The ability to recognize emotions is an important part of building intelligent computers. Extracting the emotional aspects of a situation could provide computers with a rich context to make appropriate decisions about how to interact with the user or adapt the system response. The problem that we address in this thesis is that the current methods of determining user emotion have two issues: the equipment that is required is expensive, and the majority of these sensors are invasive to the user. These problems limit the real-world applicability of existing emotion-sensing methods because the equipment costs limit the availability of the technology, and the obtrusive nature of the sensors are not realistic in typical home or office settings. Our solution is to determine user emotions by analyzing the rhythm of an individuals typing patterns on a standard keyboard. Our keystroke dynamics approach would allow for the uninfluenced determination of emotion using technology that is in widespread use today. We conducted a field study where participants keystrokes were collected in situ and their emotional states were recorded via self reports. Using various data mining techniques, we created models based on 15 different emotional states. With the results from our cross-validation, we identify our best-performing emotional state models as well as other emotional states that can be explored in future studies. We also provide a set of recommendations for future analysis on the existing data set as well as suggestions for future data collection and experimentation.

The ability to recognize emotions is an important part of building intelligent computers. Extracting the emotional aspects of a situation could provide computers with a rich context to make appropriate decisions about how to interact with the user or adapt the system response. The problem that we address in this thesis is that the current methods of determining user emotion have two issues: the equipment that is required is expensive, and the majority of these sensors are invasive to the user. These problems limit the real-world applicability of existing emotion-sensing methods because the equipment costs limit the availability of the technology, and the obtrusive nature of the sensors are not realistic in typical home or office settings. Our solution is to determine user emotions by analyzing the rhythm of an individuals typing patterns on a standard keyboard. Our keystroke dynamics approach would allow for the uninfluenced determination of emotion using technology that is in widespread use today. We conducted a field study where participants keystrokes were collected in situ and their emotional states were recorded via self reports. Using various data mining techniques, we created models based on 15 different emotional states. With the results from our cross-validation, we identify our best-performing emotional state models as well as other emotional states that can be explored in future studies. We also provide a set of recommendations for future analysis on the existing data set as well as suggestions for future data collection and experimentation.

This study examined the potential use of keystroke dynamics to create keyprints (typing fingerprints) to authenticate individuals in online assessment situations. The implications of this study are best understood in terms of the keystroke behavioral biometric. While previous studies considered the degree to which keystroke typing patterns are unique, this study was set up to determine how well keyprints are able to identify individuals when typing under various treatment conditions (copy typing, free typing, and typing with mild or moderate impediments). While authentication can be difficult when attempting to correctly identify individual users, the results of this study indicate that keyprints can be a solid indicator of negative cases (i.e., flagging situations where a typing sample is likely not the correct individual). As anticipated, typing with a temporary impediment does diminish the algorithms' ability to identify students. This is also the case when user samples are typed under conditions different from those in which the keyprint baseline signature was captured (i.e., copy versus free typing). The ability to identify individuals is also challenging when using small comparison samples. However, the ability of the system to identify negative cases functions fairly well in each instance.

<p>Biometri är en relativt ny säkerhetsmetod för datorsystem. Biometri används ofta för att ersätta eller kombineras med användarnamn och lösenord. Detta görs genom att mäta ett fysiologiskt attribut eller beteendeattribut hos användaren. Keystroke dynamics är en biometrisk metod vilken registrerar användarens sätt att skriva på tangentbordet. En stor mängd försök med keystroke dynamics har gjorts i tidigare arbeten. Många av dessa har utgått ifrån metoder vilka använder ett högt antal stickprov från användarens beteende vid tangentbordet. Optimalt är dock en metod med hög säkerhet men samtidigt använder ett lågt antal stickprov. Denna rapport introducerar en ny algoritm för implementering av keystroke dynamics, vilken jämförs med två existerande algoritmer. Denna rapport visar att den nya algoritmen har högre prestanda än de övriga två i jämförelsen</p>

<p>One important issue faced by companies is to secure their information and resources from intrusions. For accessing a resource almost every system uses the approach of assigning a unique username and a password to all legitimate users. This approach has a major drawback. If an intruder gets the above information then he can become a big threat for the company and its resources. To strengthen the computer security there are several biometric methods for identity verification which are based on the human body’s unique characteristics and behavior including fingerprints, face recognition, retina scan and signatures. However most of these techniques are expensive and requires the installation of additional hardware. </p><p>This thesis focuses on keystroke dynamics as an identity verifier, which are based on the user’s unique habitual typing rhythm. This technique is not just looking for <i>what</i> the user types but also <i>how</i> he types. This method does not require additional hardware to be installed and are therefore rather inexpensive to implement. This thesis will discuss how identity verification through keystroke characteristics can be made, what have been done in this area and give advantages and disadvantages of the technique.</p>

Biometri är en relativt ny säkerhetsmetod för datorsystem. Biometri används ofta för att ersätta eller kombineras med användarnamn och lösenord. Detta görs genom att mäta ett fysiologiskt attribut eller beteendeattribut hos användaren. Keystroke dynamics är en biometrisk metod vilken registrerar användarens sätt att skriva på tangentbordet. En stor mängd försök med keystroke dynamics har gjorts i tidigare arbeten. Många av dessa har utgått ifrån metoder vilka använder ett högt antal stickprov från användarens beteende vid tangentbordet. Optimalt är dock en metod med hög säkerhet men samtidigt använder ett lågt antal stickprov. Denna rapport introducerar en ny algoritm för implementering av keystroke dynamics, vilken jämförs med två existerande algoritmer. Denna rapport visar att den nya algoritmen har högre prestanda än de övriga två i jämförelsen

