COPE: Interactive Exploration of Co-occurrence Patterns in Spatial Time Series.

Spatial time series is a common type of data dealt with in many domains, such as economic statistics and environmental science. There have been many studies focusing on finding and analyzing various kinds of events in time series; the term 'event' refers to significant changes or occurrences of particular patterns formed by consecutive attribute values. We focus on a further step in event analysis: finding and exploring events that frequently co-occurred with a target class of similar events having occurred repeatedly over a period of time. This type of analysis can provide important clues for understanding the formation and spreading mechanisms of events and interdependencies among spatial locations. We propose a visual exploration framework COPE (Co-Occurrence Pattern Exploration), which allows users to extract events of interest from data and detect various co-occurrence patterns among them. Case studies and expert reviews were conducted to verify the effectiveness and scalability of COPE using two real-world datasets

Individuals' quality of life linked to major life events, perceived social support, and personality traits.

PURPOSE: The aim of this study was to investigate the relationship between major recent life events that occurred during the last 5 years, social and personal resources, and subjective quality of life (QoL). METHODS: A total of 1801 participants from the general population (CoLaus/PsyCoLaus study) completed the Life Events Questionnaire, the Social Support Questionnaire, the NEO Five-Factor Inventory Revised, and the Manchester Short Assessment of Quality of Life. RESULTS: Major life events were modestly associated with the QoL (about 5 % of the explained variance). However, QoL was significantly related to perceived social support and personality traits (about 37 % of the explained variance). Particularly, perceived social support, extraversion and conscientiousness personality dimensions were positively linked to life satisfaction, whereas a high level of neuroticism was negatively associated with QoL. CONCLUSION: This study highlights the negative but temporary association between critical events and QoL. However, a combination of high conscientiousness and extraversion, and positive social support may explain better variances for a high-perceived QoL

Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods

Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques. The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns. The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other. The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques. The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Early prediction of critical events in infants with single ventricle physiology in critical care using routinely collected data

Intensive care units (ICUs) provide care for critically-ill patients who require constant monitoring and the availability of specialized equipment and personnel. In this environment, a high volume of information and a high degree of uncertainty present a burden to clinicians. In specialized cohorts, such as pediatric patients with congenital heart defects (CHDs), this burden is exacerbated by increased complexity, the inadequacy of existing decision support aids, and the limited and decreasing availability of highly-specialized clinicians. Among CHD patients, infants with single ventricle (SV) physiology are one of the most complex and severely-ill sub-populations. While SV mortality rates have dropped, patient deterioration may happen unexpectedly in the period before patients undergo stage-2 palliative surgery. Even in expert hands, critical and potentially catastrophic events (CEs), such as cardiopulmonary resuscitation (CPR), emergent endotracheal intubation (EEI), or extracorporeal membrane oxygenation (ECMO) are common in SV patients, and may negatively impact morbidity, mortality, and hospital length of stay. There is a clinical need of predictive tools that help intensivists assess and forecast the advent of CEs in SV infants. Although ubiquitous, widely adopted ICU severity-of-illness scores or early warning systems (EWS), e.g., PRISM and PIM, have not met this need. They are often developed for general ICU use and do not generalize well to specialized populations. Furthermore, most EWS are developed for prediction of patient mortality. Among SV patients, however, death is semi-elective. On the other hand, prediction of CEs may help clinicians improve patient care by anticipating the advent of patient deterioration. In this dissertation, we aimed to develop and validate predictive models that achieve early and accurate prediction of CEs in infants with SV physiology. Such models may provide early and actionable information to clinicians and may be used to perform clinical interventions aimed at preventing CEs, and to reducing morbidity, mortality, and healthcare costs. We assert that our work is significant in that it addresses an unmet clinical need by achieving state-of-the-art, early prediction of patient deterioration in a challenging and vulnerable population

Theory and Applications for Advanced Text Mining

Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields

Exploring Relations Between Motivation, Metacognition, and Academic Achievement Through Variable-Centered, Person-Centered and Learning Analytic Methodologies

