126,394 research outputs found
Human Interaction Discovery in Smartphone Proximity Networks
Since humans are fundamentally social beings and interact frequently with others in their daily life, understanding social context is of primary importance in building context-aware applications. In this paper, using smartphone Bluetooth as a proximity sensor to create social networks, we present a probabilistic approach to mine human interaction types in real life. Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related. The results show that the model can automatically discover a variety of social contexts. We objectively validated our model by studying its predictive and retrieval performance
A Hybrid Approach to Finding Relevant Social Media Content for Complex Domain Specific Information Needs
While contemporary semantic search systems offer to improve classical
keyword-based search, they are not always adequate for complex domain specific
information needs. The domain of prescription drug abuse, for example, requires
knowledge of both ontological concepts and 'intelligible constructs' not
typically modeled in ontologies. These intelligible constructs convey essential
information that include notions of intensity, frequency, interval, dosage and
sentiments, which could be important to the holistic needs of the information
seeker. We present a hybrid approach to domain specific information retrieval
(or knowledge-aware search) that integrates ontology-driven query
interpretation with synonym-based query expansion and domain specific rules, to
facilitate search in social media. Our framework is based on a context-free
grammar (CFG) that defines the query language of constructs interpretable by
the search system. The grammar provides two levels of semantic interpretation:
1) a top-level CFG that facilitates retrieval of diverse textual patterns,
which belong to broad templates and 2) a low-level CFG that enables
interpretation of certain specific expressions that belong to such patterns.
These low-level expressions occur as concepts from four different categories of
data: 1) ontological concepts, 2) concepts in lexicons (such as emotions and
sentiments), 3) concepts in lexicons with only partial ontology representation,
called lexico-ontology concepts (such as side effects and routes of
administration (ROA)), and 4) domain specific expressions (such as date, time,
interval, frequency and dosage) derived solely through rules. Our approach is
embodied in a novel Semantic Web platform called PREDOSE developed for
prescription drug abuse epidemiology.
Keywords: Knowledge-Aware Search, Ontology, Semantic Search, Background
Knowledge, Context-Free GrammarComment: Accepted for publication: Journal of Web Semantics, Elsevie
Context-Aware Embeddings for Automatic Art Analysis
Automatic art analysis aims to classify and retrieve artistic representations
from a collection of images by using computer vision and machine learning
techniques. In this work, we propose to enhance visual representations from
neural networks with contextual artistic information. Whereas visual
representations are able to capture information about the content and the style
of an artwork, our proposed context-aware embeddings additionally encode
relationships between different artistic attributes, such as author, school, or
historical period. We design two different approaches for using context in
automatic art analysis. In the first one, contextual data is obtained through a
multi-task learning model, in which several attributes are trained together to
find visual relationships between elements. In the second approach, context is
obtained through an art-specific knowledge graph, which encodes relationships
between artistic attributes. An exhaustive evaluation of both of our models in
several art analysis problems, such as author identification, type
classification, or cross-modal retrieval, show that performance is improved by
up to 7.3% in art classification and 37.24% in retrieval when context-aware
embeddings are used
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
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