Browsing Habits Research Articles

E-learning Recommender System based on k-Nearest Neighbor (KNN), Singular Value Decomposition (SVD), and CoClustering Approaches

The advent of the digital age resulted in the development of the e-learning platform as a helpful resource for top-notch e-learning materials. Despite its promise, the entire scope of its potential hasn't been ultimately discovered. E-learning platforms are expected to offer information that meets customers' requirements and interests to draw users and boost profits. These suggestions are created by considering various aspects, including previous browsing habits, purchase history, demographic data, and others. E-learning platforms may improve the quality of the learning process by offering users interesting information tailored to their specific requirements and preferences by utilizing cutting-edge technology. This paper explores Machine Learning (ML) approaches used in e-learning content recommender platforms. Three ML approaches are chosen and applied to predict user interest and need Singular Value Decomposition (SVD), k-nearest Neighbor Baseline (KNNBaseline), and CoClustering. These ML approaches boost user experience by analyzing data trends and patterns of usage to deliver insights into the best customized and appropriate educational materials for every user. The previous user ratings trend is employed to derive the item ratings. Mean absolute error (MAE) and root mean square error (RMSE) are assessed to evaluate the implemented techniques' effectiveness.

Interactive Interface Design for Adaptive and User Behavioural Habits

This study aims to explore adaptive interaction design in relation to user behavior habits, optimizing the user interface (UI) of mobile applications through statistical analysis of user habits. Specifically, the research analyzes user behavior regarding landscape and portrait browsing modes when using mobile applications. The results indicate that adjusting the UI according to different browsing habits can significantly enhance user satisfaction. Therefore, adaptive interaction design strategies play a crucial role in accommodating user behavior habits, contributing to improved user experience and satisfaction.

Anatomical and Computed Tomography Study of the Mandible of the Patagonian Huemul (Hippocamelus bisulcus): Ecological and Clinical Insights.

Given the high prevalence of skeletal and mandibular diseases in the Patagonian Huemul, comprehensive anatomical studies are essential to understand the impact of nutritional deficiencies and support conservation efforts. The aim of this study is to provide a detailed anatomical characterisation of three exhumed mandibles of Patagonian huemul (Hippocamelus bisulcus) through three-dimensional reconstructions obtained from computed tomography data and semi-automatic segmentation, documenting their distinctive features. The findings revealed distinctive features in the mandible, consistent with the browsing habits of herbivores, such as a robust coronoid process, a wide and deep pterygoid fossa, a significantly lower mandibular condyle compared to other deer species and a unique configuration of the mandibular canal with a curved caudal portion and a straight rostral portion. These anatomical adaptations are likely related to the species' feeding habits and behaviour. The study also addressed the challenges of researching an endangered species, given that access to biological material is restricted by strict regulations in Chile and Argentina. These restrictions limited the available sample size and hindered the acquisition of additional specimens, which could affect the generalisation of the results. Despite these limitations, the research provides valuable anatomical insights that are fundamental for the biology, clinical practice and management of specimens. In conclusion, the mandibles of both juvenile and adult Patagonian huemul demonstrate distinctive features characteristic of browsing herbivores. The findings can serve as a basis for future comparative studies on mandibular anatomy and function in this endangered deer species as well as in other herbivorous deer.

A multiproxy approach for the feeding ecology of Late Miocene ground sloths from the southwestern Amazonia, Upper Solimões Formation, Brazil

