Visited Web Pages Research Articles

Investigation of students' use of online information in higher education using eye tracking

To successfully learn using freely available (and non-curated) Internet resources, university students need to search for, critically evaluate and select online information, and verify sources (defined as Critical Online Reasoning, COR). Recent research indicates substantial deficits in COR skills among higher education students. To support students in learning how to critically use online information for their learning, it is necessary to better understand the strategies and practices that might elicit less critically-reflective judgments about online information and thus account for such deficits. To this end, using eye tracking data, we investigate how the COR behaviors of students who critically-reflectively evaluate the credibility of online information (‘high performers’) differ from those of students who do not critically-reflectively evaluate it (‘low performers’): 19 students were divided into high and low performers according to their performance in the newly developed Critical Online Reasoning Assessment (CORA). The fixation and dwell times of both groups during CORA task processing were compared regarding time spent on the different processing steps and eye movements on the visited web pages. The results show noticeable differences between the two groups, indicating that low performers indeed approached the task rather heuristically than systematically, and that COR skills require targeted and effective training in higher education.

Compilation of Research Works that Contributed to the Development of Algerian Agricultural Nematology: A Review

Investigation on plant-parasitic nematodes (PPN) in Algeria date back to the late 19th century. Important research has been performed since this period until currently generating significant information. The aim of this literature assembling is making available an outline of the current situation of plant nematology in the country. This review is based on the available information existing at universities’ libraries and research articles obtained by visiting web pages and Google Scholar. All pertinent information was collected from 2018 until today. This review article shows three periods for Algerian agricultural nematology: First period includes the years of French colonialism and two decades after independence. Publications on PPN started in the late 19th and early 20th centuries by French and Italian researchers, working in Algeria. The second period (1980-end 1990s) was characterized by the emergence of Algerian researchers. They successively became central academics having the responsibility to educate graduate students. Their research topics were studies on geographical distribution and evaluation of damage degrees of some PPN. The third period (2000 until today) is characterized by the progression in nematological research in the country; research topics expanded and became more diverse. Collaboration between universities, National Institute of Agronomic Research and National Institute of Plant Protection are the organisations contributing to the development of nematology. Till date, a total of 38 genera and 43 species of PPN including economically important nematodes were documented in Algeria.

An Analysis of the Development and Marketing Strategy of Art Merchandise in Art Museums

There have been many limitations and lags in the commercial operation of public art institutions, while art merchandise is an important business for the commercial operation and profitability of art museums. How to make profits in the market while still building an art brand image has been the priority for art museum operators to think and study. This paper examines the new experiences and innovative initiatives of public art museums in the art merchandising business through the case of the National Museum of Modern and Contemporary Art, Korea (MMCA). First, through the experience of operating art merchandise at the MMCA, it is found that a sound archival management of art museums is conducive to boost the quality and diversity of art merchandise and to dominating the market. Secondly, the production of high-quality digital content and its display on the visited web pages are favorable to increase online sales, raise profits and advertise the brand at the same time.

Deep auto‐encoder based clustering algorithm for graph‐based web page recommendation system

SummaryWeb‐page recommendation system (RS) plays a major role in smart web systems. The core technology is the RS, which helps users find their interests or discover item preferences. With the rapid proliferation of information technology, the present era witnessed a remarkable growth in the collection and generation of web data. Every day, it is difficult to project the appropriate information to the user and adds severe complexity to the decision making procedure. Moreover, the existing RS techniques have limitations like huge computation time, high computational cost, over specialization, and sparsity problems. This work presents an effective graph‐based web page recommendation model to overcome these difficulties. Initially, the input data is preprocessed utilizing Natural Language Tool Kit executed in Python. The snapshot‐based dynamic graph model obtains the user‐web page relationship. Weight calculation of user‐item and the user‐user edge is also processed with this graph model. The user‐user edge similarity is used to obtain the user preference vector. Then, unsupervised‐deep auto encoder based density‐based clustering algorithm is proposed to cluster the user preference vector, which groups the visited web page details into k clusters. The web pages are recommended based on the highest score calculated by the page ranking method. Finally, the topmost visited web pages are recommended to web users based on the ranking score. “All the news”, MSNBC, and Weblog datasets are taken for evaluation. The proposed method is evaluated with respect to standard measures like F1 measure, accuracy, recall, precision, and root mean square error. Compared with other existing methods, the proposed technique achieved the highest accuracy, 97.99% for All the news datasets, 93.03% for the MSNBC database, and 90.07% for the Weblog database.

