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.

/pdf/toward-the-next-generation-of-recommender-systems-a-survey-4wwsv77rns.pdf

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

This opening chapter introduces the idea of social machines. The metaphor of a machine is introduced, and it is explained how it applies to social groups connected through digitally networked technologies, ubiquitous devices and large quantities of data. Social machines enable mechanistic explanations of social phenomena, without the reductionist claim that these social groups are determined by the technology. Building blocks of the social machines concept are enumerated and described, including heterogeneous human-computer networks, social computing, communication, platforms and communities, ecosystems of social machines, dynamics of networks and various ethical concepts. Examples of social machines are introduced, including Wikipedia, citizen science social machines such as Zooniverse and Eyewire, healthcare social machines and Pokemon Go! After considering ways of classifying social machines, and contrasting social machines with similar concepts such as crowdsourcing and social computing, it is argued that producing a definition of social machines would be counterproductive, such is the fluidity of the concept and its social and technical contexts. Instead, the book will consider how technologically enabled social groups can be studied through a social machine lens.

Characterising Social Machines

Many mobile apps are designed not just to support end-users’ needs, but also commercial aims. This can result in app designs that compromise end-user privacy, safety, and well-being. Since apps nowadays provide vital digital information and services, users often have no choice but to accept potentially harmful or manipulative app designs. What if, instead, individuals could customise their apps to make them safer and better suit their needs? This exploratory work examines this question through a multi-faceted approach; first, to understand user needs, we conducted a survey (n = 100) of changes users wanted in their apps, and of perceptions of risks in app repair. Second, to identify technical challenges, we developed a prototype that enables end-users to change their apps, and realised several modifications suggested by survey participants. Finally, we conduct a set of expert interviews (n = 8) to delve into the ethical and legal aspects of such a tool, and synthesise a framework of risks and opportunities of app repair.

‘We are adults and deserve control of our phones’: Examining the risks and opportunities of a right to repair for mobile apps

Semantic Web Technologies are ideally suited to build context-aware information retrieval applications. However, the geospatial aspect of context awareness presents unique challenges such as the semantic modelling of geographical references for efficient handling of spatial queries, the reconciliation of the heterogeneity at the semantic and geo-representation levels, maintaining the quality of service and scalability of communicating, and the efficient rendering of the spatial queries' results. In this paper, we describe the modelling decisions taken to solve these challenges by analysing our implementation of an intelligent planning and recommendation tool that provides location-aware advice for a specific application domain. This paper contributes to the methodology of integrating heterogeneous geo-referenced data into semantic knowledgebases, and also proposes mechanisms for efficient spatial interrogation of the semantic knowledgebase and optimising the rendering of the dynamically retrieved context-relevant information on a web frontend.

/pdf/investigating-the-use-of-semantic-technologies-in-spatial-3d2z38gdtg.pdf

Investigating The Use Of Semantic Technologies In Spatial Mapping Applications.

The paper describes ONTOCOPI, a tool for identifYing communities of practice (COPs) by analysing ontologies of the relevant working domain. COP identification is currently a resource-heavy process largely based on interviews. ONTOCOPI attempts to uncover informal COP relations by spotting patterns in the formal relations represented in ontologies, traversing the ontology from instance to instance via selected relations. Experiments to determine particular COPs from an academic ontology are described, showing how the alteration of threshold and temporal settings, and the weights applied to the ontology's relations affect the composition ofthe identified COP.

https://link.springer.com/content/pdf/10.1007%2F978-0-387-35602-0_20.pdf

ONTOCOPI and Tools for Identifying Communities of Practice

Acknowledgements iii1 Introduction 11.1 Information Overload . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 The Semantic Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Narratives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Report Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 The Problem 42.1 Narrative Theories and Knowlege Engineering . . . . . . . . . . . . . . . . 42.2 The Analogy: Semantic Web meets Narratology . . . . . . . . . . . . . . . 52.3 Interfacing with the Semantic Web . . . . . . . . . . . . . . . . . . . . . . 52.4 The Research Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Work Completed 73.1 Memories for Life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73.1.1 Narratives in the form of Dynamic Presentations . . . . . . . . . . 83.2 OntoMedia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 Future Work and Conclusions 124.1 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124.1.1 Le PhotoCOPAIN . . . . . . . . . . . . . . . . . . . . . . . . . . . 124.1.2 eChase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134.1.3 Narratives in Computer Games . . . . . . . . . . . . . . . . . . . . 134.1.3.1 Massively Multiplayer Online Role Playing Games . . . . 134.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15ii

Faculty of Engineering, Science and Mathematics School of Electronics and Computer Science Intelligence, Agents, Multimedia Group A progress report submitted for continuation towards a PhD

Nigel Shadbolt

Papers

Characterising Social Machines

‘We are adults and deserve control of our phones’: Examining the risks and opportunities of a right to repair for mobile apps

Investigating The Use Of Semantic Technologies In Spatial Mapping Applications.

ONTOCOPI and Tools for Identifying Communities of Practice

Faculty of Engineering, Science and Mathematics School of Electronics and Computer Science Intelligence, Agents, Multimedia Group A progress report submitted for continuation towards a PhD