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

Witness reports are a key building block for reputation systems in open multi-agent systems in which agents, that are owned by a variety of stakeholders, continuously enter and leave the system. However, in such open and dynamic environments, these reports can be inaccurate because of the differing views of the reporters. Moreover, due to the conflicting interests that stem from the multiple stakeholders, some witnesses may deliberately provide false information to serve their own interests. Now, in either case, if such inaccuracy is not recognised and dealt with, it will adversely affect the function of the reputation model. To this end, this paper presents a generic method that detects inaccuracy in witness reports and updates the witness’s credibility accordingly so that less credence is placed on its future reports. Our method is empirically evaluated and is shown to help agents effectively detect inaccurate witness reports in a variety of scenarios where various degrees of inaccuracy in witness reports are introduced.

https://eprints.soton.ac.uk/261136/1/trust05%2Dws.pdf

On Handling Inaccurate Witness Reports

Situation awareness is a critical element of military decision superiority in a wide variety of operational contexts Improved situation awareness can benefit operational effectiveness by facilitating the planning process, improving the quality and timeliness of decisions, and providing better feedback about the strategic and tactical consequences of military actions The military coalition environment presents a number of challenges to situation awareness research; not only in terms of the technical approaches used to enhance situation awareness, but also in terms of the models and conceptual frameworks used to analyse situation awareness This paper outlines an approach to enhancing situation awareness that is grounded in the use of Semantic Web technologies We describe the challenges to both individual and team situation awareness presented by coalition military environments, and we discuss ways in which semantic technologies might be used to address these challenges We suggest that an approach featuring domain ontologies, reasoning capabilities, semantic queries and semantic integration techniques provides the basis for an integrated framework for improving situation awareness in military coalition contexts We provide an example of our approach in the form of the InfoGlue framework for adaptive, context-aware information retrieval

Semantic Technologies and Enhanced Situation Awareness

In this paper we outline work in progress related to the construction of contextually aware pervasive computing environments, through the use of semantic and knowledge technologies. Key to this activity is modelling both where and what a user is doing at any given time. We present a prototype application to illustrate this work and describe part of its implementation.

The Use of Ontologies in Contextually Aware Environments

We outline a simple taxonomy of approaches to modelling narrative, explain how these might be realised ontologically, and describe our continuing work to apply these techniques to the problem of Memories for Life.

/pdf/ontological-approaches-to-modelling-narrative-tr8hmfappf.pdf

Ontological Approaches to Modelling Narrative

This paper describes the design and implementation of Minimal RDFS semantics based on a backward chaining approach and implemented on a clustered RDF triple store. The system presented, called 4sr, uses 4store as base infrastructure. In order to achieve a highly scalable system we implemented the reasoning at the lowest level of the quad store, the bind operation. The bind operation runs concurrently in all the data slices allowing the reasoning to be processed in parallel among the cluster. Throughout this paper we provide detailed descriptions of the architecture, reasoning algorithms, and a scalability evaluation with the LUBM benchmark. 4sr is a stable tool available under a GNU GPL3 license and can be freely used and extended by the community1.

/pdf/the-design-and-implementation-of-minimal-rdfs-backward-54mohtpw27.pdf

Nigel Shadbolt

Papers

On Handling Inaccurate Witness Reports

Semantic Technologies and Enhanced Situation Awareness

The Use of Ontologies in Contextually Aware Environments

Ontological Approaches to Modelling Narrative

The design and implementation of minimal RDFS backward reasoning in 4store