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Due to a global competition today’s business world changed dramatically whereas the ongoing trend towards global enterprise networks has lead to increasing customer expectations regarding the performance of logistics systems which have to be simultaneously reliable, robust and cost-effective. One the one hand reliability, robustness and cost-efficiency mainly depend on the availability of suitable ICTsolutions as well as their proper integration into the business processes. As a consequence various systems were developed in the past and are currently in use. On the other hand the seamless access to the data kept by all these different systems is a key issue in order to meet the requirements of logistics systems of today and tomorrow. In this context data integration represents a major challenge which is still unsolved. This paper describes a proposal for a service-oriented approach towards the seamless integration of logistics data which aims at combining existing systems and standards and thus overcoming today’s data and information barriers. By accepting existing standards and interfaces this requires innovative approaches addressing the semantic description of data, their transparent transformation between different representations as well as a common interface which can be provided to the various systems which are currently applied in the field of logistics.

International audience; In this paper, we propose a peer-to-peer approach to derive and obtain additional context data from low-level context data that may be spread over multiple domains in pervasive computing environments. In this system, peers are self-organized into a semantic peer- to-peer network as the underlying communication substrate. Context reasoning is done in a distributed fashion through logical reasoning according to a set of user-defined rules. Both pull and push services are supported in the system to enable message exchange during the reasoning process. We present our design concepts, and prove the effectiveness of our system through the prototype evaluation

Context is typically viewed at as meta-data that supplements the 'real' data of an application. In this paper, we contrast and compare the concepts of context in four different applications. We show that our notion of context needs to be extended and that wider and more flexible notions need to be supported. In particular, a key issue is how to provide systems that support computation in regard to any context.

International audience; Ubiquitous computing involves applications that adapt their behavior to every changing physical, social, and computing environment. This can be realized through proper capturing, representation, management and reasoning of constantly changing context. In this work, we propose a hybrid approach to collaborative context management. It uses semantic ontology and relational schema. System scalability is addressed by heuristic based selective loading of relevant context data into the reasoner. We have also developed a collaborative platform that performs context-aware reasoning and decisions based on the context management model. A result from the preliminary test shows that ourmodel is scalable and the platform is domain independent.

This paper reports on how we learned from an alternative practice in order to design engaging interactive technology intended for a more general user group. When investigating new types of digital photography we designed context photography, where real-time context data visually affects digital pictures as they are taken. To understand how to design for a meaningful photographic experience, we took inspiration from an amateur practice involving a particular type of analogue camera --- Lomography. This paper shows how such alternative or marginal practices can help to ground design of interactive technology in existing human interests, while at the same time leading to a novel design outcome.

Completely sequenced genomes and other types of genomics data provide us with new information to predict protein function. While classical, homology-based function prediction provides information about a proteins’ molecular function (what does the protein do at a molecular scale?), the analysis of the sequence in the context of its genome or in other types of genomics data provides information about its functional context (what are the proteins’ interaction partners, and in which biological process does it play a role?) Genomic context data are however inherently noisy. Only by combining different types of genomic(s) context data (vertical comparative genomics) or by combining the same type of genomics data from different species (horizontal comparative genomics) do they become sufficiently reliable to be used for protein function prediction. Homology-based function prediction and context-based function prediction provide complementary information about a protein’s function and can becombined to make predictions that are specific enough for experimental testing. Here we discuss the genomic coverage and reliability of combining genomics data for protein function prediction and survey predictions that have actually led to experimental confirmation. Using a number of examples we illustrate how combining the information from various types of genomics data can lead to specific protein function predictions. These include the prediction that the Ribonuclease L inhibitor (RLI) is involved in the maturation of ribosomal RNA.

