119,033 research outputs found
Context for Ubiquitous Data Management
In response to the advance of ubiquitous computing technologies, we believe that for computer systems to be ubiquitous, they must be context-aware. In this paper, we address the impact of context-awareness on ubiquitous data management. To do this, we overview different characteristics of context in order to develop a clear understanding of context, as well as its implications and requirements for context-aware data management. References to recent research activities and applicable techniques are also provided
A Call to Arms: Revisiting Database Design
Good database design is crucial to obtain a sound, consistent database, and -
in turn - good database design methodologies are the best way to achieve the
right design. These methodologies are taught to most Computer Science
undergraduates, as part of any Introduction to Database class. They can be
considered part of the "canon", and indeed, the overall approach to database
design has been unchanged for years. Moreover, none of the major database
research assessments identify database design as a strategic research
direction.
Should we conclude that database design is a solved problem?
Our thesis is that database design remains a critical unsolved problem.
Hence, it should be the subject of more research. Our starting point is the
observation that traditional database design is not used in practice - and if
it were used it would result in designs that are not well adapted to current
environments. In short, database design has failed to keep up with the times.
In this paper, we put forth arguments to support our viewpoint, analyze the
root causes of this situation and suggest some avenues of research.Comment: Removed spurious column break. Nothing else was change
Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation
Existing item-based collaborative filtering (ICF) methods leverage only the
relation of collaborative similarity. Nevertheless, there exist multiple
relations between items in real-world scenarios. Distinct from the
collaborative similarity that implies co-interact patterns from the user
perspective, these relations reveal fine-grained knowledge on items from
different perspectives of meta-data, functionality, etc. However, how to
incorporate multiple item relations is less explored in recommendation
research. In this work, we propose Relational Collaborative Filtering (RCF), a
general framework to exploit multiple relations between items in recommender
system. We find that both the relation type and the relation value are crucial
in inferring user preference. To this end, we develop a two-level hierarchical
attention mechanism to model user preference. The first-level attention
discriminates which types of relations are more important, and the second-level
attention considers the specific relation values to estimate the contribution
of a historical item in recommending the target item. To make the item
embeddings be reflective of the relational structure between items, we further
formulate a task to preserve the item relations, and jointly train it with the
recommendation task of preference modeling. Empirical results on two real
datasets demonstrate the strong performance of RCF. Furthermore, we also
conduct qualitative analyses to show the benefits of explanations brought by
the modeling of multiple item relations
Dynamic Facial Expression of Emotion Made Easy
Facial emotion expression for virtual characters is used in a wide variety of
areas. Often, the primary reason to use emotion expression is not to study
emotion expression generation per se, but to use emotion expression in an
application or research project. What is then needed is an easy to use and
flexible, but also validated mechanism to do so. In this report we present such
a mechanism. It enables developers to build virtual characters with dynamic
affective facial expressions. The mechanism is based on Facial Action Coding.
It is easy to implement, and code is available for download. To show the
validity of the expressions generated with the mechanism we tested the
recognition accuracy for 6 basic emotions (joy, anger, sadness, surprise,
disgust, fear) and 4 blend emotions (enthusiastic, furious, frustrated, and
evil). Additionally we investigated the effect of VC distance (z-coordinate),
the effect of the VC's face morphology (male vs. female), the effect of a
lateral versus a frontal presentation of the expression, and the effect of
intensity of the expression. Participants (n=19, Western and Asian subjects)
rated the intensity of each expression for each condition (within subject
setup) in a non forced choice manner. All of the basic emotions were uniquely
perceived as such. Further, the blends and confusion details of basic emotions
are compatible with findings in psychology
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