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

In this paper, we investigate a method for constructing cascades of information co-occurrence, which is suitable to trace emergent structures in information in scenarios where rich contextual features are unavailable Our method relies only on the temporal order of content-sharing activities, and intrinsic properties of the shared content itself We apply this method to analyse information dissemination patterns across the active online citizen science project Planet Hunters, a part of the Zooniverse platform Our results lend insight into both structural and informational properties of different types of identifiers that can be used and combined to construct cascades In particular, significant differences are found in the structural properties of information cascades when hashtags as used as cascade identifiers, compared with other content features We also explain apparent local information losses in cascades in terms of information obsolescence and cascade divergence; eg, when a cascade branches into multiple, divergent cascades with combined capacity equal to the original

/pdf/from-coincidence-to-purposeful-flow-properties-of-5etscekwjj.pdf

From Coincidence to Purposeful Flow? Properties of Transcendental Information Cascades

Internet is an easy medium for people to collaborate and crowdsourcing is an efficient feature of social web where people with common interest and expertise come together to solve specific problems by collective thinking and create a community. It can also be used to filter out important information from large data, remove spams, and gamification techniques are used to reward the users for their contribution and keep a sustainable environment for the growth of the community. Semantic web technologies can be used to structure the community data so it can be combined, decentralized and be used across platform. Using such tools knowledge can be enhanced and easily discovered and merged together. This paper discusses the concept of a purposive social network where people with similar interest and varied expertise come together, use crowdsourcing technique to solve a common problem and build tools for common purpose. The StackOverflow website is chosen to study the purposive network, different network ties and roles of user is studied. Linked Data is used for name disambiguation of keywords and topics for easier search and discovery of experts in a field and provide useful information that is otherwise unavailable in the website.

/pdf/linked-data-in-crowdsourcing-purposive-social-network-5ctay5u1yq.pdf

Linked data in crowdsourcing purposive social network

Shared understanding is commonly seen as essential to the success of coalition operations, and current research efforts are attempting to develop techniques and technologies to improve shared understanding in military coalition contexts. In spite of this, our understanding of what the term ‘shared understanding’ actually means is surprisingly poor. In part, this problem is attributable to the difficulty in comprehending the true nature of understanding itself, although confusions also arise about the precise nature of the differences between shared understanding and ostensibly similar constructs, such as shared mental models and shared situation awareness. In this paper, we attempt to improve our understanding of shared understanding by exploring the nature of understanding, situation awareness and mental models. Following Wittgenstein, we suggest that understanding is best conceived of as something akin to an ability, and shared understanding is, we suggest, best conceived of as the sharing of individual forms of understanding by multiple agents. We further suggest that mental models may provide a mechanistic realization for some of the performances that manifest understanding, and that situation awareness should best be seen as a particular kind of understanding, namely a dynamic form of situational understanding. In addition to discussing the nature of understanding and shared understanding, we also discuss their potential relevance to military coalition operations. We propose that shared understanding is important to coalition operations because it contributes to improvements in coalition performance, the optimal use of limited communication assets, and an improved sense of group cohesion, group solidarity and mutual trust.

Towards an Understanding of Shared Understanding in Military Coalition Contexts

A constraint based system capable of deriving an optimised layout is disclosed. The system involves identifying and recording constraints upon the relative spatial locations of components in terms of a plurality of functional relationships. The functional relationships are used to assign components to groups in accordance with the functional relationships. The relative spatial locations between the components are determined at least part in accordance with the group assignments. Preferably, the processing involves the formation of a nested hierarchy of groups and arbitration between group assignments so that each functional relationship is assigned to only one group. Beneficially, the group arbitration involves the use of at least one Partial Ordering Table with sets of functional relationships defining nodes thereof. Node comparison is then used to assess the relative strengths of the functional relationships.

An automated method of producing a layout

The next generation of systems will do more than connect people?they will invisibly orchestrate our social processes and help us achieve the previously impossible. Consumer electronics in their current form of smartphones, wearables, and sensors, along with other devices yet to be envisioned, will power this next generation of systems, providing the key mechanisms that people will use to leverage a new type of social computational power. We refer to these as social machines.

Nigel Shadbolt

Papers

From Coincidence to Purposeful Flow? Properties of Transcendental Information Cascades

Linked data in crowdsourcing purposive social network

Towards an Understanding of Shared Understanding in Military Coalition Contexts

An automated method of producing a layout

The Rise of Social Machines: The development of a human\/digital ecosystem.