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

Despite their potential value as collaborative knowledge editing systems, semantic wikis present a number of usability challenges for human end users In particular, there are several mismatches between the simple user interaction mechanisms of wikis (which are the key to the success of wikis) and the need for users to create, edit and understand structured knowledge content (eg, in the form of RDF or OWL ontologies) In this paper, we present a Controlled Natural Language (CNL) approach to collaborative ontology development using Semantic MediaWiki (SMW) In order to support the expressivity required for OWL ontology development, we extended the representational substructure of the SMW system with an OWL meta model using a template-based mechanism To improve usability, we provided a form-based guided input interface and implemented several CNL verbalizers (CNL text generation components) In particular, we developed verbalizers for the English and Chinese variants of the Rabbit CNL, as well as the Attempto Controlled English (ACE) CNL The combination of semantic wiki systems and CNL editing interfaces may provide an effective mechanism for promoting the large-scale collaborative creation of semantically-enriched online content

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A Controlled Natural Language Interface for Semantic Media Wiki

Neurotrophic factors, particularly the neurotrophin gene family of neurotrophic factors, are implicated in activity-dependent anatomical plasticity in the visual cortex and at the neuromuscular junction. Accumulating evidence implicates neurotrophic factors as possible mediators of activity-dependent competition between afferents, leading to the segregation of afferents' arbors on the target space. We present a biologically plausible mathematical model of competition for neurotrophic factors. We show that the model leads to anatomical segregation, provided that the levels of neurotrophic factors released in an activity-independent manner, or the levels available by exogenous infusion, are below a critical value, which we derive. Above this critical value, afferent segregation breaks down. We also show that the model segregates afferents even in the presence of very highly correlated patterns of afferent activity. The model is therefore ideally suited for application to the development of ocular dominance ...

Competition for neurotrophic factors: mathematical analysis

Most existing PIM tools either suffer from having data isolated by being siloed in an application or only interact with specific tools offered through specific services. The consequences are that what people can do with their own data becomes constrained by what services application developers afford. We propose a web-standards based architecture called WebBox to support easy maintenance and repurposing of one's own data for private, social or public publishing, collaboration and reuse.

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A decentralized architecture for consolidating personal information ecosystems: The WebBox

With age, injury, or disease, human memory can be significantly impaired. Current coping strategies range from simple aids such as post-it notes and calendars to, more recently, assistive devices which attempt to provide reminders at appropriate times, capture details of important events, or aid with performing complex tasks. In this paper we extend the state of the art by reporting on a pervasive system designed to 'orientate' a person with memory problems by reminding them in real time of details and personal memories of their surroundings. We describe a three-level architecture, consisting of a novel wearable sensing device, mobile phone, and Internet service, where each level is less constrained in terms of energy and offers greater computational capability. This kind of distributed architecture for a memory aid has not been reported before. The wearable device typically draws 38.5mA of current, allowing a full day's operation on a single charge. We demonstrate the operation of the system, which successfully identifies the faces of those nearby and shows their names on the mobile phone.

/pdf/a-low-power-distributed-pervasive-healthcare-system-for-2oemx27oyx.pdf

A low-power, distributed, pervasive healthcare system for supporting memory

In the context of the Semantic Web, many ontology-related operations, e.g. ontology ranking, segmentation, alignment, articulation, reuse, evaluation, can reduced to one fundamental operation: computing the similarity and/or dissimilarity among ontological entities, and in some cases among ontologies themselves. In this paper, we review standard metrics for computing distance measures and we propose a series of semantic metrics. We give a formal account of semantic metrics drawn from a variety of research disciplines, and enrich them with semantics based on standard Description Logic constructs. We argue that concept-based metrics can be aggregated to produce numeric distances at ontology-level and we speculate on the usability of our ideas in potential areas.

Nigel Shadbolt

Papers

A Controlled Natural Language Interface for Semantic Media Wiki

Competition for neurotrophic factors: mathematical analysis

A decentralized architecture for consolidating personal information ecosystems: The WebBox

A low-power, distributed, pervasive healthcare system for supporting memory

Semantic metrics