当前期刊: World Wide Web Go to current issue    加入关注   
显示样式:        排序: IF: - GO 导出
  • Reverse Approximate Nearest Neighbor Queries on Road Network
    World Wide Web (IF 2.892) Pub Date : 2020-10-14
    Xinyu Li, Arif Hidayat, David Taniar, Muhammad Aamir Cheema

    Reverse k Nearest Neighbor (RkNN) queries retrieve all objects that consider the query as one of their k most influential objects. Given a set of user U, a set of facilities F and a value k, a facility f is said to be influential to a user u if f is one of the k closest facilities to u. As a complement of RkNN query, Reverse Approximate Nearest Neighbor (RANN) query considers relaxed definition of

  • An efficient multidimensional L ∞ $L_{\infty }$ wavelet method and its application to approximate query processing
    World Wide Web (IF 2.892) Pub Date : 2020-10-10
    Xueyan Guo, Tongliang Li, Xiaoyun Li, Huanyu Zhao, Suzhen Wang, Chaoyi Pang

    Approximate query processing (AQP) has been an effective approach for real-time and online query processing for today’s query systems. It provides approximate but fast query results to users. In wavelet based AQP, queries are executed against the wavelet synopsis which is a lossy, compressed representation of the original data returned by a specific wavelet method. Wavelet synopsis optimized for \(L_{\infty

  • Using context information to enhance simple question answering
    World Wide Web (IF 2.892) Pub Date : 2020-10-01
    Lin Li, Mengjing Zhang, Zhaohui Chao, Jianwen Xiang

    With the rapid development of knowledge bases (KBs), question answering (QA) based on KBs has become a hot research issue. The KB-QA technology can be divided into two technical routes: (1) symbol based representations, such as traditional semantic parsing, and (2) distribution based embedding. With the emergence of deep learning, the development of NLP has greatly promoted. The effect of KB-QA can

  • A multi-view attention-based deep learning system for online deviant content detection
    World Wide Web (IF 2.892) Pub Date : 2020-09-30
    Yunji Liang, Bin Guo, Zhiwen Yu, Xiaolong Zheng, Zhu Wang, Lei Tang

    With the exponential growth of user-generated content, policies and guidelines are not always enforced in social media, resulting in the prevalence of deviant content violating policies and guidelines. The adverse effects of deviant content are devastating and far-reaching. However, the detection of deviant content from sparse and imbalanced textual data is challenging, as a large number of stakeholders

  • Closed sequential pattern mining for sitemap generation
    World Wide Web (IF 2.892) Pub Date : 2020-09-27
    Michelangelo Ceci, Pasqua Fabiana Lanotte

    A sitemap represents an explicit specification of the design concept and knowledge organization of a website and is therefore considered as the website’s basic ontology. It not only presents the main usage flows for users, but also hierarchically organizes concepts of the website. Typically, sitemaps are defined by webmasters in the very early stages of the website design. However, during their life

  • Robust rumor blocking problem with uncertain rumor sources in social networks
    World Wide Web (IF 2.892) Pub Date : 2020-09-27
    Jianming Zhu, Smita Ghosh, Weili Wu

    Rumormongers spread negative information throughout the social network, which may even lead to panic or unrest. Rumor should be blocked by spreading positive information from several protector nodes in the network. Users will not be influenced if they receive the positive information ahead of negative one. In many cases, network manager or government may not know the exact positions where rumor will

  • Firmware code instrumentation technology for internet of things-based services
    World Wide Web (IF 2.892) Pub Date : 2020-09-26
    Chen Chen, Jinxin Ma, Tao Qi, Baojiang Cui, Weikong Qi, Zhaolei Zhang, Peng Sun

    With the rapid development of electronic and information technology, Internet of Things (IoT) devices have become extensively utilised in various fields. Increasing attention has been paid to the performance and security analysis of IoT-based services. Dynamic instrumentation is a common process in software analysis for acquiring runtime information. However, due to the limited software and hardware