Studi Komparatif Fungsionalitas, Performance dan User experience pada Website Geopark

Indonesia has great natural resources. UNESCO recognized at least six geoparks, and Indonesia will continue to develop more geoparks in the future. However, currently, Indonesia's geoparks are not widely known. One of the reasons is that the existing websites do not pay attention to performance guidelines, information, economics, and search engine optimization even though the website is a vital marketing medium so that geoparks can be promoted widely and effectively. A comprehensive study of the website contents is required to build a good geopark website. In addition, it is also necessary to study user experience when visiting web pages. This study aims to find the standard website content on the geopark website and compare existing website performance and user experience. This study compares three geopark websites. The three websites include the Ciletuh-Palabuhanratu, Hongkong geopark, and Cliff of Fundy Geosite website. Three indicators are used: website performance, user experience, and what information should be on the geopark website. These three indicators are essential as an attraction for website visitors so they can enjoy the content of the website and they can stay longer so that the visitors can turn into potential geopark tourists. Good website performance is supported by consistent ease of providing information and speed in providing information to website visitors. In comparison, the user experience visiting the website relates to the interactive visual design, organization, and navigation structure of the website framework and the completeness of the content. We use performance and user experience metrics as evaluation tools for the three selected websites. As a result, the general availability of information is as follows: information about geoparks, UNESCO, list of tourist attractions, activities, itinerary, tourist accommodation, education, contacts, language choices, news articles, pamphlets, maps, and virtual tours. In terms of performance, the three websites have slow speeds, but in cumulative layout shifts, they have good scores.

Experience: Analyzing Missing Web Page Visits and Unintentional Web Page Visits from the Client-side Web Logs

Web logs have been widely used to represent the web page visits of online users. However, we found that web logs in Chrome’s browsing history only record 57% of users’ visited websites, i.e., nearly half of a user’s website visits are not recorded. Additionally, 5.1% of the visits recorded in the web log occur because of unconscious user actions, i.e., these page visits are not initiated from users. We created a Google Chrome plugin and recruited users to install the plugin to collect and analyze the conscious URL visits, unconscious URL visits, and “missing” URL visits (i.e., the visits unrecorded in the traditional web log). We reported the statistics of these behaviors. We showed that sorting popular website categories based on traditional web logs differs from the rankings obtained when including missing visits or excluding unintentional visits. We predicted users’ future behaviors based on three types of training data – all the visits in modern web logs, the intentional visits in web logs, and the intentional visits plus missing visits in web logs. The experimental results indicate that missing visits in web logs may contain additional information, and unintentional visits in web logs may contain more noise than information for user modeling. Consequently, we need to be careful of the observations and conclusions derived from web log analyses because the web log data could be an incomplete and noisy dataset of a user’s visited web pages.

Towards benchmark datasets for machine learning based website phishing detection: An experimental study

The increasing popularity of the Internet led to a substantial growth of e-commerce. However, such activities have main security challenges primary caused by cyberfraud and identity theft. Therefore, checking the legitimacy of visited web pages is a crucial task to secure costumers’ identities and prevent phishing attacks. The use of machine learning is widely recognized as a promising solution. The literature is rich with studies that use machine learning techniques for website phishing detection. However, their findings are dataset dependent and are far away from generalization. Two main reasons for this unfortunate state are the impracticable replication and absence of appropriate benchmark datasets for fair evaluation of systems. Moreover, phishing tactics are continuously evolving and proposed systems are not following those rapid changes. In this paper, we present a general scheme for building reproducible and extensible datasets for website phishing detection. The aim is to (1) enable comparison of systems adopting different features, (2) overtake the short-lived nature of phishing websites, and (3) keep track of the evolution of phishing tactics. For experimenting the proposed scheme, we start by adopting a refined categorization of website phishing features and we systematically select a total of 87 commonly recognized ones, we categorize them, and we made them subjects for relevance and runtime analysis. We use the collected set of features to build a dataset in light of the proposed scheme. Thereafter, we use a conceptual replication approach to check the genericity of former findings for the built dataset. Specifically, we evaluate the performance of classifiers on individual and combined categories of features, we investigate different combinations of models, and we explore the effects of filter and wrapper methods on the selection of discriminative features. The results show that Random Forest is the most predictive classifier. Features gathered from external services are the most discriminative where features extracted from web page contents are less distinguishing. Besides external service based features, some web page content features are found not suitable for runtime detection. The use of hybrid features provided the best accuracy score of 96.61%. By investigating different feature selection methods, filter-based ranking with incremental removal of less important features improved the performance up to 96.83% better than wrapper methods.

