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A Sentiment Classification Model Based on Multiple Multi-classifier Systems 1

引用

5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Yang, Kehua Liao, Chenglin Zhang, Wei College of Computer Science and Electronic Engineering Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University ChangshaHunan410082 China

ISBN: (数字)9783030242657

ISBN: (纸本)9783030242640

With the fast development of the Internet, especially the development of the Web technique, more and more people express their views on the Internet. The content of these views involve various aspects of our life, such as books, movies, musics, policies, commodities, technical services, etc. Since these views are usually subjective and possess great potential commercial and social value, the study of sentiment analysis is followed. At the early stage of sentiment analysis, researchers used lexicon based method to solve sentiment analysis problem. With the improvement in machine learning and Natural Language Processing technology, machine learning based method are becoming more and more popular in the field of sentiment analysis. In this study, we propose an ensemble sentiment classification model based on three multi-classifier systems. The proposed model uses majority voting as an ensemble method of multiple multi-classifier systems. The effectiveness of the classification model is verified on five public datasets. Experimental results show that the proposed model is superior to traditional sentiment classification methods and simple multi-classifier systems in the performance of classification accuracy. By using the sentiment classification model proposed in this paper, the accuracy improvement in different domains also show that our proposed model has certain generalization ability. © 2019, Springer Nature Switzerland AG.

关键词： Sentiment analysis

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Object-oriented program tailoring based on model 4

Object-oriented program tailoring based on model

引用

4th International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation, CDMMS2014

作者： Jin, Qing Kai Li, Jun Yi Zhang, Yan College of Information Science and Engineering Hunan University ChangshaHunan China Key Laboratory for Embedded and Network Computing of Hunan Province ChangshaHunan China

ISBN: (纸本)9783038351528

For the redundant codes caused by software reuse and rapid development in object-oriented program, the paper proposes a cutting model and designs a cutting scheme based on this model. Firstly, a cutting model is established. Then, static analysis is made for object-oriented codes and the cutting information base of objects to be clipped is established. Lastly, the cutting information base and cutting model is matched to cut the program. A test on a practical java project indicates that the cutting scheme can make the volume of the project reduce by about 21%. © (2014) Trans Tech Publications, Switzerland.

关键词： Static analysis

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Neighbor Diversification-Based Collaborative Filtering for Improving Recommendation Lists

Neighbor Diversification-Based Collaborative Filtering for I...

引用

International Workshop on Intelligent Communication and Social networks

作者： Chao Yang Cong Cong Ai Renfa Li Key Laboratory for Embedded and Network Computing of Hunan University

ISBN: (纸本)9781479909735

Recommendation systems are popular information filtering tools that help people find what they want. Accuracy is the most widely used metric for evaluating recommendation systems. Recently, many research works have focused on new measurements beyond the accuracy of recommendation systems. In this paper, we propose a neighbor diversification collaborative filtering algorithm to improve the recommendation lists. By using Movielens dataset for empirical analysis, we investigated the influence of neighbor diversity to the recommendation accuracy, diversity, novelty and coverage. Intensive experimental results proved the efficiency of our proposed algorithm for improving recommendation lists.

关键词： Recommendation System Neighbor Diversification Diversity Novelty Coverage

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Adaptive Compressed Classification for Energy Efficient Activity Recognition in Wireless Body Sensor networks

Adaptive Compressed Classification for Energy Efficient Acti...

引用

International Conference on Big Data computing and Communications (BIGCOM)

作者： Ling Xiao Yu Meng Kai Wu Key Laboratory for Embedded and Network Computing of Hunan Province China

Energy efficiency for activity recognition is a challenging task in the long-term health monitoring applications. To save the energy in wireless body sensor networks, the adaptive compressed classification framework for activity recognition is proposed. The proposed mechanism estimates the minimum activity-specific compression ratio subject to given recognition accuracy and adaptively adjusts the number of samples taken by sensor nodes based on the feedback from the result of activity recognition. A brute force algorithm is utilized to quickly find the optimal trade-off the power consumption and classification performance. With experiments on real-world activity datasets, we demonstrate 90.5% recognition accuracy with energy consumption reduced 28%.

关键词： Activity recognition Energy consumption Optimized production technology Wireless communication Base stations Body sensor networks

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Discriminative hough-voting for object detection with parts

Discriminative hough-voting for object detection with parts

引用

International Conference on Mechatronic Science, Electric Engineering and Computer

作者： Yaodong Chen Renfa Li Key Laboratory for Embedded and Network Computing of Hunan Province Changsha China

ISBN: (纸本)9781479925667

Many detection models rely on sliding-window methods to search all possible candidates and then localize true instances by linear classifiers. Such exhaustive search involves massive computation. Hough voting scheme provides an alternative way to localize objects. Typical voting-based approaches, casting independent votes for a hypothesis, ignore the mutual relevance of features. The weights of the features for voting are learnt in a simple way. These two weaknesses limit the detection performance of the voting scheme. This paper introduces a novel voting-based model. We group the model features into parts. The features in one part are dependent and can cast consistent votes for a given hypothesis. For a given hypothesis we introduce an overall score function whose parameters can be optimized in a discriminative way. We apply a latent learning framework to deal with part-level weak supervision. The experiments evaluate the proposed model on two standard datasets. We demonstrate significant improvements in detection performance comparing the start-of-the-art detection models.

关键词： Object detection Computational modeling Shape Feature extraction Training Search problems Transforms

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An Improved algorithm of parameters reconfiguration in cognitive radio networks using ant colony optimization

An Improved algorithm of parameters reconfiguration in cogni...