A rich assemblage of fossil sloths is recognized in the southwestern Brazilian Amazon, Upper Solimões Formation (Acre Basin), and represents some of the most diverse Folivora fauna from the Late Miocene of tropical South America. Here, we have used a multiproxy approach using carbon (δ13Csc) and oxygen (δ18Osc) isotope compositions of bioapatite structural carbonate, stereomicrowear, ecomorphological indices (relative muzzle width, RMW and occlusal surface area, OSA), and body mass estimates in order to suggest the habitats and diet of the mylodontids (Octodontobradys puruensis, Urumacotherium campbelli, Pseudoprepotherium venezuelanum and Mylodontinae gen. et. sp. nov.) and the nothrotheriid (cf. Mionothropus) ground sloths from localities along the banks at the Purus and Acre rivers in Amazonas and Acre states. The isotopic results (δ13Csc = −13.2 ± 1.2 ‰; δ18Osc = +24.7 ± 0.8 ‰) indicated a consistently C3 plant diet in open-canopy woodland and closed-canopy forest habitats during warm conditions across the Tortonian ages (∼10 and 8.5 Myr). U. campbelli (Urumacotherium sp., bm: 605 kg; Niterói Site) and cf. Mionothropus (bm: 163 kg; Acre 5 Site) possibly had specialized browsing habits (folivorous/frugivorous), due to the few coarse scars in relatively polished tooth surfaces and elongate skull with narrow muzzle, respectively. Both taxa, as also P. venezuelanum (bm: 733 kg; Niterói Site), showed a low OSA values suggesting limited oral food processing offset by higher fermentation capacity and/or lower metabolic requirements. Efficient capacity for oral food processing and a generalized browsing habit with consumption of high intrinsic toughness and abrasiveness items were inferred for O. puruensis (bm: 526 kg; Talismã Site), resembling the pattern observed for the Pleistocene megathere Eremotherium laurillardi from the southwestern Amazon (Acre State). A non-selective browser diet was also supported for the Mylodontinae gen. et. sp. nov. specimen from Cavalcante Site; its similar dentary morphology to the bulk-feeder taxa implies that this individual could occasionally eat C3 herbaceous/grasses.

Genrefication in Secondary School Libraries

Most school libraries organize fiction books alphabetically by an author’s last name. In order for a student to find and compare potential fiction reading material, the traditional school library model would have the student either search the library system for the topic and then go from stack to stack looking for books about the topic from any number of locations or seek advice from school library staff who would direct the student to potential titles as part of a reader’s advisory. In an effort to make fiction book selection easier for students, some school librarians have turned to genrefied fiction collections. However, there are many school librarians who feel that organizing fiction books by genre first rather than author’s last name creates more problems than it solves. This paper will explore the history behind genrefication, discuss the Paradox of Choice and patron browsing habits, and present the modern debate regarding genrefication of fiction books in school libraries.

Data-Driven Exploration of Web Browsing Habits: A Visual Analysis with BHVis

Browsing history is an eminent tool for internet users to keep track of the daily and specially visited webpages without bookmarking them. Traditional history tools have become insufficient to satisfy their purpose of addressing complex browsing patterns. Graphical interfaces are the critical solutions for these complexities. Therefore, visualising the browsing histories interactively can be helpful in this case. There are many built-in basic visualisation browsing icons for the ease of the users in different browsers, which help the user quickly access the visited pages. But these tools lack in-depth analysis of users' browsing habits. In this study, we propose a novel approach for visualising the browsing history data and browsing habits of the user. We named this system BHVis (Browsing Habits Visualisation), which visualises the user’s web page visits using different visualisation methods and visual representations to make the browsing habits of the users explicable. The main contribution of this system is to enable users to gain insight into their browsing patterns and enable self-analysis and self-improvement. The system is evaluated based on different case studies. The study shows that this system enables users to gain insights into their web usage and monitor their web browsing habits.

