Situated Support for Choice of Representations

As more and more companies are augmenting their data to include semantics it is imperative that the choices made when choosing the modelling language are well founded in knowledge about the language and the domain in question. This work demonstrates how the Semiotic Quality Framework can facilitate the choice of the most suited language for a real world application. Computational and situated features are introduced as an extension to the framework

DogOnt - Ontology Modeling for Intelligent Domotic Environments

Abstract. Home automation has recently gained a new momentum thanks to the ever-increasing commercial availability of domotic components. In this context, researchers are working to provide interoperation mechanisms and to add intelligence on top of them. For supporting intelligent behaviors, house modeling is an essential requirement to understand current and future house states and to possibly drive more complex actions. In this paper we propose a new house modeling ontology designed to fit real world domotic system capabilities and to support interoperation between currently available and future solutions. Taking advantage of technologies developed in the context of the Semantic Web, the DogOnt ontology supports device/network independent description of houses, including both “controllable ” and architectural elements. States and functionalities are automatically associated to the modeled elements through proper inheritance mechanisms and by means of properly defined SWRL auto-completion rules which ease the modeling process, while automatic device recognition is achieved through classification reasoning.

Is Context-aware Reasoning = Case-based Reasoning?

The purpose of this paper is to explore the similarities and differences and then argue for the potential synergies between two methodologies, namely Context-aware Reasoning and Case-based Reasoning, that are amongst the tools which can be used for intelligent environment (IE) system development. Through a case study supported by a review of the literature, we argue that context awareness and case based reasoning are not equal and are complementary methodologies to solve a domain specific problem, rather, the IE development paradigm must build a cooperation between these two approaches to overcome the individual drawbacks and to maximise the success of the IE systems

Evaluering av Helsebiblioteket

-Helsebiblioteket har eksistert i tre år. I den anledning har Nasjonalt kunnskapssenter for helsetjenesten bedt SINTEF IKT å evaluerer organisasjonen. Evalueringen består av to deler: en kvalitativ undersøkelse i blant administrativ- og politisk ledelse i helsevesenet, og en kvantitativ i blant Helsebibliotekets brukere. Evalueringen viser at den underliggende idé om lik tilgang til kvalitetssikret kunnskap for helseansatte oppfattes som viktig for helsevesenet. Helsebiblioteket har i denne sammenheng vært et sentralt virkemiddel. Det er dog stadig visse utfordringer særlig med hensyn til tilgjengelig, tid og infrastruktur for den enkle helseansatte. Denne ulikhet i tilgang er fortsatt gjeldende (de største hindringer ligger utenfor Helsebibliotekets virkeområde), selv om Helsebiblioteket også har bidraget til å gjøre den mindre. Helsebiblioteket som en sentral organisasjon med redaksjonell frihet betraktes som en velegnet måte å organisere lik tilgang til kvalitetssikret kunnskap. De største utfordringer er stabil finansiering og forut-sigbarhet i kildetilgang

Extended Abstract: Modelling Explanation-Aware Ambient Intelligent Systems with Problem Frames

When designing and implementing real world ambient intelligent systems, we are in need of applicable information systems engineering methods. These should supplement the knowledge engineering tools we can find in the intelligent systems area. The work presented here focuses on explanation-aware ambient intelligent systems

A Full Privacy-Preserving Scheme for Location-Based Services

Part 2: The 2014 Asian Conference on Availability, Reliability and Security, AsiaARES 2014International audienceLocation based services(LBS) pose risks to user’s privacy, as they have access to user’s identity, location and usage profile. Many approaches have been made up to deal with the privacy problems. But few of them meet the requirement of full privacy. In this paper, we propose a protocol that does not require a trusted third party and provides full privacy. We use group anonymous authentication to fulfill identity privacy, while using program obfuscation to satisfy the privacy requirement of usage profile. And we assume that there exist some geography or geometry methods to form a cloaking region to meet location privacy

Reliable online social network data collection

Large quantities of information are shared through online social networks, making them attractive sources of data for social network research. When studying the usage of online social networks, these data may not describe properly users’ behaviours. For instance, the data collected often include content shared by the users only, or content accessible to the researchers, hence obfuscating a large amount of data that would help understanding users’ behaviours and privacy concerns. Moreover, the data collection methods employed in experiments may also have an effect on data reliability when participants self-report inacurrate information or are observed while using a simulated application. Understanding the effects of these collection methods on data reliability is paramount for the study of social networks; for understanding user behaviour; for designing socially-aware applications and services; and for mining data collected from such social networks and applications. This chapter reviews previous research which has looked at social network data collection and user behaviour in these networks. We highlight shortcomings in the methods used in these studies, and introduce our own methodology and user study based on the Experience Sampling Method; we claim our methodology leads to the collection of more reliable data by capturing both those data which are shared and not shared. We conclude with suggestions for collecting and mining data from online social networks.Postprin