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Citation recommendation systems mainly help researchers find the lists of references that related to their interests effectively and automatically. The existing approaches face the issues of data sparsity and high-dimensional in large-scale bibliographic network representation, which hinder the citation recommendation performance. To address these problems, we proposed a Content-Sensitive citation representation approach for Citation Recommendation, named CSCR. Firstly, the Doc2vec model is used to generate a paper embedding according to paper content. Then, utilizing the similarity between the paper content embeddings to select the assumed neighbours of the target paper, append the auxiliary links between target paper and its new neighbours in the bibliographic network. Thirdly, distributed network representation method is implemented on appended bibliographic network to obtain the paper node embedding, which can learn interpretable lower dimension embedding for paper nodes. Finally, the embedding vectors of these papers can be used to conduct citation recommendation. Experimental results show that the proposed approach significantly outperforms other benchmark methods in Normalized Discounted Cumulative Gain (NDCG) and the positive rate (Recall).

With advanced development of Internet communication and ubiquitous computing, Social Networks are providing an important information channel for smart city construction. Therefore, analyzing Location-based Social Network is a very valuable work in achieving reasonable urban zoning. In Social Networks, a main purpose of prestige assessment is to extract influential users who are regarded as the key nodes for community detection from Onine Social Networks (OSNs). However, social relationships of users are rarely used to evaluate the popularity of physical locations and zone physical locations. In order to achieve urban area function zoning by evaluating the prestige of geographic regions based on user relationships in Location based Social Networks (LBSNs), this paper proposes a Prestige Density-Based Spatial Clustering of Applications with Noise algorithm (P-DBSCAN) by improving the existing DBSCAN algorithm. Specifically, the algorithm first calculates the centrality of users in the social network, and then converts the centrality of users into the location-centrality through the users’ check-in data. After the centrality of each location is obtained, the discrete locations are clustered according to four constraints of the given radius. After clustering, the result of urban area function zoning can be achieved. Extensive experiments are conducted for demonstrating the effectiveness of our proposed algorithm in this paper. In addition, the visualization results reveal the correctness of our proposed approach.

With the number of smart contracts growing rapidly, retrieving the relevant smart contracts quickly and accurately has become an important issue. A key step for recognizing the related smart contracts is able to classify them accurately. Different from traditional text, the smart contract is composed of several parts: source code, code comments and other useful information like account information. How to make good use of those different kinds of features for effective classification is a problem need to be solved. Inspired by this, we proposed a smart contract classification approach based on Bi-LSTM model and Gaussian LDA, which can use a variety of information as inputs of the model, including source code, comments, tags, account and other content information. Bi-LSTM is utilized to capture grammar rules and context information in source code, while Gaussian LDA model is employed to generate comments feature where the semantics of the comments are enriched by embeddings. We also use attention mechanism to focus on the more relevant features in smart contracts for tags and fuse account information to provide additional information for classification. The experimental results show that the classification performance of the proposed model is superior to other baseline models.

Quality of Service (QoS) value is usually unknown in service recommendation practice. There are some matrix factorization approaches for predicting the unknown value with a user-service model, which uses a single collaboration with the user’s neighbor when looking for different services. However, the QoS value is highly related to the service provider and participants. The services are considered in various collaboration based on different users. By considering the context of services, this paper proposes a QoS prediction model using tensor decomposition based on service collaboration called Service-oriented Tensor (SOT). The prediction approach analyzes service collaboration from other similar services and relevant users by using a three-order tensor. Compared with the traditional model, the experiment results show that the proposed model achieves better prediction accuracy.

With an increasing number of scholarly publications, accessing and retrieving appropriate papers is becoming an essential task for researchers. Citation recommendation, which can automatically provide a reference list based on a text segment, can overcome this problem. In this paper, we first construct a heterogeneous bibliographic network and deem citation recommendation as edge prediction problem, and then we develop a network representation-based edge prediction (NREP) model, which can simultaneously learn the edge prediction knowledge and the predictive representation for efficient citation recommendation. For personalized recommendation, we incorporate author information. We conduct extensive experiments on two datasets; the experimental results show that the NREP-based approach outperforms the other four state-of-the-art baseline approaches in terms of recall, mean average precision, and normalized discounted cumulative gain.