  • A general strategy for researches on Chinese “的(de)” structure based on neural network
    World Wide Web (IF 2.892) Pub Date : 2020-09-13
    Bingqing Shi, Weiguang Qu, Rubing Dai, Bin Li, Xiaohui Yan, Junsheng Zhou, Yanhui Gu, Ge Xu

    Noun phrases reflect people’s understanding of the world entities and play an important role in people’s language system, conceptual system and application system. With the Chinese “的(de)” structure, attributive noun phrases of the combined type can accommodate more words and syntactic structures, resulting in rich levels and complex semantic structures in Chinese sentences. Moreover, the Chinese elliptical

  • Improving biterm topic model with word embeddings
    World Wide Web (IF 2.892) Pub Date : 2020-09-08
    Jiajia Huang, Min Peng, Pengwei Li, Zhiwei Hu, Chao Xu

    As one of the fundamental information extraction methods, topic model has been widely used in text clustering, information recommendation and other text analysis tasks. Conventional topic models mainly utilize word co-occurrence information in texts for topic inference. However, it is usually hard to extract a group of words that are semantically coherent and have competent representation ability when

  • Temporal knowledge extraction from large-scale text corpus
    World Wide Web (IF 2.892) Pub Date : 2020-09-02
    Yu Liu, Wen Hua, Xiaofang Zhou

    Knowledge, in practice, is time-variant and many relations are only valid for a certain period of time. This phenomenon highlights the importance of harvesting temporal-aware knowledge, i.e., the relational facts coupled with their valid temporal interval. Inspired by pattern-based information extraction systems, we resort to temporal patterns to extract time-aware knowledge from free text. However

  • Existence identifications of unobserved paths in graph-based social networks
    World Wide Web (IF 2.892) Pub Date : 2020-09-01
    Huan Wang, Qiufen Ni, Jiali Wang, Hao Li, Fuchuan Ni, Hao Wang, Liping Yan

    In recent years, social networks have surged in popularity as one of the main applications of the Internet. One key aspect of social network research is exploring important unobserved network information which is not explicitly represented. This study first introduces a new path identification problem to identify the existences of unobserved paths between nodes. Given a partial social network structure

  • User interest-based recommender system for image-sharing social media
    World Wide Web (IF 2.892) Pub Date : 2020-08-22
    Kunyoung Kim, Jongmo Kim, Minhwan Kim, Mye Sohn

    Nowadays, many people use social media to communicate with others, share their interests and obtain information. As the performance of the embedded cameras on mobile phones improve, image-sharing social media became a popular tool for people to communicate with others and share their interests, which yields vast amount of data related to the users’ interests. However, only few studies pay attention

  • Application of fuzzy logic for IoT node elimination and selection in opportunistic networks: performance evaluation of two fuzzy-based systems
    World Wide Web (IF 2.892) Pub Date : 2020-08-17
    Miralda Cuka, Donald Elmazi, Makoto Ikeda, Keita Matsuo, Leonard Barolli, Makoto Takizawa

    Opportunistic Networks (OppNets) are a sub-class of DTN, designed as a specialized ad hoc network suitable for applications such as emergency responses. Unlike traditional networks, in OppNets communication opportunities are intermittent, so an end-to-end path between the source and the destination may never exist. Existing networks have already brought connectivity to a broad range of devices, such

  • RLINK: Deep reinforcement learning for user identity linkage
    World Wide Web (IF 2.892) Pub Date : 2020-08-07
    Xiaoxue Li, Yanan Cao, Qian Li, Yanmin Shang, Yangxi Li, Yanbing Liu, Guandong Xu

    User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise similarity between identities from different SN, predicting the label of identity pairs or selecting the most relevant identity pair based on the similarity scores. However, most of these methods fail to utilize the results

  • A Pseudo-document-based Topical N-grams model for short texts
    World Wide Web (IF 2.892) Pub Date : 2020-07-23
    Hao Lin, Yuan Zuo, Guannan Liu, Hong Li, Junjie Wu, Zhiang Wu

    In recent years, short text topic modeling has drawn considerable attentions from interdisciplinary researchers. Various customized topic models have been proposed to tackle the semantic sparseness nature of short texts. Most (if not all) of them follow the bag-of-words assumption, which, however, is not adequate since word order and phrases are often critical to capturing the meaning of texts. On

