Scalable semantic aware context storage

The number of connected devices collecting and distributing real-world information through various systems, is expected to soar in the coming years. As the number of such connected devices grows, it becomes increasingly difficult to store and share all these new sources of information. Several context representation schemes try to standardize this information, but none of them have been widely adopted. In previous work we addressed this challenge, however our solution had some drawbacks: poor semantic extraction and scalability. In this paper we discuss ways to efficiently deal with representation schemes' diversity and propose a novel d-dimension organization model. Our evaluation shows that d-dimension model improves scalability and semantic extraction.

Semantic features for context organization

In recent years the technological world has grown by incorporating billions of small sensing devices, collecting and sharing real-world information. As the number of such devices grows, it becomes increasingly difficult to manage all these new information sources. There is no uniform way to share, process and understand context information. In previous publications we discussed efficient ways to organize context information that is independent of structure and representation. However, our previous solution suffers from semantic sensitivity. In this paper we review semantic methods that can be used to minimize this issue, and propose an unsupervised semantic similarity solution that combines distributional profiles with public web services. Our solution was evaluated against Miller-Charles dataset, achieving a correlation of 0.6.

Deep convolutional neural networks for semantic video object segmentation

In this thesis, we propose to infer pixel-level labelling in video by utilising only object category information, exploiting the intrinsic structure of video data. Our motivation is the observation that image-level labels are much more easily to be acquired than pixel-level labels, and it is natural to find a link between the image level recognition and pixel level classification in video data, which would transfer learned recognition models from one domain to the other one. To this end, this thesis proposes two domain adaptation approaches to adapt the deep convolutional neural network (CNN) image recognition model trained from labelled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of unlabelled video data. Our proposed approaches explicitly model and compensate for the domain adaptation from the source domain to the target domain which in turn underpins a robust semantic object segmentation method for natural videos. We demonstrate the superior performance of our methods by presenting extensive evaluations on challenging datasets comparing with the state-of-the-art methods.

Aging and semantic integration in sentence processing: Testing the cognitive workload of wrap-up

The current study investigated the cognitive workload of sentence and clause wrap-up in younger and older readers. A large number of studies have demonstrated the presence of wrap-up effects, peaks in processing time at clause and sentence boundaries that some argue reflect attention to organizational and integrative semantic processes. However, the exact nature of these wrap-up effects is still not entirely clear, with some arguing that wrap-up is not related to processing difficulty, but rather is triggered by a low-level oculomotor response or the implicit monitoring of intonational contour. The notion that wrap-up effects are resource-demanding was directly tested by examining the degree to which sentence and clause wrap-up affects the parafoveal preview benefit. Older and younger adults read passages in which a target word N occurred in a sentence-internal, clause-final, or sentence-final position. A gaze-contingent boundary change paradigm was used in which, on some trials, a non-word preview of word N+1 was replaced by a target word once the eyes crossed an invisible boundary located between words N and N+1. All measures of reading time on word N were longer at clause and sentence boundaries than in the sentence-internal position. In the earliest measures of reading time, sentence and clause wrap-up showed evidence of reducing the magnitude of the preview benefit similarly for younger and older adults. However, this effect was moderated by age in gaze duration, such that older adults showed a complete reduction in the preview benefit in the sentence-final condition. Additionally, sentence and clause wrap-up were negatively associated with the preview benefit. Collectively, the findings from the current study suggest that wrap-up is cognitively demanding and may be less efficient with age, thus, resulting in a reduction of the parafoveal preview during normal reading.

A contribution to semantic description of images and videos: an application of soft biometrics

Humans have a high ability to extract visual data information acquired by sight. Trought a learning process, which starts at birth and continues throughout life, image interpretation becomes almost instinctively. At a glance, one can easily describe a scene with reasonable precision, naming its main components. Usually, this is done by extracting low-level features such as edges, shapes and textures, and associanting them to high level meanings. In this way, a semantic description of the scene is done. An example of this, is the human capacity to recognize and describe other people physical and behavioral characteristics, or biometrics. Soft-biometrics also represents inherent characteristics of human body and behaviour, but do not allow unique person identification. Computer vision area aims to develop methods capable of performing visual interpretation with performance similar to humans. This thesis aims to propose computer vison methods which allows high level information extraction from images in the form of soft biometrics. This problem is approached in two ways, unsupervised and supervised learning methods. The first seeks to group images via an automatic feature extraction learning , using both convolution techniques, evolutionary computing and clustering. In this approach employed images contains faces and people. Second approach employs convolutional neural networks, which have the ability to operate on raw images, learning both feature extraction and classification processes. Here, images are classified according to gender and clothes, divided into upper and lower parts of human body. First approach, when tested with different image datasets obtained an accuracy of approximately 80% for faces and non-faces and 70% for people and non-person. The second tested using images and videos, obtained an accuracy of about 70% for gender, 80% to the upper clothes and 90% to lower clothes. The results of these case studies, show that proposed methods are promising, allowing the realization of automatic high level information image annotation. This opens possibilities for development of applications in diverse areas such as content-based image and video search and automatica video survaillance, reducing human effort in the task of manual annotation and monitoring.

