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Efficiently Retrieving Differences between Remote Sets using Counting Bloom Filter

International Journal of Performability Engineering 2019年第7期15卷 1947-1954页

作者： Tian, Xiaomei Zhao, Huihuang Sun, Yaqi Liang, Xiaoman College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

Retrieving differences between remote sets is widely used in set reconciliation and data deduplication. Set reconciliation and data deduplication between two nodes are widely used in various network applications. The basic idea of the difference retrieving problem is that each member of a node pair has an object set and seeks to find all differences between the two remote sets. There are many methods for retrieving difference sets, such as the standard Bloom filter (SBF), counting Bloom filter (CBF), and invertible Bloom filter (IBF). In these methods, based on the standard Bloom filter or its variants, each node represents its objects using a standard Bloom filter or other Bloom filter, which is then exchanged. A receiving node retrieves different objects between the two sets according to the received SBF, CBF, or IBF. We propose a new algorithm for retrieving differences that finds differences between remote sets using counting Bloom filters' deletion operation. The theoretical analyses and experimental results show that the differences can be retrieved efficiently. Only a very small number of differences are missing in the retrieving process, and this false negative rate can be decreased to 0% by adjusting the counting Bloom filter's parameters. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Data structures

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Improving mutation pathogenicity prediction of metal-binding sites in proteins with a panoramic attention mechanism

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Pattern Recognition 2026年 169卷

作者： Yuan Zhang Jiafeng Wu Qiuye Zhao Mingyuan Dong Junsheng Deng Xieping Gao Kai Hu Dapeng Xiong Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan 411105 China Department of Computational Biology Cornell University Ithaca NY 14853 USA Weill Institute for Cell and Molecular Biology Cornell University Ithaca NY 14853 USA Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha 410081 China

Accurately identifying the pathogenicity of mutations at protein-metal binding sites is crucial for uncovering the structural and functional complexity of metalloproteins, and the molecular mechanisms behind numerous diseases. In this study, we present a novel deep learning framework, CASTLE, for the accurate pathogenicity prediction of mutations at metal-binding sites through an effective depiction of the message-passing in local environment surrounding both the metals and metal-binding sites. Specifically, CASTLE weaves multiple attention-driven units to construct comprehensive panoramic message-passing paths, enabling the capture of intricate structural patterns associated with the metal-binding conformations. In addition, CASTLE seamlessly integrates structural information at both residue and atomic levels, and further deeply fuses structural and sequence representations to ensure the incorporation of more comprehensive information. We demonstrate that CASTLE significantly outperforms other state-of-the-art methods across all datasets we evaluated, showcasing its robustness and generalization abilities. Our interpretability analysis illustrates CASTLE’s capability in capturing meaningful representations from the metal coordination-dependent environments. Moreover, the model optimization reaffirms the advantages of our model building strategy, which can effectively capture distinct binding patterns for different metal types. Overall, CASTLE provides a powerful deep learning tool that may offer valuable insights into the study of metalloprotein-related disease mechanisms and drug design.

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The Existence Theory of the Nonlinear Discrete System and the Convergence Theory of the Gummel Iteration for the PNP Equations

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Journal of Scientific Computing 2025年第1期104卷

作者： Liu, Yang Shu, Shi Yang, Ying Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan Xiangtan411105 China School of Mathematics Hunan Institute of Science and Technology Hunan Yueyang414006 China Guilin University of Electronic Technology Guangxi Guilin541004 China

The Poisson-Nernst-Planck equations play a crucial role in modeling ion mass conservation and electrostatic diffusion in various applications, such as biological ion channels, semiconductor devices, and nanopore systems. Due to the inherent strong coupling and nonlinearity, developing robust theoretical frameworks for discrete schemes and associated fast algorithms is challenging. While the finite element method and some improved methods are widely used to solve these equations, existing theoretical analyses are insufficient, particularly regarding the existence of solutions for discrete methods or nonlinear discrete systems, and the convergence of fast algorithms. This study aims to contribute to this area by proposing a framework that includes establishing the existence of solutions for an abstract discrete scheme, which encompasses schemes such as standard finite element and edge-averaged finite element methods. We establish the existence theory for the nonlinear discrete system by carefully constructing a suitable compact convex set for the fixed-point operator and then using the Brouwer’s fixed-point theorem. Additionally, we present a convergence theory for the Gummel iteration, a commonly used iteration for the Poisson-Nernst-Planck equations, associated with our abstract discrete scheme. We further explore the theoretical results for both standard and edge-averaged finite element schemes as applications of the framework. Numerical experiments are conducted to verify the theoretical results for the Gummel iteration of these two schemes. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fixed point arithmetic