  • Let’s CoRank: trust of users and tweets on social networks
    World Wide Web (IF 2.892) Pub Date : 2020-07-14
    Peiyao Li; Weiliang Zhao; Jian Yang; Quan Z. Sheng; Jia Wu

    Twitter, as one of the largest Online Social Network (OSN) platforms, is a popular media for online social communication and information dissemination. The trustworthy evaluation of information and people becomes crucial for maintaining an open and healthy OSN for our society. In this work, we develop a Coupled Dual Networks Trust Ranking (CoRank) method to evaluate the trustworthiness of users and

  • Indexing and progressive top- k similarity retrieval of trajectories
    World Wide Web (IF 2.892) Pub Date : 2020-07-06
    Nikolaos Pliakis, Eleftherios Tiakas, Yannis Manolopoulos

    In this work, we study the performance of state-of-the-art access methods to efficiently store and retrieve trajectories in spatial networks. First, we study how efficiently such methods can manage trajectory data to support indexing for data demanding applications where trajectory retrieval must be fast. At the same time, trajectory insertions, deletions and modifications should also be executed efficiently

  • Constructing dummy query sequences to protect location privacy and query privacy in location-based services
    World Wide Web (IF 2.892) Pub Date : 2020-07-06
    Zongda Wu, Guiling Li, Shigen Shen, Xinze Lian, Enhong Chen, Guandong Xu

    Location-based services (LBS) have become an important part of people’s daily life. However, while providing great convenience for mobile users, LBS result in a serious problem on personal privacy, i.e., location privacy and query privacy. However, existing privacy methods for LBS generally take into consideration only location privacy or query privacy, without considering the problem of protecting

  • Extracting representative user subset of social networks towards user characteristics and topological features
    World Wide Web (IF 2.892) Pub Date : 2020-06-30
    Yiming Zhou; Yuehui Han; An Liu; Zhixu Li; Hongzhi Yin; Wei Chen; Lei Zhao

    Extracting a subset of representative users from the original set in social networks plays a critical role in Social Network Analysis. In existing studies, some researchers focus on preserving users’ characteristics when sampling representative users, while others pay attention to preserving the topology structure. However, both users’ characteristics and the network topology contain abundant information

  • Hybrid graph convolutional networks with multi-head attention for location recommendation
    World Wide Web (IF 2.892) Pub Date : 2020-06-23
    Ting Zhong, Shengming Zhang, Fan Zhou, Kunpeng Zhang, Goce Trajcevski, Jin Wu

    Recommending yet-unvisited points of interest (POIs) which may be of interest to users is one of the fundamental applications in location-based social networks. It mainly replies on the understanding of users, POIs, and their interactions. Previous studies either develop matrix factorization-based approaches or utilize deep learning frameworks to learn better representation of users and POIs in order

  • Towards distributed node similarity search on graphs
    World Wide Web (IF 2.892) Pub Date : 2020-06-18
    Tianming Zhang, Yunjun Gao, Baihua Zheng, Lu Chen, Shiting Wen, Wei Guo

    Node similarity search on graphs has wide applications in recommendation, link prediction, to name just a few. However, existing studies are insufficient due to two reasons: (i) the scale of the real-world graph is growing rapidly, and (ii) vertices are always associated with complex attributes. In this paper, we propose an efficiently distributed framework to support node similarity search on massive

  • Fine-grained emotion classification of Chinese microblogs based on graph convolution networks
    World Wide Web (IF 2.892) Pub Date : 2020-06-17
    Yuni Lai; Linfeng Zhang; Donghong Han; Rui Zhou; Guoren Wang

    Microblogs are widely used to express people’s opinions and feelings in daily life. Sentiment analysis (SA) can timely detect personal sentiment polarities through analyzing text. Deep learning approaches have been broadly used in SA but still have not fully exploited syntax information. In this paper, we propose a syntax-based graph convolution network (GCN) model to enhance the understanding of diverse

