-shot learning tasks. Furthermore, we also propose two simple metabatch normalization methods following our result which can be integrated into recent meta-learning methods to improve their performance in few-shot © 2023, The Authors. All rights reserved.

关键词： Learning systems

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Living-skin Detection using Multi-layer Skin Property Perceived by the Structured Light

Living-skin Detection using Multi-layer Skin Property Percei...

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

作者： Zhiyu Wang Caifeng Shan Wenjin Wang College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China Department of Biomedical Engineering Southern University of Science and Technology Shenzhen China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Living-skin detection has been used to prevent the attack of face fraud in a face recognition system. In this paper, we propose a new concept that exploits the multi-layer structure property of skin for living-skin detection. We observe a significant difference in the blur of the laser spot created by the structured light on the skin and non-skin due to the characteristic properties of laser photons in skin penetration and reflection. Based on this observation, we designed a new living-skin detection algorithm to differentiate skin and non-skin based on the blur detection of laser spots. The experimental results show that the proposed setup and method have a promising performance with an averaged precision of 96.7%, averaged recall of 82.2%, and averaged F1-score of 88.6% on a dataset of 20 adult subjects. This demonstrates the effectiveness of the new concept that uses multi-layer properties of skin tissues for living-skin detection, which may lead to new solutions for face anti-spoofing.

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Enhancing Low-Latency and High-Reliability Data Forwarding for In-Vehicle Time-Sensitive Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Sun, Haitong Zhang, Haijun Guan, Wanqing Liu, Xiangnan Ma, Hui Leung, Victor C.M. Hebei Key Laboratory of Space-Air-Ground Intelligent Communication China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology Beijing Beijing100083 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden Department of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China

To satisfy the low delay, low jitter, and high success rate requirements for in-vehicle networks, IEEE 802.1 Task Group proposed Time-Sensitive Networking (TSN), which has aroused increasing attention in managing time-critical services in networks. This paper resolves the following issue: how to minimize the worst-case response time (WCRT) of frame forwarding in TSN under no-wait scenarios to guarantee low-latency transmission, stability, and high success rate for in-vehicle TSN networks? Currently, some work resolves this problem by leveraging integer linear programming, and others apply heuristic methods to avoid the time complexity problem, but the success rate is affected since they are prone to get trapped in local optima. To reach a balance between time-complexity and success rate, a scheme named Frame Dynamic Priority Scheduling Mechanism (FDPSM) is proposed, where the frame dynamic priority levels are set up and frames are scheduled accordingly. The target is to maximize the success rate and minimize the WCRT for in-vehicle networks. We evaluate the proposed mechanism from aspects such as traffic forwarding time, size of windows, number of hops, and the network load, together with success rate and the simulation results demonstrate the advantages of the FDPSM for in-vehicle TSN networks. © 2025 IEEE. All rights reserved,

关键词： Integer programming

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Modular Mix-and-Match Complementation of Büchi Automata (Technical Report)

arXiv

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arXiv 2023年

作者： Havlena, Vojtěch Lengál, Ondřej Li, Yong Šmahlíková, Barbora Turrini, Andrea Faculty of Information Technology Brno University of Technology Brno Czech Republic Department of Computer Science University of Liverpool United Kingdom State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Institute of Intelligent Software Guangzhou China

Complementation of nondeterministic Büchi automata (BAs) is an important problem in automata theory with numerous applications in formal verification, such as termination analysis of programs, model checking, or in decision procedures of some logics. We build on ideas from a recent work on BA determinization by Li et al. and propose a new modular algorithm for BA complementation. Our algorithm allows to combine several BA complementation procedures together, with one procedure for a subset of the BA’s strongly connected components (SCCs). In this way, one can exploit the structure of particular SCCs (such as when they are inherently weak or deterministic) and use more efficient specialized algorithms, regardless of the structure of the whole BA. We give a general framework into which partial complementation procedures can be plugged in, and its instantiation with several algorithms. The framework can, in general, produce a complement with an Emerson-Lei acceptance condition, which can often be more compact. Using the algorithm, we were able to establish an exponentially better new upper bound of O(4n) for complementation of the recently introduced class of elevator automata. We implemented the algorithm in a prototype and performed a comprehensive set of experiments on a large set of benchmarks, showing that our framework complements well the state of the art and that it can serve as a basis for future efficient BA complementation and inclusion checking algorithms. © 2023, CC BY.