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Collaborative Filtering with Semantic Neighbour Discovery

Nearest neighbour collaborative filtering (NNCF) algorithms are commonly used in multimedia recommender systems to suggest media items based on the ratings of users with similar preferences. However, the prediction accuracy of NNCF algorithms is affected by the reduced number of items – the subset of items co-rated by both users – typically used to determine the similarity between pairs of users. In this paper, we propose a different approach, which substantially enhances the accuracy of the neighbour selection process – a user-based CF (UbCF) with semantic neighbour discovery (SND). Our neighbour discovery methodology, which assesses pairs of users by taking into account all the items rated at least by one of the users instead of just the set of co-rated items, semantically enriches this enlarged set of items using linked data and, finally, applies the Collinearity and Proximity Similarity metric (CPS), which combines the cosine similarity with Chebyschev distance dissimilarity metric. We tested the proposed SND against the Pearson Correlation neighbour discovery algorithm off-line, using the HetRec data set, and the results show a clear improvement in terms of accuracy and execution time for the predicted recommendations.

When “happy” means “sad”: neuropsychological evidence for the right prefrontal cortex contribution to executive semantic processing

The contribution of the left inferior prefrontal cortex in semantic processing has been widely investigated in the last decade. Converging evidence from functional imaging studies shows that this region is involved in the “executive” or “controlled” aspects of semantic processing. In this study, we report a single case study of a patient, PW, with damage to the right prefrontal and temporal cortices following stroke. PW showed a problem in executive control of semantic processing, where he could not easily override automatic but irrelevant semantic processing. This case thus shows the necessary role of the right inferior prefrontal cortex in executive semantic processing. Compared to tasks previously used in the literature, our tasks placed higher demands on executive semantic processing. We suggest that the right inferior prefrontal cortex is recruited when the demands on executive semantic processing are particularly high.

A Retrospective Socio-Semantic Analysis of the PRO-VE Conferences

Part 19: Knowledge Management in Networks

ViDRILO: The Visual and Depth Robot Indoor Localization with Objects information dataset

In this article we describe a semantic localization dataset for indoor environments named ViDRILO. The dataset provides five sequences of frames acquired with a mobile robot in two similar office buildings under different lighting conditions. Each frame consists of a point cloud representation of the scene and a perspective image. The frames in the dataset are annotated with the semantic category of the scene, but also with the presence or absence of a list of predefined objects appearing in the scene. In addition to the frames and annotations, the dataset is distributed with a set of tools for its use in both place classification and object recognition tasks. The large number of labeled frames in conjunction with the annotation scheme make this dataset different from existing ones. The ViDRILO dataset is released for use as a benchmark for different problems such as multimodal place classification and object recognition, 3D reconstruction or point cloud data compression.

The conversation on political issues on Twitter: an analysis of the participation and frames in the debate on the 'Wert law' and evictions in Spain

The aim of this study is to determine which social agents are involved in the political debate on Twitter and whether the interpretive hegemony of actors that have traditionally been the most prominent is tempered by the challenge of framing shared with audiences. The relationship between the interpretations expressed and the profiles of participants is analyzed in comparison with the frames used by mainstream media. The chosen methodology combines content analysis and discourse analysis techniques on a sample of 1,504 relevant tweets posted on two political issues –the approval of the education law LOMCE and the evictions caused by the crisis, which have also been studied in the front pages of four leading newspapers in Spain. The results show a correlation between political issue singularities, frames and the type of discussion depending on the participants.

Rapporto tensore su scalare con gravità indotta: analisi dell’approssimazione di slow-roll nei frames di Jordan e Einstein

L’obbiettivo di questa tesi è quello di analizzare le conseguenze della scelta del frame (Jordan o Einstein) nel calcolo delle proprietà degli spettri primordiali generati dall’inflazione ed in particolare dell’osservabile r (rapporto tensore su scalare) al variare del potenziale del campo che genera l’espansione accelerata. Partendo dalla descrizione della teoria dell’inflazione in relatività generale, focalizzando l’attenzione sui motivi che hanno portato all’introduzione di questa teoria, vengono presentate le tecniche di utilizzo comune per lo studio della dinamica omogenea (classica) inflazionaria e di quella disomogenea (quantistica). Una particolare attenzione viene rivolta ai metodi di approssimazione che è necessario adottare per estrarre predizioni analitiche dai modelli inflazionari per poi confrontarle con le osservazioni. Le tecniche introdotte vengono poi applicate ai modelli di inflazione con gravità indotta, ovvero ad una famiglia di modelli con accoppiamento non minimale tra il campo scalare inflatonico e il settore gravitazionale. Si porrà attenzione alle differenze rispetto ai modelli con accoppiamento minimale, e verrà studiata la dinamica in presenza di alcuni potenziali derivanti dalla teoria delle particelle e diffusi in letteratura. Il concetto di “transizione tra il frame di Jordan e il frame di Einstein” viene illustrato e le sue conseguenze nel calcolo approssimato del rapporto tensore su scalare sono discusse. Infine gli schemi di approssimazione proposti vengono analizzati numericamente. Risulterà che per due dei tre potenziali presentati i metodi di approssimazione sono più accurati nel frame di Einstein, mentre per il terzo potenziale i due frames portano a risultati analitici similmente accurati.

Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.

Semantic relations: Modelling issues, proposals and possible applications

Semantic relations are an important element in the construction of ontology-based linguistic resources and models of problem domains. Nevertheless, they remain under-specified. This is a pervasive problem in both Software Engineering and Artificial Intelligence. Thus, we find semantic links that can have multiple interpretations, abstractions that are not enough to represent the relation richness of problem domains, and even poorly structured taxonomies. However, if provided with precise semantics, some of these problems can be avoided, and meaningful operations can be performed on them that can be an aid in the ontology construction process. In this paper we present some insightful issues about the representation of relations. Moreover, the initiatives aiming to provide relations with clear semantics are explained and the inclusion of their core ideas as part of a methodology for the development of ontology-based linguistic resources is proposed.