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Upper Bounds on Lifetime of Three Dimensional Extended UWB Sensor Networks*

Chinese Journal of Electronics 2023年第2期19卷 307-312页

作者： Juan Xu Changjun Jiang Aihuang Guo Yongfa Hong Shu Li Cheng Wang School of Electronics and Information Engineering Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing of the Ministry of Education Tongji University Shanghai China College of Information Science and Engineering Shandong University of Science and Technology Qingdao China

The upper bounds on lifetime of three dimensional extended Time hopping impulse radio Ultrawide band (TH-IR UWB) sensor networks are derived using percolation theory arguments. The TH-IR UWB sensor network consists of $n$ sensor nodes distributed in a cube of edge length $n^{1/3}$ according to a Poisson point process of unit intensity. It is shown that for such a static three dimensional extended TH-IR UWB sensor network, the upper bound on the lifetime is of order $O(n^{-1})$ , while in the ideal case, the upper bound on the lifetime is longer than that of a static network by a factor of $n^{2/3}$ . Therefore sensor nodes moving randomly in the deployment area can improve the upper bound on network lifetime. The results also reveal that the upper bounds on network lifetime decrease with the number of nodes $n$ , thus extended TH-IR UWB sensor networks aren't prone to be employed in large-scale network.

关键词： Wireless communication Wireless sensor networks Upper bound Protocols

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A novel collaboration management method based finance logistics management platform

International Journal of Multimedia and Ubiquitous Engineeri...

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International Journal of Multimedia and Ubiquitous Engineering 2014年第10期9卷 409-418页

作者： Wu, Zhongdai Ye, Minghai Liu, Jin School of Economics and Management Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China College of Info. Engineering Shanghai Maritime University Shanghai China

In order to solve current realistic problems such as low-efficiency in resource allocation, difficulty in headquarters integrated management, high-risk in finance, and inaccuracy and untimeliness of information sharing. On the basis of collaborative management theory and information technology, centralized standardization of information system of finance logistics as a management platform is constructed in the study. Standardization of business processes and uniform control of headquarter are initially realized, the needs of business operations as well as logistics management are effectively met, and using information technology to promote the meticulous management is achieved. Information management of finance logistics reduces the financial risk, strengthen the management of supervisors, and promote the capabilities of project management. © 2014 SERSC.

关键词： Finance

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Team of Bayesian optimization algorithms to solve task assignment problems in heterogeneous computing systems

Team of Bayesian optimization algorithms to solve task assig...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jie Li JunQi Zhang Qi Kang ChangJun Jiang Ministry of Education China Key Laboratory of Embedded System and Service Computing Department of Computer Science and Technology Tongji University Shanghai Department of Control Science and Engineering Tongji University Shanghai China

ISBN: (纸本)9781479938414

A Bayesian optimization algorithm (BOA) belongs to estimation of distribution algorithms (EDAs). It is characterized by combining a Bayesian network and evolutionary algorithms to solve nearly decomposable optimization problems. BOA is less popularly applied to solve high dimensionality complex optimization problems. A key reason is that the cost of training all dimensions by BOA becomes expensive with the increase of problem dimensionality. Since data are relatively sparse in a high dimensional space, even though BOA can train all dimensions simultaneously, the interdependent relations between different dimensions are difficult to learn. Its search ability is thus significantly reduced. In this paper, we propose a team of Bayesian optimization algorithms (TBOA) to search and learn dimensionality. TBOA consists of multiple BOAs, in which each BOA corresponds to a dimension of the solution domain and it is responsible for the search of this dimension's value region. The proposed TBOA is used to solve the real problem of task assignment in heterogeneous computing systems. Extensive experiments demonstrate that the computational cost of the overall training in TBOA is decreased very significantly while keeping high solution accuracy.

