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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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Identity-based threshold proxy re-encryption scheme from lattices and its applications

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Frontiers of information Technology & Electronic Engineering 2022年第2期23卷 258-277页

作者： Liqiang WU Yiliang HAN Xiaoyuan YANG Minqing ZHANG Key Laboratory of Network and Information Security Engineering University of People’s Armed PoliceXi'an 710086China MOE Key Laboratory of Computer Network and Information Security Xidian UniversityXi'an 710071China

Threshold proxy re-encryption(TPRE)can prevent collusion between a single proxy and a delegatee from converting arbitrary files against the wishes of the delegator through multiple proxies,and can also provide normal services even when certain proxy servers are paralyzed or damaged.A non-interactive identity-based TPRE(IB-TPRE)scheme over lattices is proposed which removes the public key *** accomplish this scheme,Shamir’s secret sharing is employed twice,which not only effectively hides the delegator’s private key information,but also decentralizes the proxy power by splitting the re-encryption *** means that a combiner can detect a misbehaving proxy server that has sent an invalid transformed ciphertext *** property is achieved by lattice-based fully homomorphic *** a result,the whole scheme is thoroughly capable of resisting quantum attacks even when they are *** security of the proposed scheme is based on the decisional learning with error hardness assumption in the standard *** typical application scenarios,including a file-sharing system based on a blockchain network and a robust key escrow system with threshold cryptography,are presented.

关键词： Post-quantum cryptography Threshold proxy re-encryption Lattices Robustness Decentralization

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Community Evolution Tracking Based on Core Node Extension and Edge Variation Discerning 17th

Community Evolution Tracking Based on Core Node Extension a...

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17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Zhuang, Qifeng Yu, Zhiyong Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

Communities exist anywhere in various complex networks, and community evolution tracking is one of the most well-liked areas of inquiry in the study of dynamic complex networks. Community evolution tracking has many applications in daily life, such as predicting social network behaviors or analyzing the spread of infectious illnesses. However, the majority of existing evolution tracking algorithms obtain community detection results before matching the community in tracking evolution events, making it difficult to trace the whole evolution of communities because of community matching errors. In addition, the majority of evolution tracking algorithms do not adequately account for the potential scenarios in community evolution, resulting in erroneous detection of evolution events. In this research, a community evolution tracking algorithm based on edge variation discerning and core node extension is proposed. First, we detect communities based on the core node extension strategy, which avoids the problem of community matching errors. Second, we track community evolution based on the edge variation discerning strategy, which fully considers various situations that may occur during the community evolution process. According to the outputs of our experiments, our system can effectively track the evolution of communities in synthetic dynamic networks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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A k-nearest neighbor query method based on trust and location privacy protection

A k-nearest neighbor query method based on trust and locatio...

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作者： Guo, Liangmin Zhu, Ying Yang, Hao Luo, Yonglong Sun, Liping Zheng, Xiaoyao School of Computer and Information Anhui Normal University Wuhu China Anhui Provincial Key Laboratory of Network and Information Security Wuhu China

Spatial query is an important supporting technology in the Internet of Things (IoT) and location-based services (LBS). The k-nearest neighbor query is widely used for spatial queries. However, user location privacy may be leaked in the query. In addition, some users are malicious or uncooperative. With the objective of overcoming these problems, a k-nearest neighbor query method based on trust and location privacy protection is proposed. First, we employ a new K-anonymity method based on cooperation to protect a query user's location privacy. In this method, the query user constructs an anonymous group by introducing a trust mechanism to incentivize cooperation among users. Then, according to the different radii of the selection area set by the query user, agent users with higher reputation values who send query requests for the query user are selected. Finally, the agent users obtain the query results from the LBS server and forward them to the query user, and the query user screens the results according to his or her real location. The experiments show that our method can effectively stimulate users to cooperate, better exclude malicious users, and improve the accuracy of the query results while protecting the privacy of the query user. © 2020 John Wiley & Sons, Ltd.