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2015 3rd International Conference on Manufacturing Engineering and Technology for Manufacturing Growth(METMG 2015)

作者： Shaoyuan Liu Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University

Recent years, cognitive radio techniques have becoming a promising solution to tackle the bottleneck of spectrum scarcity. As an essential characteristics for CRN, the cognitive parameter reconfiguration can automatically adjust the parameters to meet the user demands and adapt to the timevarying environment. However parameter reconfiguration and complex environment cognition are still challenging due to their need for high speed parallel computation and multi-objects optimization. In this aspect, ant colony optimization(ACO) technique, as an intelligent technology to solve the complex issues, is introduced to the reconfiguration process to achieve the assignment. The goal of this paper is to present a parameters reconfiguration scenario using ACO with a comprehensive fitness function for reconfiguration decision making with regard to the multiple and complex objectives. Finally, series of results have demonstrated the stability and validity of the ACO based reconfiguration algorithm in CRN.

关键词： ACO simulation An Improved algorithm of parameters reconfiguration in cognitive radio networks using ant colony optimization

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Multi-Users Cooperative Spectrum Sensing in Distributed Cognitive Radio networks with Energy Harvesting

Multi-Users Cooperative Spectrum Sensing in Distributed Cogn...

引用

2nd International Conference on Simulation and Modeling Methodologies,Technologies and Applications(SMTA 2015)

作者： H.Wang F.Z.Zeng Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University

In this paper we propose an improved sensing scheme in cognitive radio networks, which allows the secondary users to utilize the spectrum of primary users when these resources are not being used currently. We first apply multi-user cooperative spectrum sensing in cognitive radio networks to improve both energy efficiency and spectrum utility. Secondly, we investigate the energy harvesting communication model. Finally, we analysis the probability of the proposed spectrum sensing scheme, and obtain an optimal solution under the scenarios of non-energy harvesting scheme and energy harvesting scheme respectively. The simulation results demonstrate the high performance of our proposed scheme.

关键词： Multi-Users Cooperative Spectrum Sensing in Distributed Cognitive Radio networks with Energy Harvesting Radio

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A decomposition based evolutionary algorithm with angle penalty selection strategy for many-objective optimization 9th

A decomposition based evolutionary algorithm with angle pena...

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9th International Conference on Swarm Intelligence, ICSI 2018

作者： Li, Zhiyong Lin, Ke Nouioua, Mourad Jiang, Shilong Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha China PKU-HKUST Shenzhen-HongKong Institution Shenzhen China

ISBN: (纸本)9783319938141

Evolutionary algorithms (EAs) based on decomposition have shown to be promising in solving many-objective optimization problems (MaOPs). First, the population (or objective space) is divided into K subpopulations (or subregions) by a group of uniform distribution reference vectors. Later, subpopulations are optimized simultaneously. In this paper, we propose a new decomposition based evolutionary algorithm with angle penalty selection strategy for MaOPs (MOEA-APS). In the environmental selection process, in order to prevent the solutions located around the boundary of the subregion from being simultaneously selected into the next generation which will affect negatively on the performance of the algorithm, a new angle similarity measure (AS) is calculated and used to punish the dense solutions. More precisely, after selecting a good solution x for a sub population, the solutions whose angle similarity with x exceeding η or pareto dominated by x will be directly punished. Moreover, The threshold η is not fixed, but decided by the distribution of the solutions around x. This mechanism allows to improve diversity of population. The experimental results on DTLZ benchmark test problems show that the results of the proposed algorithm are very competitive comparing with four other state-of-the-art EAs for MaOPs. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Evolutionary algorithms

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A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor Data and Periodic Sampling

A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor ...

引用

2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zheng, Jianbo Yang, Chao Zheng, Fangrong Jiang, Bin College of Computer Science and Electronic Engineering Hunan University China Key Laboratory for Embedded and Network Computing of Hunan Province China School of Computational Science and Electronics Hunan Institute of Engineering China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

In recent years, bearing fault diagnosis based on deep learning has gradually become the mainstream. However, the existing studies still have some defects, such as unreasonable sampling and incomplete utilization of bearing data, limiting the further improvement of the performance of the fault diagnosis model. This paper proposes a fault diagnosis method using multi-sensor data and periodic sampling to solve the problems above. First, the vibration data of different bearing positions are fused into multi-channel fusion data to improve the defect of insufficient data utilization. Second, based on the sampling length and sampling stride, periodic sampling is carried out for the fusion data to solve the problem of unreasonable sampling. Third, the traditional convolutional neural network is adjusted to extract more detailed fault features and obtain the best recognition effect. Finally, the experimental results verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Failure analysis

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Collaborative Multi-head Contextualized Sparse Representations for Real-Time Open-Domain Question Answering 17th

Collaborative Multi-head Contextualized Sparse Representatio...

引用

17th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2022

作者： Sun, Minyu Jiang, Bin Zhou, Xinjiao Zhang, Bolin Yang, Chao College of Computer Science and Electronic Engineering Hunan University Hunan Changsha410082 China Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Hunan Changsha410082 China

ISBN: (纸本)9789819923847

An efficient method of representing and retrieving information is an essential component of open domain QA. There are question and answer models that allow for real-time responses with speed benefit and scalability. Nonetheless, due to the limitations of existing phrase models, their accuracy is low. In this paper, we improve the contextualized sparse representation to strengthen the connection between contextual information. We achieve better answer retrieval by enhancing the embedding quality of the model for phrase representation. Specifically, based on original contextualized sparse representations, we transform the single self-attention into collaborative multi-head attention so that attention heads can connect and pay attention to crucial information in different context locations. Compared with learning sparse vectors in n-gram vocabulary space by rectified self-attention, collaborative multi-head attention performs better on the SQuAD dataset. Due to the increased efficiency of critical information representation, the model improves to varying degrees on both of the two evaluation metrics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vector spaces

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