Identifying correlations between context data, user behavior, and semantic information can lead to new services that are able to adapt to different situations. This "personalization" process can be based on recommendations on content. To better support service developers in focusing mainly on the creation of their service logic, these recommendations should be provided by a generic multipurpose recommender. Therefore, this paper proposes a generic framework that delivers "contextual recommendations" that are based on the combination of previously gathered user feedback data (i.e. ratings and clickstream history), context data, and ontology-based content categorization schemes. This paper provides a detailed overview of the specification, a short description of a possible usage scenario, and a discussion of the results

The intelligent dissemination of context information to enable context-aware applications is a topic often discussed in current research. However, related approaches are not able to cope with the unknown and dynamically changing nature often found in ad-hoc peer-to-peer environments. In this paper, we propose a flexible means of gathering, interpreting, and providing context information in a distributed manner. For this purpose, we describe a request-driven model for the interaction of context providers and requesters. We incorporate a bio- inspired pheromone-based approach of intelligently storing and disseminating information based on the data really requested and needed. Semantic Web technology is utilized to obtain a loosely coupled interaction pattern and an adaptable representation of context information. Thus, desired information can be accessed by external applications and services in a flexible and transparent manner.

According to the IETF definition, "presence conveys the ability and willingness of a user to communicate across a set of devices". In this paper we use semantic techniques to ask and reason about this ability and willingness on the basis of local context, instead of conveying raw context data. We describe the architecture of a policy engine, using the N3 language, give policy examples and discuss the applicability of the techniques in small, mobile terminals.

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In everyday life there are low risk situations where resources normally need to be protected and thus access to them controlled, but at the same time the implications of occasional unauthorized access are not severe. In these situations using an authentication technique that is expensive or requires significant effort from the user may not be justified. In this paper, we introduce a question-based authentication scheme appropriate for low risk situations. The scheme utilizes the context data already collected within a smart environment to generate the questions. Despite, the limited capabilities of the smart environment used in this experiment our preliminary study shows that using simple questions based on workplace events it is possible to discriminate between genuine users and impostors.

Context-aware mobile applications and systems have been extensively explored in the last decade and in the last few years we already saw promising products on the market. Most of these applications assume that context data is highly accurate. But in practice this information is often unreliable, especially when gathered from sensors or external sources. Previous research has argued that the system usability can be improved by displaying the uncertainty to the user. The research presented in this paper shows that it is not always an advantage to show the confidence of the context-aware application to the user. We developed a system for automatic form filling on mobile devices which fills in any web form with user data stored on the mobile device. The used algorithm generates rules which indicate with which probability which input field of a form should be filled in with which value. Based on this we developed two versions of our system. One shows the uncertainty of the system and one not. We then conducted a user study which shows that the user needs slightly more time and produces slightly more errors when the confidence of the system is visualized.

In this paper, we describe our application framework (Multi-Site Navigator) for delivering context-aware services. The application framework is based on our middleware solution, named context Data Manager (XDM), using DHTs technologies as underlying substrate for integrating different contextual data generators or sensors.

The number and the diffusion of non traditional devices able to provide every-where and any time access to the Web is increasing day by day. These devices include not only cellular phones, PDAs, and terminals for disabled people, but also new kind of devices, possibly embedded into tools such as household appliances, or vehicle dashboards. The characteristics of the various devices are so different that the issues related to the delivering of information and services on the Web do not involve only the presentation aspects, but structural and navigational aspects as well. As an example consider a cellular phone: its limited computing capabilities require that information is filtered and organized as a collection of atomic units whose dimensions depend closely on specific features of the device. It turns out that a novel and fundamental requirement in this scenario is the ability of the system to adapt and personalize content delivery according to the context of the client (a human being or an application). There is no accepted definition of context, but the term is a usually adopted to indicate “a set of attributes that characterizes the capabilities of the access mechanism, the preferences of the user and other aspects of the context into which a Web page is to be delivered”. These may include the access device (even in presence of strong heterogeneity of devices), the network QoS, the user preferences, the location, the time, the language, and so on. Therefore adaptiveness usually concerns several independent coordinates. In this chapter we present models, methods and techniques for the design and the development of Web-based information systems, which are adaptive with respect to context information.

