Combinatorial locational analysis of public services in metropolitan areas. Case study in the city of Volos, Greece.

Social prosperity largely depends on spatial structure, a relation which becomes stronger in urban areas where the quality of life is menaced by several factors. Traffic, over-building, lack of open space and deficient location of services come to the fore. The latter reflects access inequality and is one of the main reasons for everyday movement difficulties of citizens. Particularly, public services, as part of the public sector, are considered to be driven by the principle of social well-fare. Therefore the study of their location gives rise to the question: how can access of city blocks to public services be evaluated and how can the results of this evaluation be combined with the monetary values assigned by the state? In this respect, the main aim of this paper is the determination of a synthetic methodological framework for the locational analysis and evaluation of public services in urban areas. The proposed approach is based on spatial analysis methods and techniques as well as on the analytical capabilities of GIS and finally leads to the definition of the locational value for each city block. The public services are classified according to served population age groups and to their yearly utilization levels. The minimum and average Manhattan distances to the services of each classification group are calculated along with the percentages of services that are closer than a critical radius to each city block. At the final step, city blocks are classified through the use of cluster analysis to the calculated distances and percentages and then ranked according to their overall accessibility to public services. Their score is utilized in the definition of their locational value and in the formulation of a combinatorial index which compares locational and land values throughout the study area. The methodological framework is applied in the city of Volos where according to the results of the analytical process the majority of city blocks (60,7%) indicates a comparatively lower locational than monetary land value.

Semantic user profiling techniques for personalised multimedia recommendation

Due to the explosion of news materials available through broadcast and other channels, there is an increasing need for personalised news video retrieval. In this work, we introduce a semantic-based user modelling technique to capture users’ evolving information needs. Our approach exploits implicit user interaction to capture long-term user interests in a profile. The organised interests are used to retrieve and recommend news stories to the users. In this paper, we exploit the Linked Open Data Cloud to identify similar news stories that match the users’ interest. We evaluate various recommendation parameters by introducing a simulation-based evaluation scheme

Patch-based semantic labelling of images.

PhDThe work presented in this thesis is focused at associating a semantics to the content of an image, linking the content to high level semantic categories. The process can take place at two levels: either at image level, towards image categorisation, or at pixel level, in se- mantic segmentation or semantic labelling. To this end, an analysis framework is proposed, and the different steps of part (or patch) extraction, description and probabilistic modelling are detailed. Parts of different nature are used, and one of the contributions is a method to complement information associated to them. Context for parts has to be considered at different scales. Short range pixel dependences are accounted by associating pixels to larger patches. A Conditional Random Field, that is, a probabilistic discriminative graphical model, is used to model medium range dependences between neighbouring patches. Another contribution is an efficient method to consider rich neighbourhoods without having loops in the inference graph. To this end, weak neighbours are introduced, that is, neighbours whose label probability distribution is pre-estimated rather than mutable during the inference. Longer range dependences, that tend to make the inference problem intractable, are addressed as well. A novel descriptor based on local histograms of visual words has been proposed, meant to both complement the feature descriptor of the patches and augment the context awareness in the patch labelling process. Finally, an alternative approach to consider multiple scales in a hierarchical framework based on image pyramids is proposed. An image pyramid is a compositional representation of the image based on hierarchical clustering. All the presented contributions are extensively detailed throughout the thesis, and experimental results performed on publicly available datasets are reported to assess their validity. A critical comparison with the state of the art in this research area is also presented, and the advantage in adopting the proposed improvements are clearly highlighted

Multi modal multi-semantic image retrieval

PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ 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 ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ 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 ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi 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

Do Speakers Build the Categories Linguists Postulate? A Usage-Based Exploration

Linguists are naturally inclined to seek maximally general categories for the description of linguistic phenomena, e.g. the present tense or the reflexive voice. It has been taken for granted that speakers use the same categories in their daily experience with language. A few studies have indicated, however, that speakers might not be able to build some general constructions that linguists postulate (see e.g. Dąbrowska 2008a; Perek 2015). If we would like for our descriptions to reflect the linguistic knowledge of native speakers, we need to empirically investigate the cognitive reality of the categories we develop. The main aim of this thesis was to investigate whether speakers build the categories linguists postulate and if so, how general these categories are. A number of corpus and experimental studies were conducted for Polish prefixed verbs and reflexive verbs, which explored categories of different levels of generality. The results of the studies suggest that speakers might build some general categories (e.g. the one for the Polish marker siebie), while they might not be able to build others (e.g. the ones for the different senses of the verbal prefix po-). These differences can be explained by the frequency with which the constructions occur as well as the nature of their typical contexts. The above result underscores the importance of empirically veryfing the categories linguists postulate. Linguists must not tacitly assume that their linguistic descriptions are cognitively real because it cannot be assessed a priori whether speakers use them or not. Since speakers might not be able to construct for categories that are established in linguistics, such as verbal prefixes, some other ‘traditional’ linguistic categories might need revisiting and empirical verification

