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arXiv 2021年

作者： Wu, Di Xie, Xiaofeng Nie, Xiang Fu, Bin Deng, Hanhui Zeng, Haibo Qin, Zhijin Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University China Department of Electrical and Computer Engineering Virginia Tech United States EECS Queen Mary University of London United Kingdom

—The rapid deployment of Internet of Things (IoT) applications leads to massive data that need to be processed. These IoT applications have specific communication requirements on latency and bandwidth, and present new features on their generated data such as time-dependency. Therefore, it is desirable to reshape the current IoT architectures by exploring their inherent nature of communication and computing to support smart IoT data process and analysis. We introduce in this paper features of IoT data, trends of IoT network architectures, some problems in IoT data analysis, and their solutions. Specifically, we view that software-defined edge computing is a promising architecture to support the unique needs of IoT data analysis. We further present an experiment on data anomaly detection in this architecture, and the comparison between two architectures for ECG diagnosis. Results show that our method is effective and feasible. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge computing

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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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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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LAMPT: LAbel Mask-Predicted Transformer for Extreme Multi-label Text Classification

LAMPT: LAbel Mask-Predicted Transformer for Extreme Multi-la...

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IEEE International Conference on Big Data

作者： Yifan Xu Minghao Zhu 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

ISBN: (纸本)9781665480468

Extreme Multi-label text Classification ( XMC) is a task of recalling the most relevant labels for each given text from an extremely large-scale label set. It is emphasized that XMC is a more complex classification task because there are two main problems: large space of labels and the labels in XMC tasks tend to be correlated. Existing methods attempt to model label correlations by viewing the XMC task as a sequence generation problem however they still suffer from (1) using the slow serial decoding strategy where labels are predicted one-by-one. (2) needing to compare a mass of label ordering strategies in the decoding stage to achieve satisfied accuracy. In this work, we propose LAbel Mask-Predicted Transformer (LAMPT) to address the both issues, which is a novel non-autoregressive generation model that (1) enriches the input raw text representation with the additional label features by fully exploiting the label dependencies, (2) allows for efficient parallel decoding thanks to its non-autoregressive decoding formulation and mask-prediced training strategy. Experimental results demonstrate that single model performance is substantially enhanced by LAMPT. On a Wiki dataset with thirty-one thousand labels, LAMPT-XLNet accuracy has gained 1.6% relative improvement on P@3 over the LightXML-XLNet. Also, the P@1 of ensemble LAMPT is 90.00%, a significant i mprovement over the state-of-the-art ensemble LightXML (transformer-based) and AttentionXML (LSTM-based), which achieve 89.45% and 87.47%, respectively.

关键词： Training Correlation Text categorization Information services Big Data Predictive models Transformers

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Multi-Label Learning Based on Transfer Learning and Label Correlation

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Computers, Materials & Continua 2019年第7期61卷 155-169页

作者： Kehua Yang Chaowei She Wei Zhang Jiqing Yao Shaosong Long College of Computer Science and Electronic Engineering and Key Laboratory for Embedded and Network Computing of Hunan Province Hunan UniversityChangsha410082China Oath Verizon Company ManhattanNew York10007USA

In recent years,multi-label learning has received a lot of ***,most of the existing methods only consider global label correlation or local label *** fact,on the one hand,both global and local label correlations can appear in real-world situation at same *** the other hand,we should not be limited to pairwise labels while ignoring the high-order label *** this paper,we propose a novel and effective method called GLLCBN for multi-label ***,we obtain the global label correlation by exploiting label semantic ***,we analyze the pairwise labels in the label space of the data set to acquire the local ***,we build the original version of the label dependency model by global and local label *** that,we use graph theory,probability theory and Bayesian networks to eliminate redundant dependency structure in the initial version model,so as to get the optimal label dependent ***,we obtain the feature extraction model by adjusting the Inception V3 model of convolution neural network and combine it with the GLLCBN model to achieve the multi-label *** experimental results show that our proposed model has better performance than other multi-label learning methods in performance evaluating.

