34,423 research outputs found
Learning Dynamic Classes of Events using Stacked Multilayer Perceptron Networks
People often use a web search engine to find information about events of
interest, for example, sport competitions, political elections, festivals and
entertainment news. In this paper, we study a problem of detecting
event-related queries, which is the first step before selecting a suitable
time-aware retrieval model. In general, event-related information needs can be
observed in query streams through various temporal patterns of user search
behavior, e.g., spiky peaks for popular events, and periodicities for
repetitive events. However, it is also common that users search for non-popular
events, which may not exhibit temporal variations in query streams, e.g., past
events recently occurred, historical events triggered by anniversaries or
similar events, and future events anticipated to happen. To address the
challenge of detecting dynamic classes of events, we propose a novel deep
learning model to classify a given query into a predetermined set of multiple
event types. Our proposed model, a Stacked Multilayer Perceptron (S-MLP)
network, consists of multilayer perceptron used as a basic learning unit. We
assemble stacked units to further learn complex relationships between neutrons
in successive layers. To evaluate our proposed model, we conduct experiments
using real-world queries and a set of manually created ground truth.
Preliminary results have shown that our proposed deep learning model
outperforms the state-of-the-art classification models significantly.Comment: Neu-IR '16 SIGIR Workshop on Neural Information Retrieval, 6 pages, 4
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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
Video Stream Retrieval of Unseen Queries using Semantic Memory
Retrieval of live, user-broadcast video streams is an under-addressed and
increasingly relevant challenge. The on-line nature of the problem requires
temporal evaluation and the unforeseeable scope of potential queries motivates
an approach which can accommodate arbitrary search queries. To account for the
breadth of possible queries, we adopt a no-example approach to query retrieval,
which uses a query's semantic relatedness to pre-trained concept classifiers.
To adapt to shifting video content, we propose memory pooling and memory
welling methods that favor recent information over long past content. We
identify two stream retrieval tasks, instantaneous retrieval at any particular
time and continuous retrieval over a prolonged duration, and propose means for
evaluating them. Three large scale video datasets are adapted to the challenge
of stream retrieval. We report results for our search methods on the new stream
retrieval tasks, as well as demonstrate their efficacy in a traditional,
non-streaming video task.Comment: Presented at BMVC 2016, British Machine Vision Conference, 201
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm usersâ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to âunannotatedâ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the âBag of Visual Wordsâ
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ânon-informative
visual wordsâ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
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a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
The Effects of Twitter Sentiment on Stock Price Returns
Social media are increasingly reflecting and influencing behavior of other
complex systems. In this paper we investigate the relations between a well-know
micro-blogging platform Twitter and financial markets. In particular, we
consider, in a period of 15 months, the Twitter volume and sentiment about the
30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We
find a relatively low Pearson correlation and Granger causality between the
corresponding time series over the entire time period. However, we find a
significant dependence between the Twitter sentiment and abnormal returns
during the peaks of Twitter volume. This is valid not only for the expected
Twitter volume peaks (e.g., quarterly announcements), but also for peaks
corresponding to less obvious events. We formalize the procedure by adapting
the well-known "event study" from economics and finance to the analysis of
Twitter data. The procedure allows to automatically identify events as Twitter
volume peaks, to compute the prevailing sentiment (positive or negative)
expressed in tweets at these peaks, and finally to apply the "event study"
methodology to relate them to stock returns. We show that sentiment polarity of
Twitter peaks implies the direction of cumulative abnormal returns. The amount
of cumulative abnormal returns is relatively low (about 1-2%), but the
dependence is statistically significant for several days after the events
Unified Embedding and Metric Learning for Zero-Exemplar Event Detection
Event detection in unconstrained videos is conceived as a content-based video
retrieval with two modalities: textual and visual. Given a text describing a
novel event, the goal is to rank related videos accordingly. This task is
zero-exemplar, no video examples are given to the novel event.
Related works train a bank of concept detectors on external data sources.
These detectors predict confidence scores for test videos, which are ranked and
retrieved accordingly. In contrast, we learn a joint space in which the visual
and textual representations are embedded. The space casts a novel event as a
probability of pre-defined events. Also, it learns to measure the distance
between an event and its related videos.
Our model is trained end-to-end on publicly available EventNet. When applied
to TRECVID Multimedia Event Detection dataset, it outperforms the
state-of-the-art by a considerable margin.Comment: IEEE CVPR 201
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