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Bo Shen
China Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education Beijing Jiaotong University, Beijing, China

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Research article
Published: 24 August 2020 in Complexity
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The opinion dynamics is a complex and interesting process, especially for the systems with a large number of individuals. It is usually hard to describe the evolutionary features of these systems. In some previous works, it has been shown that the self-excitation type model has superior performance in learning and predicting opinions. Following this line, we consider the self-excitation opinion model and study the collective behaviors of the self-excitation model. We propose a Mckean–Vlasov-type integrodifferential equation to describe the asymptotic behaviors of the model and show that the introduced equation, by coupling with the initial distribution, has the ability of capturing the influence of the self-excitation process, which describes the mutually exciting and recurrent nature of individuals. We also find that the steady-state distribution is a “contraction” of the initial distribution in the linear and bounded confidence (DW model) interaction cases, which is different from the results of the model with nonself-excitation interaction.

ACS Style

Lifu Wang; Bo Shen. The Collective Behaviors of Self-Excitation Information Diffusion Processes for a Large Number of Individuals. Complexity 2020, 2020, 1 -14.

AMA Style

Lifu Wang, Bo Shen. The Collective Behaviors of Self-Excitation Information Diffusion Processes for a Large Number of Individuals. Complexity. 2020; 2020 ():1-14.

Chicago/Turabian Style

Lifu Wang; Bo Shen. 2020. "The Collective Behaviors of Self-Excitation Information Diffusion Processes for a Large Number of Individuals." Complexity 2020, no. : 1-14.

Journal article
Published: 08 February 2020 in Neurocomputing
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Deep neural networks have been employed to analyze the sentiment of text sequences and achieved significant effect. However, these models still face the issues of weakness of pre-trained word embeddings and weak interaction between the specific aspect and the context in attention mechanism. The pre-trained word embeddings lack the specific semantic information from the context. The weak interaction results in poor attention weights and produces limited aspect dependent sentiment representation in aspect-based sentiment analysis (ABSA). In this paper, we propose a novel end-to-end memory neural network, termed Recurrent Memory Neural Network (ReMemNN), to mitigate the above-mentioned problems. In ReMemNN, to tackle weakness of pre-trained word embeddings, a specially module named embedding adjustment learning module is designed to transfer the pre-trained word embeddings into adjustment word embeddings. To tackle weak interaction in attention mechanism, a multielement attention mechanism is designed to generate powerful attention weights and more precise aspect dependent sentiment representation. Besides, an explicit memory module is designed to store these different representations and generate hidden states and representations. Extensive experimental results on all datasets show that ReMemNN outperforms typical baselines and achieve the state-of-the-art performance. Besides, these experimental results also demonstrate that ReMemNN is language-independent and dataset type-independent.

ACS Style

Ning Liu; Bo Shen. ReMemNN: A novel memory neural network for powerful interaction in aspect-based sentiment analysis. Neurocomputing 2020, 395, 66 -77.

AMA Style

Ning Liu, Bo Shen. ReMemNN: A novel memory neural network for powerful interaction in aspect-based sentiment analysis. Neurocomputing. 2020; 395 ():66-77.

Chicago/Turabian Style

Ning Liu; Bo Shen. 2020. "ReMemNN: A novel memory neural network for powerful interaction in aspect-based sentiment analysis." Neurocomputing 395, no. : 66-77.

Journal article
Published: 09 January 2020 in IEEE Access
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In recent years, with the eruptive popularity of mobile Internet and the emergence of various new IoT applications, fog computing is proposed to shift the cloud computing services towards the edge, making up for its lack of mobility support and high delay. Fog computing is customized for scenarios with scarce resources and unpredictable environments, but there is no user-centric joint optimization fog computing models designed for such scenarios. In this paper, we aim to maximize the user experience and overall system performance by jointly optimizing user association and resource allocation in the scenarios mentioned above, which can be formulated as a mix-integer non-linear programming problem. To solve the NP-hard problem, we propose a low-complexity two-step interactive optimal algorithm, named UCAA algorithm. For the user association problem, we propose a semi-definite programming based algorithm, and then further propose a Kuhn-Munkres algorithm based user association decision approximation algorithm. For the resource allocation problem, we first prove that it can be decoupled into two sub-problems, ie., transmission power selection problem and computing resource allocation problem, and solve them individually, in addition, we have given a rigorous proof that the optimal solution of the two sub-problems is the optimal solution to the original problem as well. The numerical results show that the proposed UCAA algorithm achieves better performance than conventional algorithms in terms of the value of average usercentric utility, especially in case of more user equipments (UEs), fewer fog nodes, limited computing capacity of fog nodes, lower delay tolerance, lower local computation capacity, etc., which presented to illustrate that the UCAA algorithm can significantly improve user experience and system performance in the considering fog computing scenarios.

