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Relation Classification based on Selective Entity-Aware Attention 25

Relation Classification based on Selective Entity-Aware Atte...

引用

25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Ding, Haijie Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Jiangsu Nanjing China

ISBN: (纸本)9781665405270

Relation classification aims to classify the entity pairs into a certain relation, which is an important task of natural language processing. The latest end-to-end models based on attention mechanism still have shortcomings, i.e., the attention will be gradually weakened when processing long sequences, and they cannot make use of the hidden type information of the entities. To solve these problems, we propose a relation classification model based on the selective entity-aware attention mechanism, which consists of context encoder and entity-aware attention network. In the context encoder, contextual word semantics are learned through self-attention. Entity selection is applied to adapt the fact that different words can determine each other's importance. Latent types of entities are taken as auxiliary information to make full use of the entities' hidden features. Experiments on the SemEval-2010 Task 8 dataset and TACRED show that our model outperforms the baselines without implementing any external resources or NLP tools, and the entity-aware attention indeed improve the model's performance. © 2022 IEEE.

关键词： Semantics

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CETO-Sim: A Simulation Platform for Cloud-Edge Task Offloading 25

CETO-Sim: A Simulation Platform for Cloud-Edge Task Offloadi...

引用

25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yao, Rui Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Jiangsu Nanjing China

ISBN: (纸本)9781665405270

In order to verify the effectiveness of task offloading algorithms in large-scale cloud and edge computing systems, scholars usually use simulation platforms to conduct extensive experiments. However, existing simulation platforms have unclear application scenarios and relatively homogeneous functions, which cannot fully exploit the respective advantages of cloud and edge computing. To address the above issues, this paper designs a new simulation platform, CETO-Sim (Cloud-Edge Task Offloading Simulation), for the cloud-edge aggregation computing scenario, which is convenient for users to verify the effectiveness of the task offloading algorithm. The platform adopts a modular design strategy, allowing users to invoke the various interfaces provided by the platform to simulate and build different physical entities, and to freely combine them to simulate different computing environments. The platform contains six components: task management component, network topology component, end-user component, scheduling policy component, server component and operation component. The platform simulates the task offloading process through the interaction between these components, while recording the changes of various parameters of tasks and simulated entities in real time, and finally printing out the experimental results in the form of logs. This paper presents an example of task offloading using the particle swarm algorithm in a cloud-side aggregated computing environment, demonstrating that the CETO-Sim platform has the advantages of clear architecture, simple operation, easy deployment, accurate experimental results and high simulation efficiency, and also verifying the feasibility of deploying the platform in other scenarios. © 2022 IEEE.

关键词： Simulation platform

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VECSim: A Simulation Platform for Multi-bitrate Video Caching Collaboratively in Edge Computing Network 25

VECSim: A Simulation Platform for Multi-bitrate Video Cachin...

引用

25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Chen, Peipei Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Jiangsu Nanjing China

ISBN: (纸本)9781665405270

The development of the mobile Internet has led to a shape increase in video traffic data. New Mobile Edge Computing (MEC) technology can reduce network operation and service delivery delays, so as to improving the QoE (Quality of Experience) of user. In MEC environment, this article builds a simulation platform for multi-bitrate video caching and scheduling scenarios VECSim (Video Edge CloudSim). VECSim provides a simulation environment specific to verify multi-bitrate video caching collaboratively and scheduling strategy in the Mobile Edge Computing scenarios. Unlike simple numerical simulations and complex real-world deployments, VECSim provides a simple interface that can be called by the user, besides, it can be used to provide larger-scale, more flexible request scenarios which are closer to the actual environment. Based on the edge computing simulation platform EdgeCloudSim, this platform adds and modifies quite a few functions so that it can be effectively used in multi-bitrate video caching and scheduling scenarios, which now includes seven main modules: Server Management Modules, Network Module, User Mobility Module, Task Loader Generator Module, Video Storage Module, Edge Client Management Module, and Edge Orchestrator Module. With this platform, researchers can easily and quickly verify their video caching and scheduling strategies. © 2022 IEEE.

