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IEEE International Conference on Cloud Computing Technology and Science

作者： Tianhang Guo Yiping Wen Feiran Wang Junjie Hou School of Computer Science and Engineering Hunan University of Science & Technology China Key Laboratory of Knowledge Processing & Networked Manufacturing Hunan University of Science & Technology China

Online learning is becoming increasingly popular in recent years. Personalized recommendation is particularly important for the development of online learning systems. Though LSTM model has been widely applied in various recommendations, it normally can't deal with the problem of sparse data. In this paper, we present a novel model for learning resource recommendation, named G-LSTM. Our model integrates the Generalized Matrix Factorization (GMF) with Long Short-Term Memory (LSTM) model. For evaluating our model, we prepare and analyze two datasets from Junyi Academy. Extensive experiments are conducted on the two datasets to verify the superiority of our model in both effectiveness and accuracy.

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Web Service Recommendation via Combining Doc2Vec-Based Functionality Clustering and DeepFM-Based Score Prediction

Web Service Recommendation via Combining Doc2Vec-Based Funct...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Xiangping Zhang Jianxun Liu Buqing Cao Qiaoxiang Xiao Yiping Wen Key Lab. of Knowledge Processing & Networked Manufacturing Hunan University of Science & Technology China

ISBN: (纸本)9781728111421;9781728111414

Due to the rapid growth in both the number and diversity of Web services on the Internet, it becomes increasingly difficult for developer to find the desired and appropriate Web services for Mashup creation. Even if the existing approaches show improvements in Web APIs recommendation, it is still challenging to recommend Web APIs with high accuracy and good diversity. Some of them integrate functionality clustering and the quality of service to recommend Web APIs for Mashup creation, but do not consider the high-order composition interaction relationship among functionality information, quality attributes. In this paper, we propose a novel Web APIs recommendation method via integrating the functionality clustering of service and the quality of service. In this method, it firstly obtains the functionality clustering by using Doc2Vec to cluster the description document of Web APIs. Then, the deep factorization machine model is used to extract the multi-dimension quality attributes of service and mine the high-order composition interaction relationship between them. Finally, the comparative experiments are performed on ProgrammableWeb dataset and experimental results show that our method significantly improves the performance of Web API recommendation in term of precision, recall, purity, entropy, DCG and HMD.

关键词： Mashups Quality of service Feature extraction Predictive models Data mining Clustering algorithms

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Web Service Recommendation Based on Word Embedding and Topic Model

Web Service Recommendation Based on Word Embedding and Topic...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Ting Chen Jianxun Liu Buqing Cao Zhenlian Peng Yiping Wen Run Li Key Lab. of Knowledge Processing & Networked Manufacturing Hunan University of Science & Technology China

ISBN: (纸本)9781728111421;9781728111414

Web service recommendation faces the following problems: large-scale number of Web services, the diversity of service categories, and the sparseness of service texts on the Internet. To this end, a Web service recommendation method that combines word embedding with topic models is proposed in this paper. Firstly, the English Wikipedia is used to construct a high-quality Word embedding model to obtain semantic similar words, and based on the DMM model, the GPU promotion strategy is integrated into the topic derivation process to obtain more effective service implied topics. Secondly, the obtained topic distribution vector is used to calculate the similarity. Multi-dimensional features such as similarity of Web APIs, similarity of Mashups, co-occurrence and popularity of Web APIs, are modeled by deep factorization machine to rank and predict Top-N Web APIs recommended set. Finally, experiments conducted on a real dataset, by comparing the proposed method with multiple existing Web service recommendation methods, show that the proposed approach achieves better performance.