ACS Style

Shiyuan Tong; Yun Liu; Mohamed Cheriet; Michel Kadoch; Bo Shen. UCAA: User-Centric User Association and Resource Allocation in Fog Computing Networks. IEEE Access 2020, 8, 10671 -10685.

AMA Style

Shiyuan Tong, Yun Liu, Mohamed Cheriet, Michel Kadoch, Bo Shen. UCAA: User-Centric User Association and Resource Allocation in Fog Computing Networks. IEEE Access. 2020; 8 (99):10671-10685.

Chicago/Turabian Style

Shiyuan Tong; Yun Liu; Mohamed Cheriet; Michel Kadoch; Bo Shen. 2020. "UCAA: User-Centric User Association and Resource Allocation in Fog Computing Networks." IEEE Access 8, no. 99: 10671-10685.

Journal article
Published: 19 September 2019 in Human-centric Computing and Information Sciences
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Aspect-based sentiment analysis (ABSA) is a powerful way of predicting the sentiment polarity of text in natural language processing. However, understanding human emotions and reasoning from text like a human continues to be a challenge. In this paper, we propose a model, named Attention-based Sentiment Reasoner (AS-Reasoner), to alleviate the problem of how to capture precise sentiment expressions in ABSA for reasoning. AS-Reasoner assigns importance degrees to different words in a sentence to capture key sentiment expressions towards a specific aspect, and transfers them into a sentiment sentence representation for reasoning in the next layer. To obtain appropriate importance degree values for different words in a sentence, two attention mechanisms we designed: intra attention and global attention. Specifically, intra attention captures the sentiment similarity between any two words in a sentence to compute weights and global attention computes weights by a global perspective. Experiments on all four English and four Chinese datasets show that the proposed model achieves state-of-the-art accuracy and macro-F1 results for aspect term level sentiment analysis and obtains the best accuracy for aspect category level sentiment analysis. The experimental results also indicate that AS-Reasoner is language-independent.

ACS Style

Ning Liu; Bo Shen; Zhenjiang Zhang; Zhiyuan Zhang; Kun Mi. Attention-based Sentiment Reasoner for aspect-based sentiment analysis. Human-centric Computing and Information Sciences 2019, 9, 1 -17.

AMA Style

Ning Liu, Bo Shen, Zhenjiang Zhang, Zhiyuan Zhang, Kun Mi. Attention-based Sentiment Reasoner for aspect-based sentiment analysis. Human-centric Computing and Information Sciences. 2019; 9 (1):1-17.

Chicago/Turabian Style

Ning Liu; Bo Shen; Zhenjiang Zhang; Zhiyuan Zhang; Kun Mi. 2019. "Attention-based Sentiment Reasoner for aspect-based sentiment analysis." Human-centric Computing and Information Sciences 9, no. 1: 1-17.