  • Topic representation model based on microblogging behavior analysis
    World Wide Web (IF 2.892) Pub Date : 2020-06-15
    Weihong Han, Zhihong Tian, Zizhong Huang, Shudong Li, Yan Jia

    With the development of microblogging, it has become an important way for people to obtain information, express opinions, and make suggestions. Identifying new topics quickly and accurately from the massive microblogging data plays a crucial role for recommending information and controlling public opinion. The topic representation model provides a basis for topic detection. In this paper, we propose

  • Why current differential privacy schemes are inapplicable for correlated data publishing?
    World Wide Web (IF 2.892) Pub Date : 2020-06-08
    Hao Wang, Zhengquan Xu, Shan Jia, Ying Xia, Xu Zhang

    Although data analysis and mining technologies can efficiently provide intelligent and personalized services to us, data owners may not always be willing to share their true data because of privacy concerns. Recently, differential privacy (DP) technology has achieved a good trade-off between data utility and privacy guarantee by publishing noisy outputs. Nonetheless, DP still has a risk of privacy

  • Generative temporal link prediction via self-tokenized sequence modeling
    World Wide Web (IF 2.892) Pub Date : 2020-05-29
    Yue Wang; Chenwei Zhang; Shen Wang; Philip S. Yu; Lu Bai; Lixin Cui; Guandong Xu

    We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future links for temporal networks. GLSM captures the temporal link formation patterns from the observed links with a sequence modeling framework and has the ability to generate the emerging links by inferring from the probability distribution

  • Accurate relational reasoning in edge-labeled graphs by multi-labeled random walk with restart
    World Wide Web (IF 2.892) Pub Date : 2020-05-21
    Jinhong Jung, Woojeong Jin, Ha-myung Park, U Kang

    Given an edge-labeled graph and two nodes, how can we accurately infer the relation between the nodes? Reasoning how the nodes are related is a fundamental task in analyzing network data, and various relevance measures have been suggested to effectively identify relevance between nodes in graphs. Although many random walk based models have been extensively utilized to reveal relevance between nodes

  • Extracting diverse-shapelets for early classification on time series
    World Wide Web (IF 2.892) Pub Date : 2020-05-21
    Wenhe Yan, Guiling Li, Zongda Wu, Senzhang Wang, Philip S. Yu

    In recent years, early classification on time series has become increasingly important in time-sensitive applications. Existing shapelet based methods still cannot work well on this problem. First, the effectiveness of traditional shapelet based methods would be influenced by the number of shapelet candidates. Second, it is difficult for previous methods to obtain diverse shapelets in shapelet selection

  • Multi-document semantic relation extraction for news analytics
    World Wide Web (IF 2.892) Pub Date : 2020-05-18
    Yongpan Sheng; Zenglin Xu; Yafang Wang; Gerard de Melo

    Given the overwhelming amounts of information in our current 24/7 stream of new incoming articles, new techniques are needed to enable users to focus on just the key entities and concepts along with their relationships. Examples include news articles but also business reports and social media. The fact that relevant information may be distributed across diverse sources makes it particularly challenging

  • Yet another approach to understanding news event evolution
    World Wide Web (IF 2.892) Pub Date : 2020-05-05
    Shangwen Lv; Longtao Huang; Liangjun Zang; Wei Zhou; Jizhong Han; Songlin Hu

    With information explosion on the Internet, only returning ranked documents by search engines cannot satisfy people’s requirements on news events understanding. A more intelligent news events search engine should not only retrieve all related documents about a specific event, but also provide a global view about how the event originates and evolves. In order to solve this challenge, two tasks, event

  • DP-FL: a novel differentially private federated learning framework for the unbalanced data
    World Wide Web (IF 2.892) Pub Date : 2020-04-30
    Xixi Huang; Ye Ding; Zoe L. Jiang; Shuhan Qi; Xuan Wang; Qing Liao

    Security issues of artificial intelligence attract many attention in many research fields and industries, such as face recognition, medical care, and client services. Federated learning is proposed by Google, which can prevent the leakage of data during the AI training because each enterprise only needs to exchange training parameters without data sharing. In this paper, we present a novel differentially