Representation of the image of the minor in the publicity circulated via the Internet

The representation of the minor in advertisements is a topic that is scarcely studied and reflected in the bibliography, in social paediatrics as well as audio-visual communication. The aim of the present study is to describe how the minor is represented in the publicity that is conveyed through the internet, and whether or not that representation is adequate. An analysis was made of all the advertisements in which minors appeared during a period of two months, on three days a week, on the most visited web pages in Spain. The evaluation on how the minor was represented was carried out with an evaluation tool, constructed by the authors, based on European and Spanish legislation, which showed a high concordance between evaluators. A descriptive analysis was performed on the categorical variables, and the inter-dependent relationship was established between them using the chi-squared test. A total of 173 advertisements were identified in which at least one minor appeared, which was generally represented solo (63.5%), in a natural environment (36.9%) and of school age (44.5%). The web pages of the "general press" are those that most frequently show advertisement in which minors are represented, through the use of banners (82%). In the majority of cases the inadequate use of the figure of the minor takes place using tactical representation, and not just strategic. The image of the minor in the publicity that is conveyed via the most visited web pages in Spain is inadequate in 3 of every 3 advertisements. This misuse of the minor is usually seen in as unjustifiably strategic, and favouring non-positive values or that they favour situations of inequality.

Representation of the image of the minor in the publicity circulated via the Internet

IntroductionThe representation of the minor in advertisements is a topic that is scarcely studied and reflected in the bibliography, in social paediatrics as well as audio-visual communication. The aim of the present study is to describe how the minor is represented in the publicity that is conveyed through the internet, and whether or not that representation is adequate. Material and methodsAn analysis was made of all the advertisements in which minors appeared during a period of two months, on three days a week, on the most visited web pages in Spain. The evaluation on how the minor was represented was carried out with an evaluation tool, constructed by the authors, based on European and Spanish legislation, which showed a high concordance between evaluators. A descriptive analysis was performed on the categorical variables, and the inter-dependent relationship was established between them using the chi-squared test. ResultsA total of 173 advertisements were identified in which at least one minor appeared, which was generally represented solo (63.5%), in a natural environment (36.9%) and of school age (44.5%). The web pages of the “general press” are those that most frequently show advertisement in which minors are represented, through the use of banners (82%). In the majority of cases the inadequate use of the figure of the minor takes place using tactical representation, and not just strategic. ConclusionsThe image of the minor in the publicity that is conveyed via the most visited web pages in Spain is inadequate in 2 of every 3 advertisements. This misuse of the minor is usually seen in as unjustifiably strategic, and favouring non-positive values or that they favour situations of inequality.

Online social network trend discovery using frequent subgraph mining

Graph mining has become a well-established discipline within the domain of data mining. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale graph data mining to be conducted. Frequent subgraph mining (FSM) plays a very significant role in graph mining, attracting a great deal of attention in different domains, such as Bioinformatics, web data mining and social networks. Online social networks (SNs) play an important role in today’s Internet. These social networks contain huge amounts of data and present a challenging problem. FSM has been used in SNs to identify the frequent pattern trends existing in the network. A frequent pattern trend is defined as a sequence of time-stamped occurrences (support) value for specific frequent pattern that exist in the data. For example, most active researchers, most visited web pages or users’ navigation patterns over the web are few to mention. In the past few years, social network trend mining has been an active area of research. Many graph mining algorithms have been proposed, but a very limited effort exists for capturing an important dimension of SNs, which is trends discovery. Therefore, this paper introduces a novel FSM approach, called A-RAFF (A RAnked Frequent pattern-growth Framework), to discovering and comparing the frequent pattern trends exist in the social network data. Furthermore, the social network frequent pattern trend analysis has been evaluated using two standard social networks, Facebook-like network and the famous MSNBC news network datasets. Consequently, the discovered trends will help the underlying social networks to further enhance their platforms for the betterment of the users as well as for their business growth.

El mundo social de la discapacidad. Resultados de las páginas web

El interés deestetrabajo esexponer losresultados delanálisis delas distintas páginas web que ofrecen información y/o servicios relacionados con el mundo de la discapacidad. El análisis que se ha llevado a cabo pretende, únicamente, conocer las características más comunes de las páginas web visitadas (asociaciones, entidades corporativas relacionadas con la discapacidad) y proponer el uso de determinados elementos que mejoren las cualidades de navegación en dichas páginas en beneficio del usuario. Para ello, hemos analizado las páginas web de las principales asociaciones nacionales relacionadas con el mundo de la discapacidad.

