Basics of qualitative research: Grounded theory procedures and techniques

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

pdf/explanation-in-artificial-intelligence-insights-from-the-zw0ypc9t15.pdf

Explanation in artificial intelligence: Insights from the social sciences

Computing devices and applications are now used beyond the desktop, in diverse environments, and this trend toward ubiquitous computing is accelerating. One challenge that remains in this emerging research field is the ability to enhance the behavior of any application by informing it of the context of its use. By context, we refer to any information that characterizes a situation related to the interaction between humans, applications, and the surrounding environment. Context-aware applications promise richer and easier interaction, but the current state of research in this field is still far removed from that vision. This is due to 3 main problems: (a) the notion of context is still ill defined, (b) there is a lack of conceptual models and methods to help drive the design of context-aware applications, and (c) no tools are available to jump-start the development of context-aware applications. In this anchor article, we address these 3 problems in turn. We first define context, identify categories of contextual information, and characterize context-aware application behavior. Though the full impact of context-aware computing requires understanding very subtle and high-level notions of context, we are focusing our efforts on the pieces of context that can be inferred automatically from sensors in a physical environment. We then present a conceptual framework that separates the acquisition and representation of context from the delivery and reaction to context by a context-aware application. We have built a toolkit, the Context Toolkit, that instantiates this conceptual framework and supports the rapid development of a rich space of context-aware applications. We illustrate the usefulness of the conceptual framework by describing a number of context-aware applications that have been prototyped using the Context Toolkit. We also demonstrate how such a framework can support the investigation of important research challenges in the area of context-aware computing.

/pdf/a-conceptual-framework-and-a-toolkit-for-supporting-the-3ct19ji2mn.pdf

A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications

The Flickr30k dataset has become a standard benchmark for sentence-based image description. This paper presents Flickr30k Entities, which augments the 158k captions from Flickr30k with 244k coreference chains linking mentions of the same entities in images, as well as 276k manually annotated bounding boxes corresponding to each entity. Such annotation is essential for continued progress in automatic image description and grounded language understanding. We present experiments demonstrating the usefulness of our annotations for text-to-image reference resolution, or the task of localizing textual entity mentions in an image, and for bidirectional image-sentence retrieval. These experiments confirm that we can further improve the accuracy of state-of-the-art retrieval methods by training with explicit region-to-phrase correspondence, but at the same time, they show that accurately inferring this correspondence given an image and a caption remains really challenging.

/pdf/flickr30k-entities-collecting-region-to-phrase-4qzczb5v7u.pdf

Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models

This paper describes RESOLVE, a system that uses decision trees to learn how to classify conference phrases in the domain of business joint ventures An experiment is presented in which the performance of RESOLVE is compared to the performance of a manually engineered set of rules for the same task The results show that decision trees achieve higher performance than the rules in two of three evaluation metrics developed for the coreference task In addition to achieving better performance than the rules, RESOLVE provides a framework that facilitates the exploration of the types of knowledge that are useful for solving the conference problem

Using decision trees for conference resolution

UbiComp 2008, the Tenth International Conference on Ubiquitous computing, is the premier forum for the presentation of research results in all areas relating to the design, implementation, deployment and evaluation of ubiquitous computing technologies. The conference brings together leading researchers, from a variety of disciplines, perspectives and geographical areas, who have been exploring the implications of computing as it moves beyond the desktop and becomes increasingly interwoven into the fabrics of our lives.

A total of 226 submissions---160 full papers and 66 notes---were evaluated by our international Program Committee, composed of 31 of the leading researchers in the field of ubiquitous computing. Each submission was reviewed by two members of the program committee and two or more external reviewers, and after an online discussion phase 80 papers were chosen for consideration at a 2-day PC meeting. Each of these was evaluated by an additional PC member before the meeting. After this rigorous process, a total of 42 submissions---37 full papers and 5 notes---were accepted for publication in these proceedings, representing an overall acceptance rate of 19%. We are pleased to note that the set of accepted papers constitutes the largest number of papers accepted to a UbiComp conference to date.

Proceedings of the 10th international conference on Ubiquitous computing

This paper describes RESOLVE, a system that uses decision trees to learn how to classify coreferent phrases in the domain of business joint ventures. An experiment is presented in which the performance of RESOLVE is compared to the performance of a manually engineered set of rules for the same task. The results show that decision trees achieve higher performance than the rules in two of three evaluation metrics developed for the coreference task. In addition to achieving better performance than the rules, RESOLVE provides a framework that facilitates the exploration of the types of knowledge that are useful for solving the coreference problem.

/pdf/using-decision-trees-for-coreference-resolution-55ev1oupzq.pdf

Using Decision Trees for Coreference Resolution

Artifacts and surfaces that can display digital content are proliferating at a steady rate. Many of these displays will be peripheral, i.e., used for content that is not directly related to one's primary activities. However, what kinds of content would people want to see on such peripheral displays? We have begun to investigate the use of peripheral displays in three workplace contexts: within an individual office (Uni-Cast), outside an individual office (OutCast) and in a common area (GroupCast).

/pdf/unicast-outcast-groupcast-three-steps-toward-ubiquitous-3jtnxutbc7.pdf

UniCast, OutCast & GroupCast: Three Steps Toward Ubiquitous, Peripheral Displays

CIRCUS is a conceptual analyzer that produces semantic case frame representations for input sentences. Although space does not permit us to give a full technical description of CIRCUS, we will attempt to convey some sense of sentence analysis via CIRCUS. For more details, please consult [2] and [1].

University of Massachusetts: description of the CIRCUS system as used for MUC-4

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