With the increasing number of scientific papers, researchers find it more and more difficult to obtain relevant and appropriate papers to cite. Citation recommendation aims to overcome this problem by providing a reference paper list for a given manuscript. In this paper, we propose a bibliographic network representation (BNR) model, which simultaneously incorporates bibliographic network structure and content of different kinds of objects (authors, papers, and venues) for efficient recommendation. The proposed model also makes personalized citation recommendation possible, which is a new issue that a few papers addressed in the past. When conducting experiments on the ACL Anthology Network and DBLP datasets, the results demonstrate that the proposed BNR-based citation recommendation approach is able to achieve considerable improvement over other network representation-based citation recommendation approaches. The performance of the personalized recommendation approach is also competitive with the non-personalized recommendation approach.

Information retrieval-based Web service discovery approach suffers from the semantic sparsity problem caused by lacking of statistical information when the Web services are described in short texts. To handle this problem, external information is often utilized to improve the discovery performance. Inspired by this, we propose a novel Web service discovery approach based on a neural topic model and leveraging Web service labels. More specifically, words in Web services are mapped into continuous embeddings, and labels are integrated by a neural topic model simultaneously for embodying external semantics of the Web service description. Based on the topic model, the services are interpreted into hierarchical models for building a service querying and ranking model. Extensive experiments on several datasets demonstrated that the proposed approach achieves improved performance in terms of F-measure. The results also suggest that leveraging external information is useful for semantic sparse Web service discovery.

The rapid growth of scientific papers makes it difficult to find relevant and appropriate citations. Context-aware citation recommendation aims to overcome this problem by providing a list of scientific papers given a short passage of text. In this paper, we propose a long–short-term memory (LSTM)-based model for context-aware citation recommendation, which first learns the distributed representations of the citation contexts and the scientific papers separately based on LSTM, and then measures the relevance based on the learned distributed representation of citation contexts and the scientific papers. Finally, the scientific papers with high relevance scores are selected as the recommendation list. In particular, we try to incorporate author information, venue information, and content information in scientific paper distributed vector representation. Furthermore, we integrate author information of the given context in citation context distributed vector representation. Thus, the proposed model makes personalized context-aware citation recommendation possible, which is a new issue that few papers addressed in the past. When conducting experiments on the ACL Anthology Network and DBLP data sets, the results demonstrate the proposed LSTM-based model for context-aware citation recommendation is able to achieve considerable improvement over previous context-aware citation recommendation approaches. The personalized recommendation approach is also competitive with the non-personalized recommendation approach.

Many state-of-the-art citation recommendation methods have been proposed for finding a list of reference papers for a given manuscript, among which the graph-based method has gained particular attention, due to its flexibility for incorporating various information that embodies user’s preferences. To achieve a more synthetic, accurate, and personalized recommendation result than the previous graph-based methods, this paper proposes a new graph-based recommendation framework that exploiting diversified link information in a bibliographic network and the concise query information that embodies the specific requirement of user comprehensively. The proposed framework not only performs mutual reinforcement rules on all available multiple types of relations in a multi-layered graph but also incorporates the query information into the multi-layered mutual reinforcement schema to construct a multi-layered mutually reinforced query-focused (MMRQ) citation recommendation approach. Extensive experiments have been conducted on a subset of anthology network data set. Experimental results of Recall measures, normalized discounted cumulative gain measures, and case study all demonstrate that our MMRQ method obtains a superior citation recommendation.

Although there has been a lot of research in multi-label learning task, little attention has been paid on the weak label problem, in which only a subset of labels has been assigned to each instance in the training set. The extreme form of weak label learning is to predict all the labels from just one label set in the training phase. In this paper, we focus on dealing with this kind of weak label learning task, which is commonly met in old legacy information system, and it is also called “Hercules Learning.”We propose a label-group-optimization-based Hercules learning algorithm, which divides the entire label set into multiple groups according to the classifier's capability to distinguish them, so for each group, we can train a classifier which can predict instance's label within the group with high accuracy. The experimental results show that our algorithm is obviously superior to the existing weak label learning algorithm.

In the era of big scholarly data, citation recommendation is playing an increasingly significant role as it solves information overload issues by automatically suggesting relevant references that align with researchers' interests. Many state-of-the-art models have been utilized for citation recommendation, among which graph-based models have garnered significant attention, due to their flexibility in integrating rich information that influences users' preferences. Co-authorship is one of the key relations in citation recommendation, but it is usually regarded as a binary relation in current graph-based models. This binary modeling of co-authorship is likely to result in information loss, such as the loss of strong or weak relationships between specific research topics. To address this issue, we present a fine-grained method for co-authorship modeling that incorporates the co-author network structure and the topics of their published articles. Then, we design a three-layered graph-based recommendation model that integrates fine-grained co-authorship as well as author-paper, paper-citation, and paper-keyword relations. Our model effectively generates query-oriented recommendations using a simple random walk algorithm. Extensive experiments conducted on a subset of the anthology network data set for performance evaluation demonstrate that our method outperforms other models in terms of both Recall and NDCG.

