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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

International Encyclopedia of the Social and Behavioral Sciences (vol. 10)

Smartphones with touchscreen-based interfaces are increasingly used by non-technical groups including the elderly. However, application developers have little understanding of how senior users interact with their products and of how to design senior-friendly interfaces. As an initial study to assess standard mobile touchscreen interfaces for the elderly, we conducted performance measurements and observational evaluations of 20 elderly participants. The tasks included performing basic gestures such as taps, drags, and pinching motions and using basic interactive components such as software keyboards and photo viewers. We found that mobile touchscreens were generally easy for the elderly to use and a week's experience generally improved their proficiency. However, careful observations identified several typical problems that should be addressed in future interfaces. We discuss the implications of our experiments, seeking to provide informal guidelines for application developers to design better interfaces for elderly people.

/pdf/elderly-user-evaluation-of-mobile-touchscreen-interactions-59bwynjvss.pdf

Elderly user evaluation of mobile touchscreen interactions

Turn-by-turn navigation is a useful paradigm for assisting people with visual impairments during mobility as it reduces the cognitive load of having to simultaneously sense, localize and plan. To realize such a system, it is necessary to be able to automatically localize the user with sufficient accuracy, provide timely and efficient instructions and have the ability to easily deploy the system to new spaces. We propose a smartphone-based system that provides turn-by-turn navigation assistance based on accurate real-time localization over large spaces. In addition to basic navigation capabilities, our system also informs the user about nearby points-of-interest (POI) and accessibility issues (e.g., stairs ahead). After deploying the system on a university campus across several indoor and outdoor areas, we evaluated it with six blind subjects and showed that our system is capable of guiding visually impaired users in complex and unfamiliar environments.

NavCog: a navigational cognitive assistant for the blind

We first discuss the difficulties that blind people face in trying to lie in society, because of the lack of accessible information resources, and then consider the potential of the Web as a new information resource for the blind. After describing how blind people in Japan currently access the Web, we give an overview of our system for nonvisual Web access. Our system has five special characteristics. One is the use of a numeric keypad for surfing the Net, with a key assignment designed for intuitive operation. The second is a fast- forward key for quick reading. The next two are that hyperlinks are read in a female voice and HTML tags are converted into voice data. The fifth is that the system can be synchronized with Netscape Navigator. After evaluating the system and offering some conclusions, we discuss our plans for future work.

User interface of a Home Page Reader

These days, accessibility-related regulations and guidelines have been accelerating the improvement of Web accessibility. One of the accelerating factors is the development and deployment of accessibility evaluation tools for authoring time and repair time. They mainly focus on creating compliant Web sites by analyzing the HTML syntax of pages, and report that pages are compliant when there are no syntactical errors. However, such compliant pages are often not truly usable by blind users. This is because current evaluation tools merely check if the HTML tags are appropriately used to be compliant with regulations and guidelines. It would be better if such tools paid more attention to real usability, especially on time-oriented usability factors, such as the speed to reach target content, the ease of understanding the page structure, and the navigability, in order to help Web designers to create not simply compliant pages but also usable pages for the blind. Therefore, we decided to develop Accessibility Designer (aDesigner), which has capabilities to visualize blind users' usability by using colors and gradations. The visualization function allows Web designers to grasp the weak points in their pages, and to recognize how accessible or inaccessible their pages are at a glance. In this paper, after reviewing the related work, we describe our approach to visualize blind users' usability followed by an overview of Accessibility Designer. We then report on our evaluations of real Web sites using Accessibility Designer. After discussing the results, we conclude the paper.

/pdf/accessibility-designer-visualizing-usability-for-the-blind-1lwmbsbh7u.pdf

Accessibility designer: visualizing usability for the blind

Web content is under the control of site owners, and therefore the site owners have the responsibility to make their content accessible. This is a basic assumption of Web accessibility. Users who want access to inaccessible content must ask the site owners for help. However, the process is slow and too often the need is mooted before the content becomes accessible. Social Accessibility is an approach to drastically reduce the burden on site owners and to shorten the time to provide accessible Web content by allowing volunteers worldwide to - renovate' any webpage on the Internet. Users encountering Web access problems anywhere at any time will be able to immediately report the problems to a social computing service. Volunteers can be quickly notified, and they can easily respond by creating and publishing the requested accessibility metadata--also helping any other users who encounter the same problems. Site owners can learn about the methods for future accessibility renovations based on the volunteers' external metadata. There are two key technologies to enable this process, the external metadata that allows volunteers to annotate existing Web content, and the social computing service that supports the collaborative renovations. In this paper, we will first review previous approaches, and then propose the Social Accessibility approach. The scenario, implementation, and results of a pilot service are introduced, followed by discussion of future directions.

http://webinsight.cs.washington.edu/papers/social-accessibility.pdf

Chieko Asakawa

Papers

Elderly user evaluation of mobile touchscreen interactions

NavCog: a navigational cognitive assistant for the blind

User interface of a Home Page Reader

Accessibility designer: visualizing usability for the blind

Social accessibility: achieving accessibility through collaborative metadata authoring