Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

Approaching water stress in the Alps: Transdisciplinary co-production of systems, target and transformation knowledge

There is increasing recognition that transdisciplinary approaches are needed to create suitable knowledge for sustainable water management. However, there is no common understanding of what transdisciplinary research may be and there is very limited debate on potentials and challenges regarding its implementation. Against this background, this paper presents a conceptual framework for transdisciplinary co-production of knowledge in water management projects oriented towards more sustainable use of water. Moreover, first experiences with its implementation are discussed. In so doing, the focus lies on potentials and challenges related to the co-production of systems, target and transformation knowledge by researchers and local stakeholders.

Ethnosciences - A step towards the integration of scientific and non-scientific forms of knowledge in the management of natural resources for the future

Integration of indigenous knowledge and ethnoscientific approaches into contemporary frameworks for conservation and sustainable management of natural resources will become increasingly important in policies on an international and national level. We set the scene on how this can be done by exploring the key conditions and dimensions of a dialogue between ‘ontologies’ and the roles, which ethnosciences could play in this process. First, the roles which ethnosciences in the context of sustainable development were analysed, placing emphasis on the implications arising when western sciences aspire to relate to indigenous forms of knowledge. Secondly, the contributions of ethnosciences to such an ‘inter-ontological dialogue’ were explored, based on an ethnoecological study of the encounter of sciences and indigenous knowledge in the Andes of Bolivia, and reviewed experiences from mangrove systems in Kenya, India and Sri Lanka, and from case-studies in other ecosystems world-wide.

A distributed, semiotic-inductive and human-Oriented approach to web-scale knowledge retrieval

Web-scale knowledge retrieval can be enabled by distributed information retrieval, clustering Web clients to a large-scale computing infrastructure for knowledge discovery from Web documents. Based on this infrastructure, we propose to apply semiotic (i.e., sub-syntactical) and inductive (i.e., probabilistic) methods for inferring concept associations in human knowledge. These associations can be combined to form a fuzzy (i.e.,gradual) semantic net representing a map of the knowledge in the Web. Thus, we propose to provide interactive visualizations of these cognitive concept maps to end users, who can browse and search the Web in a human-oriented, visual, and associative interface.

Multiple Sclerosis: Current Knowledge and Future Outlook

Background: Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system characterized by demyelination and axonal loss. The etiology of MS is unknown; however, environmental and genetic factors play a key role in the development of MS. Diagnostic criteria have been adapted to facilitate earlier diagnosis with increased sensitivity and specificity. Our understanding of the pathophysiology of MS has deepened considerably in recent years, resulting in different therapies to modify the disease course. Furthermore, several drugs have lately shown efficacy in phase III studies and their approval is expected in the near future. As treatment options expand, a future challenge will be to find the optimal treatment for the individual patient. Summary: This mini-review gives an overview of the current knowledge of MS with emphasis on the latest diagnostic criteria and both current and upcoming treatment options. Key Messages: Treatment of MS changes rapidly as the knowledge and therapeutic options in MS expand. Clinical Impact: Diagnosis of MS is based on McDonald criteria. MS therapy can be divided into relapse, disease-modifying and symptomatic treatment. Relapses are commonly treated with intravenous methylprednisolone. First-line therapy consists of either interferon-β, glatiramer acetate or teriflunomide. In general, agents used as escalation therapies (natalizumab, fingolimod and mitoxantrone) are more potent than the agents used for first-line therapy; however, these have potentially serious side effects and should be used with care.

Context Aware e-Learning Objects and their Types from the Perspective of the Object-Oriented Paradigm

The contribution of this article demonstrates how to identify context-aware types of e-Learning objects (eLOs) derived from the subject domains. This perspective is taken from an engineering point of view and is applied during requirements elicitation and analysis relating to present work in constructing an object-oriented (OO), dynamic, and adaptive model to build and deliver packaged e-Learning courses. Consequently, three preliminary subject domains are presented and, as a result, three primitive types of eLOs are posited. These types educed from the subject domains are of structural, conceptual, and granular nature. Structural objects are responsible for the course itself, conceptual objects incorporate adaptive and logical interoperability, while granular objects congregate granular assets. Their differences, interrelationships, and responsibilities are discussed. A major design challenge relates to adaptive behaviour. Future research addresses refinement on the subject domains and adaptive hypermedia systems.