Systematic Review on Privacy Categorization

In the modern digital world users need to make privacy and security choices that have far-reaching consequences. Researchers are increasingly studying people's decisions when facing with privacy and security trade-offs, the pressing and time consuming disincentives that influence those decisions, and methods to mitigate them. This work aims to present a systematic review of the literature on privacy categorization, which has been defined in terms of profile, profiling, segmentation, clustering and personae. Privacy categorization involves the possibility to classify users according to specific prerequisites, such as their ability to manage privacy issues, or in terms of which type of and how many personal information they decide or do not decide to disclose. Privacy categorization has been defined and used for different purposes. The systematic review focuses on three main research questions that investigate the study contexts, i.e. the motivations and research questions, that propose privacy categorisations; the methodologies and results of privacy categorisations; the evolution of privacy categorisations over time. Ultimately it tries to provide an answer whether privacy categorization as a research attempt is still meaningful and may have a future

Conceptualising digital transformation in SMEs: an ecosystemic perspective

Purpose: Supported by a service ecosystem that is increasingly immersed into digital transformation, small- and medium-sized enterprises (SMEs) have access to turnkey information technology (IT) applications, which may come free of charge but not free of concerns. The purpose of this paper is to explore a group conceptualisation and associated perceptions of IT issues within an ecosystem that includes three subgroup profiles: entrepreneurs, IT professionals and socioeconomic support professionals. Design/methodology/approach: Using group concept mapping, a bottom-up and participatory mixed methods-based approach, a concept map was estimated, based on a list of items, to define seven clusters pertaining to issues and challenges of adoption and use of turnkey IT applications in SMEs of less than 20 employees. Perceptions measures of relative importance and feasibility were obtained by subgroup profiles. Findings: The relative importance and relative feasibility measures for the seven clusters indicate significant statistical differences in ratings among the subgroup profiles. A discussion on the importance of relational capital in addressing challenges of digital transformation in SMEs is developed. Originality/value: Results highlight signifiant differences concerning key dimensions in the adoption and use of IT from the perspective of three subgroup profiles of actors within the ecosystem. First, the results stress the need to develop a shared understanding of IT challenges. Second, they suggest policymakers could use these conceptual representations to further develop and strengthen the IT-related support agenda for SMEs, especially the smaller ones (e.g. training programs, business support and coaching initiatives, etc.). © 2019, Emerald Publishing Limited

Parameter-free agglomerative hierarchical clustering to model learners' activity in online discussion forums