DIGITAL TRANSFORMATION VULNERABILITIES: ASSESSING THE RISKS AND STRENGTHENING CYBER SECURITY

Digital transformation has revolutionized the way organizations operate, enabling them to leverage advanced technologies for increased efficiency and productivity. However, along with the benefits, digital transformation also brings new vulnerabilities and risks, particularly in the realm of cybersecurity. This abstract aims to provide an overview of the vulnerabilities associated with digital transformation and highlight the importance of assessing risks and strengthening cybersecurity measures to mitigate potential threats. The rapid adoption of cloud computing, Internet of Things devices, and interconnected systems has expanded the attack surface for cybercriminals. Organizations must recognize the potential vulnerabilities introduced by these technologies, such as data breaches, system failures, and unauthorized access. Understanding the specific risks associated with digital transformation is crucial for developing effective cybersecurity strategies. Assessing risks involves evaluating the organization’s digital infrastructure, identifying potential weak points, and determining the likelihood and potential impact of various threats. This process enables organizations to prioritize cybersecurity efforts and allocate resources effectively. It also involves evaluating the security measures in place, including firewalls, intrusion detection systems, encryption protocols, and employee training programs, to ensure they are up to date and resilient against emerging threats. Strengthening cybersecurity requires a multi-layered approach that encompasses technical measures, organizational policies, and a culture of security awareness. Implementing robust security controls, such as regular software updates, network segmentation, and access controls, helps fortify digital systems against attacks. Additionally, establishing incident response plans and conducting regular security audits aids in identifying vulnerabilities and responding swiftly to cyber incidents. Furthermore, fostering a culture of cybersecurity awareness among employees is essential. Educating staff on best practices, such as strong password management, phishing awareness, and safe browsing habits, empowers them to become the first line of defence against cyber threats.

Educational Information Refinement with Application Using Massive-Scale Data Mining

In the manuscript, we propose a novel online learning mechanism based on educational data mining. By leveraging the computer-assisted information-based learning guidance platform, we collect the relevant information of students’ login platform and resource browsing. Subsequently, we preprocess these students’ login data based on which the statistical analysis of students’ login and resource browsing habits are learned through a decision-making mechanism. The decision tree algorithm discovers the underlying factors that influence it from million-scale real-world instructors/students. In this way, instructors and teaching staff can effectively grasp the learning process of students according to the analyzing results. Based on this, the target teaching content integration and teaching model construction can be realized accordingly. This can substantially improve the effectiveness and quality of online learning. In the evaluation stage, we observe that the strategy to deploy a virtual lab environment vigorously brings greater flexibility in the allocation of computing resources to educational institutions. In an ideal sandboxed laboratory context, students can obtain and create an internal network and then have accession to all the computers conveniently. By doing so, gathering savvy skills gives them the workability to build architectures based on mined data. In this work, we adopt the so-called EDUCloud to provide a personal cloud network connection that can flexibly deploy the mined laboratory-related data to facilitate online learning between instructors and students.

Public Health Shock, Intervention Policies, and Health Behaviors: Evidence from COVID-19

In response to the COVID-19 pandemic, many countries, including the U.S., adopted intervention policies aimed at averting the spread. However, these policies may have led to significant changes in public health behaviors. We use Google search queries to examine how state government actions are associated with people’s internet searches (internet browsing habits) related to health behaviors. We employ the differences-in-differences method to determine the link between disease outbreak, associated intervention policies, and changes in health behavior related searches. Our findings show that school closures, restaurant restrictions, and stay-at-home orders lead to a significant rise in searches for workout, physical activity, exercise, takeout, liquor, and wine. Moreover, people’s concerns regarding weight loss, diet, nutrition, restaurant, and fast food substantially decline following stay-at-home orders. Our event-study results indicate that changes in health behaviors began weeks before stay-at-home orders were implemented contemporaneously with emergency declarations and other partial closures. These findings suggest that people’s health behaviors are notably affected by state government’s intervention policies.

Pester power- The most powerful weapon of kids

The internets have taken up the majority of children's leisure time, drastically altering their media habits. Even the dinner table, where parents used to instill many positive values and wholesome ideas in their children, has been supplanted with watching television. Children today are so technologically aware that they practically know how everything works on the internet, and their browsing habits expose them to more of these sites and products around the world that they may then demand from their parents. Children's growing consumerism is blamed for the emergence of Pester Power. Due to the deluge of new TV (television) content, this trend has emerged. Nowadays, almost all marketers seek to promote their goods directly to the young target audience. The composition of Indian families has shifted from joint families to nuclear or extended families as a result of influences from the west. Indian consumerism has been greatly influenced by exposure to the globalised world and changing family dynamics. Kids are ruling the consumer market as three distinct segments are represented by them. The research aims to study the influence of cartoon character, celebrity endorsement on child’ pester power. The study also proves that there is positive relationship between humour in advertisements with child buying behaviour.