关键词： Bayesian networks multi-label learning global and local label correlations transfer learning

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keyphrase Generation Based on Self-Attention Mechanism

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Computers, Materials & Continua 2019年第8期61卷 569-581页

作者： Kehua Yang Yaodong Wang Wei Zhang Jiqing Yao Yuquan Le College of Computer Science and Electronic Engineering and Key Laboratory for Embedded and Network Computing of Hunan Province Hunan UniversityChangsha410082China Oath Verizon Company ManhattanNew York10007USA

keyphrase greatly provides summarized and valuable *** information can help us not only understand text semantics,but also organize and retrieve text content *** task of automatically generating it has received considerable attention in recent *** the previous studies,we can see many workable solutions for obtaining *** method is to divide the content to be summarized into multiple blocks of text,then we rank and select the most important *** disadvantage of this method is that it cannot identify keyphrase that does not include in the text,let alone get the real semantic meaning hidden in the *** approach uses recurrent neural networks to generate keyphrases from the semantic aspects of the text,but the inherently sequential nature precludes parallelization within training examples,and distances have limitations on context *** works have demonstrated the benefits of the self-attention mechanism,which can learn global text dependency features and can be *** by the above observation,we propose a keyphrase generation model,which is based entirely on the self-attention *** is an encoder-decoder model that can make up the above disadvantage *** addition,we also consider the semantic similarity between keyphrases,and add semantic similarity processing module into the *** proposed model,which is demonstrated by empirical analysis on five datasets,can achieve competitive performance compared to baseline methods.

关键词： keyphrase generation self-attention mechanism encoder-decoder framework

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Activity-specific caloric expenditure estimation from kinetic energy harvesting in wearable devices

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Pervasive and Mobile computing 2020年 67卷 101185-101185页

作者： Xiao, Ling Wu, Kai Tian, Xiaobing Luo, Juan College of Computer Science and Electronic Engineering Hunan University Changsha0731 China Key Laboratory for Embedded and Network Computing of Hunan Province China

Accurate and efficient estimation of caloric expenditure during daily activities is desirable in tracking personal activity and health. Kinetic energy harvesting (KEH) has created an opportunity for wearable devices with limited battery power to realize long-term human health monitoring. We postulate to utilize KEH device for calorie estimation instead of accelerometer considering that the kinetic energy is produced when the user expends some calories by doing the bodily movement. In this paper, we propose to use the output voltage of KEH for activity intensity classification and develop activity-specific regression models combined with anthropometric characteristics for caloric expenditure estimation by random forest. To validate our approach, we build a KEH hardware platform and collect the dataset of seven different activities in free-living conditions from ten participants. Experiment results show that our approach achieves an accuracy of 95. 7% to classify intensity of activity and yields a R2 coefficient of 0.93 for regression model and a root mean square error (RMSE) of 1.09 kcal/min to estimate calorie expenditure by only using the output voltage of KEH. The comparison experiments validate the effectiveness of KEH device for caloric expenditure estimation act as a substitute for accelerometer in terms of estimation accuracy and energy saving. © 2020 Elsevier B.V.

关键词： Energy harvesting

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CSCL-DTI: predicting drug-target interaction through cross-view and self-supervised contrastive learning