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Improved Security for Android System based on Multi-Chaotic Maps using a Novel Image Encryption Algorithm

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International Journal of Performability Engineering 2019年第6期15卷 1692-1701页

作者： Jiao, Ge Li, Lang Zou, Yi College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

Aimed at the unsatisfactory effect of security in mobile cloud computing, we propose a novel image encryption algorithm by using the Improved Fruit Fly Optimization Algorithm-Artificial Bee Colony (hereafter referred to as IFOA-ABC). First, the feature of task scheduling function in mobile cloud computing is described to set up the task scheduling function;second, the population of fruit fly optimization algorithm is initialized by using orthogonal array and quantitative techniques, and the boundary of fruit fly optimization algorithm is processed, the step size in search is adjusted dynamically, and the artificial bee colony algorithm is used to help fruit fly optimization algorithm develop the global optimal solution;in the simulation experiment, the IFOA-ABC algorithm has obvious advantages over other intelligent algorithms in the comparison of four indicators of mobile cloud task scheduling, which means that the proposed algorithm can effectively improve the privacy and efficiency of cloud computing scheduling. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Image enhancement

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A Time-Driven Cloudlet Placement Strategy for Workflow Applications in Wireless Metropolitan Area Networks

Research Square

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Research Square 2021年

作者： Zhang, Jianshan Li, Ming Zheng, Xianghan Hsu, Ching-Hsien College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory Network Computing and Intelligent Information Processing Fuzhou China Department of Computer Science and Information Engineering Asia University Taichung Taiwan

With the rapid development of mobile technology, mobile applications have increasing requirements for computational resources, and mobile devices can no longer meet these requirements. Mobile edge computing (MEC) has emerged in this context and has brought innovation into the working mode of traditional cloud computing. By provisioning edge server placement, the computing power of the cloud centre is distributed to the edge of the network. The abundant computational resources of edge servers compensate for the lack of mobile devices and shorten the communication delay between servers and users. Constituting a specific forms of edge servers, cloudlets have been widely studied within academia and industry in recent years. However, existing studies have mainly focused on computation offloading for general computing tasks under fixed cloudlet placement positions. They ignored the impact on computation offloading results from cloudlet placement positions and data dependencies among mobile application components. In this paper, we study the cloudlet placement problem based on Workflow Applications (WAs) in Wireless Metropolitan Area Networks (WMANs). We devise a cloudlet placement strategy based on a Particle swarm optimization algorithm using Genetic algorithm operators with the Encoding Library updating mode (PGEL), which enables the cloudlet to be placed in appropriate positions. The simulation results show that the proposed strategy can obtain a near-optimal cloudlets placement scheme, and compared with other classic algorithms, this algorithm can reduce the execution time of WAs by 15.04%-44.99%. © 2021, CC BY.

关键词： computation offloading

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X-Architecture Steiner Minimal Tree Construction Based on Discrete Differential Evolution 15th

X-Architecture Steiner Minimal Tree Construction Based on Di...

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15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2019

作者： Wu, Hailin Xu, Saijuan Zhuang, Zhen Liu, Genggeng College of Mathematics and Computer Sciences Fuzhou University Fuzhou350116 China Department of Information Engineering Fujian Business University Fuzhou350012 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

ISBN: (纸本)9783030324551

As the best connection model for the multi-pin net of the non-Manhattan architecture global routing problem, the X-architecture Steiner Minimum Tree (XSMT) construction is a Non-deterministic Polynomial hard (NP-hard) problem. The Differential Evolution (DE) algorithm has shown good application effect in solving various NP-hard problems. For this reason, based on the idea of DE algorithm, this paper proposes an XSMT construction algorithm for solving this problem. First of all, because the traditional DE algorithm is designed for continuous problems, the optimization ability is limited in solving discrete problems. This paper proposes a novel crossover operator and mutation operator. At the same time, in order to maintain the effectiveness of the evolutionary algorithm, an Edge-to-Point coding strategy suitable for evolutionary algorithms is proposed to better preserve the optimal substructure of the population. Finally, in order to speed up the convergence speed and quality of the algorithm, this paper proposes an initial solution based on the minimum tree generation algorithm. Experiments show that the effectiveness of the proposed algorithm and related strategies can construct a high-quality XSMT solution. © Springer Nature Switzerland AG, 2020.

