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Reducing energy consumption optimization selection of path transmission routing algorithm in opportunistic networks

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High Technology Letters 2015年第3期21卷 321-327页

作者：吴嘉 Yi Xi Chen Zhigang school of software Central South University

Opportunistic networks are random networks and do not communicate with each other among respective communication *** situation leads to great difficulty in message *** paper proposes a reducing energy consumption optimal selection of path transmission(OSPT) routing algorithm in opportunistic *** algorithm designs a dynamic random network topology,creates a dynamic link,and realizes an optimized selected *** algorithm solves a problem that nodes are unable to deliver messages for a long time in opportunistic *** to the simulation experiment,OSPT improves deliver ratio,and reduces energy consumption,cache time and transmission delay compared with the Epidemic Algorithm and Spray and Wait Algorithm in opportunistic networks.

关键词： opportunistic networks routing algorithm deliver ratio energy consumption transmission delay cache time

Out of sight, out of mind: a distance-aware forgetting strategy for adaptive random testing

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Science China(Information Sciences) 2017年第9期60卷 78-98页

作者： Chengying MAO Tsong Yueh CHEN Fei-Ching KUO school of software and Communication Engineering Jiangxi University of Finance and Economics Department of Computer Science and software Engineering Swinburne University of Technology

Adaptive random testing(ART) achieves better failure-detection effectiveness than random testing by increasing the diversity of test cases. However, the intention of ensuring even spread of test cases inevitably causes an overhead problem. Although two basic forgetting strategies(i.e. random forgetting and consecutive retention) were proposed to reduce the computation cost of ART, they only considered the temporal distribution of test cases. In the paper, we presented a distance-aware forgetting strategy for the fixed size candidate set version of ART(DF-FSCS), in which the spatial distribution of test cases is taken into consideration. For a given candidate, the test cases out of its "sight" are ignored to reduce the distance computation cost. At the same time, the dynamic adjustment for partitioning and the second-round forgetting are adopted to ensure the linear complexity of DF-FSCS algorithm. Both simulation analysis and empirical study are employed to investigate the efficiency and effectiveness of DF-FSCS. The experimental results show that DF-FSCS significantly outperforms the classical ART algorithm FSCS-ART in efficiency, and has comparable failure-detection effectiveness. Compared with two basic forgetting methods, DF-FSCS is better in both efficiency and effectiveness. In contrast with a typical linear-time ART algorithm RBCVT-Fast, our algorithm requires less computational overhead and exhibits the similar failure-detection capability. In addition, DF-FSCS has more reliable performance than RBCVT-Fast in detecting failures for the programs with high-dimensional input domain.

关键词： adaptive random testing software testing test cases computational overhead diversity

Smart Care: Energy-Efficient Long-Term Physical Activity Tracking Using Smartphones

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Tsinghua Science and Technology 2015年第4期20卷 348-363页

作者： Hui Liu Rui Li Sicong Liu Shibian Tian Junzhao Du school of software and Institute of software Engineering Xidian Universityand Science and Technology on Infomation Transmission and Dissemination in Communication Networks Lab.

Lack of physical activity is becoming a killer of our healthy life. As a solution for this negative impact, we propose SmartCare to help users to set up a healthy physical activity habit. SmartCare can monitor a user＇s activities over a long time, and then provide activity quality assessment and suggestion. SmartCare consists of three parts, activity recognition, energy saving, and health feedback. Activity recognition can recognize nine kinds of daily activities. A hybrid classifier that uses less power and memory with satisfactory accuracy was designed and implemented by utilizing the periodicity of target activity. In addition, a learning-based energy saver was introduced to reduce energy consumption by adjusting sampling rates and the set of features adaptively. Based on the type and duration of the activity recorded, health feedback in terms of the calorie burned was given. The system could provide quantitative activity quality assessment and recommend future physical activity plans. Through extensive real-life testing, the system is shown to achieve an average recognition accuracy of 98.0% with a minimized energy expenditure.

关键词： physical activity tracking hybrid classifier health feedback

Beyond pixels:Learning from multimodal hyperspectral superpixels for land cover classification

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Science China(Technological Sciences) 2022年第4期65卷 802-808页

作者： HONG DanFeng WU Xin YAO Jing ZHU XiaoXiang Key Laboratory of Computational Optical Imaging Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China school of Computer Science(national pilot software Engineering school) Beijing University of Posts and TelecommunicationsBeijing 100876China Remote Sensing Technology Institute German Aerospace Center(DLR)Wessling 82234Germany Data Science in Earth Observation Technical University of MunichMunich 80333Germany

Despite tons of advanced classification models that have recently been developed for the land cover mapping task,the monotonicity of a single remote sensing data source,such as only using hyperspectral data or multispectral data,hinders the classification accuracy from being further improved and tends to meet the performance *** this reason,we develop a novel superpixel-based subspace learning model,called Supace,by jointly learning multimodal feature representations from HS and MS superpixels for more accurate LCC *** can learn a common subspace across multimodal RS data,where the diverse and complementary information from different modalities can be better combined,being capable of enhancing the discriminative ability of to-be-learned features in a more effective *** better capture semantic information of objects in the feature learning process,superpixels that beyond pixels are regarded as the study object in our Supace for *** experiments have been conducted on two popular hyperspectral and multispectral datasets,demonstrating the superiority of the proposed Supace in the land cover classification task compared with several well-known baselines related to multimodal remote sensing image feature learning.