  • End-to-end relation extraction based on bootstrapped multi-level distant supervision
    World Wide Web (IF 2.892) Pub Date : 2020-04-24
    Ying He; Zhixu Li; Qiang Yang; Zhigang Chen; An Liu; Lei Zhao; Xiaofang Zhou

    Distant supervised relation extraction has been widely used to identify new relation facts from free text, since the existence of knowledge base helps these models to build a large dataset with few human intervention and low costs of manpower and time. However, the existing Distant Supervised models are all based on the single-node classifier so that they suffer from the serious false categorization

  • Decision-based evasion attacks on tree ensemble classifiers
    World Wide Web (IF 2.892) Pub Date : 2020-04-20
    Fuyong Zhang; Yi Wang; Shigang Liu; Hua Wang

    Learning-based classifiers are found to be susceptible to adversarial examples. Recent studies suggested that ensemble classifiers tend to be more robust than single classifiers against evasion attacks. In this paper, we argue that this is not necessarily the case. In particular, we show that a discrete-valued random forest classifier can be easily evaded by adversarial inputs manipulated based only

  • Rebalancing the car-sharing system with reinforcement learning
    World Wide Web (IF 2.892) Pub Date : 2020-04-20
    Changwei Ren; Lixingjian An; Zhanquan Gu; Yuexuan Wang; Yunjun Gao

    With the sharing economy boom, there is a notable increase in the number of car-sharing corporations, which provided a variety of travel options and improved convenience and functionality. Owing to the similarity in the travel patterns of the urban population, car-sharing system often faces the problem of imbalance in the number of shared cars within the spatial distribution, especially during the

  • Embedding geographic information for anomalous trajectory detection
    World Wide Web (IF 2.892) Pub Date : 2020-04-17
    Ding Xiao; Li Song; Ruijia Wang; Xiaotian Han; Yanan Cai; Chuan Shi

    Anomalous trajectory detection is a crucial task in trajectory mining fields. Traditional anomalous trajectory detection methods mainly focus on the differences of a new trajectory and the historical trajectory with density and isolation techniques, which may suffer from the following two disadvantages. (1) They cannot capture the sequential information of the trajectory well. (2) They cannot make

  • A deep-learning model for urban traffic flow prediction with traffic events mined from twitter
    World Wide Web (IF 2.892) Pub Date : 2020-03-14
    Aniekan Essien, Ilias Petrounias, Pedro Sampaio, Sandra Sampaio

    Abstract Short-term traffic parameter forecasting is critical to modern urban traffic management and control systems. Predictive accuracy in data-driven traffic models is reduced when exposed to non-recurring or non-routine traffic events, such as accidents, road closures, and extreme weather conditions. The analytical mining of data from social networks – specifically twitter – can improve urban traffic

  • Understanding a bag of words by conceptual labeling with prior weights
    World Wide Web (IF 2.892) Pub Date : 2020-04-14
    Haiyun Jiang; Deqing Yang; Yanghua Xiao; Wei Wang

    In many natural language processing tasks, e.g., text classification or information extraction, the weighted bag-of-words model is widely used to represent the semantics of text, where the importance of each word is quantified by its weight. However, it is still difficult for machines to understand a weighted bag of words (WBoW) without explicit explanations, which seriously limits its application

  • A detailed review of D2D cache in helper selection
    World Wide Web (IF 2.892) Pub Date : 2020-04-11
    Tong Wang; Yunfeng Wang; Xibo Wang; Yue Cao

    With the rapid development of communication networks, the information interaction between heterogeneous networks such as the Internet of Things (IoT) and vehicle ad-hoc networks (VANETs) is becoming more and more common. In cellular networks, the proximity devices may share files directly without going through the eNBs, which is named Device-to-Device (D2D) communications. It has been considered as

  • Multi-source knowledge fusion: a survey
    World Wide Web (IF 2.892) Pub Date : 2020-04-08
    Xiaojuan Zhao; Yan Jia; Aiping Li; Rong Jiang; Yichen Song