Systems for recommending scientific papers mainly help researchers to find a list of references that related to the researcher's interest effectively and automatically. Many state-of-the-art technique have been used for recommendation system, however, the traditional approaches has the issues of data scarcities and cold start, and existing recommended approaches with network representation only focus on one aspect of node information and cannot leverage content information. In this paper, we proposed a Citation Recommendation method with a Content-Aware bibliographic network representation, called CR-CA, whose recommended process contains two levels: (1) At the node content level, the proposed approach calculates similarities between the target and candidate papers, selecting an initial seed set of papers; (2) At the citation network structure level, this approach exploits citation relationship between papers to study latent representation of the scientific papers based on a deep natural language method–DeepWalk. The proposed approach was tested on the AAN dataset demonstrate that this approach outperforms baseline algorithms, in the true positive rate (Recall) and normalized discounted cumulative gain (NDCG).

The communication links of multi-agent systems (MASs) generate random time delays, which significantly affect accuracy and real-time control. A Markov chain is established at the input end and output end of a communication link to express this time delay and is introduced to asynchronous data fusion at an intelligent location. The influence of random time delays and data fusion on the positioning accuracy and real-time performance is investigated. A distributed fusion simulation system is established based on MASs and employed to simulate the random delay in the fusion of multi-sensor data. Different fusion algorithms are tested by changing the upper boundary of the time delay. The test results indicate the existence of a boundary between the data fusion and the time delay. The fusion accuracies on both sides of the boundary significantly differ. This difference is helpful for selecting sensors and fusion algorithms for different sampling frequencies in multi-agent collaborative control systems, improving the positioning accuracy of the system and effectively controlling computation costs.

As the scale of integrated circuits continuously increasing, guaranteeing intensity of testing and coverage rate of verification in the design phase absolutely is becoming severe challenges. As a solution, Gate Level Information Flow Tracking (GLIFT) method is able to precisely measure all the logical information flows in the underlying hardware to prevent information leakage resulting from these harmful flows of information. However, preliminary research work for GLIFT mainly focused on the basic theories as well as the generation algorithms, which only can track logic formalization under the Boolean logic system. These approaches ignored that digital circuits are typically described by multi-valued logic during hardware design and verification. To address that issue, we present to expand the GLIFT method for multi-valued logic system. In this paper, the label propagation rule set is respectively derived for four-valued and nine-valued logic systems by extending the label propagation set for Boolean logic. Moreover, in order to support the characteristics of multi-valued logic in label propagation, the formal descriptions of GLIFT logic for primitive gates are improved in computing hardware as well. The experimental results demonstrate that the extended method is consistent with the label propagation rule of the multi-valued logic system.

Cloud service resource allocation is an essential task in cloud computing. The cloud service resource allocation problem is modeled as an optimization problem, and is solved via different particle swarm optimization (PSO) variants in this paper. The aim of our method is to minimize the delay and the price at the same time. Based on the experimental results, it could be conducted that the good performance could be achieved via PSO algorithms. The future research is to utilize PSO algorithms on solving more real-world problems, especially with other quality of service problems.

Reasonable house price prediction is a meaningful task, and the house clustering is an important process in the prediction. In this paper, we propose the method of Multi-Scale Affinity Propagation(MSAP) aggregating the house appropriately by the landmark and the facility. Then in each cluster, using Linear Regression model with Normal Noise(LRNN) predicts the reasonable price, which is verified by the increasing number of the renting reviews. Experiments show that the precision of the reasonable price prediction improved greatly via the method of MSAP.

Service topic detection is one of the most important techniques in service information extraction, clustering and recommendation. Comparing with short text corpus in social network, service description corpus possesses higher dimensionality and more diversity. It is difficult to detect topics from a large number of service descriptions. To address these challenges, we proposed a new LDA (Latent Dirichlet Allocation) model based topic detection method, referred to as CV-LDA (Context sensitive word Vector based LDA). It utilizes a word embedding based method that generate context sensitive vector to cluster the words for decreasing dimensionality. Through topic perplexity analysis in the real-world dataset, it is obvious that topics detected by our method has a lower perplexity, comparing with word frequency weighing based vectors.