In this paper we propose a comprehensive framework for therepresentation and translation of context information in adaptiveWeb based applications.We consider the various formats used today to express context data (HTTPheader, XML files over specific DTD's, RDF, CC/PP, and so on) and show thatthe primitives adopted by all of them can be classified into a ratherlimited set of basic types. We then define, building on these basic types,a general formalism for context information called GPM that embeds theseheterogeneous formats. It turns out that GPM can be used to model a varietyof contexts in a uniform way and therefore provides a powerful toolsupporting the design and analysis of context aware applications.We also address the problem of translating context information from onerepresentation into another. This is achieved by using GPM as anintermediate representation level. Finally, we describe architecture andfunctionality of a prototype implementing the translation process andillustrate experimental results supporting the effectiveness of the overallapproach.

Independent component analysis (ICA) (Hyvarinen et al., 2001) applied on word context data gives distinct features which reflect syntactic and semantic categories. The analysis gives features or categories that are both explicit and can easily be interpreted by humans. This result can be obtained without any human supervision or tagged corpora that would have some predetermined morphological, syntactic or semantic information. The results include both an emergence of clear distinctive categories or features and a distributed representation. This is based on the fact that a word may belong to several categories simultaneously in a graded manner. Within cognitive linguistics we wish that our model can provide additional understanding on potential cognitive mechanisms in natural language learning and understanding. Our approach is based on the assumption that much of the linguistic knowledge is emergent in nature and based on specific learning mechanisms. In the full paper, we will provide a brief introduction to independent component analysis, review the earlier published results (Honkela and Hyvarinen, 2004), provide the very latest results, compare our model with some other approaches based on unsupervised learning paradigm and discuss in general the potential of the ICA model within the theme of modelling language, cognition and action.

Much context data comes from mobile, transient, and unreliable sources. Such resources are best specified by descriptive names identifying what data is needed rather than which source is to provide it. The design of descriptive names has important consequences, but until now little attention has been focused on this problem. We propose a descriptive naming system for providers of context data that provides more flexibility and power than previous naming systems by classifying data providers into “provider kinds” that are organized in an evolving hierarchy of subkinds and superkinds. New provider kinds can be inserted in the hierarchy not only as subkinds, but also as superkinds, of existing provider kinds. Our names can specify arbitrary boolean combinations of arbitrary tests on data-source attributes, yielding expressive power not found in naming schemes based on attribute matching.

Given time and location information about digital photographs we can automatically generate an abundance of related contextual metadata, using off-the-shelf and Web-based data sources. Among these are the local daylight status and weather conditions at the time and place a photo was taken. This metadata has the potential of serving as memory cues and filters when browsing photo collections, especially as these collections grow into the tens of thousands and span dozens of years.We describe the contextual metadata that we automatically assemble for a photograph, given time and location, as well as a browser interface that utilizes that metadata. We then present the results of a user study and a survey that together expose which categories of contextual metadata are most useful for recalling and finding photographs. We identify among still unavailable metadata categories those that are most promising to develop next.

This paper presents mathematical modeling for manufactured product disassembly planning. It takes into account the recent research that has been done with regard to environmental aspects, economic constraints and actual operational disassembly factories. The method is based on a linear programming formalism and aims to integrate in a single model: product, system and context data. These data represent structural and end-of-life description of product, organization and resources of disassembly system, and the political and economical conjunctures. This model serves to find the optimal disassembly plan by optimizing the disassembly process profit as an objective function within an optimization problem. A set of constraints represents many product, system and context features.

Sensor data is structured and generally lacks of meaning by itself, but life-logging data (time, location, etc.) out of sensor data can be utilized to create lots of meaningful information combined with social data from social networks like Facebook and Twitter. There have been many platforms to produce meaningful information and support human behavior and context-awareness through integrating diverse mobile, social, and sensing input streams. The problem is that these platforms do not guarantee the performance in terms of the processing time and even let the accuracy of output data be addressed by new studies in each area where the platform is applied. Thus, this study proposes an improved platform which builds a knowledge base for context awareness by applying distributed and parallel computing approach considering the characteristics of sensor data that is collected and processed in real-time, and compares the proposed platform with existing platforms in terms of performance. The experiment shows the proposed platform is an advanced platform in terms of processing time. We reduce the processing time by 40% compared with existing platform. The proposed platform also guarantees the accuracy compared with existing platform.