Journal article
Published: 02 September 2019 in Knowledge-Based Systems
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Aspect-based sentiment analysis (ABSA) is a type of fine-grained sentiment analysis. Previous work in ABSA is mostly based on recurrent neural networks (RNNs). However, RNNs employed in ABSA have some weaknesses, such as lacking position invariance and lacking sensitivity to local key patterns. Meanwhile, a convolutional neural network (CNN) addresses the limitations in RNN, but itself is weak at capturing long-distance dependency and modeling sequence information. Moreover, the attention mechanism employed in ABSA may introduce some noise that is detrimental to capturing important sentiment expressions. In this paper, we assume that a sentence consists of some sentiment clues, and a sentence clue consists of multiple words. Based on this, we propose a novel neural network structure, named the Gated Alternate Neural Network (GANN), to address the limitations mentioned above. In GANN, a specially designed module, named the Gate Truncation RNN (GTR), is used to learn informative aspect-dependent sentiment clue representations. In these representations, the relative distance between each context word and aspect target, the sequence information, and semantic dependency within a sentiment clue are concurrently encoded. To filter out noise, a gating mechanism is designed to control information flow to obtain more precise representations. Convolution and pooling mechanisms are employed to capture key local sentiment clue features and acquire the position invariance of features. To verify the effect and generalization of GANN, we conducted abundant experiments on four Chinese and three English datasets. The experimental results show that GANN achieves state-of-the-art results and indicate that our proposed model is language-independent.

ACS Style

Ning Liu; Bo Shen. Aspect-based sentiment analysis with gated alternate neural network. Knowledge-Based Systems 2019, 188, 105010 .

AMA Style

Ning Liu, Bo Shen. Aspect-based sentiment analysis with gated alternate neural network. Knowledge-Based Systems. 2019; 188 ():105010.

Chicago/Turabian Style

Ning Liu; Bo Shen. 2019. "Aspect-based sentiment analysis with gated alternate neural network." Knowledge-Based Systems 188, no. : 105010.

Journal article
Published: 12 September 2018 in Sensors
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Big data gathered from real systems, such as public infrastructure, healthcare, smart homes, industries, and so on, by sensor networks contain enormous value, and need to be mined deeply, which depends on a data storing and retrieving service. HBase is playing an increasingly important part in the big data environment since it provides a flexible pattern for storing extremely large amounts of unstructured data. Despite the fast-speed reading by RowKey, HBase does not natively support multi-conditional query, which is a common demand and operation in relational databases, especially for data analysis of ubiquitous sensing applications. In this paper, we introduce a method to construct a linear index by employing a Hilbert space-filling curve. As a RowKey generating schema, the proposed method maps multiple index-columns into a one-dimensional encoded sequence, and then constructs a new RowKey. We also provide a R-tree-based optimization to reduce the computational cost of encoding query conditions. Without using a secondary index mode, experimental results indicate that the proposed method has better performance in multi-conditional queries.

ACS Style

Bo Shen; Yi-Chen Liao; Dan Liu; Han-Chieh Chao. A Method of HBase Multi-Conditional Query for Ubiquitous Sensing Applications. Sensors 2018, 18, 3064 .

AMA Style

Bo Shen, Yi-Chen Liao, Dan Liu, Han-Chieh Chao. A Method of HBase Multi-Conditional Query for Ubiquitous Sensing Applications. Sensors. 2018; 18 (9):3064.

Chicago/Turabian Style

Bo Shen; Yi-Chen Liao; Dan Liu; Han-Chieh Chao. 2018. "A Method of HBase Multi-Conditional Query for Ubiquitous Sensing Applications." Sensors 18, no. 9: 3064.

Journal article
Published: 16 July 2018 in Sensors
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With the rapid development of information technology, large-scale personal data, including those collected by sensors or IoT devices, is stored in the cloud or data centers. In some cases, the owners of the cloud or data centers need to publish the data. Therefore, how to make the best use of the data in the risk of personal information leakage has become a popular research topic. The most common method of data privacy protection is the data anonymization, which has two main problems: (1) The availability of information after clustering will be reduced, and it cannot be flexibly adjusted. (2) Most methods are static. When the data is released multiple times, it will cause personal privacy leakage. To solve the problems, this article has two contributions. The first one is to propose a new method based on micro-aggregation to complete the process of clustering. In this way, the data availability and the privacy protection can be adjusted flexibly by considering the concepts of distance and information entropy. The second contribution of this article is to propose a dynamic update mechanism that guarantees that the individual privacy is not compromised after the data has been subjected to multiple releases, and minimizes the loss of information. At the end of the article, the algorithm is simulated with real data sets. The availability and advantages of the method are demonstrated by calculating the time, the average information loss and the number of forged data.

