User Browsing Research Articles

BiOnIC: A Catalog of User Interactions with Biomedical Ontologies.

BiOnIC is a catalog of aggregated statistics of user clicks, queries, and reuse counts for access to over 200 biomedical ontologies. BiOnIC also provides anonymized sequences of classes accessed by users over a period of four years. To generate the statistics, we processed the access logs of BioPortal, a large open biomedical ontology repository. We publish the BiOnIC data using DCAT and SKOS metadata standards. The BiOnIC catalog has a wide range of applicability, which we demonstrate through its use in three different types of applications. To our knowledge, this type of interaction data stemming from a real-world, large-scale application has not been published before. We expect that the catalog will become an important resource for researchers and developers in the Semantic Web community by providing novel insights into how ontologies are explored, queried and reused. The BiOnIC catalog may ultimately assist in the more informed development of intelligent user interfaces for semantic resources through interface customization, prediction of user browsing and querying behavior, and ontology summarization. The BiOnIC catalog is available at: http://onto-apps.stanford.edu/bionic.

Investigation of Heterogeneous Approach to Fact Invention of Web Users’ Web Access Behaviour

World Wide Web consists of a huge volume of different types of data. Web mining is one of the fields of data mining wherein there are different web services and a large number of web users. Web user mining is also one of the fields of web mining. The web users’ information about the web access is collected through different ways. The most common technique to collect information about the web users is through web log file. There are several other techniques available to collect web users’ web access information; they are through browser agent, user authentication, web review, web rating, web ranking and tracking cookies. The web users find it difficult to retrieve their required information in time from the web because of the huge volume of unstructured and structured information which increases the complexity of the web. Web usage mining is very much important for various purposes such as organizing website, business and maintenance service, personalization of website and reducing the network bandwidth. This paper provides an analysis about the web usage mining techniques. Â

Eye tracking the user experience – An evaluation of ontology visualization techniques

Various ontology visualization techniques have been developed over the years, offering essential interfaces to users for browsing and interacting with ontologies, in an effort to assist with ontology understanding. Yet few studies have focused on evaluating the usability of existing ontology visualization techniques. This paper presents an eye-tracking user study that evaluates two commonly used ontology visualization techniques, namely, indented list and graph. The eye-tracking experiment and analysis presented in this paper complements the set of existing evaluation protocols for ontology visualization. In addition, the results found from this study contribute to a greater understanding of the strengths and weaknesses of the two visualization techniques, and in particular, how and why one is more effective than the other. Based on approximately 500MB of eye movement data containing around 30 million rows of gaze data generated by a Tobii eye tracker, we found evidence suggesting indented lists are more efficient at supporting information searches and graphs are more efficient at supporting information processing. Keywords: eye tracking, ontology visualization evaluation, usability study, gaze data processing, statistical analysis * Corresponding author. E-mail: bofu@uvic.ca. † Current affiliation: Google Inc.

Poe in Cyberspace: The “Mobile-Friendly” Sites

Poe in Cyberspace: The “Mobile-Friendly” Sites

Automatic recognition of males and females among web browser users based on behavioural patterns of peripherals usage

Purpose The purpose of this paper is to answer the question whether it is possible to recognise the gender of a web browser user on the basis of keystroke dynamics and mouse movements. Design/methodology/approach An experiment was organised in order to track mouse and keyboard usage using a special web browser plug-in. After collecting the data, a number of parameters describing the users’ keystrokes, mouse movements and clicks were calculated for each data sample. Then several machine learning methods were used to verify the stated research question. Findings The experiment showed that it is possible to recognise males and females on the basis of behavioural characteristics with an accuracy exceeding 70 per cent. The best results were obtained while using Bayesian networks. Research limitations/implications The first limitation of the study was the restricted contextual information, i.e. neither the type of web page browsed nor the user activity was taken into account. Another is the narrow scope of the respondent group. Future work should focus on gathering data from more users covering a wider age range and should consider the context. Practical implications Automatic gender recognition could be used in profiling a user to create personalised websites or as an additional feature in automatic identification for security reasons. It might be also considered as a confirmation of declared gender in web-based surveys. Social implications As not all users perceive personalised ads and websites as beneficial, this application requires the analysis of a user perspective to provide value to the consumer without privacy violation. Originality/value Behavioural characteristics, such as mouse movements and keystroke dynamics, have already been used for user authentication and emotion recognition, but applying these data to gender recognition is an original idea.