关键词： Automata theory

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Sparsity Aware of TF-IDF Matrix to Accelerate Oblivious Document Ranking and Retrieval

Sparsity Aware of TF-IDF Matrix to Accelerate Oblivious Docu...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zeshi Zhang Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Ministry of Education Key Laboratory of Computer Vision and System China Faculty of Engineering University of Bristol Bristol U.K.

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Due to cloud security concerns, there is an increasing interest in information retrieval systems that can support private queries over public documents. It is desirable for oblivious document ranking and retrieval in public cloud at lower cost and faster speed without revealing query-related information. Currently, the term frequency-inverse document frequency (TF-IDF) and private information retrieval (PIR) techniques are used to solve this problem, but the encryption operation time is over dominant. Motivated by the observation of the sparsity of the TF-IDF matrix, we propose an efficient approach for oblivious document ranking and retrieval, called E-Coeus. It takes advantage of the high sparsity of the TF-IDF matrix to rearrange the matrix. Our method accelerates the speed of PIR inadvertently retrieving documents and reduces the user retrieval delay time. In a stand-alone experiment for a TF-IDF matrix of 1.2M rows and 64K columns with the sparsity of 10%, E-Coeus improves the document ranking and retrieval performance by 23% over the state-of-the-art approach, Coeus. With cluster of 64 machines, E-Coeus improves the performance by 34% over Coeus when the TF-IDF matrix sparsity is 30%.

关键词： Time-frequency analysis Privacy Costs Cloud computing security Information retrieval Encryption Delays

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Affiliation-based Local Community Detection across Multiple Networks

arXiv

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arXiv 2024年

作者： Ni, Li Xie, Zhou Zhang, Yiwen Luo, Wenjian Sheng, Victor S. School of Computer Science and Technology Anhui University Anhui Hefei230601 China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen518055 China School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China Department of Computer Science Texas Tech University LubbockTX79409 United States

Real-world networks are often constructed from different sources or domains, including various types of entities and diverse relationships between networks, thus forming multi-domain networks. A single network typically fails to capture the complete graph structure and the diverse relationships among multiple networks. Consequently, leveraging multiple networks is crucial for a comprehensive detection of community structures. Most existing local community detection methods discover community structures by integrating information from different views on multi-view networks. However, methods designed for multi-view networks are not suitable for multi-domain networks. Therefore, to mine communities from multiple networks, we propose a Local Algorithm for Multiple networks with node Affiliation, called LAMA, which is suitable for both multi-view and multi-domain networks. The core idea of LAMA is to optimize node affiliations by maximizing the quality of communities within each network while ensuring consistency in community structures across multiple networks. The algorithm iteratively optimizes node affiliations and expands the community outward based on affiliations to detect the community containing the seed node. Experimental results show that LAMA outperforms comparison algorithms on two synthetic datasets and five real datasets. Copyright © 2024, The Authors. All rights reserved.

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Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation

SSRN

引用

SSRN 2023年

作者： Li, Ping Zhang, Yu Yuan, Li Xiao, Huaxin Lin, Binbin Xu, Xianghua School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Electrical and Computer Engineering Peking University Beijing China College of Systems Engineering National University of Defense Technology Changsha China College of Computer Science Zhejiang University Hangzhou China

Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge. However, previous methods do not fully use spatial-temporal context and fail to tackle this challenging task in real-time. This motivates us to develop an efficient Long-Short Temporal Attention network (termed LSTA) for unsupervised VOS task from a holistic view. Specifically, LSTA consists of two dominant modules, i.e., Long Temporal Memory and Short Temporal Attention. The former captures the long-term global pixel relations of the past frames and the current frame, which models constantly present objects by encoding appearance pattern. Meanwhile, the latter reveals the short-term local pixel relations of one nearby frame and the current frame, which models moving objects by encoding motion pattern. To speedup the inference, the efficient projection and the locality-based sliding window are adopted to achieve nearly linear time complexity for the two light modules, respectively. Extensive empirical studies on several benchmarks have demonstrated promising performances of the proposed method with high © 2023, The Authors. All rights reserved.

关键词： Pixels

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