ACS Style

Yancheng Shi; Zhenjiang Zhang; Han-Chieh Chao; Bo Shen. Data Privacy Protection Based on Micro Aggregation with Dynamic Sensitive Attribute Updating. Sensors 2018, 18, 2307 .

AMA Style

Yancheng Shi, Zhenjiang Zhang, Han-Chieh Chao, Bo Shen. Data Privacy Protection Based on Micro Aggregation with Dynamic Sensitive Attribute Updating. Sensors. 2018; 18 (7):2307.

Chicago/Turabian Style

Yancheng Shi; Zhenjiang Zhang; Han-Chieh Chao; Bo Shen. 2018. "Data Privacy Protection Based on Micro Aggregation with Dynamic Sensitive Attribute Updating." Sensors 18, no. 7: 2307.

Journal article
Published: 23 June 2016 in Sensors
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Data aggregation has been considered as an effective way to decrease the data to be transferred in sensor networks. Particularly for wearable sensor systems, smaller battery has less energy, which makes energy conservation in data transmission more important. Nevertheless, wearable sensor systems usually have features like frequently dynamic changes of topologies and data over a large range, of which current aggregating methods can’t adapt to the demand. In this paper, we study the system composed of many wearable devices with sensors, such as the network of a tactical unit, and introduce an energy consumption-balanced method of data aggregation, named LDA-RT. In the proposed method, we develop a query algorithm based on the idea of ‘happened-before’ to construct a dynamic and energy-balancing routing tree. We also present a distributed data aggregating and sorting algorithm to execute top-k query and decrease the data that must be transferred among wearable devices. Combining these algorithms, LDA-RT tries to balance the energy consumptions for prolonging the lifetime of wearable sensor systems. Results of evaluation indicate that LDA-RT performs well in constructing routing trees and energy balances. It also outperforms the filter-based top-k monitoring approach in energy consumption, load balance, and the network’s lifetime, especially for highly dynamic data sources.

ACS Style

Bo Shen; Jun-Song Fu. A Method of Data Aggregation for Wearable Sensor Systems. Sensors 2016, 16, 954 .

AMA Style

Bo Shen, Jun-Song Fu. A Method of Data Aggregation for Wearable Sensor Systems. Sensors. 2016; 16 (7):954.

Chicago/Turabian Style

Bo Shen; Jun-Song Fu. 2016. "A Method of Data Aggregation for Wearable Sensor Systems." Sensors 16, no. 7: 954.

Research article
Published: 01 January 2016 in IET Networks
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At present, research on users’ preferences is becoming increasingly widespread. Traditional research used a simple cosine similarity algorithm to calculate the similarity between users and just focused on one factor. So, a more complete method is described for researching preferences by considering more factors of the network to analyse data. Two personalisation recommendation algorithms are given based on users’ preferences. These two algorithms mainly work on two aspects: one is the content-based recommendation algorithm. It uses a cosine similarity algorithm to calculate the similarity between users, uses LDA to obtain improvement and uses screens according to a user impact factor. The other is a relation-based recommendation algorithm. It uses the PersonalRank algorithm to calculate the similarity, and uses the weight of users’ communication behaviours to improve the algorithm. Finally, micro-blog users’ data are analysed in detail, and the consequences of the two recommendation methods are fused based on the content of the results and recommendations. As a result, we obtained more accurate recommendation results and verified the feasibility of the algorithm by experimental analysis.

ACS Style

Bo Shen; Bao‐Wen Hu; Huan Zhang. Method for the analysis of the preferences of network users. IET Networks 2016, 5, 8 -12.

AMA Style

Bo Shen, Bao‐Wen Hu, Huan Zhang. Method for the analysis of the preferences of network users. IET Networks. 2016; 5 (1):8-12.

Chicago/Turabian Style

Bo Shen; Bao‐Wen Hu; Huan Zhang. 2016. "Method for the analysis of the preferences of network users." IET Networks 5, no. 1: 8-12.

Conference paper
Published: 04 August 2015 in Business Information Systems
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This paper focuses on analyzing how to improve the HBase non RowKey’s query and designing an approach to improve its performance. Analyzing and summarizing the advantages and disadvantages of index technology applied in typical application scenarios, we design a secondary index approach for HBase based on the pre-partition. Through experiments, we found this method can effectively improve the query performance of HBase on non RowKey column, have little effect on the performance of the original data writing and reduce data redundancy. Compared with other methods, this approach has certain performance advantages.