关键词： Mobile edge computing

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An Answer Summarization Scheme Based on Multilayer Attention Model 26

An Answer Summarization Scheme Based on Multilayer Attention...

引用

26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Xu, Xiaolong Dong, Yihao Song, Jian Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China

ISBN: (纸本)9798350331684

At present, deep learning technologies have been widely used in the field of natural language process, such as text summarization. In CQA, the answer summary could help users get a complete answer quickly. There are still some problems with the current answer summary scheme, such as semantic inconsistency, repetition of words, etc. In order to solve this, we propose a novel scheme Answer Summarization based on Multi-layer Attention Scheme (ASMAM). Based on the traditional Seq2Seq, we introduce self-attention and multi-head attention scheme respectively during sentence and text encoding, which could improve text representation ability of the model. In order to solve "long distance dependence"of RNN and too many parameters of LSTM, we all use GRU as the neuron at the encoder and decoder sides. Experiments over the Yahoo! Answers dataset demonstrate that the coherence and fluency of the generated summary are all superior to the benchmark model in ROUGE evaluation system. © 2023 IEEE.

关键词： Semantics

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AtM-DNN: A Multimodal Attention Fusion Network with Auxiliary Function for Sentiment Classification 25

AtM-DNN: A Multimodal Attention Fusion Network with Auxiliar...

引用

25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Huang, Ji Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China

ISBN: (纸本)9781665405270

Multimodal sentiment classification is an important research attracting many scientists' attention in natural language processing. In most multimodal sentiment research, each modal of the dataset is labeled with a unified label. However, this unified label of multimodal data may limit the model to obtain the different information between multimodal in the training process. To address the above issues, this paper proposes AtM-DNN, a model based on multimodal attention fusion network in collaboration with auxiliary function for multimodal sentiment classification. Different single-modal encoders are used to map the original data of different modal into vectors of different information dimensions. Attention mechanism is introduced to control the information weight of each modal in the interaction, so as to remove the redundancy of information. Besides, each modal is introduced into the loss function as an auxiliary function to assist the calculation of the final loss function. Experiments on CH-SIMS dataset show that the attention mechanism of each modal can exactly improve the accuracy and F1 of sentiment classification of each single-modal, and also can improve the accuracy and F1 of multimodal sentiment classification. Although the collaboration of auxiliary function can improve the effect of multimodal sentiment classification, it is likely to reduce the effect of each single-modal. And these models are better than the single-modal sentiment classification model. © 2022 IEEE.

关键词： Neural networks

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MDPFL:A Multiple Differential Privacy Protection Method based on Federated Learning 26

MDPFL:A Multiple Differential Privacy Protection Method base...

引用

26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Xinyu Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China

ISBN: (纸本)9798350331684

With the continuous development of artificial intelligence technology, machine learning in distributed network systems, such as IoVflntemet of Vehicles), will inevitably lead to privacy leakage. At present, there are lots of problems in federated learning, differential privacy and other machine learning privacy protection schemes, such as high loss of availability and the need of a fully trusted third-party server. In order to solve these problems, we propose MDPFL(Multiple Differential Privacy based on Federated Learning) algorithm. The algorithm combines with the differential privacy model in each stage of federated learning to solve the problem of curious third-party data collectors obtaining users' original data in the process of machine learning. Meanwhile, the algorithm does not adopt a decentralized machine learning scheme directly, but uses a double noise adding mode with the existence of the third-party data collectors. We use Laplacian mechanism in the central server and GRR mechanism in local clients to ensure the goal of stability of the effect of the machine ¡earning model. The algorithm is compared with FedAvg, CDP, LDP algorithm in accuracy and loss rate which based on EMNIST data sets. In different global sensitivities, the model training effect is consistent with FedAvg algorithm while comparing with LDP algorithm, the speed of model convergence is improved. © 2023 IEEE.

关键词： Learning algorithms

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Transfer learning with active queries for relational data modeling across multiple information networks 25th

Transfer learning with active queries for relational data mo...