关键词： Web services Semantics Data models Collaboration Filtering Encyclopedias

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Towards Energy-Efficient Scheduling with Batch processing for Instance-Intensive Cloud Workflows

Towards Energy-Efficient Scheduling with Batch Processing fo...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Zhibin Wang Yiping Wen Jinjun Chen Buqing Cao Feiran Wang Key Laboratory of Knowledge Processing & Networked Manufacturing Hunan University of Science & Technology China School of Computer Science and Engineering Hunan University of Science & Technology China

ISBN: (纸本)9781728111421;9781728111414

Instance-intensive workflow applications are widely used in the areas of electronic commerce, advanced manufacture, etc. However, existing studies normally do not consider the problem of reducing their energy consumption by utilizing the advantages of batch processing strategy and the characters of instance-intensive workflow applications. This paper presents an Energy-efficient Instance-intensive Cloud workflows scheduling method with Batch processing, named EICB. Technically, the method is promoted with strategies to merge several activity instances and balance resource utilization of physical machines (PMs) to improve energy efficiency for instance-intensive cloud workflows. The effectiveness and efficiency of the proposed method are validated by extensive experiments.

关键词： Batch production systems Energy consumption Cloud computing Resource management Energy efficiency Job shop scheduling Monitoring

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Design and Analysis of Improved Hash-based RFID Lightweight Mutual Authentication Protocol

Design and Analysis of Improved Hash-based RFID Lightweight ...

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作者： Xiaochun Chen Yali Shao Yanfang He Zhangbing Li Guangdong Polytechnic College Key Lab of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology

Since the existing radio frequency identification(RFID) mutual authentication protocols encounter the challenges such as security risks, an improved Hash-based lightweight mutual authentication protocol is put forward. The proposed protocol exploits one-way Hash function, of the transmitted message and updating dynamic shared key, better overcome the security risks of RFID. In this protocol, database takes on numerous computations, while tag only executes simple XOR,JION and Hash operations. Through BAN logic formal security proof, security performance analysis,efficiency analysis, this protocol can make up for the denial of service attack, updating key and de-synchronization, which are exist in the same kind of RFID. Therefore, the proposed protocol suits large-scale and low-cost radio frequency identification(RFID) system.

关键词： radio frequency identification(RFID) security risks authentication protocols BAN logic Hash function dynamic shared key

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Personalized query expansion utilizing multi-relational social data

Personalized query expansion utilizing multi-relational soci...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Xuan Wu Dong Zhou Yu Xu Séamus Lawless Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology Xiangtan Hunan China ADAPT Centre School of Computer Science and Statistics Dublin 2 Ireland

ISBN: (纸本)9781538607572

Social tagging systems have been widely used as a way to annotate and categorize Web resources. However, users often use unrestricted vocabulary to tag and describe resources. On the contrast, annotators of Web documents may use very different words to describe the same concept. In the past few years, numerous personalized query expansion methods have been proposed to tackle the vocabulary mismatch problem. Many of them are based on the probabilistic-based techniques or graph-based techniques, but they ignored the multi-relational characteristics existed in the social data. In this paper, we explore multiple semantic relationships from social tagging systems, including relationships between tags, between words and between tags and words. Three affinity graphs are built based on the features derived from tags and words. In addition, we incorporate pseudo-relevance feedback information obtained from top-ranked documents to regularize the smoothness of multiple associations over the three affinity graphs. The key of this paper is considering above three affinity graphs into a novel query expansion model and aim to produce better personalized search results. Experiments conducted on a real-world dataset validate the effectiveness of the proposed approach.

关键词： Tagging Semantics Vocabulary Data models Probabilistic logic Computational modeling knowledge engineering

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Enhanced personalized search using social data

Enhanced personalized search using social data

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2016 Conference on Empirical Methods in Natural Language processing, EMNLP 2016

作者： Zhou, Dong Lawless, Séamus Wu, Xuan Zhao, Wenyu Liu, Jianxun Key Laboratory of Knowledge Processing and Networked Manufacturing and School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan411201 China ADAPT Centre Knowledge and Date Engineering Group School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland

ISBN: (纸本)9781945626258

Search personalization that considers the social dimension of the web has attracted a significant volume of research in recent years. A user profile is usually needed to represent a user's interests in order to tailor future searches. Previous research has typically constructed a profile solely from a user's usage information. When the user has only limited activities in the system, the effect of the user profile on search is also constrained. This research addresses the setting where a user has only a limited amount of usage information. We build enhanced user profiles from a set of annotations and resources that users have marked, together with an external knowledge base constructed according to usage histories. We present two probabilistic latent topic models to simultaneously incorporate social annotations, documents and the external knowledge base. Our web search strategy is achieved using personalized social query expansion. We introduce a topical query expansion model to enhance the search by utilizing individual user profiles. The proposed approaches have been intensively evaluated on a large public social annotation dataset. Results show that our models significantly outperformed existing personalized query expansion methods which use user profiles solely built from past usage information in personalized search. © 2016 Association for Computational Linguistics