The three studies that comprise this dissertation examine relations between student characteristics, motivations, metacognitive learning processes, and academic achievement. Methodologically, the dissertation demonstrated the potential of multiple types of approaches and data resource types. By employing multiple approaches including variable-centered, person-centered, and learning analytics, researchers can understand learning processes from various angles. In addition, through this triangulation by multiple types of methodological approaches, educational theories could be more thoroughly verified and supported by various empirical findings. Multiple types of data resources are related to analytical methods. The purpose of the first paper was to examine relations between achievement goals and metacognitive learning behaviors using a clustering analysis and visualization. A clustering analysis conducted with achievement goals produced three goal profiles; 1) mastery-approach, 2) performance-approach, and 3) performance-avoidance identified three goal profiles. The profiles include High Approach, High Mastery, and High Goal Endorsement groups. The finding demonstrated that students in the High Mastery group, who had greater use of the self-assessment tool, obtained higher final grades than other groups could be explained from the perspective of SRL. In addition, learners motivated by mastery approach goals engaged in the greater use of self-assessment quizzes. Students in the High Mastery group also used the tools earlier than other two groups for exam 2. As the most frequently used pattern, sequential pattern mining discovered the repeated use of self-assessment quizzes to monitor their learning. More students in the High Mastery group employ this pattern of metacognitive events than students in the High Performance and High-Goal endorsement groups, particularly during sessions in weeks before exams. A subsequent analysis revealed that for all exams, students who conducted a repeated behavior pattern indicative of metacognitive monitoring and control outperformed those who did not. From the research, it is confirmed that the person-centered analysis provided authentic and generalizable groups and afforded observation of the learning behaviors of learners with typical combinations of goals. In addition, sequential patterns provide instructor more interesting information on learning processes than the frequency of accesses. The purpose of the second research was to identify motivational profiles based on multiple types of motivations including self-efficacy, achievement goals, and expectancy-value from an integrative perspective. For this research, a LPA was conducted with ten types of motivational constructs and three kinds of metacognitive learning processes. The LPA identified four motivational profiles; 1) High Cost, 2) High Performance Goals, 3) High Goals and Values, and 4) Low Performance Goals, and three metacognitive profiles; 1) Infrequent metacognitive processing. 2) Checking performance and planning, and 3) Self-assessment. Student demographic information significantly influenced the membership of motivational profiles. Older students tend to have higher self-efficacy, mastery-approach, and values, but low cost than younger ones. In addition, compared to Caucasian and Asian students, underrepresented students tend to be more motivated by higher goals and values than high cost or high performance goals. Lastly, female students are more likely to be members of High performance goals and High goals and values than High cost oriented and Low performance goals and cost than males. In terms of the relations profiles with academic achievement, Low Performance Goals group showed the best performance. Among metacognitive profile groups, students in Checking performance and planning, and Self-assessment demonstrated similar academic performance. The investigation of relations between two profile groups demonstrated that students in the High cost group are more likely to be a member of self-assessment group than checking performance and planning as well as of a member of an infrequent metacognitive process than checking performance and planning. In addition, students in high performance and goals and high goals and values groups relative to the low performance goals group more likely to be a member of the infrequent metacognitive process than checking performance and planning. The findings of this research provide authentic motivation status and metacognition learning process as well as their relations. Addition, this research figured out specific motivational profiles through the multiple types of motivations from the integrative perspective. Therefore, instructors can provide more effective and specific interventions to students who have difficulty utilizing metacognitive learning processes, considering motivational status based on multiple motivations. In addition, instructors can understand motivational profiles by demographics so at the beginning of the semester in which the information on students is not enough to identify students learning processes, they intervene students based on demographic information. The purpose of the third paper was to consider the relative importance of capturing demographic, motivational and metacognitive processes as potential predictors of learning outcomes, and appraises them alongside both traditional prediction modeling approaches in higher education, and emergent methods, sequence pattern mining, arising from the field of educational data mining. The sequence pattern mining discovered the repeated use of self-assessment quizzes in Biology and repeated use of planning contents in Math. A regression model with combined resource types demonstrated the improved predictive power than models with individual resource types. Also, theory-aligned behaviors designed based on metacognitive learning processes better improved the accuracy of the model than non-theory-aligned behaviors automatically provided by the system. Lastly, when applying the same prediction model, the model better explained the variance of academic achievement in Biology in which metacognitive supporting tools designed based on an educational theory than that in Math that has few theory-aligned behavior variables. Therefore, this study emphasizes the importance of existing ambient data from university systems. Also, log data generated by systems such as LMS allows researchers to examine the same data in different ways with no need for additional data collection. Lastly, educational theory and contexts should be taken into consideration in designing courses and developing the prediction models. Therefore, instructors and researchers, in designing courses, the consideration of educational theories and contexts is the essential process. This dissertation provides insight regarding authentic relations between motivation, metacognition, and academic achievement. Specifically, instructors can understand how multiple types of motivations work together, and the motivational profiles influence metacognitive learning strategies. In courses, by examining motivational profiles, instructors can provide more effective intervention with which students change their resolve their weak learning easier. Practically, by investigating each type of predictor from data resources including demographic, motivation, and behavioral variables, findings from this dissertation can enable researchers to prioritize development of prediction models to identify students who are more likely to experience failure in courses. Additionally, instructors can figure out the importance of interpreting variables through educational theories and in context through the comparison of courses with differing instructional designs. Further, by appraising these results in light of theory, instructors can take action to improve student’s learning outcomes by adjusting the design of their courses