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  • Attributed Collaboration Network Embedding for Academic Relationship Mining
    ACM Trans. Web (IF 1.157) Pub Date : 2020-11-21
    Wei Wang; Jiaying Liu; Tao Tang; Suppawong Tuarob; Feng Xia; Zhiguo Gong; Irwin King

    Finding both efficient and effective quantitative representations for scholars in scientific digital libraries has been a focal point of research. The unprecedented amounts of scholarly datasets, combined with contemporary machine learning and big data techniques, have enabled intelligent and automatic profiling of scholars from this vast and ever-increasing pool of scholarly data. Meanwhile, recent

  • Investigating and Modeling the Web Elements’ Visual Feature Influence on Free-viewing Attention
    ACM Trans. Web (IF 1.157) Pub Date : 2020-11-05
    Sandeep Vidyapu; Vijaya Saradhi Vedula; Samit Bhattacharya

    User attentional analyses on web elements help in synthesis and rendering of webpages. However, majority of the existing analyses are limited in incorporating the intrinsic visual features of text and images. This study aimed to analyze the influence of elements’ visual features (font-size, font-family, color, etc., for text; and brightness, color, intensity, etc., for images) besides their position

  • A Structured and Linguistic Approach to Understanding Recovery and Relapse in AA
    ACM Trans. Web (IF 1.157) Pub Date : 2020-11-05
    Shawn Bailey; Yue Zhang; Arti Ramesh; Jennifer Golbeck; Lise Getoor

    Alcoholism, also known as Alcohol Use Disorder (AUD), is a serious problem affecting millions of people worldwide. Recovery from AUD is known to be challenging and often leads to relapse at various points after enrolling in a rehabilitation program such as Alcoholics Anonymous (AA). In this work, we present a structured and linguistic approach using hinge-loss Markov random fields (HL-MRFs) to understand

  • CBPCS: A Cache-block-based Service Process Caching Strategy to Accelerate the Execution of Service Processes
    ACM Trans. Web (IF 1.157) Pub Date : 2020-10-29
    Jian Cao; Tingjie Jia; Siyou Qian; Haiyan Zhao; Jie Wang

    With the development of cloud computing and the advent of the Web 2.0 era, composing a set of Web services as a service process is becoming a common practice to provide more functional services. However, a service process involves multiple service invocations over the network, which incurs a huge time cost and could become a bottleneck to performance. To accelerate its execution, we propose an engine-side

  • Decoupled Variational Embedding for Signed Directed Networks
    ACM Trans. Web (IF 1.157) Pub Date : 2020-10-28
    Xu Chen; Jiangchao Yao; Maosen Li; Ya Zhang; Yanfeng Wang

    Node representation learning for signed directed networks has received considerable attention in many real-world applications such as link sign prediction, node classification, and node recommendation. The challenge lies in how to adequately encode the complex topological information of the networks. Recent studies mainly focus on preserving the first-order network topology that indicates the closeness

  • Emotions Behind Drive-by Download Propagation on Twitter
    ACM Trans. Web (IF 1.157) Pub Date : 2020-08-25
    Amir Javed; Pete Burnap; Matthew L. Williams; Omer F. Rana

    Twitter has emerged as one of the most popular platforms to get updates on entertainment and current events. However, due to its 280-character restriction and automatic shortening of URLs, it is continuously targeted by cybercriminals to carry out drive-by download attacks, where a user’s system is infected by merely visiting a Web page. Popular events that attract a large number of users are used

  • Analyzing Genetic Testing Discourse on the Web Through the Lens of Twitter, Reddit, and 4chan
    ACM Trans. Web (IF 1.157) Pub Date : 2020-08-25
    Alexandros Mittos; Savvas Zannettou; Jeremy Blackburn; Emiliano De Cristofaro

    Recent progress in genomics has enabled the emergence of a flourishing market for direct-to-consumer (DTC) genetic testing. Companies like 23andMe and AncestryDNA provide affordable health, genealogy, and ancestry reports, and have already tested tens of millions of customers. Consequently, news, experiences, and views on genetic testing are increasingly shared and discussed on social media. At the