Nowcasting of Lumber Futures Price with Google Trends Index Using Machine Learning and Deep Learning Models

Abstract Firms engaged in producing, processing, marketing, or using lumber and lumber products always invest in futures markets to reduce the risk of lumber price volatility. The accurate prediction of real-time prices can help companies and investors hedge risks and make correct market decisions. This paper explores whether Internet browsing habits can accurately nowcast the lumber futures price. The predictors are Google Trends index data related to lumber prices. This study offers a fresh perspective on nowcasting the lumber price accurately. The novel outlook of employing both machine learning and deep learning methods shows that despite the high predictive power of both the methods, on average, deep learning models can better capture trends and provide more accurate predictions than machine learning models. The artificial neural network model is the most competitive, followed by the recurrent neural network model.

Political Polarization in Online News Consumption

Political polarization appears to be on the rise, as measured by voting behavior, general affect towards opposing partisans and their parties, and contents posted and consumed online. Research over the years has focused on the role of the Web as a driver of polarization. In order to further our understanding of the factors behind online polarization, in the present work we collect and analyze Web browsing histories of tens of thousands of users alongside careful measurements of the time spent browsing various news sources. We show that online news consumption follows a polarized pattern, where users' visits to news sources aligned with their own political leaning are substantially longer than their visits to other news sources. Next, we show that such preferences hold at the individual as well as the population level, as evidenced by the emergence of clear partisan communities of news domains from aggregated browsing patterns. Finally, we tackle the important question of the role of user choices in polarization. Are users simply following the links proffered by their Web environment, or do they exacerbate partisan polarization by intentionally pursuing like-minded news sources? To answer this question, we compare browsing patterns with the underlying hyperlink structure spanned by the considered news domains, finding strong evidence of polarization in partisan browsing habits beyond that which can be explained by the hyperlink structure of the Web.

Web Browsing Habits of Healthcare Professions Students in Gross Anatomy Laboratory.

Personal computer use for educational purposes by the healthcare professions students has become ubiquitous. Although the effect of computer-based dissection instructions has been studied, there is a paucity of information regarding student browsing habits of information available on the Internet. Although current students have favorable attitudes toward accessing anatomical information online, web browsing habits have not yet been investigated specifically in the dissection laboratory setting. The purpose of this study was to describe the browsing habits of the healthcare professions students in the gross anatomy setting using a retrospective analysis. Files containing web browser history were retrieved from desktop computers in the gross anatomy laboratory and custom code was written to parse them into comma separated value files. Each web address was categorized and descriptive statistics was calculated. Browser history for 24 computers was analyzed from June 2013 to January 2015. During this period, students accumulated 100,857 webpage visits. Most often, students performed a Google search for anatomy (22.0% of all visits) and non-anatomy related (20.6% of all visits) information. Students also used the web browser to access various entertainment (16.4% of all visits) and productivity related services (15.9% of all visits). This analysis revealed a large volume of webpage visits by the healthcare professions students in the gross anatomy laboratory. A wide diversity of anatomy and non-anatomy related webpages were visited. Future analyses could be directed at examination of when in relation to class time students accessed the information, how browsing habits change over time, and what anatomical structures were most commonly searched for.

The blame game: Using eDNA to identify species-specific tree browsing by red deer (Cervus elaphus) and roe deer (Capreolus capreolus) in a temperate forest