L'anàlisi de l'activitat dels estudiants en els fòrums de discussió online implica un problema de modelització altament depenent del context, el qual pot ser plantejat des d'aproximacions tant teòriques com empíriques. Quan aquest problema és abordat des de l'àmbit de la mineria de dades, l'enfocament més comunament adoptat és el de la classificació no supervisada (o clustering), donant lloc, d'aquesta manera, a un escenari de clustering en el qual el nombre real de clústers és a priori desconegut. Per tant, aquesta aproximació revela una qüestió subjacent, la qual no és sinó un dels problemes més coneguts del paradigma del clustering: l'estimació del nombre de clústers, habitualment seleccionat per l'usuari concorde a algun tipus de criteri subjectiu que pot comportar fàcilment l'aparició de biaixos indesitjats en els models obtinguts. Amb l'objectiu d'evitar qualsevol intervenció de l'usuari en l'etapa de clustering, dos nous criteris d'unió entre clústers són proposats en la present tesi, els quals, al seu torn, permeten la implementació d'un nou algorisme de clustering jeràrquic aglomeratiu lliure de paràmetres. Un complet conjunt d'experiments indica que el nou algorisme de clustering és capaç de proporcionar solucions de clustering òptimes enfront d'una gran varietat d'escenaris de clustering, sent capaç de bregar amb diferents classes de dades, així com de millorar el rendiment ofert pels algorismes de clustering més àmpliament emprats en la pràctica. Finalment, una estratègia d'anàlisi de dues etapes basada en el paradigma del clustering subespaial és proposada a fi d'abordar adequadament el problema de la modelització de la participació dels estudiants en les discussions asíncrones. Combinada amb el nou algorisme clustering, l'estratègia proposada demostra ser capaç de limitar la intervenció subjectiva de l'usuari a les etapes d'interpretació del procés d'anàlisi i de donar lloc a una completa modelització de l'activitat duta a terme pels estudiants en els fòrums de discussió online.El análisis de la actividad de los estudiantes en los foros de discusión online acarrea un problema de modelización altamente dependiente del contexto, el cual puede ser planteado desde aproximaciones tanto teóricas como empíricas. Cuando este problema es abordado desde el ámbito de la minería de datos, el enfoque más comúnmente adoptado es el de la clasificación no supervisada (o clustering), dando lugar, de este modo, a un escenario de clustering en el que el número real de clusters es a priori desconocido. Por tanto, esta aproximación revela una cuestión subyacente, la cual no es sino uno de los problemas más conocidos del paradigma del clustering: la estimación del número de clusters, habitualmente seleccionado por el usuario acorde a algún tipo de criterio subjetivo que puede conllevar fácilmente la aparición de sesgos indeseados en los modelos obtenidos. Con el objetivo de evitar cualquier intervención del usuario en la etapa de clustering, dos nuevos criterios de unión entre clusters son propuestos en la presente tesis, los cuales, a su vez, permiten la implementación de un nuevo algoritmo de clustering jerárquico aglomerativo libre de parámetros. Un completo conjunto de experimentos indica que el nuevo algoritmo de clustering es capaz de proporcionar soluciones de clustering óptimas frente a una gran variedad de escenarios de clustering, siendo capaz de lidiar con diferentes clases de datos, así como de mejorar el rendimiento ofrecido por los algoritmos de clustering más ampliamente utilizados en la práctica. Finalmente, una estrategia de análisis de dos etapas basada en el paradigma del clustering subespacial es propuesta a fin de abordar adecuadamente el problema de la modelización de la participación de los estudiantes en las discusiones asíncronas. Combinada con el nuevo algoritmo clustering, la estrategia propuesta demuestra ser capaz de limitar la intervención subjetiva del usuario a las etapas de interpretación del proceso de análisis y de dar lugar a una completa modelización de la actividad llevada a cabo por los estudiantes en los foros de discusión online.The analysis of learners' activity in online discussion forums leads to a highly context-dependent modelling problem, which can be posed from both theoretical and empirical approaches. When this problem is tackled from the data mining field, a clustering-based perspective is usually adopted, thus giving rise to a clustering scenario where the real number of clusters is a priori unknown. Hence, this approach reveals an underlying problem, which is one of the best-known issues of the clustering paradigm: the estimation of the number of clusters, habitually selected by user according to some kind of subjective criterion that may easily lead to the appearance of undesired biases in the obtained models. With the aim of avoiding any user intervention in the cluster analysis stage, two new cluster merging criteria are proposed in the present thesis, which allow to implement a novel parameter-free agglomerative hierarchical algorithm. A complete set of experiments indicate that the new clustering algorithm is able to provide optimal clustering solutions in the face of a great variety of clustering scenarios, both having the ability to deal with different kinds of data and outperforming clustering algorithms most widely used in practice. Finally, a two-stage analysis strategy based on the subspace clustering paradigm is proposed to properly tackle the issue of modelling learners' participation in the asynchronous discussions. In combination with the new clustering algorithm, the proposed strategy proves to be able to limit user's subjective intervention to the interpretation stages of the analysis process and to lead to a complete modelling of the activity performed by learners in online discussion forums

Modelling the acquisition of natural language categories

The ability to reason about categories and category membership is fundamental to human cognition, and as a result a considerable amount of research has explored the acquisition and modelling of categorical structure from a variety of perspectives. These range from feature norming studies involving adult participants (McRae et al. 2005) to long-term infant behavioural studies (Bornstein and Mash 2010) to modelling experiments involving artificial stimuli (Quinn 1987). In this thesis we focus on the task of natural language categorisation, modelling the cognitively plausible acquisition of semantic categories for nouns based on purely linguistic input. Focusing on natural language categories and linguistic input allows us to make use of the tools of distributional semantics to create high-quality representations of meaning in a fully unsupervised fashion, a property not commonly seen in traditional studies of categorisation. We explore how natural language categories can be represented using distributional models of semantics; we construct concept representations for corpora and evaluate their performance against psychological representations based on human-produced features, and show that distributional models can provide a high-quality substitute for equivalent feature representations. Having shown that corpus-based concept representations can be used to model category structure, we turn our focus to the task of modelling category acquisition and exploring how category structure evolves over time. We identify two key properties necessary for cognitive plausibility in a model of category acquisition, incrementality and non-parametricity, and construct a pair of models designed around these constraints. Both models are based on a graphical representation of semantics in which a category represents a densely connected subgraph. The first model identifies such subgraphs and uses these to extract a flat organisation of concepts into categories; the second uses a generative approach to identify implicit hierarchical structure and extract an hierarchical category organisation. We compare both models against existing methods of identifying category structure in corpora, and find that they outperform their counterparts on a variety of tasks. Furthermore, the incremental nature of our models allows us to predict the structure of categories during formation and thus to more accurately model category acquisition, a task to which batch-trained exemplar and prototype models are poorly suited