Harnessing agile concepts for the development of intelligent systems

Traditional and current approaches to intelligent systems design, have led to the creation of sophisticated and computationally-intensive packages and environments, for a wide range of applications. This paper proposes methods with which to extend the functionality of such systems, borrowing knowledge management concepts from the field of Agile Manufacturing. As such, this paper proposes that the future of intelligent systems design should be based not only upon the continuing development of artificial intelligence techniques, but also effective methods for harnessing human skills and core competencies to achieve these aims

Harnessing Technology: new modes of technology-enhanced learning: opportunities and challenges

A report commissioned by Becta to explore the potential impact on education, staff and learners of new modes of technology enhanced learning, envisaged as becoming available in subsequent years. A generative framework, developed by the researchers is described, which was used as an analytical tool to relate the possibilities of the technology described to learning and teaching activities. This report is part of the curriculum and pedagogy strand of Becta's programme of managed research in support of the development of Harnessing Technology: Next Generation Learning 2008-14. A system-wide strategy for technology in education and skills. Between April 2008 and March 2009, the project carried out research, in three iterative phases, into the future of learning with technology. The research has drawn from, and aims to inform, all UK education sectors

The Evidence Hub: harnessing the collective intelligence of communities to build evidence-based knowledge

Conventional document and discussion websites provide users with no help in assessing the quality or quantity of evidence behind any given idea. Besides, the very meaning of what evidence is may not be unequivocally defined within a community, and may require deep understanding, common ground and debate. An Evidence Hub is a tool to pool the community collective intelligence on what is evidence for an idea. It provides an infrastructure for debating and building evidence-based knowledge and practice. An Evidence Hub is best thought of as a filter onto other websites — a map that distills the most important issues, ideas and evidence from the noise by making clear why ideas and web resources may be worth further investigation. This paper describes the Evidence Hub concept and rationale, the breath of user engagement and the evolution of specific features, derived from our work with different community groups in the healthcare and educational sector

Harnessing mobile technology for classroom learning

Educational institutions are reluctant adopt mobile computing and Wireless technologies. This is because this technology remains relatively expensive compared to traditional computing technologies, mobile devices are inherently personal and can be difficult to use as a teaching tool to groups of learners, and Short Message Services and Multimedia Message Services (SMS/MMS) are expensive and limited in functional scope despite their popularity amongst young people. In this paper, we describe a component of a prototype learning environment named Quest where we propose anew way of harnessing mobile technology for learning that negates these drawbacks. In Quest we have demonstrated that the information gathering capabilities of mobile phones can be harnessed to aid learners research

Language engineering - a champion for European culture

Language is key to culture. It is a direct cultural medium as well as a means of recording and providing access to non-lingual elements of culture. Language is also fundamental to a sense of cultural identity. For this reason, it is vital, in a changing Europe, that we preserve the multi-lingual character of our society in order to move successfully towards closer co-operation at a political, economic, and social level. Language engineering is the application of knowledge of language to the development of computer software which can recognise, understand, interpret, and generate human language in all its forms. The paper provides a high level view of the ‘state of the art’ in language engineering and indicates ways in which it will have a profound impact on our culture in the future. It shows how advances in language engineering are an important aid in maintaining cultural diversity in a multi-lingual European society, while enabling the development of social cohesion across cultural and national divides. It addresses issues raised by the prospect of the Multi-lingual Information Society, including education, human communication with technology and information management, as well as aspects of digital cities such as tele-presence in digital libraries, virtual art galleries and electronic museums. The paper raises the issue of language as a factor in cultural domination, showing the contribution that language engineering can make towards countering it. The paper also raises a number of controversial issues concerning the likely benefits arising from the ways in which language is likely to influence the culture of Europe

Developing a comprehensive framework for multimodal feature extraction

Feature extraction is a critical component of many applied data science workflows. In recent years, rapid advances in artificial intelligence and machine learning have led to an explosion of feature extraction tools and services that allow data scientists to cheaply and effectively annotate their data along a vast array of dimensions---ranging from detecting faces in images to analyzing the sentiment expressed in coherent text. Unfortunately, the proliferation of powerful feature extraction services has been mirrored by a corresponding expansion in the number of distinct interfaces to feature extraction services. In a world where nearly every new service has its own API, documentation, and/or client library, data scientists who need to combine diverse features obtained from multiple sources are often forced to write and maintain ever more elaborate feature extraction pipelines. To address this challenge, we introduce a new open-source framework for comprehensive multimodal feature extraction. Pliers is an open-source Python package that supports standardized annotation of diverse data types (video, images, audio, and text), and is expressly with both ease-of-use and extensibility in mind. Users can apply a wide range of pre-existing feature extraction tools to their data in just a few lines of Python code, and can also easily add their own custom extractors by writing modular classes. A graph-based API enables rapid development of complex feature extraction pipelines that output results in a single, standardized format. We describe the package's architecture, detail its major advantages over previous feature extraction toolboxes, and use a sample application to a large functional MRI dataset to illustrate how pliers can significantly reduce the time and effort required to construct sophisticated feature extraction workflows while increasing code clarity and maintainability

GAMES: A new Scenario for Software and Knowledge Reuse

Games are a well-known test bed for testing search algorithms and learning methods, and many authors have presented numerous reasons for the research in this area. Nevertheless, they have not received the attention they deserve as software projects. In this paper, we analyze the applicability of software and knowledge reuse in the games domain. In spite of the need to find a good evaluation function, search algorithms and interface design can be said to be the primary concerns. In addition, we will discuss the current state of the main statistical learning methods and how they can be addressed from a software engineering point of view. So, this paper proposes a reliable environment and adequate tools, necessary in order to achieve high levels of reuse in the games domain

Citizen Science 2.0 : Data Management Principles to Harness the Power of the Crowd

Citizen science refers to voluntary participation by the general public in scientific endeavors. Although citizen science has a long tradition, the rise of online communities and user-generated web content has the potential to greatly expand its scope and contributions. Citizens spread across a large area will collect more information than an individual researcher can. Because citizen scientists tend to make observations about areas they know well, data are likely to be very detailed. Although the potential for engaging citizen scientists is extensive, there are challenges as well. In this paper we consider one such challenge – creating an environment in which non-experts in a scientific domain can provide appropriate and accurate data regarding their observations. We describe the problem in the context of a research project that includes the development of a website to collect citizen-generated data on the distribution of plants and animals in a geographic region. We propose an approach that can improve the quantity and quality of data collected in such projects by organizing data using instance-based data structures. Potential implications of this approach are discussed and plans for future research to validate the design are described

A System for Accessible Artificial Intelligence

While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them. We believe that AI has matured to the point where it should be an accessible technology for everyone. We present an ongoing project whose ultimate goal is to deliver an open source, user-friendly AI system that is specialized for machine learning analysis of complex data in the biomedical and health care domains. We discuss how genetic programming can aid in this endeavor, and highlight specific examples where genetic programming has automated machine learning analyses in previous projects.Comment: 14 pages, 5 figures, submitted to Genetic Programming Theory and Practice 2017 worksho