CSCL-DTI: predicting drug-target interaction through cross-v...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuan Lin Xi Zhang Zu-Guo Yu Yahui Long Xiangxiang Zeng Philip S. Yu College of Computer Science Xiangtan University China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China Singapore Immunology Network (SIgN) Agency for Science Technology and Research (A*STAR) Singapore College of Information Science and Engineering Hunan University China Department of Computer Science University of Illinois Chicago USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurately predicting drug-target interactions (DTI) is a critical step in drug discovery. Existing methods of DTI prediction primarily employ Simplified Molecular-Input Line-Entry System (SMILES) sequences or molecular graphs to learn drug representations. However, the features learned by such single-view approach is prone to incomplete. While some multiview methods that consider the views of both SMILES sequences and molecular graphs have been developed, these methods often fall in short in capturing potential interactions between views. In this work, we propose a novel dual contrastive learning framework CSCL-DTI for DTI prediction. First, we design a contrastive-enhanced cross-view representation learning (CVRL) to learn representations for drugs. In this module, Transformer-based and graph convolutional network (GCN)-based encoders are separately adopted to learn view-specific representations, followed by contrastive learning to enrich the representations by accounting for the potential interplay between local chemical context and topological structure. Second, we combine Transformer with self-supervised contrastive learning (SSCL) to learn representations for targets by modelling protein amino acids sequences. The scheme allows to effectively preserve the intrinsic characteristics of the sequences. Finally, we introduce a bilinear attention network to obtain an integrated representation by adaptively incorporating drug and target representations. Benchmarking experiments on two datasets demonstrated that CSCL-DTI 1 outperforms six state-of-the-art methods.

关键词： Drugs Proteins Representation learning Protein engineering Adaptation models Contrastive learning Predictive models Transformers Amino acids Diffusion tensor imaging

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Interpretable multi-view attention network for drug-drug interaction prediction

Interpretable multi-view attention network for drug-drug int...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xuan Lin Qi Wen Sijie Yang Zu-Guo Yu Yahui Long Xiangxiang Zeng College of Computer Science Xiangtan University China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China Singapore Immunology Network (SIgN) Agency for Science Technology and Research(A*STAR) Singapore College of Information Science and Engineering Hunan University China

Drug-drug interaction (DDI) plays an increasingly crucial role in drug discovery. Predicting potential DDI is also essential for clinical research. Given the high cost and risk of wet-lab experiments, in-silico DDI prediction is an alternative choice. Recently, deep learning methods have been developed for DDI prediction. However, most of existing methods focus on feature extraction from either molecular SMILES sequences or drug interactive networks, ignoring the valuable complementary information that can be derived from these two views. In this paper, we propose a novel interpretable Multi-View Attention network (MVA-DDI) for DDI prediction. MVA-DDI can effectively extracts drug representations from different perspectives to improve DDI prediction. Specifically, for a given drug, we design a transformer-based encoder and a graph convolutional networkbased encoder to learn sequence and graph representations from SMILES sequence and molecular graph, respectively. To fully exploit the complementary information between the sequence and molecular views, an attention mechanism is further adopted to adaptively aggregate the sequence and graph representations by taking the importance of different views into accounts, generating the final drug representations. Comparison experiments demonstrated that our MVA-DDI 1 model achieved superior performance to state-of-the-art models on DDI prediction.

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Energy optimization on dynamic multiple functions automotive cyber-physical systems 21

Energy optimization on dynamic multiple functions automotive...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Liu, Yan Xie, Guoqi Jin, Linlin Li, Renfa Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha Hunan China

ISBN: (纸本)9781728120584

A new generation of automotive electronic systems is a typical cyber-physical systems (CPS) based on an integrated architecture, in which multiple functions can be executed on one electronic control unit (ECU) and one function can be assigned to different ECU. Heterogeneity and dynamics are essential characteristics of automotive CPS (ACPS). Considering heterogeneity and dynamics of ACPS, optimizing functional safety and energy consumption in system design is still a challenge. In this paper, we focus on the collaborative optimization of functional safety and energy consumption with global method in the context of prioritizing functional safety. Considering the requirements of functional safety, we propose a deadline driven processor merging for multiple dynamic functions (DPMMDF) algorithm to maximizes the number of functions that meet deadlines when a function arrives dynamically from the perspective of global optimization. Further, we use a global energy saving for multiple dynamic functions (GESMDF) algorithm, combined with the integrated architecture features, to optimize the energy consumption of ACPS by selecting multiple candidate executable ECUs for a task. Experimental results show that combination of the proposed two algorithms can effectively reduce deadline missed ratio (DMR), and minimize energy consumption compared with existing methods. © 2019 IEEE.

关键词： Global optimization

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