关键词： Bayes methods Sociology Statistics Optimization Computational efficiency Joints Probability distribution

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A Novel ICD Coding Method Based on Associated and Hierarchical Code Description Distillation

arXiv

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arXiv 2024年

作者： Zhang, Bin Wang, Junli Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China

ICD(International Classification of Diseases) coding involves assigning ICD codes to patients visit based on their medical notes. ICD coding is a challenging multilabel text classification problem due to noisy medical document inputs. Recent advancements in automated ICD coding have enhanced performance by integrating additional data and knowledge bases with the encoding of medical notes and codes. However, most of them ignore the code hierarchy, leading to improper code assignments. To address these problems, we propose a novel method based on associated and hierarchical code description distillation (AHDD) for better code representation learning and avoidance of improper code *** utilize the code description and the hierarchical structure inherent to the ICD codes. Therefore, in this paper, we leverage the code description and the hierarchical structure inherent to the ICD codes. The code description is also applied to aware the attention layer and output layer. Experimental results on the benchmark dataset show the superiority of the proposed method over several state-of-the-art baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Routing Strategy for SDN Large Flow Based on Deep Reinforcement Learning

Routing Strategy for SDN Large Flow Based on Deep Reinforcem...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yu Ke Junli Wang Chungang Yan Jiamin Yao Key Laboratory of Embedded System and Service Computing Collaborative Innovation Center for Financial Network Security (Tongji University) Ministry of Education National (Province-Ministry Joint) Tongji University Shanghai China

With the expansion of network scale, traditional routing strategies in Software-Defined Networking (SDN) have difficulty adapting to fast traffic variability. The large flow in the network occupies 80% of the network traffic. An unreasonable schedule of the large flow will lead to the emergence of bottleneck links and reduce the network performance. To solve the above problems, this paper proposes a different routing method, called Routing Strategy for SDN Large Flow Based on Deep Reinforcement Learning (RSDRL). Considering the links in the network that can carry large flow are limited, RSDRL can schedule large flow firstly and generate routing strategies adaptively. At the same time, in order to avoid the occurrence of congestion, RSDRL quantifies the degree of congestion in the network to implement routing. Experiments show that the proposed model has better Quality of service (QoS) performance under the test traffic, realizing a reasonable schedule of traffic.

关键词： Deep learning Training Knowledge engineering Schedules Adaptation models Reinforcement learning Quality of service

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Correct after Answer: Enhancing Multi-Span Question Answering with Post-Processing Method

arXiv

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arXiv 2024年

作者： Lin, Jiayi Zhang, Chenyang Tong, Haibo Zhang, Dongyu Hong, Qingqing Hou, Bingxuan Wang, Junli Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China

Multi-Span Question Answering (MSQA) requires models to extract one or multiple answer spans from a given context to answer a question. Prior work mainly focuses on designing specific methods or applying heuristic strategies to encourage models to predict more correct predictions. However, these models are trained on gold answers and fail to consider the incorrect predictions. Through a statistical analysis, we observe that models with stronger abilities do not predict less incorrect predictions compared with other models. In this work, we propose Answering-Classifying-Correcting (ACC) framework, which employs a post-processing strategy to handle incorrect predictions. Specifically, the ACC framework first introduces a classifier to classify the predictions into three types and exclude "wrong predictions", then introduces a corrector to modify "partially correct predictions". Experiments on several MSQA datasets show that ACC framework significantly improves the Exact Match (EM) scores, and further analysis demostrates that ACC framework efficiently reduces the number of incorrect predictions, improving the quality of predictions.1 © 2024, CC BY-NC-ND.

关键词： Prediction models

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Property analysis of composable web services

Telkomnika (Telecommunication Computing Electronics and Cont...

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Telkomnika (Telecommunication computing Electronics and Control) 2016年第2期14卷 734-740页

作者： Liu, Wei Wang, Lu Du, Yuyue Wang, Pin Yan, Chun State Key Laboratory of Mining Disaster Prevention and Control Shandong University of Science and Technology Qingdao China College of Information Science and Engineering Shandong University of Science Technology Qingdao China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Web services are basic components constructing a flexible business process software. By composing multiple Web services, a complicated business process across organization and departments can be formed. This paper present a formal model of composable Web services: composed service process nets (CSPNs). Properties of Composable Web services are analyzed based on CSPNs. The relation between a CSPN and its corresponding Web service process subnets are discussed. The property analysis methods of CSPNs are come up with. A dynamic property of CSPNs can be determined based on static net structures by means of the proposed methods. © 2016 Universitas Ahmad Dahlan.