Fast prediction of web user browsing behaviours using most interesting patterns

The prediction of users’ browsing behaviours is essential for putting appropriate information on the web. The browsing behaviours are stored as navigational patterns in web server logs. These weblogs are used to predict the frequently accessed patterns of web users, which can be used to predict user behaviour and to collect business intelligence. However, owing to the exponentially increasing weblog size, existing implementations of frequent-pattern-mining algorithms often take too much time and generate too many redundant patterns. This article introduces the most interesting pattern-based parallel FP-growth (MIP-PFP) algorithm. MIP-PFP is an improved implementation of the parallel FP-growth algorithm and implemented on the Apache Spark platform for extracting frequent patterns from huge weblogs. Experiments were performed on openly available National Aeronautics and Space Administration (NASA) weblog data to test the effectiveness of the MIP-PFP algorithm. The results were compared with existing implementation of PFP algorithms. The results suggest that the MIP-PFP algorithm running on Apache Spark reduced the execution time by a factor of more than 10 times. The effect of sequence length that has been used as input to the MIP-PFP algorithm was also evaluated with different interestingness parameters including support, confidence, lift, leverage, cosine, and conviction. It is observed from experimental results that only sequences of length greater than three produced a very low value of support for these interestingness measures.

Query Recommendation Based on User Browsing History

Query Recommendation Based on User Browsing History

Research of Data-Aiming Mining Algorithm in Cloud Environment

Cloud computing contains a huge amount of data, which are featured as being widely distributed, heterogeneous, and dynamic. Thus, aiming at how to mine useful parts in these information, this paper proposes an Apriori algorithm based on cloud computing and introduces cost-sensitive learning and non-filter matrix to find k frequency set and uses the method of generating association rules to improve effectiveness of data mining. Simulation experiments show that mining algorithm in this paper is highly effective and suitable for data mining in the context of cloud computing. With the emergence of the concept cloud computing, more and more information is shared and spread through the Internet. How can carry out data mining in cloud computing became an important means of access to information (1). Literature (2) data mining solutions in the cloud computing environment is proposed, through the cloud computing and cloud computing services, describes the problem solving mechanism for data mining services. Literature (3) proposed a data processing method based on cloud computing, based on mining user browsing preference path. Experiments show that the mining algorithm for large amount of data logs, accuracy and efficiency than ordinary users preferred path mining algorithm based on frequency increased. Literature (4) proposed a new framework for data mining in the field of cloud platforms and cloud computing environments are described mechanism of data mining in the field of service-oriented. Literature (5) is a method of data mining based on cloud computing technology: large data sets and mining tasks on multiple computers in parallel, experiments show that use cloud computing technology to handle large data sets in a cluster, you can significantly improve the efficiency of data mining. Literature (6) for cloud computing resources prediction model based on data mining technology, results showed that this model not only improves the prediction accuracy of cloud computing resources, and reduce the complexity of modeling, improves the efficiency of models, to cloud computing resources provides a new way of building models. Literature (7) proposed data mining services view, cloud services task force has analyzed data mining advantages and challenges, data mining and prediction of cloud point data mining development trend of cloud services, providing data mining services framework for cloud services. This paper proposes an Apriori algorithm based on cloud computing and introduces cost-sensitive learning and non-filter matrix to find k

Concept indexing and expansion for social multimedia websites based on semantic processing and graph analysis

ABSTRACTThis study presents a human-centric technique for social video expansion based on semantic processing and graph analysis. The objective is to increase metadata of an online video and to explore related information, thereby facilitating user browsing activities. To analyze the semantic meaning of a video, shots and scenes are firstly extracted from the video on the server side. Subsequently, this study uses annotations along with ConceptNet to establish the underlying framework. Detailed metadata, including visual objects and audio events among the predefined categories, are indexed by using the proposed method. Furthermore, relevant online media associated with each category are also analyzed to enrich the existing content. With the above-mentioned information, users can easily browse and search the content according to the link analysis and its complementary knowledge. Experiments on a video dataset are conducted for evaluation. The results show that our system can achieve satisfactory performance, thereby demonstrating the feasibility of the proposed idea.

