People learn whenever and wherever possible, and whatever they like or encounter--Mathematics, Drama, Art, Languages, Physics, Philosophy, and so on. With the bursting of knowledge, evaluation of one's possession of knowledge becomes increasingly difficult. There are a lot of demands to evaluate one's understanding of a piece of knowledge. Assessment of understanding of knowledge is conventionally through tests or interviews, but they have some limitations such as low-efficiency and not-comprehensive. This paper proposes a method called Understanding Tree to estimate one's understanding of knowledge, by keeping track of his her learning activities. It overcomes some limitations of traditional methods, hence complements traditional methods.
Many research fields focus on the collection of personal information, such as lifelogging, expertise finding, and personal informatics. Personal informatics is a class of tools that help people collect personally relevant information for the purpose of self-reflection and gaining self-knowledge @cite_2 @cite_28 @cite_9 . Various tools have been developed to help people collect and analyze different kinds of personal information, such as location @cite_8 , finances @cite_10 , food @cite_4 , weight @cite_7 @cite_24 , and physical activity @cite_0 . Knowledge Model and Understanding Tree facilitate a new type of personal informatics tool that helps people discover their expertise and deficiencies in a more accurate way, by quantitatively assessing an individual's possession of knowledge.
@cite_4 @cite_7 @cite_8 @cite_28 @cite_9 @cite_24 @cite_0 @cite_2 @cite_10
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People strive to obtain self-knowledge. A class of systems called personal informatics is appearing that help people collect and reflect on personal information. However, there is no comprehensive list of problems that users experience using these systems, and no guidance for making these systems more effective. To address this, we conducted surveys and interviews with people who collect and reflect on personal information. We derived a stage-based model of personal informatics systems composed of five stages (preparation, collection, integration, reflection, and action) and identified barriers in each of the stages. These stages have four essential properties: barriers cascade to later stages; they are iterative; they are user-driven and or system-driven; and they are uni-faceted or multi-faceted. From these properties, we recommend that personal informatics systems should 1) be designed in a holistic manner across the stages; 2) allow iteration between stages; 3) apply an appropriate balance of automated technology and user control within each stage to facilitate the user experience; and 4) explore support for associating multiple facets of people's lives to enrich the value of systems. Crisis informatics is a multidisciplinary field combining computing and social science knowledge of disasters; its central tenet is that people use personal information and communication technology to respond to disaster in creative ways to cope with uncertainty. We study and develop computational support for collection and sociobehavioral analysis of online participation (i.e., tweets and Facebook posts) to address challenges in disaster warning, response, and recovery. Because such data are rarely tidy, we offer lessons—learned the hard way, as we have made every mistake described below—with respect to the opportunities and limitations of social media research on crisis events. There are many studies that collect and store life log for personal memory. The paper explains how a system can create someone's life log in an inexpensive way to share daily life events with family or friends through socialnetwork or messaging. In the modern world where people are usually busier than ever, family members are geographically distributed due to globalization of companies and humans are inundated with more information than they can process, ambient communications through mobile media or internet based communication can provide rich social connections to friends and family. People can stay connected to their loving ones ubiquitously that they care about by sharing awareness information in a passive way. For users who wish to have a persistent existence in a virtual world - to let their friends know about their current activity or to inform their caretakers - new technology is needed. Research that aims to bridge real life and the virtual worlds (e.g., Second Life, Face book etc.) to simulate virtual living or logging daily events, while challenging and promising, is currently rare. Only very recently the mapping of real-world activities to virtual worlds has been attempted by processing multiple sensors data along with inference logic for realworld activities. Detecting or inferring human activity using such simple sensor data is often inaccurate, insufficient and expensive. Hence, this paper proposes to infer human activity from environmental sound cues and common sense knowledge, which is an inexpensive alternative to other sensors (e.g., accelerometers) based approaches. Because of their ubiquity, we believe that mobile phones or hand-held devices (HHD) are ideal channels to achieve a seamless integration between the physical and virtual worlds. Therefore, the paper presents a prototype to log daily events by a mobile phone based application by inferring activities from environmental sound cues. To the best of our knowledge, this system pioneers the use of environmental sound based activity recognition in mobile computing to reflect one's real-world activity in virtual worlds. Security informatics is an emerging field of study focusing on the development and evaluation of advanced information technologies and systems for national and homeland security-related applications. Spatio-temporal hotspot analysis is an important component of security informatics since location and time are two critical aspects of most security-related events. The outputs of such analyses can provide useful information to guide the activities aimed at preventing, detecting, and responding to security problems. This paper reports a computational study carried out to evaluate the effectiveness of two prominent spatio-temporal hotspot analysis techniques, i.e., scan statistics and risk-adjusted clustering, in two selected security-related applications including infectious disease informatics and crime analysis. This paper also proposes a new technique based on support vector machines. Preliminary experiments have demonstrated positively that this new approach can be a viable analysis alternative in security informatics. Personal media collections are often viewed and managed along the social dimension, the places we spend time at and the people we see, thus tools for