    Multi-source knowledge fusion is one of the important research topics in the fields of artificial intelligence, natural language processing, and so on. The research results of multi-source knowledge fusion can help computer to better understand human intelligence, human language and human thinking, effectively promote the Big Search in Cyberspace, effectively promote the construction of domain knowledge

  • Flexible sensitive K -anonymization on transactions
    World Wide Web (IF 2.892) Pub Date : 2020-04-01
    Yu-Chuan Tsai; Shyue-Liang Wang; I-Hsien Ting; Tzung-Pei Hong

    In recent years, privacy breaches have been a great concern on the published data. Only removing one’s personal identification information is not sufficient to protect individual’s privacy. Privacy preservation technology for published data is devoted to preventing re-identification and retaining the useful information in published data. In this work, we propose a novel algorithm to deal with sensitive

  • Personalized top- n influential community search over large social networks
    World Wide Web (IF 2.892) Pub Date : 2020-03-31
    Jian Xu; Xiaoyi Fu; Yiming Wu; Ming Luo; Ming Xu; Ning Zheng

    User-centered analysis is one of the aims of online community search. In this paper, we study personalized top-n influential community search that has a practical application. Given an evolving social network, where every edge has a propagation probability, we propose a maximal pk-Clique community model, that uses a new cohesive criterion. The criterion requires that the propagation probability of

  • Scene text reading based cloud compliance access
    World Wide Web (IF 2.892) Pub Date : 2020-03-31
    Hezhong Pan; Chuanyi Liu; Shaoming Duan; Peiyi Han; Binxing Fang

    Cloud Compliance Access system assure that cloud users could utilize resources of cloud platform while conforming to regulations and the specific functions of this system were User Behavior Analyze and Data Sanitization. However, the problem of information extraction from images data is still to be addressed for both of these functions. The authors, in this paper, ventures to propose a new cloud compliance

  • Measurement and analysis on large-scale offline mobile app dissemination over device-to-device sharing in mobile social networks
    World Wide Web (IF 2.892) Pub Date : 2020-03-27
    Xiaofei Wang; Chenyang Wang; Xu Chen; Xiaoming Fu; Jinyoung Han; Xin Wang

    Recently, the issue of offloading cellular data while reducing the duplicated cellular transmission has gained more and more attention. Several studies have shown that sharing contents through Device-to-Device (D2D) to offload traffic to local connections nearby can offer better performance for mobile users. Nevertheless, most existing proposals are somewhat confined to small-scale data sets or limited

  • Topic based time-sensitive influence maximization in online social networks
    World Wide Web (IF 2.892) Pub Date : 2020-03-26
    Huiyu Min; Jiuxin Cao; Tangfei Yuan; Bo Liu

    With the exponential expansion of online social networks (OSNs), an extensive research on information diffusion in OSNs has been emerged in recent years. One of the key research is influence maximization (IM). IM is a problem to find a seed set with k nodes, so as to maximize the range of information propagation in OSNs. Most research adopts general greedy, degree discount, and centrality measures

  • Mining health knowledge graph for health risk prediction
    World Wide Web (IF 2.892) Pub Date : 2020-03-20
    Xiaohui Tao; Thuan Pham; Ji Zhang; Jianming Yong; Wee Pheng Goh; Wenping Zhang; Yi Cai

    Nowadays classification models have been widely adopted in healthcare, aiming at supporting practitioners for disease diagnosis and human error reduction. The challenge is utilising effective methods to mine real-world data in the medical domain, as many different models have been proposed with varying results. A large number of researchers focus on the diversity problem of real-time data sets in classification

  • Efficient targeted influence minimization in big social networks
    World Wide Web (IF 2.892) Pub Date : 2020-03-19
    Xinjue Wang; Ke Deng; Jianxin Li; Jeffery Xu Yu; Christian S. Jensen; Xiaochun Yang

    An online social network can be used for the diffusion of malicious information like derogatory rumors, disinformation, hate speech, revenge pornography, etc. This motivates the study of influence minimization that aim to prevent the spread of malicious information. Unlike previous influence minimization work, this study considers the influence minimization in relation to a particular group of social

