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.

Data Mining Practical Machine Learning Tools and Techniques

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

The present historical moment may seem a particularly inopportune time to review Bowling Alone, Robert Putnam's latest exploration of civic decline in America. After all, the outpouring of volunteerism, solidarity, patriotism, and self-sacrifice displayed by Americans in the wake of the September 11 terrorist attacks appears to fly in the face of Putnam's central argument: that \"social capital\" -defined as \"social networks and the norms of reciprocity and trustworthiness that arise from them\" (p. 19)'has declined to dangerously low levels in America over the last three decades. However, Putnam is not fazed in the least by the recent effusion of solidarity. Quite the contrary, he sees in it the potential to \"reverse what has been a 30to 40-year steady decline in most measures of connectedness or community.\"' As an example of how the current \"war on terrorism\" could generate a durable civic renewal, Putnam points to the burst in civic practices that occurred during and after World War II, which he says \"permanently marked\" the generation that lived through it and had a \"terrific effect on American public life over the last half-century.\" 3 If Americans can follow this example and channel their current civic

Bowling alone: The collapse and revival of American community

The greatest contributor of CO2 emissions in the average American household is personal transportation. Because transportation is inherently a mobile activity, mobile devices are well suited to sense and provide feedback about these activities. In this paper, we explore the use of personal ambient displays on mobile phones to give users feedback about sensed and self-reported transportation behaviors. We first present results from a set of formative studies exploring our respondents' existing transportation routines, willingness to engage in and maintain green transportation behavior, and reactions to early mobile phone "green" application design concepts. We then describe the results of a 3-week field study (N=13) of the UbiGreen Transportation Display prototype, a mobile phone application that semi-automatically senses and reveals information about transportation behavior. Our contributions include a working system for semi-automatically tracking transit activity, a visual design capable of engaging users in the goal of increasing green transportation, and the results of our studies, which have implications for the design of future green applications.

/pdf/ubigreen-investigating-a-mobile-tool-for-tracking-and-511ydmc5gy.pdf

UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits

We present a technique for evaluating the usability and effectiveness of ambient displays. Ambient displays are abstract and aesthetic peripheral displays portraying non-critical information on the periphery of a user's attention. Although many innovative displays have been published, little existing work has focused on their evaluation, in part because evaluation of ambient displays is difficult and costly. We adapted a low-cost evaluation technique, heuristic evaluation, for use with ambient displays. With the help of ambient display designers, we defined a modified set of heuristics. We compared the performance of Nielsen's heuristics and our heuristics on two ambient displays. Evaluators using our heuristics found more, severe problems than evaluators using Nielsen's heuristics. Additionally, when using our heuristics, 3-5 evaluators were able to identify 40--60% of known usability issues. This implies that heuristic evaluation is an effective technique for identifying usability issues with ambient displays.

/pdf/heuristic-evaluation-of-ambient-displays-59hvfoy6tp.pdf

Heuristic evaluation of ambient displays

We conducted a questionnaire-based study of the relative importance of two factors, inquirer and situation, in determining the preferred accuracy of personal information disclosed through a ubiquitous computing system. We found that privacy preferences varied by inquirer more than by situation. That is, individuals were more likely to apply the same privacy preferences to the same inquirer in different situations than to apply the same privacy preferences to different inquirers in the same situation. We are applying these results to the design of a user interface for managing everyday privacy in ubiquitous computing.

http://www.mscs.mu.edu/~praveen/Research/MobileInterrMgmt/Related%20Works%20_%20MIIM/21_Lederer_2003.pdf

Who wants to know what when? privacy preference determinants in ubiquitous computing

This paper discusses the complex relationship between pervasive computing and environmental sustainability. Pervasive computing naturally lends itself to many of environmental sustainability's core challenges. These are well-aligned with pervasive computing's vision, which promises to deliver computational intelligence embedded in the physical world, human enterprises, and people's lives.

Environmental Sustainability

In this paper, we investigate how the choice of media for capture and access affects the diary study method. The diary study is a method of understanding participant behavior and intent in situ that minimizes the effects of observers on participants. We first situate diary studies within a framework of field studies and review related literature. We then report on three diary studies we conducted that involve photographs, audio recordings, location information and tangible artifacts. We then analyze our findings, specifically addressing the following questions: How do context information and episodic memory prompts captured by participants vary with media? In what way do different media "jog" memory? How do different media affect the diary study process? These questions are particularly important for diary studies because they can be especially useful as compared to other methods when a participant intends to do an action but does not or when actions are particularly difficult to sense. We also built and tested a tool based on participant and researcher frustrations with the method. Our contribution includes suggested modifications to traditional diary techniques that enable annotation and review of captured media; a new variation on the diary study appropriate for researchers using digital capture media; and a lightweight tool to support it, motivated by past work and findings from our studies.

Jennifer Mankoff

Papers

UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits

Heuristic evaluation of ambient displays

Who wants to know what when? privacy preference determinants in ubiquitous computing

Environmental Sustainability

When participants do the capturing: the role of media in diary studies