The purpose of this observational study of the activity context in which preschool children engaged in positive social interactions was to produce a data source that could be used to assist in the identification of socially withdrawn children, and to select efficient training contexts in which to develop the social behavior repertoire of withdrawn youngsters. Eleven high-rate and 10 low-rate social interactors, ranging in age from 36 to 72 months, were observed for 12 10-minute sessions across a 3-month period. Six activity contexts were coded by trained observers: Observer, Isolate, Parallel, Game, Cooperative, and Fantasy. The results indicated that: (a) Low-rate children engaged in significantly more Isolate and Observer activity than did high-rate children. (b) High-rate children engaged in significantly more Cooperative and Fantasy activity than did low-rate children. (c) There were no significant differences between groups for Game and Parallel activities. Results are discussed in terms of the concurrent validity between frequency of interaction measures and the activity context data, the developmental progression of social skills acquisition, and the selection of treatment contexts for withdrawn children.

The large scale works that most deeply mark the fate of a territory are certainly the transport infrastructure (broad lines of road and rail links, ports, airports, intermodal nodes), which have important economic implications for the entire production and commercial apparatus located in the industrial and urban areas. The preventive analysis to verify the quality of the infrastructure projects is entrusted to the feasibility studies, which are the most appropriate tool to evaluate the opportunity to make a public investment. The assessment of the effects of transport infrastructure is a key element of regional planning and, in particular, the planning of new infrastructure. However, it is important not to underestimate the assessment culture, even in the case of renovation of existing works, where both the accurate detection of the context data and the verification of both impacts that can be generated by the interventions, is very often neglected.

Detailed information on the early musical responses and productivity of normal children prior to 2 years of age is notably absent, except for such prodigies as Mozart. In contrast, other areas of child development such as language acquisition have been widely studied. This void led to the present exploratory study of three children of three contrasting musical backgrounds. One family included parents who were professional musicians, one family had parents who were musically oriented but not musicians by profession, and a third family had parents who were not musically oriented. The study involved the observation of each child in relevant family settings over a period from birth to 2 years of age and provided an earlier period of observation (0–12 months), more family context data, and more child performance data than the other studies presently in the literature (McKernon, 1979; Holahan, Chapter 5, this volume) of children from 1 to 2 1/2 years.

Unmanned aerial vehicles (UAVs), also known as drones, provides unique functionalities, which allows area surveillance, inspection, surveying, unarmed cargo, armed attack machines, and aerial photography. Drones are susceptible to GPS spoofing attacks, integrity attacks and de-authentication attacks, which can allow criminals to access data, intercept the drone and use it commit a crime. Thus, this paper is presented to report on potential attacks against the Parrot Bebop 2 drone, and the ability for an investigator to collect evidence about the attacks on the drone. This paper aims at examining the possibility of establishing ownership and collecting data to reconstruct events, linking the drone controller with the drone to prove ownership, flight origins and other potentially useful information necessary to identify the proprietor of a crime. In addition, we have also proposed small-scale drone ontology for modelling drone context data, and simple forensic processing framework for small-scale drones.

Ensuring high quality education is an important priority of state educational policy in the Russian Federation. Recently, the quality of education, which directly depends on many factors, has been widely discussed. The aim of the study is a statistical analysis of the results of regional qualimetric monitoring of the quality of knowledge based on contextual indicators that affect the educational results of students. As the main tools used correlation and regression analysis. The described technique was tested in the Pskov region. The results of the study provide relevant information to all interested parties: state and municipal levels of education management, heads and teachers of educational institutions, parents and students.