关键词： Evolutionary algorithms

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Characterization for a generic construction of bent functions and its consequences

arXiv

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arXiv 2023年

作者： Li, Yanjun Gao, Jinjie Kan, Haibin Peng, Jie Zheng, Lijing Chen, Changhui Institute of Statistics and Applied Mathematics Anhui University of Finance and Economics Anhui Bengbu233030 China School of Computer Sciences Fudan University Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Shanghai Engineering Research Center of Blockchain Shanghai200433 China Yiwu Research Institute of Fudan University Yiwu City322000 China Mathematics and Science College of Shanghai Normal University Guilin Road #100 Shanghai200234 China The School of Mathematics and Physics University of South China Hunan Hengyang421001 China

In this letter, we give a characterization for a generic construction of bent functions. This characterization enables us to obtain another efficient construction of bent functions and to give a positive answer on a p... 详细信息

关键词： Hadamard transforms

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An ontology-supported inquiry learning technique

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 16-28页

作者： Xue, Xingsi Zhang, Jie Yang, Chaofan Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou Fujian350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin City Guanxi Province537000 China School of Computer Science and Mathematics Intelligent Information Processing Research Center Fujian University of Technology No.33 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology No.1 Jinji Road Guilin Guangxi541004 China

Although Inquiry-based Learning (IBL) offers compelling opportunities for the engineering education, one of the challenges to the implementation of it is that the learner always lacks of the background knowledge. To provide him with the necessary knowledge when making the inquiry, this paper proposes an Ontology Supported Inquiry Learning technique (OSIL), which works on the basis of two ontologies, i.e. curriculum ontology and the learner’s ontology. The experimental results show that the constructed curriculum ontology and learner ontology is of high-quality, and the answers recommended by OSIL almost perfectly match with the true answers. From the experimental results, we can see that it is a feasible way of using the software engineering and prototype evolution method to construct the curriculum and learner ontology, and the individual inquiry based on two ontologies is able to provide the learners with high-quality personalized services. © 2021, Taiwan Ubiquitous information. All rights reserved.

关键词： Curricula

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Mobile Crowd-Sensing System Based on Participant Selection 15th

Mobile Crowd-Sensing System Based on Participant Selection

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15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Tu, Chunyu Han, Lei Wang, Leye Yu, Zhiyong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou China Key Lab of High Confidence Software Technologies Peking University Beijing China

ISBN: (纸本)9783030642426

Mobile Crowdsensing (MCS) is often accompanied by various adverse objectives when performing data collection, which makes it difficult to collect accurate data of the entire target area with low cost. Therefore, how to collect a small part of the data to accurately infer the other data of the entire target area is a crucial issue. People’s daily trajectories usually follow a certain pattern, for example, students go to school, company employees go to work. This pattern allows us to find participants who often pass through fixed areas within a certain time span, leading their collected partial data are near optimal for the entire data collection task. This paper models the problem and develops multiple methods to improve the participant selection and the data recovery. Particularly, we use a random method, reinforcement learning, and greedy algorithm to handle this problem and compare the differences among these methods by experiments. © 2020, Springer Nature Switzerland AG.

关键词： Reinforcement learning

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FireMatch: A Semi-Supervised Video Fire Detection Network Based on Consistency and Distribution Alignment

arXiv

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arXiv 2023年

作者： Lin, Qinghua Li, Zuoyong Zeng, Kun Fan, Haoyi Li, Wei Zhou, Xiaoguang School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Automation Beijing University of Post and Telecommunications Beijing100876 China School of Economics and Management Minjiang University Fuzhou350121 China

Deep learning techniques have greatly enhanced the performance of fire detection in videos. However, video-based fire detection models heavily rely on labeled data, and the process of data labeling is particularly costly and time-consuming, especially when dealing with videos. Considering the limited quantity of labeled video data, we propose a semi-supervised fire detection model called FireMatch, which is based on consistency regularization and adversarial distribution alignment. Specifically, we first combine consistency regularization with pseudo-label. For unlabeled data, we design video data augmentation to obtain corresponding weakly augmented and strongly augmented samples. The proposed model predicts weakly augmented samples and retains pseudo-label above a threshold, while training on strongly augmented samples to predict these pseudo-labels for learning more robust feature representations. Secondly, we generate video cross-set augmented samples by adversarial distribution alignment to expand the training data and alleviate the decline in classification performance caused by insufficient labeled data. Finally, we introduce a fairness loss to help the model produce diverse predictions for input samples, thereby addressing the issue of high confidence with the non-fire class in fire classification scenarios. The FireMatch achieved an accuracy of 76.92% and 91.81% on two real-world fire datasets, respectively. The experimental results demonstrate that the proposed method outperforms the current state-of-the-art semi-supervised classification methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Fires

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