The Role for Virtual Patients in the Future of Medical Education

The medical education community is working-across disciplines and across the continuum-to address the current challenges facing the medical education system and to implement strategies to improve educational outcomes. Educational technology offers the promise of addressing these important challenges in ways not previously possible. The authors propose a role for virtual patients (VPs), which they define as multimedia, screen-based interactive patient scenarios. They believe VPs offer capabilities and benefits particularly well suited to addressing the challenges facing medical education. Well-designed, interactive VP-based learning activities can promote the deep learning that is needed to handle the rapid growth in medical knowledge. Clinically oriented learning from VPs can capture intrinsic motivation and promote mastery learning. VPs can also enhance trainees' application of foundational knowledge to promote the development of clinical reasoning, the foundation of medical practice. Although not the entire solution, VPs can support competency-based education. The data created by the use of VPs can serve as the basis for multi-institutional research that will enable the medical education community both to better understand the effectiveness of educational interventions and to measure progress toward an improved system of medical education.

The advantage and necessity of the Christian revelation, shewn from the state of religion in the ancient heathen world : especially with respect to the knowledge and worship of the one true God : a rule of moral duty : and a state of future rewards and punishments : to which is prefixed, a preliminary discourse on natural and revealed religion /

Mode of access: Internet.

The advantage and necessity of the Christian revelation, shewn from the state of religion in the antient heathen world: especially with respect to the knowledge and worship of the one true god: a rule of moral duty: and a state of future rewards and punishments. To which is prefixed, a preliminary discourse on natural and revealed religion. In two volumes.

At head of title: Finley's edition.

The use of electropalatography in the assessment and treatment of acquired motor speech disorders in adults: Current knowledge and future directions

Electropalatography (EPG) has been employed to measure speech articulation since the mid-1970s. This technique has predominately been used in experimental phonetic research and in the diagnosis and treatment of articulation disorders in children. However, there is a growing body of research employing EPG to diagnose and treat articulatory impairment associated with acquired motor speech disorder (MSD) in adults. The purpose of this paper was to (1) review the findings of studies pertaining to the assessment and treatment of MSDs in adults using EPG, (2) highlight current methodologies employed, and (3) discuss the potential limitations of EPG in the assessment and treatment of MSDs and examine directions for future applied research and treatment studies.

Knowledge representation with ontologies: present challenges future possibilities

Ontologies have become the knowledge representation medium of choice in recent years for a range of computer science specialities including the Semantic Web, Agents, and Bio-informatics. There has been a great deal of research and development in this area combined with hype and reaction. This special issue is concerned with the limitations of ontologies and how these can be addressed, together with a consideration of how we can circumvent or go beyond these constraints. The introduction places the discussion in context and presents the papers included in this issue.

Knowledge representation with ontologies:the present and future

Recently, we have seen an explosion of interest in ontologies as artifacts to represent human knowledge and as critical components in knowledge management, the semantic Web, business-to-business applications, and several other application areas. Various research communities commonly assume that ontologies are the appropriate modeling structure for representing knowledge. However, little discussion has occurred regarding the actual range of knowledge an ontology can successfully represent.

Knowledge management in the energy sector:review and future directions

Purpose – The purpose of this paper is to examine the state of knowledge management (KM) in the energy sector and more broadly, and consider future directions for research and practice. Design/methodology/approach – The paper reviews the literature on KM and the practice of KM as relevant to the energy sector. Findings – There are many examples of good practice in KM in the sector, and some organisations, especially in the oil industry, are seen as leaders in KM practice. However, other organisations have yet to embark on explicit KM initiatives or projects at all. In addition, some parts of the energy sector discuss KM without any reference to the more general KM literature. Originality/value – Although some parts of the energy sector have justifiably earned a good reputation for KM, other parts are completely unaware of the field, as is apparent from the literature. This review helps to raise awareness and guide future work.

A Semantic-Oriented Architecture of a Functional Module for Personalized and Adaptive Access to the Knowledge in a Multimedia Digital Library

This article presents the principal results of the doctoral thesis “Semantic-oriented Architecture and Models for Personalized and Adaptive Access to the Knowledge in Multimedia Digital Library” by Desislava Ivanova Paneva-Marinova (Institute of Mathematics and Informatics), successfully defended before the Specialised Academic Council for Informatics and Mathematical Modelling on 27 October, 2008.