7,095 research outputs found
Creating a test collection to evaluate diversity in image retrieval
This paper describes the adaptation of an existing test collection
for image retrieval to enable diversity in the results set to be
measured. Previous research has shown that a more diverse set of
results often satisfies the needs of more users better than standard
document rankings. To enable diversity to be quantified, it is
necessary to classify images relevant to a given theme to one or
more sub-topics or clusters. We describe the challenges in
building (as far as we are aware) the first test collection for
evaluating diversity in image retrieval. This includes selecting
appropriate topics, creating sub-topics, and quantifying the overall
effectiveness of a retrieval system. A total of 39 topics were
augmented for cluster-based relevance and we also provide an
initial analysis of assessor agreement for grouping relevant
images into sub-topics or clusters
Structural Regularities in Text-based Entity Vector Spaces
Entity retrieval is the task of finding entities such as people or products
in response to a query, based solely on the textual documents they are
associated with. Recent semantic entity retrieval algorithms represent queries
and experts in finite-dimensional vector spaces, where both are constructed
from text sequences.
We investigate entity vector spaces and the degree to which they capture
structural regularities. Such vector spaces are constructed in an unsupervised
manner without explicit information about structural aspects. For concreteness,
we address these questions for a specific type of entity: experts in the
context of expert finding. We discover how clusterings of experts correspond to
committees in organizations, the ability of expert representations to encode
the co-author graph, and the degree to which they encode academic rank. We
compare latent, continuous representations created using methods based on
distributional semantics (LSI), topic models (LDA) and neural networks
(word2vec, doc2vec, SERT). Vector spaces created using neural methods, such as
doc2vec and SERT, systematically perform better at clustering than LSI, LDA and
word2vec. When it comes to encoding entity relations, SERT performs best.Comment: ICTIR2017. Proceedings of the 3rd ACM International Conference on the
Theory of Information Retrieval. 201
A resource-frugal probabilistic dictionary and applications in (meta)genomics
Genomic and metagenomic fields, generating huge sets of short genomic
sequences, brought their own share of high performance problems. To extract
relevant pieces of information from the huge data sets generated by current
sequencing techniques, one must rely on extremely scalable methods and
solutions. Indexing billions of objects is a task considered too expensive
while being a fundamental need in this field. In this paper we propose a
straightforward indexing structure that scales to billions of element and we
propose two direct applications in genomics and metagenomics. We show that our
proposal solves problem instances for which no other known solution scales-up.
We believe that many tools and applications could benefit from either the
fundamental data structure we provide or from the applications developed from
this structure.Comment: Submitted to PSC 201
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
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