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Long-term Measurement and Analysis of the Free Proxy Ecosystem

Free web proxies promise anonymity and censorship circumvention at no cost. Several websites publish lists of free proxies organized by country,... (more)

Fast and Practical Snippet Generation for RDF Datasets

Triple-structured open data creates value in many ways. However, the reuse of datasets is still challenging. Users feel difficult to assess the... (more)

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About TWEB

The journal Transactions on the Web (TWEB) publishes refereed articles reporting the results of research on Web content, applications, use, and related enabling technologies.

The scope of TWEB is described on the Call for Papers page. Authors are invited to submit original research papers for consideration by following the directions on the Author Guidelines page.

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Forthcoming Articles
An Outsourcing Model for Alert Analysis in a Cybersecurity Operations Center

A typical Cybersecurity Operations Center (CSOC) is a service organization. It hires and trains analysts, whose task is to perform analysis of alerts that were generated while monitoring the client?s networks. Due to ever-increasing financial and infrastructure burden on a CSOC driven by the rapidly growing demand for security services, it would become prohibitively expensive to continually expand the size of a CSOC in order to meet the demands in the future. An alternative solution is to outsource the alert analysis process to on-demand analysts, in order to provide scalable CSOC service to its clients with features such as, 1) higher throughput, 2) higher quality, and 3) more economical service than the current in-house service. This paper presents a novel two-step sequential mixed integer programming optimization method that is used in the development of a new decision-support business model for outsourcing the alert analysis process. It is demonstrated that through this model, a CSOC can effectively deliver its alert management services with the above-mentioned features. Results indicate that the model is scalable, computationally viable, realtime implementable, and can deliver CSOC services that meet the service level agreement (SLA) between the CSOC and its client.

Improving the Accuracy of the Video Popularity Prediction Models through user grouping and video popularity classification

This paper proposes a novel approach for enhancing the video popularity prediction models. Using the proposed approach, we enhance three popularity prediction techniques that outperform the accuracy of the prior state-of-the-art solutions. The major components of the proposed approach are two novel mechanisms for "\textit{user grouping}" and "\textit{content classification}". The user grouping method is an unsupervised clustering approach that divides the users into an adequate number of user groups with similar interests. The content classification approach identifies the classes of videos with similar popularity growth trends. To predict the popularity of the newly-released videos, our proposed popularity prediction model trains its parameters in each user group and its associated video popularity classes. Evaluations are performed through a 5-fold cross validation and on a dataset containing one month video request records of 26,706 number of BBC iPlayer users. Using the proposed grouping technique, user groups of similar interest and up to 2 video popularity classes for each user group were detected. Our analysis shows that the accuracy of the proposed solution outperforms the state-of-the-art including SH, ML, MRBF models on average by 45\%, 33\% and 24\%, respectively. Finally, we discuss how various systems in the network and service management domain such as cache deployment, advertising and video broadcasting technologies benefit from our findings to illustrate the implications.

Efficient Pairwise Penetrating-Rank Similarity Retrieval

Many web applications demand a measure of similarity between two entities, such as collaborative filtering, web document ranking, linkage prediction, and anomaly detection. P-Rank (Penetrating- Rank) has been accepted as a promising graph-based similarity measure as it provides a compre- hensive way of encoding both incoming and outgoing links into assessment. However, the existing method to compute P-Rank is iterative in nature and rather cost-inhibitive. Moreover, the accuracy estimate and stability issues for P-Rank computation have not been addressed. In this paper, we consider the optimization techniques for P-Rank search that encompasses its accuracy, stability and computational efficiency. (1) The accuracy estimation is provided for P-Rank iterations, with the aim to find out the number of iterations, k, required to guarantee a desired accuracy. (2) A rigorous bound on the condition number of P-Rank is obtained for stability analysis. Based on this bound, it can be shown that P-Rank is stable and well-conditioned when the damping factors are chosen to be suitably small. (3) Two matrix-based algorithms, applicable to digraphs and undirected graphs, are respectively devised for efficient P-Rank computation, which improves the computational time from O(kn3) to O(?n2+ ?6) for digraphs, and to O(?n2) for undirected graphs, where n is the number of vertices in the graph, and ? (? n) is the target rank of the graph. Moreover, our pro- posed algorithms can significantly reduce the memory space of P-Rank computations from O(n2) to O(?n + ?4) for digraphs, and to O(?n) for undirected graphs, respectively. Finally, extensive experiments on real-world and synthetic datasets demonstrate the usefulness and efficiency of the proposed techniques for P-Rank similarity assessment on various networks.

'The Best of Both Worlds!' Integration of Web Page and Eye Tracking Data Driven Approaches for Automatic AOI Detection

Web pages are comprised of different kinds of elements (menus, adverts, etc). Segmenting pages into their elements has long been important in understanding how people experience those pages, and in making those experiences 'better'. Many approaches have been proposed which relate the resultant elements with the underlying source code, however, they do not consider users' interactions. Another group of approaches analyses eye movements of users to discover areas that interest or attract them (AOIs). Although these approaches consider how users interact with web pages, they do not relate AOIs with the underlying source code. We propose a novel approach which integrates web page and eye tracking data driven approaches for automatic AOI detection. This approach segments an entire web page into its AOIs by considering users' interactions and relates AOIs with the underlying source code. Based on the Adjusted Rand Index measure, our approach provides the most similar segmentation to the ground truth segmentation compared to its individual components.

A Survey of Figurative Language and its Computational Detection in Online Social Networks

The frequent usage of figurative language on online social networks, especially on Twitter, has the potential to mislead traditional sentiment analysis and recommender systems. Due to the extensive use of slangs, bashes, flames, and non-literal texts, tweets are a great source of figurative language, such as sarcasm, irony, metaphor, simile, hyperbole, humor, and satire. Starting with a brief introduction of figurative language and its various categories, this paper presents an in-depth survey of the state-of-the-art techniques for computational detection of seven different figurative language categories, mainly on Twitter. For each figurative language category, we present details about the characterizing features, datasets, and state-of-the-art computational detection approaches. Finally, we discuss open challenges and future directions of research for each figurative language category.

Time-Aspect-Sentiment Recommendation Models Based on Novel Similarity Measure Methods

The explosive growth of e-commerce has led to the development of recommendation system which identifies a set of items that meet the user's personalized interest. However, the timeliness of historical data and the implicity of feedbacks pose severe challenges for existing recommendation methods. To alleviate these problems, we exploit the user's consumption behaviors from the perspectives of user and item, by modeling both item level and user level respectively, where the item level reflects the grade of the item, and the user level represents the user's consumption behavior. In this paper, we collect the description information and the reviews of the items from publicly websites, then adopt sentiment analysis techniques to model the similarity on user level and product level respectively. In particular, we extend traditional latent factor model and propose two novel methods--Item Similarity Matrix Factorization (ISMF) and User Similarity Matrix Factorization (USMF) by introducing two novel similarity measuring methods. In ISMF and USMF, the consistency between latent factors and explicit aspects are naturally incorporated into the learning of latent factors of users and items, so as to predict the users' preferences on different products more accurately. Experimental evaluations on the real datasets show that our methods outperform the competitive approaches.

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