OpenMinTeD: A Platform Facilitating Text Mining of Scholarly Content

The OpenMinTeD platform aims to bring full text Open Access scholarly content from a wide range of providers together with Text and Data Mining (TDM) tools from various Natural Language Processing frameworks and TDM developers in an integrated environment. In this way, it supports users who want to mine scientific literature with easy access to relevant content and allows running scalable TDM workflows in the cloud

God’s Extended Mind

The traditional doctrine of divine omniscience ascribes to God the fully exercised power to know all truths. but why is God’s excellence with respect to knowing not treated on a par with his excellence with respect to doing, where the latter requires only that God have the power to do all things? The prima facie problem with divine ”omni-knowledgeability’ -- roughly, being able to know whatever one wants to know whenever one wants to know it -- is that knowledge requires an internal representation, whereas mere ”knowledgeability’ does not. I argue to the contrary that knowledge does not require an internal representation, and that even if it did, an omni-knowledgeable God would satisfy this requirement. omni-knowledgeability therefore represents a distinct understanding of God’s cognitive excellence while satisfying the traditional insistence on full omniscience

Is Understanding Reducible?

Despite playing an important role in epistemology, philosophy of science, and more recently in moral philosophy and aesthetics, the nature of understanding is still much contested. One attractive framework attempts to reduce understanding to other familiar epistemic states. This paper explores and develops a methodology for testing such reductionist theories before offering a counterexample to a recently defended variant on which understanding reduces to what an agent knows

Technological Spaces: An Initial Appraisal

In this paper, we propose a high level view of technological spaces (TS) and relations among these spaces. A technological space is a working context with a set of associated concepts, body of knowledge, tools, required skills, and possibilities. It is often associated to a given user community with shared know-how, educational support, common literature and even workshop and conference regular meetings. Although it is difficult to give a precise definition, some TSs can be easily identified, e.g. the XML TS, the DBMS TS, the abstract syntax TS, the meta-model (OMG/MDA) TS, etc. The purpose of our work is not to define an abstract theory of technological spaces, but to figure out how to work more efficiently by using the best possibilities of each technology. To do so, we need a basic understanding of the similarities and differences between various TSs, and also of the possible operational bridges that will allow transferring the results obtained in one TS to other TS. We hope that the presented industrial vision may help us putting forward the idea that there could be more cooperation than competition among alternative technologies. Furthermore, as the spectrum of such available technologies is rapidly broadening, the necessity to offer clear guidelines when choosing practical solutions to engineering problems is becoming a must, not only for teachers but for project leaders as well

Do they practice what we teach? Follow-up evaluation of a Schema Therapy training programme

This study evaluated a three-day Schema Therapy training programme for trainee clinical psychologists. The training used an experiential model of learning, which was intended to encourage the transfer of knowledge and techniques from the learning environment into clinical practice. Using a mixed-methods approach, the training programme was evaluated in terms of: (1) self-reported changes in knowledge, confidence and willingness to use Schema Therapy-informed techniques; (2) whether the training was integrated into clinical practice; and (3) the perceived barriers/facilitators to achieving practice integration. Participants – 17 of the 19 trainee clinical psychologists enrolled on the Schema Therapy training programme – completed assessments immediately pre- and post-training. Participants were subsequently followed-up for reassessment three months after the training. Group- and individual-level analyses showed that most participants reported training-related gains in knowledge and confidence; these were largely sustained at follow-up, and were associated with post-training practice integration of Schema Therapy concepts and techniques. Analysis of qualitative data identified factors moderating use of training in practice. Findings of the study have implications for future delivery and evaluation of training in cognitive-behavioural therapies

Do they practice what we teach? Follow-up evaluation of a Schema Therapy training programme

This study evaluated a three-day Schema Therapy training programme for trainee clinical psychologists. The training used an experiential model of learning, which was intended to encourage the transfer of knowledge and techniques from the learning environment into clinical practice. Using a mixed-methods approach, the training programme was evaluated in terms of: (1) self-reported changes in knowledge, confidence and willingness to use Schema Therapy-informed techniques; (2) whether the training was integrated into clinical practice; and (3) the perceived barriers/facilitators to achieving practice integration. Participants – 17 of the 19 trainee clinical psychologists enrolled on the Schema Therapy training programme – completed assessments immediately pre- and post-training. Participants were subsequently followed-up for reassessment three months after the training. Group- and individual-level analyses showed that most participants reported training-related gains in knowledge and confidence; these were largely sustained at follow-up, and were associated with post-training practice integration of Schema Therapy concepts and techniques. Analysis of qualitative data identified factors moderating use of training in practice. Findings of the study have implications for future delivery and evaluation of training in cognitive-behavioural therapies

Learning perceptual schemas to avoid the utility problem

This paper describes principles for representing and organising planning knowledge in a machine learning architecture. One of the difficulties with learning about tasks requiring planning is the utility problem: as more knowledge is acquired by the learner, the utilisation of that knowledge takes on a complexity which overwhelms the mechanisms of the original task. This problem does not, however, occur with human learners: on the contrary, it is usually the case that, the more knowledgeable the learner, the greater the efficiency and accuracy in locating a solution. The reason for this lies in the types of knowledge acquired by the human learner and its organisation. We describe the basic representations which underlie the superior abilities of human experts, and describe algorithms for using equivalent representations in a machine learning architecture