Analyzing and eliminating phishing threats in IoT, network and other Web applications using iterative intersection

In today’s cyber era, Internet of Things (IoT) based products are increasingly adopted by users for various purposes. Accesses to these systems are facilitated via a web application to the end user. Traditionally, Phishing attacks were targeted toward banking and financial systems. With the rise in usage of IoT, the attack surface increases. Along with IoT specific attacks, attackers are targeting users with Phishing to steal passwords in order to gain access to IoT devices like security cameras. Phishing is an online attack that has been around for more than two decades. Though there are advanced prevention and detection mechanisms designed and developed by researchers and organizations, statistics show that Phishing has been on the rise. Often, there is a monetary incentive for the bad actor that carries out a phishing attack. This motivates attackers to advance their evasion mechanisms and maintain the status quo as a race between detection and evasion. A methodology Phish-Sec was introduced which paves a way to counter Phishing attacks in a pro-active manner by aggregating signatures of legitimate websites at the source. Phish-Sec involves determining uniqueness across ‘n’ websites. This manuscript provides the mathematical solution using intersection to determine the uniqueness of a visited web page. Iterative intersection is incorporated with Phish-sec to facilitate poison avoidance in its back-end system. By this, Phish-Sec can be expanded to a variety of applications, including non-financial based systems like IoT. It is proved that the overall efficiency of Phish-Sec increases along with its expansion capabilities. The true positive achieved by phish-sec is 99.15% which is 0.15% higher.

Identification of Web Site Reliability through Data Scrapping at Web Crawler’s Navigation

Searching a specified content on the web site is like epistle a single character in bunch of pages. When the user enters their keyword into any search engines, it takes that in to web server mining process for collecting the entire terms related to that entered key phrase. Few pages gives legal and authenticated matter for the user, which they really wanted to access. Whereas many other pages are bringing them some unwanted and malicious codes of pages or virus activity pages to harm user’s activities and the system’s functions. Generally a web page attacks the targeted system by faulty instructions and malevolent programs through some sort of intrusion methodologies are called as phishing. In this attacking method user is set to access unknown or illegal sites by the way of accessing some unidentified websites link imbedding with legal site contents. Once victim’s system performance got compromised then hackers started to do attack. To avoid this kind of molestations, user needs to understand reliability of web page’s contents before started to continue browsing. This research paper is going to present web crawler architecture, design complexities and implementation for scrapping web contents from visited web pages for indentifying their reliability and freshness.

Web Revisitation using Content and Context Tokens

Right now, the Internet is the most common medium people use to obtain access to knowledge. One's requirements vary from a single word sense to specialized papers and self-education. Revisiting previously visited web pages is one of the most common and unpleasant activities a person does. In fact, a significant primary feedback technique is included in tailoring the individual's memory of vigor and re-examination patterns. Our successful control of environment and object recollections, including decay and reinforcing techniques, may imitate device retention and retention mechanisms. This proposed solution takes into consideration the normal human retrieval method of utilizing episodic and textual memory signals for fast retrieval and includes a different site revisitation mechanism known as the "Web Page Preview" via meaning and information gained through user discovery and web page visits. The underlying methods for the collection, preservation, degradation and usage of meaning and quality of consumer memories for page re-discovery are addressed. Our 3 month user study says that of the time, location, and activity context factors in Web Page Prep, activity is the best recall guide, and context + content based re-discovery provides the highest results relative to context and content individual rediscovery. This concept, if completed, can be used to improve web surfing functionality by providing users a variety of apps with a rather simple UI.

A Filtering Process to Enhance Topic Detection and Labelling

Abstract In the digitization air, it is very important to detect and analyze the related topics to some discussions, occurred in social media or to label some visited web pages or documents. This information could be very helpful to the process of personalization as well as user satisfaction. There are various and different methods that study and deal with a huge data to provide insights into user behaviors. In this paper, we propose a filtering process that enhances topic detection and labelling. The latter aims to compact the result delivered by inferential algorithms such as Latent Dirichlet Allocation and Dirichlet Mixture Model. Our filtering process relies on words dependency on each contextual use for delivering high correlated label. Indeed, we use Word2vec as well as N-grams to eliminate non-significant words in each topic. We also use Hellinger distance to aggregate redundant words to the appropriate topic. Besides, we eliminate the non-reliable topics according to some metric. We associate this proposal to different topic-modeling algorithms. Experiments demonstrate the effectiveness of the made association between inferential model and our filtering process compared to the state of the art. We also use different textual data to validate our proposal.

