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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.

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.

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.

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.

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.

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.

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.

Online businesses require a deep understanding of their customers’ interests to innovate and develop new products and services. Users, on the other hand, rarely express their interests explicitly. The purpose of this study is to predict users’ implicit interest in products of an online store based on their mouse behavior through various product page elements.

First, user mouse behavior data is collected throughout an online store website. Next, several mouse behavioral features on the product pages elements are extracted and finally, several models are extracted using machine learning techniques to predict a user’s interest in a product.

The results indicate that focusing on mouse behavior on various page elements improves user preference prediction accuracy compared to other available methods.

User mouse behavior was used to predict consumer preferences in this study, therefore gathering additional data on user demography, personality dimensions and emotions may significantly aid in accurate prediction.

Mouse behavior is the most repeated behavior during Web page browsing through personal computers and laptops. It has been referred to as implicit feedback in some studies and an effective way to ascertain user preference. In these studies, mouse behavior is only assessed throughout the entire Web page, lacking a focus on different page elements. It is assumed that in online stores, user interaction with key elements of a product page, such as an image gallery, user reviews, a description and features and specifications, can be highly informative and aid in determining the user’s interest in that product.

Many computer related injuries occur from mouse use. The mouse is the primary input device by virtue of its universal availability, yet it is not the best for all‐round work. Alternative devices such as the track ball, touch pad, and graphics tablet can be used more effectively for precise cursor control, while reducing stress and the potential for injury. Though these devices may seem exotic or a luxury, they are in fact affordable and well‐suited to daily use by everyone. This article describes the mouse and its inherent weaknesses, and offers more practical alternatives. These alternatives may help reduce injury‐producing activities, and may also improve productivity and creativity by allowing more precision cursor controls.

Developing attention-aware systems and interfaces based on eye tracking technology could revolutionize mainstream human–computer interaction to make the interaction between human beings and computers more intuitive, effective and immersive than can be achieved traditionally using a computer mouse. This paper aims to propose an eye-controlled interactive reading system (ECIRS) that uses human eyes instead of the traditional mouse to control digital text to support screen-based digital reading.

This study uses a quasi-experimental design to examine the effects of an experimental group and a control group of learners who, respectively, used the ECIRS and a mouse-controlled interactive reading system (MCIRS) to conduct their reading of two types of English-language text online – pure text and Q&A-type articles on reading comprehension, cognitive load, technology acceptance, and reading behavioural characteristics. Additionally, the effects of learners with field-independent (FI) and field-dependence (FD) cognitive styles who, respectively, used the ECIRS and MCIRS to conduct their reading of two types of English-language text online – pure text and Q&A-type articles on reading comprehension are also examined.

Analytical results reveal that the reading comprehension of learners in the experimental group significantly exceeded those in the control group for the Q&A article, but the difference was insignificant for the pure text article. Moreover, the ECIRS improved the reading comprehension of field-independent learners more than it did that of field-dependent learners. Moreover, neither the cognitive loads of the two groups nor their acceptance of the technology differed significantly, whereas the reading time of the experimental group significantly exceeded that of the control group. Interestingly, for all articles, the control group of learners read mostly from top to bottom without repetition, whereas most of the learners in the experimental group read most paragraphs more than once. Clearly, the proposed ECIRS supports deeper digital reading than does the MCIRS.

This study proposes an emerging ECIRS that can automatically provide supplementary information to a reader and control a reading text based on a reader’s eye movement to replace the widely used mouse-controlled reading system on a computer screen to effectively support digital reading for English language learning. The implications of this study are that the highly interactive reading patterns of digital text with ECIRS support increase motivation and willingness to learn while giving learners a more intuitive and natural reading experience as well as reading an article online with ECIRS support guides learners’ attention in deeper digital reading than does the MCIRS because of simultaneously integrating perceptual and cognitive processes of selection, awareness and control based on human eye movement.

The paper aims to describe the sensors used for interfacing with consumer electronic devices.

The paper describes the types of sensors employed in user‐interface devices such as trackballs, mice, touch pads, touch screens and gesture‐based systems. It concludes with a brief consideration of brain‐computer interface technology.

It is shown that a diverse range of sensors is used to interface with consumer electronics. They are based on optical, electrical, acoustic and solid‐state (MEMS) technologies. In the longer term, many may ultimately be replaced by sensors that interpret thought by detecting brain waves.

The paper provides a timely review of the sensors used to interface with consumer electronics. These constitute a very large and rapidly growing market.

The purpose of this paper is to report on how researchers at Penn State University used video screen capture technology to learn more about student usage of the library's databases.

The process of identifying and implementing a novel way to capture and analyze the human‐computer interaction is outlined and discussed.

Because of the drawbacks associated with both formal, direct physical observation of research participants and videotaping participants, video screen capture technology is chosen as a better way to track human‐computer interaction.

Video screen capture technology is an inexpensive, user‐friendly way to enhance electronic resource usability studies in any library. Research files can be easily exported into coding software for data analysis.

The paper examines a new, non‐invasive way to capture student research behavior. It shows how any library could use this same technology to conduct research on how their resources are being used by their user population.

The purpose of this paper is to describe an electro-mechanical lumped parameters model used for the simulation of an energy harvester device.

The model is taking into account the main features of both mechanical and electromagnetic phenomena keeping the computational burden as low as possible to insert it inside an optimisation loop.

The simulation tool is then used to design the main parameters of an energy harvester able to supply a computer mouse by converting mechanical energy provided by the computer user.

The use of a multi-physics analysis tool inside one optimisation loop is a difficult task that requires the honing of all the modules involved in the performance evaluation. The developed approach has shown to be reliable, efficient and has been a key factor in the development of a new product.

Discusses the confusion in terminology used between IBM compatible machines and Macintosh systems. Provides a glossary of Macintosh terms and how they relate to IBM language.

This paper aims to design a compact scheme of behavioural biometric-based user authentication, develop an adaptive mechanism that selects an appropriate classifier in an adaptive way and conduct a study to explore the effect of this mechanism.

As a study, the proposed adaptive mechanism was implemented using a cost-based metric, which enables mobile phones to adopt a less costly classifier in an adaptive way to build the user normal-behaviour model and detect behavioural anomalies.

The user study with 50 participants indicates that our proposed mechanism can positively affect the authentication performance by maintaining the authentication accuracy at a relatively high and stable level.

The authentication accuracy can be further improved by incorporating other appropriate classifiers (e.g. neural networks) and considering other touch-gesture-related features (e.g. the speed of a touch).

This work explores the effect of adaptive mechanism on behavioural biometric-based user authentication. The results should be of interest for software developers and security specialists in deciding whether to implement such a mechanism for enhancing authentication performance on mobile phones.

The user study with 50 participants indicates that this mechanism can positively affect the authentication performance by maintaining the authentication accuracy at a relatively high and stable level. To the best of our knowledge, our work is an early work discussing the implementation of an adaptive mechanism on a mobile phone.