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

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

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Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

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 increasingly widespread use of 'smart' devices has raised multifarious ethical concerns regarding their use in domestic spaces. Previous work examining such ethical dimensions has typically either involved empirical studies of concerns raised by specific devices and use contexts, or alternatively expounded on abstract concepts like autonomy, privacy or trust in relation to 'smart homes' in general. This paper attempts to bridge these approaches by asking what features of smart devices users consider as rendering them 'smart' and how these relate to ethical concerns. Through a multimethod investigation including surveys with smart device users (n=120) and semi-structured interviews (n=15), we identify and describe eight types of smartness and explore how they engender a variety of ethical concerns including privacy, autonomy, and disruption of the social order. We argue that this middle ground, between concerns arising from particular devices and more abstract ethical concepts, can better anticipate potential ethical concerns regarding smart devices.

Strangers in the Room: Unpacking Perceptions of 'Smartness' and Related Ethical Concerns in the Home

As manufacturers shift their focus from selling products to providing services, designers must therefore increasingly consider the life-cycle requirements in addition to conventional design parameters. To identify possible areas of concern, engineers must consider knowledge gained through the life cycle of a related product. However, because of the size and distributed nature of a company’s operation, engineers often do not have access to front-line maintenance data. Additionally, the large number of documents generated during the design and operation of a product makes it impractical to manually review all documents thoroughly during a design task. This paper presents a prototype knowledge-based document repository for an aeroengine manufacturer. The developed system searches and analyzes distributed document resources, and provides engineers with a summary view of the underlying knowledge. The aim is to aid engineers in creating design requirements that incorporate maintenance issues. Unlike existing document repositories and digital libraries, our approach is knowledge based, where users browse summary reports instead of following suggested links. To test the validity of our architecture, we have developed and deployed a prototype of our knowledge-based document repository. The repository has been demonstrated to and validated by the engine design community

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Knowledge Transfer: From Maintenance to Engine Design

We address the life cycle of semantic web based knowledge management from ontology modelling to instance generation and reuse. We illustrate through a semantic web based knowledge management approach the potential of applying semantic web technologies in GEODISE, an e-Science pilot project in the domain of Engineering Design Search and Optimization (EDSO). In particular, we show how ontologies and semantically enriched instances are acquired through knowledge acquisition and resource annotation. This is illustrated not only in Protege with an OWL plug-in, but also in a light weight function annotator customized for resource providers to semantically describe their own resources to be published. In terms of reuse, advice mechanisms, in particular a knowledge advisor based on semantic matching, are designed to consume the semantic information and facilitate service discovery, assembly and configuration in a real problem solving environment. An implementation has demonstrated the integration of the advisor in a text mode domain script editor and a GUI mode workflow composition environment. Our research work shows the potential of using semantic web technology to manage and reuse knowledge in e-Science.

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Semantic Web based Content Enrichment and Knowledge Reuse in e-Science

EXPLAIN: Experiments in planning and instruction.

The information processing capabilities of humans enable them to opportunistically draw and integrate knowledge from nearly any information source. However, the integration of digital, structured data from diverse sources remains difficult, due to problems of heterogeneity that arise when data modelled separately are brought together. In this paper, we present an investigation of the feasibility of extending Personal Information Management (PIM) tools to support lightweight, user-driven mixing of previously un-integrated data, with the objective of allowing users to take advantage of the emerging ecosystems of structured data currently becoming available. In this study, we conducted an exploratory, sequential, mixed-method investigation, starting with two pre-studies of the data integration needs and challenges, respectively, of Web-based data sources. Observations from these pre-studies led to DataPalette, an interface that introduced simple co-reference and group multi-path-selection mechanisms for working with terminologically and structurally heterogeneous data. Our lab study showed that participants readily understood the new interaction mechanisms which were introduced. Participants made more carefully justified decisions, even while weighing a greater number of factors, moreover expending less effort, during subjective-choice tasks when using DataPalette, than with a control set-up.

Nigel Shadbolt

Papers

Strangers in the Room: Unpacking Perceptions of 'Smartness' and Related Ethical Concerns in the Home

Knowledge Transfer: From Maintenance to Engine Design

Semantic Web based Content Enrichment and Knowledge Reuse in e-Science

EXPLAIN: Experiments in planning and instruction.

Carpé data: supporting serendipitous data integration in personal information management