关键词： classification hyperspectral image land cover multimodal multispectral image remote sensing subspace learning superpixels

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Learning from group supervision: the impact of supervision deficiency on multi-label learning

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Science China(Information Sciences) 2021年第3期64卷 5-17页

作者： Miao XU Lan-Zhe GUO school of Information Technology and Electrical Engineering The University of Queensland RIKEN Center for Advanced Intelligence Project national Key Laboratory for Novel software Technology Nanjing University

Multi-label learning studies the problem where one instance is associated with multiple *** supervised multi-label learning has attracted considerable research attention because of the annotation difficulty. Majority of the studies on weakly supervised multi-label learning assume that one group of weak annotations is available for each instance; however, none of these studies considers multiple groups of weak annotations that can be easily acquired through crowdsourcing. Recent studies on crowdsourced multilabel learning observed that the current query strategies do not agree well with human habits and that data cannot be collected as expected. Therefore, this study aims to design a new query strategy in accordance with human behavior patterns to obtain multiple groups of weak annotations. Further, a learning algorithm is proposed based on neural networks for such type of data. In addition, this study qualitatively and empirically analyzes factors in the proposed query strategy that may impact further learning and provides insights to obtain better query strategy with respect to future crowdsourcing in case of multi-label data.

关键词： multi-label learning weakly supervised leaning weakly supervised multi-label learning query strategy group supervision

Graph Enhanced Transformer for Aspect Category Detection

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Journal of Computer Science & Technology 2023年第3期38卷 612-625页

作者：陈晨王厚峰朱晴晴柳军飞 Office of the Cyberspace Affairs Commission Peking UniversityBeijing 100871China school of Computer Science Peking UniversityBeijing 100871China school of software and Microelectronics Peking UniversityBeijing 100871China national Engineering Research Center for software Engineering Peking UniversityBeijing 100871China

Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category detection as a flat classification problem. However, aspect categories are inter-related, and they are usually organized with a hierarchical tree structure. To leverage the structure information, this paper proposes a hierarchical multi-label classification model to detect aspect categories and uses a graph enhanced transformer network to integrate label dependency information into prediction features. Experiments have been conducted on four widely-used benchmark datasets, showing that the proposed model outperforms all strong baselines.

关键词： aspect based sentiment analysis aspect category detection hierarchical multi-label classification transformer network

A fractal and scale-free model of complex networks with hub attraction behaviors

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Science China(Information Sciences) 2015年第1期58卷 178-187页

作者： KUANG Li ZHENG BoJin LI DeYi LI YuanXiang SUN Yu State Key Laboratory of software Engineering Computer SchoolWuhan University College of Computer Science South-Central University For Nationalities school of software Tsinghua University school of Computer and Electronics and Information Guangxi University

It is widely believed that fractality of complex networks originate from hub repulsion behaviors(anticorrelation or disassortativity), which means that large degree nodes tend to connect with small degree *** hypothesis was demonstrated by a dynamical growth model, which evolves as the inverse renormalization procedure, proposed by Song et al. Now we find that the dynamical growth model is based on the assumption that all the cross-box links have the same probability e to link to the most connected nodes inside each ***, we modify the growth model by adopting the flexible probability e, which makes hubs to have higher probability to connect with hubs than non-hubs. With this model, we find that some fractal and scale-free networks have hub attraction behaviors(correlation or assortativity). The results are the counter-examples of former beliefs. Actually, the real-world collaboration network of movie actors also is fractal and shows assortative mixing.

关键词： scale-free fractal network self-similarity fractal dimension hub attraction

Research of Consistency Maintenance Mechanism in Real-Time Collaborative Multi-View Business Modeling

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Journal of Shanghai Jiaotong university(Science) 2015年第1期20卷 86-92页

作者：蔡鸿明计晓峰步丰林 school of software Shanghai Jiaotong University

Real-time collaborative editing(RTCE)can support a group of people collaboratively work from distributed locations at the same ***,consistency maintenance is one key challenge when different types of conflicts *** a common synchronous mechanism is proposed to support consistency maintenance in the process of multi-view business *** on operation analysis on different views of models in the real-time collaborative editing system,detection of potential conflicts is realized by means of a decision-making *** consistency maintenance provides a comprehensive and applicable conflicts detection and resolution for collaborative business ***,a prototype of collaborative multi-view business modeling system is introduced to verify the *** point is that the mechanism proposes a comprehensive solution for collaborative multi-view business modeling.

关键词： computer support cooperative work(CSCW) multi-view business modeling consistency maintenance software engineering

A Depth Video Coding In-Loop Median Filter Based on Joint Weighted Sparse Representation

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Wuhan University Journal of Natural Sciences 2016年第4期21卷 351-357页

作者： Lü Haitao YIN Cao CUI Zongmin HU Jinhui school of Information Science and Technology Jiujiang University national Engineering Research Center for Multimedia software/school of Computer Wuhan University

The existing depth video coding algorithms are generally based on in-loop depth filters, whose performance are unstable and easily affected by the outliers. In this paper, we design a joint weighted sparse representation-based median filter as the in-loop filter in depth video codec. It constructs depth candidate set which contains relevant neighboring depth pixel based on depth and intensity similarity weighted sparse coding, then the median operation is performed on this set to select a neighboring depth pixel as the result of the filtering. The experimental results indicate that the depth bitrate is reduced by about 9% compared with anchor method. It is confirmed that the proposed method is more effective in reducing the required depth bitrates for a given synthesis quality level.

关键词： depth video coding virtual view synthesis joint weighted sparse representation