关键词： Web services

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A Dual-stage Attention Based SDN Traffic Prediction Method

A Dual-stage Attention Based SDN Traffic Prediction Method

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Chenjing Gao Junli Wang Chungang Yan Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai China

Traffic matrix is the main research object of traffic prediction in software-defined networking. Accurate and timely traffic matrix prediction plays an important role in avoiding network congestion. While various methods have been proposed in previous studies to solve the SDN traffic prediction problem, few of them consider both inter- and intra-flow features. In this paper, we propose a dual-stage attention based Traffic Prediction method for the SDN traffic matrix prediction task. First, our method uses temporal pattern attention as the inter-flow attention mechanism before the encoding stage to realize adaptive feature extraction. Then, temporal attention is introduced as the intra-flow attention mechanism before the decoding stage to capture long-term temporal dependencies. Finally, we use the autoregressive module to handle the highly dynamic SDN traffic volume. Experimental results show that our proposed SDN traffic prediction method can capture more traffic features and improve the SDN traffic matrix prediction performance to some extent.

关键词： Network topology Time series analysis Prediction methods Machine learning Feature extraction Graph neural networks Encoding

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FedDDB: Clustered Federated Learning based on Data Distribution Difference 22

FedDDB: Clustered Federated Learning based on Data Distribut...

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Proceedings of the 2022 5th International Conference on Algorithms, computing and Artificial Intelligence

作者： Chengyu You Zihao Lu Junli Wang Chungang Yan Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education China and National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University China

ISBN: (纸本)9781450398336

Clustered federated learning is a federated learning method based on multi-task learning. It groups similar clients into the same clusters and shares model parameters to solve the problem that the joint model is trapped in local optima on Non-IID data. Most of the existing clustered federated learning methods are based on the difference of model parameters for clients clustering. During the client model training process, the model parameters are biased and the clustering result is affected due to insufficient samples and missing eigenvalues in the dataset. In this paper, we develop a clustered federated learning method based on data distribution difference (FedDDB) in the dataset level. The method in this paper focuses on the distribution of label probability and eigenvalues, analyzes the difference of data distribution difference between clients and measures the distance between datasets which is used for client clustering. Every cluster will be trained independently and in parallel on the cluster center model. At the beginning of each round of training, the client clustering process needs to be repeated. We conduct relevant experiments and demonstrate the effectiveness of our method.

关键词： Clustering method

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Behavioral Authentication for Security and Safety

arXiv

引用

arXiv 2023年

作者： Wang, Cheng Tang, Hao Zhu, Hangyu Zheng, Junhan Jiang, Changjun Department of Computer Science and Technology Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China

The issues of both system security and safety can be dissected integrally from the perspective of behavioral appropriateness. That is, a system is secure or safe can be judged by whether the behavior of certain agent(s) is appropriate or not. Specifically, a so-called appropriate behavior involves the right agent performing the right actions at the right time under certain conditions. Then, according to different levels of appropriateness and degrees of custodies, behavioral authentication can be graded into three levels, i.e., the authentication of behavioral Identity, Conformity, and Benignity. In a broad sense, for the security and safety issue, behavioral authentication is not only an innovative and promising method due to its inherent advantages but also a critical and fundamental problem due to the ubiquity of behavior generation and the necessity of behavior regulation in any system. By this classification, this review provides a comprehensive examination of the background and preliminaries of behavioral authentication. It further summarizes existing research based on their respective focus areas and characteristics. The challenges confronted by current behavioral authentication methods are analyzed, and potential research directions are discussed to promote the diversified and integrated development of behavioral authentication. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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