DECT: Distributed Evolving Context Tree for Understanding User Behavior Pattern Evolution

Internet user behavior models characterize user browsing dynamics or the transitions among web pages. The models help Internet companies improve their services by accurately targeting customers and providing them the information they want. For instance, specific web pages can be customized and prefetched for individuals based on sequences of web pages they have visited. Existing user behavior models abstracted as time-homogeneous Markov models cannot efficiently model user behavior variation through time. This demo presents DECT, a scalable time-variant variable-order Markov model. DECT digests terabytes of user session data and yields user behavior patterns through time. We realize DECT using Apache Spark and deploy it on top of Yahoo! infrastructure. We demonstrate the benefits of DECT with anomaly detection and ad click rate prediction applications. DECT enables the detection of higher-order path anomalies and provides deep insights into ad click rates with respect to user visiting paths.

CATOS (Computer Aided Training/Observing System): Automating animal observation and training.

In animal behavioral biology, an automated observing/training system may be useful for several reasons: (a) continuous observation of animals for documentation of specific, irregular events, (b) long-term intensive training of animals in preparation for behavioral experiments, (c) elimination of potential cues and biases induced by humans during training and testing. Here, we describe an open-source-based system named CATOS (Computer Aided Training/Observing System) developed for such situations. There are several notable features in this system. CATOS is flexible and low cost because it is based on free open-source software libraries, common hardware parts, and open-system electronics based on Arduino. Automated video condensation is applied, leading to significantly reduced video data storage compared to the total active hours of the system. A data-viewing utility program helps a user browse recorded data quickly and more efficiently. With these features, CATOS has the potential to be applied to many different animal species in various environments such as laboratories, zoos, or even private homes. Also, an animal's free access to the device without constraint, and a gamified learning process, enhance the animal's welfare and enriches their environment. As a proof of concept, the system was built and tested with two different species. Initially, the system was tested for approximately 10 months with a domesticated cat. The cat was successfully and fully automatically trained to discriminate three different spoken words. Then, in order to test the system's adaptability to other species and hardware components, we used it to train a laboratory rat for 3 weeks.

PRUB: A Privacy Protection Friend Recommendation System Based on User Behavior

The fast developing social network is a double-edged sword. It remains a serious problem to provide users with excellent mobile social network services as well as protecting privacy data. Most popular social applications utilize behavior of users to build connection with people having similar behavior, thus improving user experience. However, many users do not want to share their certain behavioral information to the recommendation system. In this paper, we aim to design a secure friend recommendation system based on the user behavior, called PRUB. The system proposed aims at achieving fine-grained recommendation to friends who share some same characteristics without exposing the actual user behavior. We utilized the anonymous data from a Chinese ISP, which records the user browsing behavior, for 3 months to test our system. The experiment result shows that our system can achieve a remarkable recommendation goal and, at the same time, protect the privacy of the user behavior information.

Time-ordered collaborative filtering for news recommendation

Faced with hundreds of thousands of news articles in the news websites, it is difficult for users to find the news articles they are interested in. Therefore, various news recommender systems were built. In the news recommendation, these news articles read by a user is typically in the form of a time sequence. However, traditional news recommendation algorithms rarely consider the time sequence characteristic of user browsing behaviors. Therefore, the performance of traditional news recommendation algorithms is not good enough in predicting the next news article which a user will read. To solve this problem, this paper proposes a time-ordered collaborative filtering recommendation algorithm (TOCF), which takes the time sequence characteristic of user behaviors into account. Besides, a new method to compute the similarity among different users, named time-dependent similarity, is proposed. To demonstrate the efficiency of our solution, extensive experiments are conducted along with detailed performance analysis.

