Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

From the Publisher: 
A Question of Identity 
Life on the Screen is a fascinating and wide-ranging investigation of the impact of computers and networking on society, peoples' perceptions of themselves, and the individual's relationship to machines. Sherry Turkle, a Professor of the Sociology of Science at MIT and a licensed psychologist, uses Internet MUDs (multi-user domains, or in older gaming parlance multi-user dungeons) as a launching pad for explorations of software design, user interfaces, simulation, artificial intelligence, artificial life, agents, "bots," virtual reality, and "the on-line way of life." 
Turkle's discussion of postmodernism is particularly enlightening. She shows how postmodern concepts in art, architecture, and ethics are related to concrete topics much closer to home, for example AI research (Minsky's "Society of Mind") and even MUDs (exemplified by students with X-window terminals who are doing homework in one window and simultaneously playing out several different roles in the same MUD in other windows). Those of you who have (like me) been turned off by the shallow, pretentious, meaningless paintings and sculptures that litter our museums of modern art may have a different perspective after hearing what Turkle has to say. 
This is a psychoanalytical book, not a technical one. However, software developers and engineers will find it highly accessible because of the depth of the author's technical understanding and credibility. Unlike most other authors in this genre, Turkle does not constantly jar the technically-literate reader with blatant errors or bogus assertions about how things work. Although I personally don't have time or patience for MUDs,view most of AI as snake-oil, and abhor postmodern architecture, I thought the time spent reading this book was an extremely good investment.

스크린 위의 삶 = Life on the screen : identity in the age of the internet

Trust: The Social Virtues and the Creation of Prosperity

The goal of this dissertation is to find and provide the basis for a managerial tool that allows a firm to easily express its business logic. The methodological basis for this work is design science, where the researcher builds an artifact to solve a specific problem. In this case the aim is to provide an ontology that makes it possible to explicit a firm's business model. In other words, the proposed artifact helps a firm to formally describe its value proposition, its customers, the relationship with them, the necessary intra- and inter-firm infrastructure and its profit model. Such an ontology is relevant because until now there is no model that expresses a company's global business logic from a pure business point of view. Previous models essentially take an organizational or process perspective or cover only parts of a firm's business logic. The four main pillars of the ontology, which are inspired by management science and enterprise- and processmodeling, are product, customer interface, infrastructure and finance. The ontology is validated by case studies, a panel of experts and managers. The dissertation also provides a software prototype to capture a company's business model in an information system. The last part of the thesis consists of a demonstration of the value of the ontology in business strategy and Information Systems (IS) alignment.
Structure of this thesis:
The dissertation is structured in nine parts:
Chapter 1 presents the motivations of this research, the research methodology with which the goals shall be achieved and why this dissertation present a contribution to research.
Chapter 2 investigates the origins, the term and the concept of business models. It defines what is meant by business models in this dissertation and how they are situated in the context of the firm. In addition this chapter outlines the possible uses of the business model concept.
Chapter 3 gives an overview of the research done in the field of business models and enterprise ontologies.
Chapter 4 introduces the major contribution of this dissertation: the business model ontology. In this part of the thesis the elements, attributes and relationships of the ontology are explained and described in detail.
Chapter 5 presents a case study of the Montreux Jazz Festival which's business model was captured by applying the structure and concepts of the ontology. In fact, it gives an impression of how a business model description based on the ontology looks like.
Chapter 6 shows an instantiation of the ontology into a prototype tool: the Business Model Modelling Language BM2L. This is an XML-based description language that allows to capture and describe the business model of a firm and has a large potential for further applications.
Chapter 7 is about the evaluation of the business model ontology. The evaluation builds on literature review, a set of interviews with practitioners and case studies.
Chapter 8 gives an outlook on possible future research and applications of the business model ontology. The main areas of interest are alignment of business and information technology IT/information systems IS and business model comparison. Finally, chapter 9 presents some conclusions.

The business model ontology a proposition in a design science approach

Previous studies have examined various aspects of user behavior on the Web, including general information-seeking patterns, search engine use, and revisitation habits. Little research has been conducted to study how users navigate and interact with their Web browser across different information-seeking tasks. We have conducted a field study of 21 participants, in which we logged detailed Web usage and asked participants to provide task categorizations of their Web usage based on the following categories: Fact Finding, Information Gathering, Browsing, and Transactions. We used implicit measures logged during each task session to provide usage measures such as dwell time, number of pages viewed, and the use of specific browser navigation mechanisms. We also report on differences in how participants interacted with their Web browser across the range of information-seeking tasks. Within each type of task, we found several distinguishing characteristics. In particular, Information Gathering tasks were the most complex; participants spent more time completing this task, viewed more pages, and used the Web browser functions most heavily during this task. The results of this analysis have been used to provide implications for future support of information seeking on the Web as well as direction for future research in this area. © 2007 Wiley Periodicals, Inc.

