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OBOME - Ontology based opinion mining in UBIPOL
Ontologies have a special role in the UBIPOL system, they help to structure the policy related context, provide conceptualization for policy domain and use in the opinion mining process. In this work we presented a system called Ontology Based Opinion Mining Engine (OBOME) for analyzing a domain-specific opinion corpus by first assisting the user with the creation of a domain ontology from the corpus. We determined the polarity of opinion on the various domain aspects. In the former step, the policy domain aspect has are identified (namely which policy category is represented by the concept). This identification is supported by the policy modelling ontology, which describe the most important policy – related classes and structure. Then the most informative documents from the corpus are extracted and asked the user to create a set of aspects and related keywords using these documents. In the latter step, we used the corpus specific ontology to model the domain and extracted aspect-polarity associations using grammatical dependencies between words. Later, summarized results are shown to the user to analyze and store. Finally, in an offline process policy modeling ontology is updated
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
Where previous reviews on content-based image retrieval emphasize on what can
be seen in an image to bridge the semantic gap, this survey considers what
people tag about an image. A comprehensive treatise of three closely linked
problems, i.e., image tag assignment, refinement, and tag-based image retrieval
is presented. While existing works vary in terms of their targeted tasks and
methodology, they rely on the key functionality of tag relevance, i.e.
estimating the relevance of a specific tag with respect to the visual content
of a given image and its social context. By analyzing what information a
specific method exploits to construct its tag relevance function and how such
information is exploited, this paper introduces a taxonomy to structure the
growing literature, understand the ingredients of the main works, clarify their
connections and difference, and recognize their merits and limitations. For a
head-to-head comparison between the state-of-the-art, a new experimental
protocol is presented, with training sets containing 10k, 100k and 1m images
and an evaluation on three test sets, contributed by various research groups.
Eleven representative works are implemented and evaluated. Putting all this
together, the survey aims to provide an overview of the past and foster
progress for the near future.Comment: to appear in ACM Computing Survey
Towards a Holistic Approach to Designing Theory-based Mobile Health Interventions
Increasing evidence has shown that theory-based health behavior change
interventions are more effective than non-theory-based ones. However, only a
few segments of relevant studies were theory-based, especially the studies
conducted by non-psychology researchers. On the other hand, many mobile health
interventions, even those based on the behavioral theories, may still fail in
the absence of a user-centered design process. The gap between behavioral
theories and user-centered design increases the difficulty of designing and
implementing mobile health interventions. To bridge this gap, we propose a
holistic approach to designing theory-based mobile health interventions built
on the existing theories and frameworks of three categories: (1) behavioral
theories (e.g., the Social Cognitive Theory, the Theory of Planned Behavior,
and the Health Action Process Approach), (2) the technological models and
frameworks (e.g., the Behavior Change Techniques, the Persuasive System Design
and Behavior Change Support System, and the Just-in-Time Adaptive
Interventions), and (3) the user-centered systematic approaches (e.g., the
CeHRes Roadmap, the Wendel's Approach, and the IDEAS Model). This holistic
approach provides researchers a lens to see the whole picture for developing
mobile health interventions
Simultaneous Inference of User Representations and Trust
Inferring trust relations between social media users is critical for a number
of applications wherein users seek credible information. The fact that
available trust relations are scarce and skewed makes trust prediction a
challenging task. To the best of our knowledge, this is the first work on
exploring representation learning for trust prediction. We propose an approach
that uses only a small amount of binary user-user trust relations to
simultaneously learn user embeddings and a model to predict trust between user
pairs. We empirically demonstrate that for trust prediction, our approach
outperforms classifier-based approaches which use state-of-the-art
representation learning methods like DeepWalk and LINE as features. We also
conduct experiments which use embeddings pre-trained with DeepWalk and LINE
each as an input to our model, resulting in further performance improvement.
Experiments with a dataset of 356K user pairs show that the proposed
method can obtain an high F-score of 92.65%.Comment: To appear in the proceedings of ASONAM'17. Please cite that versio
Optical tomography: Image improvement using mixed projection of parallel and fan beam modes
Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be defined by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The findings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam
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