ACS Style

Bo Shen; Han-Chieh Chao; Weijia Xu. Multi-Column Query Method Research and Optimization on HBase. Business Information Systems 2015, 414 -421.

AMA Style

Bo Shen, Han-Chieh Chao, Weijia Xu. Multi-Column Query Method Research and Optimization on HBase. Business Information Systems. 2015; ():414-421.

Chicago/Turabian Style

Bo Shen; Han-Chieh Chao; Weijia Xu. 2015. "Multi-Column Query Method Research and Optimization on HBase." Business Information Systems , no. : 414-421.

Journal article
Published: 22 October 2014 in Sensors
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This paper presents an integrated model aimed at obtaining robust and reliable results in decision level multisensor data fusion applications. The proposed model is based on the connection of Dempster-Shafer evidence theory and an extreme learning machine. It includes three main improvement aspects: a mass constructing algorithm to build reasonable basic belief assignments (BBAs); an evidence synthesis method to get a comprehensive BBA for an information source from several mass functions or experts; and a new way to make high-precision decisions based on an extreme learning machine (ELM). Compared to some universal classification methods, the proposed one can be directly applied in multisensor data fusion applications, but not only for conventional classifications. Experimental results demonstrate that the proposed model is able to yield robust and reliable results in multisensor data fusion problems. In addition, this paper also draws some meaningful conclusions, which have significant implications for future studies.

ACS Style

Bo Shen; Yun Liu; Jun-Song Fu. An Integrated Model for Robust Multisensor Data Fusion. Sensors 2014, 14, 19669 -19686.

AMA Style

Bo Shen, Yun Liu, Jun-Song Fu. An Integrated Model for Robust Multisensor Data Fusion. Sensors. 2014; 14 (10):19669-19686.

Chicago/Turabian Style

Bo Shen; Yun Liu; Jun-Song Fu. 2014. "An Integrated Model for Robust Multisensor Data Fusion." Sensors 14, no. 10: 19669-19686.

Book chapter
Published: 13 November 2013 in Lecture Notes in Electrical Engineering
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Support Vector Machine is a kind of algorithm used for classifying linear and nonlinear data, which not only has a solid theoretical foundation, but is more accurate than other sorting algorithms in many areas of applications, especially in dealing with high-dimensional data. It is not necessary for us to get the specific mapping function in solving quadratic optimization problem of SVM, and the only thing we need to do is to use kernel function to replace the complicated calculation of the dot product of the data set, reducing the number of dimension calculation. This paper introduces the theoretical basis of support vector machine, summarizes the research status and analyses the research direction and development prospects of kernel function.

ACS Style

Lijuan Liu; Bo Shen; Xing Wang. Research on Kernel Function of Support Vector Machine. Lecture Notes in Electrical Engineering 2013, 260, 827 -834.

AMA Style

Lijuan Liu, Bo Shen, Xing Wang. Research on Kernel Function of Support Vector Machine. Lecture Notes in Electrical Engineering. 2013; 260 ():827-834.

Chicago/Turabian Style

Lijuan Liu; Bo Shen; Xing Wang. 2013. "Research on Kernel Function of Support Vector Machine." Lecture Notes in Electrical Engineering 260, no. : 827-834.

Journal article
Published: 30 June 2013 in Journal of Convergence Information Technology
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ACS Style

Bo Shen -; YingSi Zhao -. Optimization and Application of OPTICS Algorithm on Text Clustering. Journal of Convergence Information Technology 2013, 8, 375 -383.

AMA Style

Bo Shen -, YingSi Zhao -. Optimization and Application of OPTICS Algorithm on Text Clustering. Journal of Convergence Information Technology. 2013; 8 (11):375-383.

Chicago/Turabian Style

Bo Shen -; YingSi Zhao -. 2013. "Optimization and Application of OPTICS Algorithm on Text Clustering." Journal of Convergence Information Technology 8, no. 11: 375-383.