Use of the EFSA Standard Sample Description ver. 2.0 (SSD2) for the reporting of data on the control of pesticide residues in food and feed according to Regulation (EC) No 396/2005

EFSA Supporting PublicationsVolume 12, Issue 12 918E External scientific reportOpen Access Use of the EFSA Standard Sample Description ver. 2.0 (SSD2) for the reporting of data on the control of pesticide residues in food and feed according to Regulation (EC) No 396/2005 National Sanitary Veterinary and for Food Safety Authority of Romania (ANSVSA), National Sanitary Veterinary and for Food Safety Authority of Romania (ANSVSA) National Sanitary Veterinary and for Food Safety Authority of Romania (ANSVSA)Search for more papers by this author National Sanitary Veterinary and for Food Safety Authority of Romania (ANSVSA), National Sanitary Veterinary and for Food Safety Authority of Romania (ANSVSA) National Sanitary Veterinary and for Food Safety Authority of Romania (ANSVSA)Search for more papers by this author First published: 17 December 2015 https://doi.org/10.2903/sp.efsa.2015.EN-918 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. Published date: 17 December 2015 Question number: EFSA-Q-2014-00487 AboutReferencesRelatedInformationPDFPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessClose modalShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL References EFSA (European Food Safety Authority), 2010. Standard sample description for food and feed. EFSA Journal 2010; 8(1):1457, 54 pp. doi:10.2903/j.efsa.2010.1457 EFSA (European Food Safety Authority), 2014a. Use of the EFSA Standard Sample Description for the reporting of data on the control of pesticide residues in food and feed according to Regulation (EC) No 396/2005 (2013 Data Collection). EFSA Journal 2014; 12(1):3545 60 pp. doi:10.2903/j.efsa.2014.3545 EFSA (European Food Safety Authority), 2014b. Guidance on the data exchange version 2.0. EFSA Journal 2014; 12(12):3945, 173 pp. doi:10.2903/j.efsa.2014.3945 EFSA (European Food Safety Authority), 2015a. The EFSA Data Warehouse access rules. EFSA supporting publication 2015:EN-76, 18 pp. EFSA (European Food Safety Authority), 2015b. The 2013 European Union report on pesticide residues in food. EFSA Journal 2015; 13(3):4038, 169 pp, doi:10.2903/j.efsa.2015.4105 EFSA (European Food Safety Authority), 2015c. Guidelines for reporting data on residues of veterinary medicinal products. EFSA supporting publication 2015:EN-783. 77 pp. European Commission, 2013. Guidance document on analytical quality control and validation procedures for pesticide residues analysis in food and feed. SANCO/12571/2013. Volume12, Issue12December 2015918E ReferencesRelatedInformation RecommendedUse of the EFSA Standard Sample Description for the reporting of data on the control of pesticide residues in food and feed according to Regulation (EC) No 396/2005European Food Safety Authority, EFSA JournalUse of the EFSA Standard Sample Description for the reporting of data on the control of pesticide residues in food and feed according to Regulation (EC) No 396/2005 (revision 1)European Food Safety Authority, EFSA JournalUse of the EFSA Standard Sample Description (SSD) for the reporting of data on the control of pesticide residues in food and feed according to Regulation (EC) No 396/2005 (Version: 2013 Data Collection)European Food Safety Authority, EFSA JournalStandard Sample Description ver. 2.0European Food Safety Authority, EFSA JournalEFSA Catalogue browser User GuideEuropean Food Safety Authority (EFSA), Sofia Ioannidou, EFSA Supporting Publications

The Online Informal Learning of English

Tablets, smartphones, smart watches, Web 2.0, the time is ripe for a comprehensive study of the incidental English language learning which takes place as users browse, graze, and shoot the breeze online. Geoffrey Sockett, Professor of Linguistics at Paris Descartes University, does precisely that in this book, which consists of an introduction, a conclusion, and six substantive chapters (2–7) exploring how online informal learning of English (OILE) takes place, and the implications of this for classroom teaching. In the introduction, Sockett makes it clear that he regards OILE as encompassing many online activities such as watching video material, listening to pop music, online reading, social networking, and other interactive online practices which involve the use of English by non-native speakers. It is also clear from the outset (see, for instance, p. 4) that much of the evidence adduced will come from the context of French learners of English, unsurprising given the workplace of the author. The book provides extensive evidence of the practices of language learners during the period 2009–2013.