Efficient Mining of Interest Patterns on Click Stream Data

Nowadays, large amount of data is generated daily in e-commerce applications as click stream data. Because of the availability of this tremendous amount of data analyzing the user browsing behaviour and finding frequent navigation patterns of different web pages accessed by web users is an key element for retailers to optimize the website and personalized the web services of different e-commerce websites. User browsing behaviour is evaluated based on user interests on web pages or products. There are different parameters are considered while analyzing the click stream data for calculating frequent navigation patterns and context based customer behaviour in online data bases. In this paper we developed different models for optimizing and personalizing web service and sequential frequent patterns using the parameters: browsing path, frequently visited web pages, time duration of web pages and user interest. These novel models uses the parameters and applied on click stream data to optimize the web pages and improve the personalized recommendation.

User profiling for web personalization using multi agent and DBSCAN based approach

The user experience is enhanced by the Web Personalization System (WPS), which depends on the User's Interests (UI) and references are stored in the User Profile (UP). The profiles should be able to adapt and reproduce the change of user’s behavior for such system. Existing web page Recommendation Systems (RS) are still limited by several problems, some of which are the problem of recommending web pages to a new user whose browsing history is not available (Cold Start), sparse data structures (Sparsity), and the problem of over-specialization. In this paper, the UI has been tracked and Dynamic User Profiles have been maintained by introducing a method called Density-Based Spa-tial Clustering of Applications with Noise-User Profiling (DBSCAN-UP). The mapping web pages, construct the ontological concepts, which represent the UI, and the interests of users are learned by the reference ontology, which are used to map the visited web pages. The process of storage, management and adaptation of UI is facilitated by multi-agent system. The different user browsing behaviors learning and adapting capability is built in the proposed system and the efficiency of the DBSCAN-UP model is evaluated by the series of experi-ments. The accuracy of the DBSCAN-UP was achieved up to 5% compared to the existing methods.

Personalizing Search via Automated Analysis of Interests and Activities

We formulate and study search algorithms that consider a user's prior interactions with a wide variety of content to personalize that user's current Web search. Rather than relying on the unrealistic assumption that people will precisely specify their intent when searching, we pursue techniques that leverage implicit information about the user's interests. This information is used to re-rank Web search results within a relevance feedback framework. We explore rich models of user interests, built from both search-related information, such as previously issued queries and previously visited Web pages, and other information about the user such as documents and email the user has read and created. Our research suggests that rich representations of the user and the corpus are important for personalization, but that it is possible to approximate these representations and provide efficient client-side algorithms for personalizing search. We show that such personalization algorithms can significantly improve on current Web search.

Query based approach for referrer field analysis of log data using web mining techniques for ontology improvement

This work presents a new framework as to how web mining is helpful for information retrieval, using ontology and web log files. Ontology plays a major role in the retrieval of semantic data. The researcher has already constructed the string instrument ontology using protege 5.0, which helps in refining the web search in music domain. The researcher has proposed a novel approach for ontology management in which the ontology is continuously updated using the knowledge extracted/discovered from the analysis of the log file (specifically the data related to the referrer field) in form of new concepts and new relationships between new and/or existing concepts. The goal of this study is to use data mining algorithms to analyse visitors and visited web pages of the website and somehow characterise or distinguish them in some way. During this the researcher has collected ‘guitar’ web access log from guitar selling website of 363 days of the year 2016. After pre-processing of this log file, two new feature sets have been extracted from ‘guitar’ log file and constructed two files namely ‘File1’ and ‘File 2’. File 2 is also known as query log. Further clustering (EM), association rule finding (Apriori) and sequential patterns (n-gram) algorithms have been applied for suggestions of new concepts to continuously update and improve the existing ontology from time to time.

Personal Web Library: organizing and visualizing Web browsing history

PurposeModern Web browsers all provide a history function that allows users to see a list of URLs they have visited in chronological order. The history log contains rich information but is seldom used because of the tedious nature of scrolling through long lists. This paper aims to propose a new way to improve users’ Web browsing experience by analyzing, clustering and visualizing their browsing history.Design/methodology/approachThe authors developed a system called Personal Web Library to help users develop awareness of and understand their Web browsing patterns, identify their topics of interest and retrieve previously visited Web pages more easily.FindingsUser testing showed that this system is usable and attractive. It found that users can easily see patterns and trends at different time granularities, recall pages from the past and understand the local context of a browsing session. Its flexibility provides users with much more information than the traditional history function in modern Web browsers. Participants in the study gained an improved awareness of their Web browsing patterns. Participants mentioned that they were willing to improve their time management after viewing their browsing patterns.Practical implicationsAs more and more daily activities rely on the internet and Web browsers, browsing data captures a large part of users’ lives. Providing users with interactive visualizations of their browsing history can facilitate personal information management, time management and other meta-level activities.Originality/valueThis paper aims to help users gain insights into and improve their Web browsing experience, the authors hope that the work they conducted can spur more research contributions in this underdeveloped yet important area.