关键词： User profile

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The Expert System of Locomotive Running Gear Based on Sematic Network

The Expert System of Locomotive Running Gear Based on Semati...

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International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS)

作者： Jie Tao Yilun Liu Yiping Wen Jiahui Su Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology Xianz'Ian China College of Mechanical and Electrical Engineering Central South University Changsha China Light Alloy Research Institute Central South University Changsha China

ISBN: (纸本)9781509009855

As an important part of artificial intelligence, expert systems have widely used in the field of mechanical fault diagnosis. But with the development of the large data and cloud computing, some systems' hardware scale have inflated, which makes the energy consumption become a problem to be solved in the expert system. However the traditional database system is hard to satisfy the semantics need of the knowledge repository management, and it spends a lot of time and energy to complete the data management and reasoning. For this reason, the paper presents an approach to construct the fault diagnosis system based on semantic networks, and focus on the research of semantic knowledge organization, management, inference mechanism and knowledge acquisition. In the experiments, we built the model of locomotive's diagnosis expert system. Compared with the relationship database, the proposed approach was more accurate and robust than other method.

关键词： Semantics Expert systems Databases Fault diagnosis Data models Cognition knowledge acquisition

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A Nonlinear System Stable Control Design by Firefly Algorithm and Extreme Learning Machine

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Journal of Physics: Conference Series 2018年第1期1069卷

作者： Wenxin Yu Junnian Wang Mu Li Zhenheng Wang Xianglei Bo Juan Chen Dan Jiang School of Information and Electrical Engineering Hunan University of Science and Technology Hunan Pro. Xiangtan 411201 P.R. China College of Electrical & Information Engineering Hunan University Changsha 410082 China School of Physics and Electronics Hunan University of Science and Technology Hunan Pro. Xiangtan 411201 P.R. China Key Laboratory of Knowledge Processing Networked Manufacturing Hunan University of Science and Technology Hunan Pro. Xiangtan 411201 P.R. China Automobile NCO Academy Army Military Transportation University Bengbu 233011 China National Local Joint Engineering Laboratory of Marine Mineral Resources Exploration Equipment and Safety Technology Hunan University of Science and Technology Hunan Pro. Xiangtan 411201 P.R. China

In this paper, we introduce a novel iterative method for finding a series of fixed point and design a steady controllability approach for nonlinear systems by using Firefly algorithm and ELM. First, we construct a fixed-point search model, but it is just only looking for a fixed point. Because of the need of non-linear dynamic systems, we need to find a series of fixed point under certain circumstances. Then, we apply the Firefly Algorithm and ELM to design a steady controllability approach. Finally, we have an example to verify the method we have proposed. Through the method, we get a series of fixed point, which make dynamic Nonlinear Systems or nonlinear circuit is under a relatively stable state.

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Utilizing Sub-topic Units for Patent Prior-Art Search

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Chinese Journal of Electronics 2014年第3期23卷 480-483页

作者： ZHOU Dong LIU Jianxun ZHANG Sanrong Key Laboratory of Knowledge Processing and Networked Manufacturing & School of Computer Science and Engineering Hunan University of Science and Technology

One of the defining challenges in patent prior-art search is the problem of representing a long, technical document as a query. Previously work on this problem has concentrated on single query representations of the patent application. In the following paper, we describe an approach which uses multiple query representations generated from semantically coherent passages extracted from patent documents. We validate our technique in an experiment using the CLEF-IP 2011 patent search collection. Our system achieves statistically significant improvements over various state-of-art query generation techniques.

关键词： Patent information retrieval Sub-topic units Multiple query representations Collaborative filtering Patent ranking

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