CookiExt: Patching the browser against session hijacking attacks

Session cookies constitute one of the main attack targets against client authentication on the Web. To counter these attacks, modern web browsers implement native cookie protection mechanisms based on the HttpOnly and Secure ags. While there is a general understanding about the eectiveness of these defenses, no formal result has so far been proved about the security guarantees they convey. With the present paper we provide the rst such result, by presenting a mechanized proof of noninterference assessing the robustness of the HttpOnly and Secure cookie ags against both web and network attackers with the ability to perform arbitrary XSS code injection. We then develop CookiExt, a browser extension that provides client-side protection against session hijacking, based on appropriate agging of session cookies and automatic redirection over HTTPS for HTTP requests carrying these cookies. Our solution improves over existing client-side defenses by combining protection against both web and network attacks, while at the same time being designed so as to minimise its eects on the user's browsing experience. Finally, we report on the experiments we carried out to practically evaluate the eectiveness of our approach.

<i>Water Expert</i>: a conceptualized framework for development of a rule-based decision support system for distribution system decontamination

Abstract. Significant drinking water contamination events pose a serious threat to public and environmental health. Water utilities often must make timely, critical decisions without evaluating all facets of the incident. The data needed to enact informed decisions are inevitably dispersant and disparate, originating from policy, science, and heuristic contributors. Water Expert is a functioning hybrid decision support system (DSS) and expert system framework that emphasizes the meshing of parallel data structures in order to expedite and optimize the decision pathway. Delivered as a thin-client application through the user's web browser, Water Expert's extensive knowledgebase is a product of inter-university collaboration that methodically pieced together system decontamination procedures. Decontamination procedures are investigated through consultation with subject matter experts, literature review, and prototyping with stakeholders. This paper discusses the development of Water Expert, analyzing the development process underlying the DSS and the system's existing architecture specifications. Water Expert constitutes the first system to employ a combination of deterministic and heuristic models which provide decontamination solutions for water distribution systems. Results indicate that the decision making process following a contamination event is a multi-disciplinary effort. This contortion of multiple inputs and objectives limit the ability of the decision maker to find optimum solutions without technological intervention.

Exploiting Implicit User Activity for Media Recommendation

This thesis explores in depth how to exploit the user browsing behavior, and in particular the referrer URL, to understand the interest of the users. The aim is, first, to understand the preferences of the users from their navigation patterns, i.e., from the implicit actions of the users. Then, to exploit this information to personalize the content offered by the service provider. The key findings from our studies allowed us to propose innovative solutions to perform recommendation and ranking of media content. We show how the browsing logs are extremely meaningful also for cold-start problem -- estimating the preferences of newcomers.

Computerised Recommendations on E-Transaction Finalisation by Means of Machine Learning

Abstract Nowadays a vast majority of businesses are supported or executed online. Website-to-user interaction is extremely important and user browsing activity on a website is becoming important to analyse. This paper is devoted to the research on user online behaviour and making computerised advices. Several problems and their solutions are discussed: to know user behaviour online pattern with respect to business objectives and estimate a possible highest impact on user online activity. The approach suggested in the paper uses the following techniques: Business Process Modelling for formalisation of user online activity; Google Analytics tracking code function for gathering statistical data about user online activities; Naïve Bayes classifier and a feedforward neural network for a classification of online patterns of user behaviour as well as for an estimation of a website component that has the highest impact on a fulfilment of business objective by a user and which will be advised to be looked at. The technique is illustrated by an example.

Computerised recommendations on e-transaction finalisation by means of machine learning

Nowadays a vast majority of businesses are supported or executed online. Website-to-user interaction is extremely important and user browsing activity on a website is becoming important to analyse. This paper is devoted to the research on user online behaviour and making computerised advices. Several problems and their solutions are discussed: to know user behaviour online pattern with respect to business objectives and estimate a possible highest impact on user online activity. The approach suggested in the paper uses the following techniques: Business Process Modelling for formalisation of user online activity; Google Analytics tracking code function for gathering statistical data about user online activities; Naive Bayes classifier and a feedforward neural network for a classification of online patterns of user behaviour as well as for an estimation of a website component that has the highest impact on a fulfilment of business objective by a user and which will be advised to be looked at. The technique is illustrated by an example.