关键词： Location based services

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Code Generation From Flowcharts with Texts: A Benchmark Dataset and An Approach

Code Generation From Flowcharts with Texts: A Benchmark Data...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Liu, Zejie Hu, Xiaoyu Zhou, Deyu Li, Lin Zhang, Xu Xiang, Yanzheng Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Nanjing Origin Information Technology Company China

Currently, researchers focus on generating codes from the requirement documents. However, current approaches still perform poorly on some requirements needing complex problem-solving skills. In reality, to tackle such complex requirements, instead of directly translating requirement documents into codes, software engineers write codes via unified modeling language diagrams, such as flowcharts, an intermediate tool to analyze and visualize the system. Therefore, we propose a new source code generation task, that is, to generate source code from flowcharts with texts. We manually construct a benchmark dataset containing 320 flowcharts with their corresponding source codes. Obviously, it is not straightforward to employ the current approaches for the new source code generation task since (1) the flowchart is a graph that contains various structures, including loop, selection, and others which is different from texts;(2) the connections between nodes in the flowchart are abundant and diverse which need to be carefully handled. To solve the above problems, we propose a two-stage code generation model. In the first stage, a structure recognition model is employed to transform the flowchart into pseudo-code containing the structural conventions of a typical programming language such as while, if. In the second stage, a code generation model is employed to convert the pseudo-code into code. Experimental results show that the proposed approach can achieve some improvement over the baselines. © 2022 Association for Computational Linguistics.

关键词： Flowcharting

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Decomposition-Based Classifier Chains for Multi-Dimensional Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 176-191页

作者： Jia, Bin-Bin Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou730050 China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing210096 China

In multi-dimensional classification, the semantics of objects are characterized by multiple class variables from different dimensions. To model the dependencies among class variables, one natural strategy is to build a number of multiclass classifiers in a chaining structure, one per dimension, where the subsequent classifiers on the chain augment the feature space with all labeling information used by the preceding classifiers. However, it is shown that this strategy cannot compete with existing state-of-the-art approaches via comparative studies. One possible reason is that inaccurate predictions of preceding classifiers would degenerate the performance of subsequent ones. Besides, it is more difficult to learn a multiclass classifier than a binary one with the same accuracy, and better performance can be expected if the multi-dimensional classification problem can be solved by building multiple binary classifiers in a chaining structure. Based on these conjectures, this article proposes an approach, which builds a chain of binary classifiers to solve the multi-dimensional classification problem with the help of one-versus-one decomposition. To address the issue that different one-versus-one decomposed problems involve different training examples, the feature space is augmented with the binary predictions of preceding classifiers on the chain to train the subsequent ones. To alleviate the effect of the specified chaining order, the ensemble version of the proposed approach is further investigated. Comparative studies over 20 benchmark datasets clearly show the superiority of the proposed approach against the state-of-the-art multi-dimensional classification baselines. © 2020 IEEE.

关键词： Machine learning

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A Semi-supervised Protein Complex Identification Algorithm Based on Sparseness Constraint

A Semi-supervised Protein Complex Identification Algorithm B...

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2024 International Conference on Applied Mathematics, Modelling and Statistics Application, AMMSA 2024

作者： Liu, Guangming Dang, Fengqing Wang, Bin Peng, Yonghong Wang, Xiaofan Tan, Li School of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Technology Xi'An University of Technology Shaanxi Xi'an China Faculty of Science and Engineering Computing and Information Science Cambridge United Kingdom School of Computer and Information Engineering Beijing Technology and Business University Beijing China

Recognising protein complexes in protein interaction networks is crucial, but poses a major challenge due to the frequency of noisy interactions. These networks typically involve numerous protein complexes, with each protein generally only participating in a few complexes. Current recognition models often ignore this aspect. To address this problem, we present a semi-supervised protein complex identification algorithm that extends non-negative matrix factorization (NMF) with sparsity constraints. In contrast to conventional approaches that apply a global sparsity constraint to the entire matrix, our method imposes individual sparsity constraints on protein membership indicator vectors. This targeted strategy controls the algorithm more effectively. Our experimental results with yeast and human protein interaction networks show that our algorithm achieves higher accuracy in identifying protein complexes than leading contemporary methods. © Published under licence by IOP Publishing Ltd.

关键词： Non-negative matrix factorization

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Slew-Driven Layer Assignment for Advanced Non-default-rule Wires 20th

Slew-Driven Layer Assignment for Advanced Non-default-rule W...