  • Cold-start Point-of-interest Recommendation through Crowdsourcing
    ACM Trans. Web (IF 1.157) Pub Date : 2020-08-25
    Pramit Mazumdar; Bidyut Kr. Patra; Korra Sathya Babu

    Recommender system is a popular tool that aims to provide personalized suggestions to user about items, products, services, and so on. Recommender system has effectively been used in online social networks, especially the location-based social networks for providing suggestions for interesting places known as POIs (points-of-interest). Popular recommender systems explore historical data to learn users’

  • Early Detection of Social Media Hoaxes at Scale
    ACM Trans. Web (IF 1.157) Pub Date : 2020-08-18
    Arkaitz Zubiaga; Aiqi Jiang

    The unmoderated nature of social media enables the diffusion of hoaxes, which in turn jeopardises the credibility of information gathered from social media platforms. Existing research on automated detection of hoaxes has the limitation of using relatively small datasets, owing to the difficulty of getting labelled data. This, in turn, has limited research exploring early detection of hoaxes as well

  • Dynamic Offloading of Web Application Execution Using Snapshot
    ACM Trans. Web (IF 1.157) Pub Date : 2020-07-28
    Hyuk-Jin Jeong; Inchang Jeong; Soo-Mook Moon

    Mobile web platforms are facing new demands for emerging applications, such as machine learning or augmented reality, which require significant computing powers beyond that of current mobile hardware. Computation offloading can accelerate these apps by offloading the computation-intensive parts of an app from a client to a powerful server. Unfortunately, previous studies of offloading in the field

  • On Scalability of Association-rule-based Recommendation: A Unified Distributed-computing Framework
    ACM Trans. Web (IF 1.157) Pub Date : 2020-06-21
    Zhiang Wu; Changsheng Li; Jie Cao; Yong Ge

    The association-rule-based approach is one of the most common technologies for building recommender systems and it has been extensively adopted for commercial use. A variety of techniques, mainly including eligible rule selection and multiple rules combination, have been developed to create effective recommendation. Unfortunately, little attention has been paid to the scalability concern of rule-based

  • Roaming Through the Castle Tunnels: An Empirical Analysis of Inter-app Navigation of Android Apps
    ACM Trans. Web (IF 1.157) Pub Date : 2020-06-27
    Yun Ma; Ziniu Hu; Diandian Gu; Li Zhou; Qiaozhu Mei; Gang Huang; Xuanzhe Liu

    Smartphone applications (a.k.a., apps) have become indispensable in our everyday life and work. In practice, accomplishing a task on smartphones may require the user to navigate among various apps. Unlike Web pages that are inherently interconnected through hyperlinks, apps are usually isolated building blocks, and the lack of direct links between apps has compromised the efficiency of task completion

  • QoS-aware Automatic Web Service Composition with Multiple Objectives
    ACM Trans. Web (IF 1.157) Pub Date : 2020-05-16
    Soumi Chattopadhyay; Ansuman Banerjee

    Automatic web service composition has received a significant research attention in service-oriented computing over decades of research. With increasing number of web services, providing an end-to-end Quality of Service (QoS) guarantee in responding to user queries is becoming an important concern. Multiple QoS parameters (e.g., response time, latency, throughput, reliability, availability, success

  • PatternRank+NN
    ACM Trans. Web (IF 1.157) Pub Date : 2020-05-02
    Zhijun Xiao; Cuiping Li; Hong Chen

    We propose a ranking framework, called PatternRank+NN, for expanding a set of seed entities of a particular class (i.e., entity set expansion) from Web search queries. PatternRank+NN consists of two parts: PatternRank and NN. Unlike the traditional methods, PatternRank brings user behaviors into entity set expansion from Web search queries. PatternRank is a Markov chain which simulates the Web search

    ACM Trans. Web (IF 1.157) Pub Date : 2020-05-02
    Huijun Wu; Chen Wang; Richard Nock; Wei Wang; Jie Yin; Kai Lu; Liming Zhu

    Sharing a pre-trained machine learning model, particularly a deep neural network via prediction APIs, is becoming a common practice on machine learning as a service (MLaaS) platforms nowadays. Although deep neural networks (DNN) have shown remarkable successes in many tasks, they are also criticized for the lack of interpretability and transparency. Interpreting a shared DNN model faces two additional

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