Open PhD Workshop on Technology-Enhanced Learning and Semantics, Software and Services

The 7FP project SISTER focuses, especially, on strengthening the PhD and PostDoc level of education and training of researchers, and thus attracting more young scientists to the research profession and retaining them. The project SISTER is structured around two ICT strategic research areas - Software and services, and Intelligent Content and Semantics. The research workshops and seminars will support the research in the particular area through brainstorming sessions, discussions and strategic planning. Some of them will be of benefit to the PhD students and Post Docs and the advancement in their careers, while others will be devoted to further research collaboration in selected EU research programmes. The main research areas addressed are: Creation of digital libraries with intelligent content. Semantic annotation of digital content - Creation of ontologies for the digital content in the libraries. Semantic annotation of the learning materials in the repositories. The created ontologies and their semantic annotation will allow searching materials using semantic web techniques. Development of adaptive intelligent learning systems based on intelligent ontologies and digital learning materials. New innovative pedagogical approaches, assessment models and organisational models for lifelong competence development. Software for the effective support of users who create, store, use and exchange knowledge resources, learning activities, units of learning and competence development programmes within a learning network. Models and tools for competence development into a common, easy to use infrastructure. Training programs to learn users how to work with the infrastructure, and to train instructors and companies (specifically SMEs) to deliver services using the infrastructure. Responsive environments for technology-enhanced learning higher education and business organisations "that motivate, engage and inspire learners, and which can be embedded in the business processes and human resources management systems of organisations". Special attention will be given to using the research outcomes related to Intelligent Content and Semantics and Digital Libraries for building intelligent Adaptive and intuitive learning systems and Web 2.0 oriented applications. Development of a semantics-based reference frameworks for the conceptualisation of learning content, learning objectives, and teaching strategies, and the implementation of pedagogically-driven and semantically-enhanced adaptive learning systems. This will lead to consolidating existing theoretical and technological frameworks for explicitly modelling educational content, teaching strategies, and learner characteristics, and integrating them under a common semantic model.Open PhD Workshop on Technology-Enhanced Learning and Semantics, Software and Services in conjunction with the 13th International Conference on Artificial Intelligence: Methodology, Systems, Applications - AI@Work (AIMSA 2008) 04-06 September, Varna, Bulgari

Let us first agree on what the term "semantics" means: An unorthodox approach to an age-old debate

Traditionally, semantics has been seen as a feature of human language. The advent of the information era has led to its widespread redefinition as an information feature. Contrary to this praxis, I define semantics as a special kind of information. Revitalizing the ideas of Bar-Hillel and Carnap I have recreated and re-established the notion of semantics as the notion of Semantic Information. I have proposed a new definition of information (as a description, a linguistic text, a piece of a story or a tale) and a clear segregation between two different types of information - physical and semantic information. I hope, I have clearly explained the (usually obscured and mysterious) interrelations between data and physical information as well as the relation between physical information and semantic information. Consequently, usually indefinable notions of "information", "knowledge", "memory", "learning" and "semantics" have also received their suitable illumination and explanation

SemML: Reusable ML for condition monitoring in discrete manufacturing

Machine learning (ML) is gaining much attention for data analysis in manufacturing. Despite the success, there is still a number of challenges in widening the scope of ML adoption. The main challenges include the exhausting effort of data integration and lacking of generalisability of developed ML pipelines to diverse data variants, sources, and domain processes. In this demo we present our SemML system that addresses these challenges by enhancing machine learning with semantic technologies: by capturing domain and ML knowledge in ontologies and ontology templates and automating various ML steps using reasoning. During the demo the attendees will experience three cunningly-designed scenarios based on real industrial applications of manufacturing condition monitoring at Bosch, and witness the power of ontologies and templates in enabling reusable ML pipelines

Implementation of Advanced Technologies in Ontology-based E-learning Model

Paper discusses how advanced technologies like educational software and Web 2.0 tools could be successfully integrated in e-learning in order to create and deliver high quality e-learning content using ontology-based model. We propose here a possible solution for implementation of advanced technologies in this model and how different social tools and educational software could be used in ontology as RLOs. We describe advantages of the solution and how set requirements for effective e-learning are satisfied by the suggested model for integration of new technologies

A Review Study On Role Of AI In Healthcare

Artificial intelligence in healthcare is an overarching term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to mimic human cognition in the analysis, presentation, and comprehension of complex medical and health care data. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data. This paper presents a review study on role of AI in healthcare in short.

Tourism mobile and recommendation systems - a state of the art

Recommendation systems have been growing in number for the last fifteen years. To evolve and adapt to the demands of the actual society, many paradigms emerged giving birth to even more paradigms and hybrid approaches. Mobile devices have also been under an incredible growth rate in every business area, and there are already lots of mobile based systems to assist tourists. This explosive growth gave birth to different mobile applications, each having their own advantages and disadvantages. Since recommendation and mobile systems might as well be integrated, this work intends to present the current state of the art in tourism mobile and recommendation systems, as well as to state their advantages and disadvantages

CAD2Real: Deep learning with domain randomization of CAD data for 3D pose estimation of electronic control unit housings

Electronic control units (ECUs) are essential for many automobile components, e.g. engine, anti-lock braking system (ABS), steering and airbags. For some products, the 3D pose of each single ECU needs to be determined during series production. Deep learning approaches can not easily be applied to this problem, because labeled training data is not available in sufficient numbers. Thus, we train state-of-the-art artificial neural networks (ANNs) on purely synthetic training data, which is automatically created from a single CAD file. By randomizing parameters during rendering of training images, we enable inference on RGB images of a real sample part. In contrast to classic image processing approaches, this data-driven approach poses only few requirements regarding the measurement setup and transfers to related use cases with little development effort.Comment: Proc. 30. Workshop Computational Intelligence, Berlin, 202

CAD2Real: Deep learning with domain randomization of CAD data for 3D pose estimation of electronic control unit housings

Electronic control units (ECUs) are essential for many automobile components, e.g. engine, anti-lock braking system (ABS), steering and airbags. For some products, the 3D pose of each single ECU needs to be determined during series production. Deep learning approaches can not easily be applied to this problem, because labeled training data is not available in sufficient numbers. Thus, we train state-of-the-art artificial neural networks (ANNs) on purely synthetic training data, which is automatically created from a single CAD file. By randomizing parameters during rendering of training images, we enable inference on RGB images of a real sample part. In contrast to classic image processing approaches, this data-driven approach poses only few requirements regarding the measurement setup and transfers to related use cases with little development effort