49,179 research outputs found
Enriching MPEG-7 user models with content metadata
MPEG-7 is an XML-based standard that provides tools for creating rich and structured multimedia content metadata. However, only an extremely limited range of preferences can be specified for user models and multimedia content metadata created by other parts of the standard cannot be fully exploited. This results in a very incomplete mapping of user models to content models. We present an approach to address the problem by representing user models by means of existing MPEG-7 content description tools
Goal-based self-contextualization
Abstract. System self-contextualizability is the system ability to autonomously adapt its behavior to the uncontrollable relevant context to keep its objectives satisfied. Self-contextualizable system must have alternative behaviors each fitting to a set of contexts. We propose to start considering context at the level of requirements engineering, adopting Tropos goal model to express requirements and complementing it with our proposed context analysis. We define variation points on goal model where a context-based decision might need to be taken, and propose constructs to analyze context. While goal analysis provides constructs to hierarchically analyze goals and discover alternative sets of tasks to be executed to satisfy a goal, our proposed context analysis provides constructs to hierarchically analyze context and discover alternative sets of facts to be monitored to verify a context.
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Generation of multi-modal dialogue for a net environment
In this paper an architecture and special purpose markup language for simulated affective face-to-face communication is presented. In systems based on this architecture, users will be able to watch embodied conversational agents interact with each other in virtual locations on the internet. The markup language, or Rich Representation Language (RRL), has been designed to provide an integrated representation of speech, gesture, posture and facial animation
Explainable Reasoning over Knowledge Graphs for Recommendation
Incorporating knowledge graph into recommender systems has attracted
increasing attention in recent years. By exploring the interlinks within a
knowledge graph, the connectivity between users and items can be discovered as
paths, which provide rich and complementary information to user-item
interactions. Such connectivity not only reveals the semantics of entities and
relations, but also helps to comprehend a user's interest. However, existing
efforts have not fully explored this connectivity to infer user preferences,
especially in terms of modeling the sequential dependencies within and holistic
semantics of a path. In this paper, we contribute a new model named
Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for
recommendation. KPRN can generate path representations by composing the
semantics of both entities and relations. By leveraging the sequential
dependencies within a path, we allow effective reasoning on paths to infer the
underlying rationale of a user-item interaction. Furthermore, we design a new
weighted pooling operation to discriminate the strengths of different paths in
connecting a user with an item, endowing our model with a certain level of
explainability. We conduct extensive experiments on two datasets about movie
and music, demonstrating significant improvements over state-of-the-art
solutions Collaborative Knowledge Base Embedding and Neural Factorization
Machine.Comment: 8 pages, 5 figures, AAAI-201
ConXsense - Automated Context Classification for Context-Aware Access Control
We present ConXsense, the first framework for context-aware access control on
mobile devices based on context classification. Previous context-aware access
control systems often require users to laboriously specify detailed policies or
they rely on pre-defined policies not adequately reflecting the true
preferences of users. We present the design and implementation of a
context-aware framework that uses a probabilistic approach to overcome these
deficiencies. The framework utilizes context sensing and machine learning to
automatically classify contexts according to their security and privacy-related
properties. We apply the framework to two important smartphone-related use
cases: protection against device misuse using a dynamic device lock and
protection against sensory malware. We ground our analysis on a sociological
survey examining the perceptions and concerns of users related to contextual
smartphone security and analyze the effectiveness of our approach with
real-world context data. We also demonstrate the integration of our framework
with the FlaskDroid architecture for fine-grained access control enforcement on
the Android platform.Comment: Recipient of the Best Paper Awar
Bipolarity in the querying of temporal databases
A database represents part of reality by containing data representing properties of real objects or concepts. To many real-world concepts or objects, time is an essential aspect and thus it should often be (implicitly) represented by databases, making these temporal databases. However, like other data, the time-related data in such databases may also contain imperfections such as uncertainties. One of the main purposes of a database is to allow the retrieval of information or knowledge deduced from its data, which is often done by querying the database. Because users may have both positive and negative preferences, they may want to query a database in a bipolar way. Moreover, their demands may have some temporal aspects. In this paper, a novel technique is presented, to query a valid-time relation containing uncertain valid-time data in a heterogeneously bipolar way, allowing every elementary query constraint a specific temporal constraint
Non-functional Property based service selection: A survey and classification of approaches
In recent years there has been much effort dedicated to developing approaches for service selection based on non-functional properties. It is clear that much progress has been made, and by considering the individual approaches there is some overlap in functionality, but obviously also some divergence. In this paper we contribute a classification of approaches, that is, we define a number of criteria which allow to differentiate approaches. We use this classification to provide a comparison of existing approaches and in that sense provide a survey of the state of the art of the field. Finally we make some suggestions as to where the research in this area might be heading and which new challenges need to be addressed
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