extracting and using this information are required. We present novel algorithms for identifying socially significant places termed social spheres unobtrusively from GPS traces of daily life, and label them as one of Home, Work, or Other, with quantitative evaluation of 9 months taken from 5 users. We extract locational co-presence of these users and formulate a novel measure of social tie strength based on frequency of interaction, and the nature of spheres it occurs within. Comparative user studies of a multimedia browser designed to demonstrate the utility of social metadata indicate the usefulness of a simple interface allowing navigation and filtering in these terms. We note the application of social context is potentially much broader than personal media management, including context-aware device behaviour, life logs, social networks, and location-aware information services. Extensive amounts of knowledge and data stored in medical databases require the development of specialized tools for storing, accessing, analysis, and effectiveness usage of stored knowledge and data. Intelligent methods such as neural networks, fuzzy sets, decision trees, and expert systems are, slowly but steadily, applied in the medical fields. Recently, rough set theory is a new intelligent technique was used for the discovery of data dependencies, data reduction, approximate set classification, and rule induction from databases. In this paper, we present a rough set method for generating classification rules from a set of observed 360 samples of the breast cancer data. The attributes are selected, normalized and then the rough set dependency rules are generated directly from the real value attribute vector. Then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Experimental results from applying the rough set analysis to the set of data samples are given and evaluated. In addition, the generated rules are also compared to the well-known ID3 classifier algorithm. The study showed that the theory of rough sets seems to be a useful tool for inductive learning and a valuable aid for building expert systems. Most data mining algorithms and tools stop at discovered customer models, producing distribution information on customer profiles. Such techniques, when applied to industrial problems such as customer relationship management (CRM), are useful in pointing out customers who are likely attritors and customers who are loyal, but they require human experts to postprocess the mined information manually. Most of the postprocessing techniques have been limited to producing visualization results and interestingness ranking, but they do not directly suggest actions that would lead to an increase the objective function such as profit. Here, we present a novel algorithm that suggest actions to change customers from an undesired status (such as attritors) to a desired one (such as loyal) while maximizing objective function: the expected net profit. We develop these algorithms under resource constraints that are abound in reality. The contribution of the work is in taking the output from an existing mature technique (decision trees, for example), and producing novel, actionable knowledge through automatic postprocessing. The advent of the Internet has made the transmission of personally identifiable information more common and often unintended by the user. As personal information becomes more accessible, individuals worry that businesses misuse the information that is collected while they are online. Organizations have tried to mitigate this concern in two ways: (1) by offering privacy policies regarding the handling and use of personal information and (2) by offering benefits such as financial gains or convenience. In this paper, we interpret these actions in the context of the information-processing theory of motivation. Information-processing theories, also known as expectancy theories in the context of motivated behavior, are built on the premise that people process information about behavior-outcome relationships. By doing so, they are forming expectations and making decisions about what behavior to choose. Using an experimental setting, we empirically validate predictions that the means to mitigate privacy concerns are associated with positive valences resulting in an increase in motivational score. In a conjoint analysis exercise, 268 participants from the United States and Singapore face trade-off situations, where an organization may only offer incomplete privacy protection or some benefits. While privacy protections (against secondary use, improper access, and error) are associated with positive valences, we also find that financial gains and convenience can significantly increase individuals' motivational score of registering with a Web site. We find that benefits-monetary reward and future convenience-significantly affect individuals' preferences over Web sites with differing privacy policies. We also quantify the value of Web site privacy protection. Among U.S. subjects, protection against errors, improper access, and secondary use of personal information is worth @math 44.62. Finally, our approach also allows us to identify three distinct segments of Internet users-privacy guardians, information sellers, and convenience seekers. Bioinformatics, a field devoted to the interpretation and analysis of biological data using computational techniques, has evolved tremendously in recent years due to the explosive growth of biological information generated by the scientific community. Soft computing is a consortium of methodologies that work synergistically and provides, in one form or another, flexible information processing capabilities for handling real-life ambiguous situations. Several research articles dealing with the application of soft computing tools to bioinformatics have been published in the recent past; however, they are scattered in different journals, conference proceedings and technical reports, thus causing inconvenience to readers, students and researchers. This book, unique in its nature, is aimed at providing a treatise in a unified framework, with both theoretical and experimental results, describing the basic principles of soft computing and demonstrating the various ways in which they can be used for analyzing biological data in an efficient manner. Interesting research articles from eminent scientists around the world are brought together in a systematic way such that the reader will be able to understand the issues and challenges in this domain, the existing ways of tackling them, recent trends, and future directions. This book is the first of its kind to bring together two important research areas, soft computing and bioinformatics, in order to demonstrate how the tools and techniques in the former can be used for efficiently solving several problems in the latter.