Increasing deer populations in many temperate regions can affect tree regeneration, resulting in severe long-term impacts on forest structure, composition and diversity. Of the most common deer species in Europe — red deer (Cervus elaphus) and roe deer (Capreolus capreolus) — roe deer are generally thought to have the highest impact on palatable tree species owing to their feeding niche. Although browsing and its potential consequences are well researched, less is known about the influence of specific deer species within multi-species ungulate communities on specific tree species. Environmental DNA (eDNA) allows the determination of species-specific browsing habits without the need for direct observations, facilitating effective targeting of management interventions. In this study eDNA was used to elucidate the browsing patterns of these two deer species in the temperate forest of the Bavarian Forest National Park, Germany and analysed the influence of tree species, management type and height of browsing, on the success rate of the method. Samples were collected from twigs used in feeding trials from enclosures containing red deer or roe deer and from naturally browsed twigs in three different management types within the national park. eDNA was successfully amplified from 98% of the feeding trial samples, and the correct deer species was identified for all samples. eDNA was successfully amplified from approximately 50% of the naturally browsed samples. Neither management type, tree species, nor height of browsing had any significant influence on the success of the method. For silver fir and rowan, no significant difference was found in the proportion of browsing events attributable to roe or red deer, when the two deer species occur at similar densities. These results indicate that roe deer might not always be disproportionately responsible for the browsing of palatable tree species as expected from its food niche. Roe deer were significantly more responsible for browsing at lower heights than red deer. Although not statistically significant, roe deer were more responsible for browsing in intact forest compared to bark-beetle-impact forest, with the opposite relationship for red deer.

Analyzing the Browsing Basket: A Latent Interests-Based Segmentation Tool

The increasing importance of online distribution channels is paralleled by a rising interest in gaining insights into the customer journey to online purchases. In this paper we propose an easy-to-implement two-step procedure that enables online marketing managers to disentangle the complex interrelationships hidden behind observed Internet browsing behavior across websites. Utilizing the procedure allows managers to gain a better understanding why Internet users are visiting their website(s) and how these visits are related to purchases. In the first step, the procedure uncovers latent interests underlying online users' browsing behavior. In the second step, we segment the online users based on their uncovered latent interests. This way, online marketers may understand how segment-specific combinations of latent interests are linked to purchase behavior. We apply the procedure to ComScore clickstream data across 472 websites. We show that there is considerable heterogeneity among online users both regarding online browsing habits, combinations of latent interests, and their conversion into online purchases. For example, some users are interested in apparel and travel service opposed to users who are interested in entertainment tickets. Our empirical analysis confirms that a relatively small fraction of online users realize 70% of online spending. In addition, we detect substantial segment-specific differences of shopping behavior across categories, the most important product categories being apparel as well as food & beverages. Our descriptive perspective comes up with surprising associations among the websites which can be interesting for online marketers.

The pre-modern self in post-modern times: The rhetoric of privacy in the work of Walter J. Ong, S.J.1

The Edward Snowden revelations of leaked National Security Agency documents in 2013 received considerable attention in the scholarly and popular media. More recently, Facebook and other social media sites have come under scrutiny for their role in the 2016 US elections. Surveys of attitudes consistently indicate that Americans fear identity theft and other problems arising from Internet use. Yet Americans consistently turn over their personal data to Google, Facebook and other large companies. An important 2015 study of Internet browsing habits showed that users, after an initial concern with privacy-enhancing technologies following the Snowden revelations, quickly returned to sports and celebrity gossip as loci of interest. This article teases out reasons for the apparent contradiction. It uses Walter J. Ong’s work, Norbert Elias’s study of manners, along with more recent scholarship on privacy, on the early book trade and on aspects of the early modern period to argue that the contemporary concept of privacy is historically contingent. Historical circumstances and the development of certain technologies encouraged the development of privacy and the self. Current circumstances and technologies are contributing to their decay. This article grows out of two preliminary addresses, one given at the 2016 meeting of the Media Ecology Association in Bologna and the other at a meeting of the Gonzaga University Socratic Club in 2018.

An inexpensive and open‐source method to study large terrestrial animal diet and behaviour using time‐lapse video and GPS

