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

A specification tool for planning systems

The open world of the (Semantic) Web is a global information space offering diverse materials of disparate qualities, and the opportunity to re-use, aggregate, and integrate these materials in novel ways. The advent of Linked Data brings the potential to expose data on the Web, creating new challenges for data consumers who want to integrate these data. One challenge is the ability, for users, to elicit the reliability and/or the accuracy of the data they come across. In this paper, we describe a light-weight provenance extension for the voiD vocabulary that allows data publishers to add provenance metadata to their datasets. These provenance metadata can be queried by consumers and used as contextual information for integration and inter-operation of information resources on the Semantic Web.

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Provenance in Linked Data Integration

Portraying matters as other than they truly are is an important part of everyday human communication. In this paper, we use a survey to examine ways in which people fabricate, omit or alter the truth online. Many reasons are found, including creative expression, hiding sensitive information, role-playing, and avoiding harassment or discrimination. The results suggest lying is often used for benign purposes, and we conclude that its use may be essential to maintaining a humane online society.

Self Curation, Social Partitioning, Escaping from Prejudice and Harassment: the Many Dimensions of Lying Online

Ramine Tinati, Elena Simperl, Markus Luczak-Roesch, Max Van Kleek, Nigel Shadbolt, University ofSouthampton1. INTRODUCTIONOnline citizen science can be seen as a form of collective intelligence Levy [1997] and Woolley et al.´[2010] in which the wisdom of the crowd is applied to the Web to advance scientiﬁc knowledge [Prestop-nik and Crowston 2012] Thus far, online citizen science projects [Bonney et al. 2009] have appliedmillions of volunteers to solving problems in a wide array of scientiﬁc domains, ranging from the clas-siﬁcation of galaxies [Fortson et al. 2011] to the completion of protein folding networks [Khatib et al.2011].Central to many of these projects are online messaging or discussion facilities designed to allowvolunteers to ask one another questions and advice. Such facilities have in many cases yielded sub-stantial, dedicated self-sustaining online communities. In this paper, we examine participation in suchcommunities; speciﬁcally, whether participation in online discussion inﬂuences task completion withinand across 10 distinct projects of a shared citizen science platform, the Zooniverse

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Collective Intelligence in Citizen Science - A Study of Performers and Talkers

Reference is of fundamental importance in natural language semantics. In Formal Semantics, reference is regarded as an absolute relation between expressions and referents. Thus construed, reference is independent of the cognitive states of processors. Various Formal theories of reference are examined. We review the solutions which these theories offer to the referential problems associated with 'Opaque Contexts'. Such problems must be resolved if the Formal concept of reference is to be maintained. Formal explanations of other referential phenomena are also examined, viz. 'specificity', 'expressive responsibility', and 'referentiality'. The thesis demonstrates that certain paradoxes arise as a result of using the same logical apparatus to describe all of these separate phenomena. It is argued that reference is not an absolute and invariant relation in language. Despite this claim, it is argued that a theory of the effectiveness of referential acts is still possible. A system of referential description is presented that represents crucial aspects of the process of performing and understanding referential acts. It is proposed that generating and interpreting natural language is best explained as a process of constructing cognitive models. The elements involved in constructing such models indicate that the state of a language processor is the most important determinant of the mechanics of the referential act. The apparatus embodied in the system is used to explain 'specificity', 'expressive responsibility', etc., without recourse to the logical apparatus of scope. The system is also deployed in the analysis of discourse data. This data is derived from a task in which the participants, initially, do not have equal knowledge about the likely objects of discourse reference. The analysis reveals that referential expressions are constructed, used and interpreted, modulo the intentional states of the processors. These states include; the kinds of high-level 'resource allocation' strategies that are in force at any point in the discourse, the beliefs processor's have about the domain of discourse, and the beliefs they have about the beliefs of their fellow interlocutors.

Nigel Shadbolt

Papers

A specification tool for planning systems

Provenance in Linked Data Integration

Self Curation, Social Partitioning, Escaping from Prejudice and Harassment: the Many Dimensions of Lying Online

Collective Intelligence in Citizen Science - A Study of Performers and Talkers

Constituting reference in natural language : the problem of referential opacity