<p>In the age of a networking revolution, when the Internet has changed not only the way we see computing, but also the whole society, we constantly face new challenges in the area of user verification. It is often the case that the login-id password pair does not provide a sufficient level of security. Other, more sophisticated techniques are used: one-time passwords, smart cards or biometric identity verification. The biometric approach is considered to be one of the most secure ways of authentication. </p><p>On the other hand, many biometric methods require additional hardware in order to sample the corresponding biometric feature, which increases the costs and the complexity of implementation. There is however one biometric technique which does not demand any additional hardware – user identification based on keyboard statistics. This thesis is focused on this way of authentication. </p><p>The keyboard statistics approach is based on the user’s unique typing rhythm. Not only what the user types, but also how she/he types is important. This report describes the statistical analysis of typing samples which were collected from 20 volunteers, as well as the implementation and testing of the identity verification system, which uses the characteristics examined in the experimental stage.</p>

Distance education has seen rapid growth over the recent decades. The rapid development of Information Communication Technology [ICT] has been one of the main drivers of this growth in distance education. However, distance education and ICT themselves posts challenges to both students and educators alike. This thesis finds its basis in the problem of high failure rates and quality assurance issues in the Bachelor of Information Technology [BIT] distance degree programme conducted by the University of Colombo School of Computing in Sri Lanka. A Formative Assessment for Distance Education [FADE] model that promotes the development of and assesses higher-order skills in a collaborative online distance-learning environment was designed based on a methodological approach involving design-based research. The main study was focussed on two main problems, plagiarism in distance education [part A] and the use of technology to address the issues of learning and assessment [part B]. Research questions arising from different aspects of the design required the use of multiple methodologies. Issues of plagiarism in technology aided assessment in distance education put forward questions that required the use of a quasi-experiment and a literature survey. The empirical material of this phase of the study comprised of keystroke logs and questionnaire data. The design and evaluation of the FADE model employed a mixed method two-phase sequential explanatory strategy. The empirical material of this phase of the study comprised of questionnaires, observations coding, interviews and examination and registry data. The quasi-experimental data was analysed using a fuzzy logic engine. The questionnaire, observation coding and examination and registry data were statistically analysed and interviews were used to interpret and explain finding. The results of the part A of the study indicate that there are keystroke patterns for individuals that are stable within and across different tasks. However, the results of the literature review on plagiarism suggested the use of both technological and pedagogical approaches to plagiarism. Part B of the study, showed relationships between higher order thinking demonstrated by students and their course results and attitudes.  Collaborative learning skills demonstrated by students showed relationships to the students’ purpose of use of the FADE forum and their experience on the social web. This study finds that both technological tools and pedagogical practices have to be used in conjunction to limit the possibility of plagiarism. With reference to assessment with a focus on the development of higher order thinking, the study indicates that assessment should be based on the student’s perspective, the purpose and aim of the assessment and the assessment environment. Furthermore, the study finds that in distance education collaboration seems particularly important.

Emotions play a significant role in many human mental activities, including decision-making, motivation, and cognition. Various intelligent and expert systems can be empowered with emotionally intelligent capabilities, especially systems that interact with humans and mimic human behaviour. However, most current methods in affect recognition studies use intrusive, lab-based, and expensive tools which are unsuitable for real-world situations. Inspired by studies on keystrokes dynamics, this thesis investigates the effectiveness of diagnosing users’ affect through their typing behaviour in an educational context. To collect users’ typing patterns, a field study was conducted in which subjects used a dialogue-based tutoring system built by the researcher. Eighteen dialogue features associated with subjective and objective ratings for users’ emotions were collected. Several classification techniques were assessed in diagnosing users’ affect, including discrimination analysis, Bayesian analysis, decision trees, and neural networks. An artificial neural network approach was ultimately chosen as it yielded the highest accuracy compared with the other methods. To lower the error rate, a hierarchical classification was implemented to first classify user emotions based on their valence (positive or negative) and then perform a finer classification step to determining which emotions the user experienced (delighted, neutral, confused, bored, and frustrated). The hierarchical classifier was successfully able to diagnose users' emotional valence, while it was moderately able to classify users’ emotional states. The overall accuracy obtained from the hierarchical classifier significantly outperformed previous dialogue-based approaches and in line with some affective computing methods.

Privacy of personal health records is of ultimate importance. Unfortunately, it is easy to obtain illegal access to electronic health records under insufficient security precautions. Access control based on token or username/password is not adequate for applications in health domain which require heightened security. Currently, electronic signature mechanisms are being employed as a strong alternative to classic methods. In addition, biometrics provide more precise results in comparison to electronic signature methods. However, applicability of biometrics in this field has been prohibited by factors such as the need for special hardware, increased implementation costs, and invasiveness of the biometry sensors (eg. iris topology, fingerprint). Behavioral biometrics such as speech, and keystroke dynamics are easier to implement, and do not suffer from the disadvantages mentioned for the static biometrics. Especially, using keystroke dynamics for user authentication is more advantageous than other advanced biometrics because the implementation is inexpensive and continuous identity control is plausible. The aim of this study is to show the feasibility of merging a biometry-based advanced identity verification method together with an initial access control procedure such as password check. In this study, we provide an authentication framework based on measuring similarity of the typing characteristics of medical reporters, while they are typing medical reports. We have made a prototype of the system and provided classification of keystroke timings for each operator. We have generated a testbed and measured similarity of typing patterns of 5 medical reporters upon typing 4 different kinds of medical reports. Our system performs with hundred percent accuracy in identifying the authorized operators from the reports they type. In current practice, electronic signatures are indispensable for health information systems, but our study shows that keystroke dynamics can easily be included in this chain for increased security.