Abstract The behaviour of free‐ranging animals is difficult to study, especially on the large spatial and temporal scales relevant to long‐lived large species. Animal‐borne video and environmental data collection systems (AVEDs) record behaviour and other data in real time as animals conduct daily activities. However, few studies have combined systematically collected, long‐term AVED foraging data with environmental and movement data to test hypotheses on animal foraging. Additionally, AVEDs are often either prohibitively expensive, or require extensive fabrication and programming knowledge. The video and coordinate animal‐mounted system (VACAMS) is an animal‐mounted data collection system based on a modified GoPro® action camera platform that records short, first ‘person’ perspective videos of animal behaviour on an automated time‐lapse schedule. As most videos are georeferenced, researchers can return to the locations of specific behaviours and collect accurate, fine‐grained data on non‐woody vegetation and other habitat characteristics that may influence animal behaviour. Moreover, VACAMS are inexpensive and easy to use. This study describes VACAMS preliminary data on cattle foraging and a hypothesis exploring free‐ranging cattle browsing habits throughout the rainy season in the tropical dry forest of Sonora, Mexico. I generated a database of vegetation types consumed by cows each month (annual, woody and leaf litter) and compared actual vegetation type frequencies to a priori assumptions based on seasonal patterns of forage availability. During the monsoons, when palatable vegetation was abundant, frequencies of annual and woody perennial vegetation in cattle diets did not differ from month to month. When the rains ceased and palatable vegetation became scarce, cows switched to leaf litter, dead annual vegetation, twigs and dried leguminous fruits. Open‐source software and commercially available hardware make VACAMS financially attainable for many researchers, land managers, students and other user groups. VACAMS could be used on a range of domestic and semi‐domestic free‐ranging animals, particularly in dense forests where conventional observations are impossible. With improvements to GPS battery life and durability, the weakest points of the system, VACAMS could also potentially apply to studies of other large terrestrial animals.

Multiscale entropy rate analysis of complex mobile agents.

Accurate prediction of the motion of objects is a central scientific goal. For deterministic or stochastic processes, models exist which characterize motion with a high degree of reliability. For complex systems, or those where objects have a degree of agency, characterizing motion is far more challenging. The information entropy rate of motion through a discrete space can place a limit on the predictability of even the most complex or history-dependent actor, but the variability in measured encountered locations is inexorably tied to the spatial and temporal resolutions of those measurements. This relation depends on the path of the actor in ways that can be used to derive a general law in closed form relating the mobility entropy rate to different spatial and temporal resolutions, and the path properties within each cell along the path. Correcting for spatial and temporal effects through regression yields the path properties and a measure of mobility entropy rate robust to changes in dimension, allowing comparison of mobility entropy rates between datasets. Employing this measure on empirical datasets yields novel findings, from the similarity of taxicabs to drifters, to the predictable motions of undergraduates, to the browsing habits of Canadian moose.

The dietary ecology of the extinct springbok Antidorcas bondi

The common springbok (Antidorcas marsupialis), with its characteristic dorsal fan and stotting movement is the national animal of South Africa. In the Pleistocene, this species shared the subcontinent with the extinct Bond's springbok (Antidorcas bondi), and differences in their ecological relationship has been of long-standing interest to palaeontologists. Brink (1987) proposed that Bond's springbok, as a characteristic species for the Florisian Land Mammal Age, was an essential part of a Pleistocene grazing community that likely occupied open grasslands in the interior. This niche contrasts markedly with the flexible browsing habits of the extant springbok, which can tolerate arid habitats. Here we revisit the dietary ecological history of A. bondi by means of published species distribution data through time, and carbon stable isotope data. The results suggest that its specialist grazing ecology remained stable from the Mid-Pleistocene onwards until its extinction in the early Holocene (c. 8-10ka). The results are discussed in the context of Pleistocene palaeoenvironmental change in southern Africa.

Contrasting Web Robot and Human Behaviors with Network Models

The web graph is a commonly-used network representation of the hyperlink structure of a website. A network of similar structure to the web graph, which we call the session graph has properties that reflect the browsing habits of the agents in the web server logs. In this paper, we apply session graphs to compare the activity of humans against web robots or crawlers. Understanding these properties will enable us to improve models of HTTP traffic, which can be used to predict and generate realistic traffic for testing and improving web server efficiency, as well as devising new caching algorithms. We apply large-scale network properties, such as the connectivity and degree distribution of human and Web robot session graphs in order to identify characteristics of the traffic which would be useful for modeling web traffic and improving cache performance. We find that the empirical degree distributions of session graphs for human and robot requests on one Web server are best fit by different theoretical distributions, indicating at a difference in the processes which generate the traffic.