  • Web event evolution trend prediction based on its computational social context
    World Wide Web (IF 2.892) Pub Date : 2020-03-14
    Junyu Xuan; Xiangfeng Luo; Jie Lu; Guangquan Zhang

    Predicting future trends of Web events can help significantly improve the quality of Web services, e.g., improving the user satisfaction of news websites. Existing approaches in this regard are based mainly on temporal patterns mined with the assumption that enough temporal data is available on hand. However, most Web events do not have a long lifecycle, but a burst property, which drastically reduces

  • Decentralized Knowledge Acquisition for Mobile Internet Applications
    World Wide Web (IF 2.892) Pub Date : 2020-03-13
    Jing Jiang; Shaoxiong Ji; Guodong Long

    Mobile internet applications on smart phones dominate large portions of daily life for many people. Conventional machine learning-based knowledge acquisition methods collect users’ data in a centralized server, then train an intelligent model, such as recommendation and prediction, using all the collected data. This knowledge acquisition method raises serious privacy concerns, and also violates the

  • An enhanced wildcard-based fuzzy searching scheme in encrypted databases
    World Wide Web (IF 2.892) Pub Date : 2020-03-13
    Jiaxun Hua; Yu Liu; He Chen; Xiuxia Tian; Cheqing Jin

    Under the overwhelming trend in Cloud Computing, Cloud Databases possessing high scalability / high availability / high parallel performance have become a prevalent paradigm of data outsourcing. In consideration of security and privacy, both individuals and enterprises prefer to outsource service data in encrypted form. Unfortunately, most encrypted databases cannot support such complicated queries

  • Towards a smarter directional data aggregation in VANETs
    World Wide Web (IF 2.892) Pub Date : 2020-03-12
    Sabri Allani; Taoufik Yeferny; Richard Chbeir; Sadok Ben Yahia

    In the last decade, Vehicular Ad hoc NETworks (VANETs) have attracted researchers, automotive companies and public governments, as a new communication technology to improve the safety of transportation systems aiming at offering smooth driving and safer roads. In this respect, a new Traffic Information System (TIS) has benefited from VANET services. The ultimate goal of a TIS consists in properly informing

  • Processing capability and QoE driven optimized computation offloading scheme in vehicular fog based F-RAN
    World Wide Web (IF 2.892) Pub Date : 2020-03-12
    Tianpeng Ye; Xiang Lin; Jun Wu; Gaolei Li; Jianhua Li

    The Fog Computing was proposed to extend the computing task to the network edge in lots of Internet of Things (IoT) scenario, such as Internet of Vehicle (IoV). However, the unbalanced data processing requirement caused by the uneven distribution of vehicles in time and space limits the service capability of IoV. To enhance the flexibility and data processing capability, we propose a hybrid fog architecture

  • Multi-view network embedding with node similarity ensemble
    World Wide Web (IF 2.892) Pub Date : 2020-03-12
    Weiwei Yuan; Kangya He; Chenyang Shi; Donghai Guan; Yuan Tian; Abdullah Al-Dhelaan; Mohammed Al-Dhelaan

    Node similarity is utilized as the most popular guidance for network embedding: nodes more similar in a network should still be more similar when mapping node information from a high-dimensional vector space to a low-dimensional vector space. Most existing methods preserve a single node similarity in the network embedding, which can merely preserve one-side network structural information. Though some

  • Exploring nonnegative and low-rank correlation for noise-resistant spectral clustering
    World Wide Web (IF 2.892) Pub Date : 2020-03-12
    Zheng Wang; Lin Zuo; Jing Ma; Si Chen; Jingjing Li; Zhao Kang; Lei Zhang

    Clustering has been extensively explored in pattern recognition and data mining in order to facilitate various applications. Due to the presence of data noise, traditional clustering approaches may become vulnerable and unreliable, thereby degrading clustering performance. In this paper, we propose a robust spectral clustering approach, termed Non-negative Low-rank Self-reconstruction (NLS), which

  • Medical treatment migration behavior prediction and recommendation based on health insurance data
    World Wide Web (IF 2.892) Pub Date : 2020-03-12
    Lin Cheng; Yuliang Shi; Kun Zhang