引用

25th International Conference on Neural Information processing, ICONIP 2018

作者： Chen, Ke-Jia Zhang, Kai Jiang, Xi-Lin Wang, Yunyun Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China

ISBN: (纸本)9783030041816

This paper studies the relationship prediction problem in multi-network scenarios, aiming to overcome the network sparsity challenge where the labeled data (connected node pairs) are much less than the unlabeled data (unconnected node pairs). The TAQIL framework is proposed by using transfer learning to get knowledge from the related source networks and then use active learning to query the labels of the most informative instances from the oracle in the target network. A new query function is also proposed in order to better use the parameters output by the transfer learning method. The alternate use of transfer learning and active learning allows adaptive transfer of knowledge across multiple networks to mitigate cold start and meantime improve the prediction accuracy with active queries in the target network. The experimental results on both non-network datasets and network datasets demonstrate the significant improvement in prediction accuracy compared with several benchmark methods and related state-of-art methods. © Springer Nature Switzerland AG 2018.

关键词： Forecasting

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Wireless Information and Power Transmission for Two-Directional Traffic in Spatial-Division Multiple Access Networks

Wireless Information and Power Transmission for Two-Directio...

引用

International Conference on Communications, Signal processing, and Systems, CSPS 2018

作者： Zhao, Juan Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210003 China

ISBN: (纸本)9789811362637

Both the power and the information are transmitted wirelessly, respectively, in downlink and uplink traffics for spatial-division multiple access networks. The receiver in uplink is equipped with multiple antennas to support the multiple access *** the first frequency band, the transmitter sends power wirelessly to multiple users at the same time. In the second frequency band, all the users utilize the received power, respectively, to send information wirelessly to the receiver (the same node in the downlink). The objective is to achieve the best energy efficiency over the whole networks while assuring the required quality of service in terms of the minimum throughput over all the unlink traffics. The unknown parameter is the transmit power in downlink. The difficulty is to deal with the hard derivations over the closed-form solution to the complicated problem. The proposed method successfully obtains the optimal solution in terms of the closed form. Numerical simulations verify the good performance achieved by our proposed method. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Power transmission

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Protocol Design of Efficient Access for Laptop in Wi-Fi Networks with Guaranteed security

Protocol Design of Efficient Access for Laptop in Wi-Fi Netw...

引用

International Conference on Communications, Signal processing, and Systems, CSPS 2018

作者： Zhao, Juan Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210003 China

ISBN: (纸本)9789811362637

Computer security is very important since three decades ago, which becomes quite challenging especially in the wireless access scenarios. In this paper, we summarize the technologies of the development over the computer security. Then, we propose a couple of iterative algorithms to carry out the laptop selection to wirelessly access the computer networks with the existence of the eavesdropper. The goal of designing the algorithms is, respectively, to maximize the secured throughput of the wireless access channel and maximize the energy efficiency for the target laptop with the existence of the eavesdropper. Simulations demonstrate the effectiveness of the proposed algorithms. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Wi-Fi

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SNNLog: A Log Parsing Scheme with Siamese Network and Fixed Depth Tree in Networks 26

SNNLog: A Log Parsing Scheme with Siamese Network and Fixed ...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Sun, Lei Xu, Xiaolong Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China

ISBN: (纸本)9798350331684

Syslog records critical information of network when the system is running, and has been used to help practitioners carry out various network maintenance and operation activities. Because of abundance of syslog, automated log analysis technology is needed to complete the above activities. Usually, log parsing is an important part of log analysis. Previous log parsing methods have good achievements, but they heavily rely on well-designed regular expressions and ignore semantic information of log. To solve these problems, we propose a novel log parsing method SNNLog, which uses Siamese Network to assess the similarity between log messages, and the similarity is used for the subsequent parsing. In addition, after parsing, SNNLog merges log similar events, reducing the misjudgment rate caused by non-numeric token variables at the beginning of messages. We evaluated on five publicly accessible log datasets and the results show that compared with SOTA algorithms such as LogMine, Spell, Drain and MoLFI, SNNLog has an F1-score of 0.999 on five datasets, and parsing accuracy of four datasets is the highest. © 2023 IEEE.

关键词： Semantics

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