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Proceedings of the 20th Web information Systems and Applications Conference, WISA 2023

作者： Lu, Ren Zhang, Wei Jiang, Lieqiu Liu, Genggeng College of Computer and Data Science Fuzhou University Fuzhou350116 China Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350116 China

ISBN: (纸本)9789819962211

With the rapid increase in circuit density in Very Large Scale Integration, the proportion of interconnect delay in circuit timing is also increasing. This makes the importance of layer assignment algorithms increasingly prominent in circuit design. However, most previous layer assignment algorithms prioritize optimizing timing exclusively from the perspective of interconnect delay, thereby disregarding the impact of slew violations on circuits. Therefore, this paper proposes a slew-driven layer assignment algorithm, which considers the timing of different routing layers and introduces non-default-rule wires to design a layer assignment algorithm that can significantly optimize delay, congestion, and slew violations. This algorithm mainly includes three key technologies: 1) Introducing non-default-rule wires technology to optimize timing and adopting a negotiation based approach to ensure that the final routing scheme does not have overflow;2) Proposing a slew prioritization strategy that comprehensively considers slew and interconnect delay during the routing process;3) Proposing a timing critical awareness strategy to further optimize the slew and interconnect delay without worsening the overflow. The experimental results show that the proposed algorithm has significant effects on optimizing delay and reducing slew violations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wire

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Optimal replication strategy for mitigating burst traffic in information-centric satellite networks: a focus on remote sensing image transmission

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Frontiers of information Technology & Electronic Engineering 2024年第6期25卷 791-808页

作者： Ziyang XING Xiaoqiang DI Hui QI Jing CHEN Jinhui CAO Jinyao LIU Xusheng LI Zichu ZHANG Yuchen ZHU Lei CHEN Kai HUANG Xinghan HUO Jilin Key Laboratory of Network and Information Security Changchun 130022China School of Computer Science and Technology Changchun University of Science and TechnologyChangchun 130022China Information Center Changchun University of Science and TechnologyChangchun 130022China

information-centric satellite networks play a crucial role in remote sensing applications,particularly in the transmission of remote sensing ***,the occurrence of burst traffic poses significant challenges in meeting the increased bandwidth *** content delivery networks are ill-equipped to handle such bursts due to their pre-deployed *** this paper,we propose an optimal replication strategy for mitigating burst traffic in information-centric satellite networks,specifically focusing on the transmission of remote sensing *** strategy involves selecting the most optimal replication delivery satellite node when multiple users subscribe to the same remote sensing content within a short time,effectively reducing network transmission data and preventing throughput degradation caused by burst traffic *** formulate the content delivery process as a multi-objective optimization problem and apply Markov decision processes to determine the optimal value for burst traffic *** address these challenges,we leverage federated reinforcement learning ***,we use bloom filters with subdivision and data identification methods to enable rapid retrieval and encoding of remote sensing *** software-based simulations using a low Earth orbit satellite constellation,we validate the effectiveness of our proposed strategy,achieving a significant 17%reduction in the average delivery *** paper offers valuable insights into efficient content delivery in satellite networks,specifically targeting the transmission of remote sensing images,and presents a promising approach to mitigate burst traffic challenges in information-centric environments.

关键词： information-centric satellite network Burst traffic Content delivery Federated reinforcement learning Mixed-integer linear programming model Bloom filter Dynamic network

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SCREADER: Prompting Large Language Models to Interpret scRNA-seq Data 24

SCREADER: Prompting Large Language Models to Interpret scRNA...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Li, Cong Long, Qingqing Zhou, Yuanchun Xiao, Meng Institute of Zoology Chinese Academy of Sciences State Key Laboratory of Stem Cell and Reproductive Biology China University of Chinese Academy of Sciences China Chinese Academy of Sciences Computer Network Information Center China

ISBN: (纸本)9798331530631

Large language models (LLMs) have demonstrated remarkable advancements, primarily due to their capabilities in modeling the hidden relationships within text sequences. This innovation presents a unique opportunity in the field of life sciences, where vast collections of single-cell omics data from multiple species provide a foundation for training foundational models. However, the challenge lies in the disparity of data scales across different species, hindering the development of a comprehensive model for interpreting genetic data across diverse organisms. In this study, we propose an innovative hybrid approach that integrates the general knowledge capabilities of LLMs with domain-specific representation models for single-cell omics data interpretation. We begin by focusing on genes as the fundamental unit of representation. Gene representations are initialized using functional descriptions, leveraging the strengths of mature language models such as LLaMA-2. By inputting single-cell gene-level expression data with prompts, we effectively model cellular representations based on the differential expression levels of genes across various species and cell types. In the experiments, we constructed developmental cells from humans and mice, specifically targeting cells that are challenging to annotate. We evaluated our methodology through basic tasks such as cell annotation and visualization analysis. The results demonstrate the efficacy of our approach compared to other methods using LLMs, highlighting significant improvements in accuracy and interoperability. Our hybrid approach enhances the representation of single-cell data and offers a robust framework for future research in cross-species genetic analysis. 1 © 2024 IEEE.

关键词： Data Mining Large Language Model Prompt Learning Single Cell RNA Data

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