    How to accurately predict the future medical treatment behaviors of patients from the historical health insurance data has become an important research issue in healthcare. In this paper, an Attention-based Bidirectional Gated Recurrent Unit (AB-GRU) medical treatment migration prediction model is proposed to predict which hospital patients will go to in the future. The model considers the impact of

  • Leveraging citation influences for Modeling scientific documents
    World Wide Web (IF 2.892) Pub Date : 2020-03-11
    Yue Qian; Yu Liu; Xiujuan Xu; Quan Z. Sheng

    This paper studies a link-text algorithm to model scientific documents by citation influences, which is applied to document clustering and influence prediction. Most existing link-text algorithms ignore the different weights of citation influences that cited documents have on the corresponding citing document. In fact, citation influences reveal the latent structure of citation networks which is more

  • Deep learning for heterogeneous medical data analysis
    World Wide Web (IF 2.892) Pub Date : 2020-03-07
    Lin Yue; Dongyuan Tian; Weitong Chen; Xuming Han; Minghao Yin

    At present, how to make use of massive medical information resources to provide scientific decision-making for the diagnosis and treatment of diseases, summarize the curative effect of various treatment schemes, and better serve the decision-making management, medical treatment, and scientific research, has drawn more and more attention of researchers. Deep learning, as the focus of most concern by

  • High-performance docker integration scheme based on OpenStack
    World Wide Web (IF 2.892) Pub Date : 2020-03-07
    Sijie Yang; Xiaofeng Wang; Xiaoxue Wang; Lun An; Guizhu Zhang

    As an emerging technology in cloud computing Docker is becoming increasingly popular due to its high speed high efficiency and portability. The integration of Docker with OpenStack has been a hot topic in research and industrial areas e.g. as an emulation platform for evaluating cyberspace security technologies. This paper introduces a high-performance Docker integration scheme based on OpenStack that

  • Link prediction of scientific collaboration networks based on information retrieval
    World Wide Web (IF 2.892) Pub Date : 2020-03-07
    Dmytro Lande; Minglei Fu; Wen Guo; Iryna Balagura; Ivan Gorbov; Hongbo Yang

    Link prediction plays an important role in scientific collaboration networks, and can favourably affect the organization of international scientific projects. In this paper, a meta-path computed prediction (MPCP) algorithm for link prediction among scientists and publications is presented. The MPCP algorithm is based on a heterogeneous information network model composed of authors and keywords in articles

  • Learning social representations with deep autoencoder for recommender system
    World Wide Web (IF 2.892) Pub Date : 2020-03-07
    Yiteng Pan; Fazhi He; Haiping Yu

    With the development of online social media, it attracts increasingly attentions to utilize social information for recommender systems. Based on the intuition that users are influenced by their social friends, these methods are capable of addressing the data sparse problem and improving the performance of recommender systems. However, these methods model the influences between each pair of users independently

  • Leveraging burst in twitter network communities for event detection
    World Wide Web (IF 2.892) Pub Date : 2020-03-04
    Jeffery Ansah; Lin Liu; Wei Kang; Jixue Liu; Jiuyong Li

    Detecting protest events using social media is an important task with many useful applications to emergency services, law enforcement agencies, and other stakeholders. A plethora of research on event detection using social media has presented myriad approaches relying on tweet contents (text) to solve the event detection problem, with notable improvements over time. Despite the myriad of existing research

  • Outsourced data integrity verification based on blockchain in untrusted environment
    World Wide Web (IF 2.892) Pub Date : 2020-03-04
    Kun Hao; Junchang Xin; Zhiqiong Wang; Guoren Wang

    Outsourced data, as the significant component of cloud service, has been widely used due to its convenience, low overhead, and high flexibility. To guarantee the integrity of outsourced data, data owner (DO) usually adopts a third party auditor (TPA) to execute the data integrity verification scheme. However, during the verification process, DO cannot fully confirm the reliability of the TPA, and handing

Contents have been reproduced by permission of the publishers.
Springer 纳米技术权威期刊征稿
ACS ES&T Engineering
ACS ES&T Water
ACS Publications填问卷