/pdf/a-field-study-characterizing-web-based-information-seeking-3dlutkfkaf.pdf

A field study characterizing Web-based information-seeking tasks

The combination of the computer and the Internet has resulted in the emergence of cybergenre, a new class of genre. Cybergenre can be characterized by the triple , where functionality refers to the capabilities afforded by this new medium. When an existing genre initially migrates to this new medium, it is usually as a faithful reproduction of the existing genre in both content and form with little new functionality. It may then evolve into a variant cybergenre as it incorporates functionality afforded by the computer and Internet. Cybergenres also include novel genres, either not based on previously existing genres or substantially different from existing genres on the basis of increased functionality. These novel genres may have either persistent or virtual instantiations. This paper proposes a taxonomy of these cybergenres and examines the evolution of the news cybergenre and the mathematics dictionary cybergenre within the context of this taxonomy.

http://web.cs.dal.ca/~shepherd/pubs/evolution.pdf

The evolution of cybergenres

Under the influence of a new medium, a genre may evolve into variants of the original genre or even into new genre. Genres, even through such evolution, are normally characterized by the tuple , and little attention is paid to the functionality afforded by the new medium. The combination of the computer and the Internet, however, has resulted in the emergence of the cybergenre, a new class of genre characterized by the triple . Users approach instances of cybergenre with certain expectations with respect to functionality, as well as to form and content. This paper examines the "functionality" attribute of various cybergenres in an attempt to identify the essential functionality that this new medium affords. This should help us not only to understand the influence of this medium on genre, but also to use genre effectively in the design of computer and network-based applications.

The functionality attribute of cybergenres

Objective: Most children with mental health disorders do not receive timely care because of access barriers. These initial trials aimed to determine whether distance interventions provided by nonprofessionals could significantly decrease the proportion of children diagnosed with disruptive behavior or anxiety disorders compared with usual care. Method: In three practical randomized controlled trials, 243 children (80 with oppositional-defiant, 72 with attention-deficit/hyperactivity, and 91 with anxiety disorders) were stratified by DSM-IV diagnoses and randomized to receive the Strongest Families intervention (treatment) or usual care (control). Assessments were blindly conducted and evaluated at 120, 240, and 365 days after randomization. The intervention consisted of evidence-based participant materials (handbooks and videos) and weekly telephone coach sessions. The main outcome was mental health diagnosis change. Results: Intention-to-treat analysis showed that for each diagnosis significant treatment effects were found at 240 and 365 days after randomization. Moreover, in the overall analysis significantly more children were not diagnosed as having disruptive behavior or anxiety disorders in the treatment group than the control group (120 days: 2 1 13.05, p .001, odds ratio 2.58, 95% confidence interval 1.54‐4.33; 240 days: 2 1 20.46, p .001, odds ratio 3.44, 95% confidence interval 1.99‐5.92; 365 days: 2 1 13.94, p .001, odds ratio 2.75, 95% confidence interval 1.61‐4.71). Conclusions: Compared with usual care, telephone-based treatments resulted in significant diagnosis decreases among children with disruptive behavior or anxiety. These interventions hold promise to increase access to mental health services. Clinical trial registration information—Strongest Families: Pediatric Disruptive Behaviour Disorder, http://www.clinicaltrials.gov, NCT00267579; Strongest Families: Pediatric Attention-Deficit/Hyperactivity Disorder, http://www.clinicaltrials.gov, NCT00267605; and Strongest Families: Pediatric Anxiety, http://www.clinicaltrials.gov, NCT00267566. J. Am. Acad. Child Adolesc. Psychiatry, 2011;50(11):1162‐1172. Key Words: child attentiondeficit/hyperactivity disorder, child anxiety disorder, tele-mental health, distance treatment, health service access

Telephone-Based Mental Health Interventions for Child Disruptive Behavior or Anxiety Disorders: Randomized Trials and Overall Analysis

While researchers have been studying user activity on the Web since its inception, there remains a lack of understanding of the high level tasks in which users engage on the Web. We have recently conducted a field study in which participants were asked to annotate all web usage with a task description and categorization. Based on our analysis of participants’ recorded tasks during the field study, as well as previous research, we have developed a goal-based classification of information tasks which describes user activities on the Web.

/pdf/a-goal-based-classification-of-web-information-tasks-212qrc6hbf.pdf

Carolyn Watters

Papers

A field study characterizing Web-based information-seeking tasks

The evolution of cybergenres

The functionality attribute of cybergenres

Telephone-Based Mental Health Interventions for Child Disruptive Behavior or Anxiety Disorders: Randomized Trials and Overall Analysis

A Goal‐based Classification of Web Information Tasks