A three-pressure-sensor (3PS) system for monitoring ankle supination torque during sport motions

This study presented a three-pressure-sensor (3PS) system for monitoring ankle supination torque during sport motions. Five male subjects wore a pair of cloth sport shoes and performed 10 trials of walking, running, cutting, vertical jump-landing and stepping-down motions in a random sequence. A pair of pressure insoles (Novel Pedar model W, Germany) was inserted in the shoes for the measurement of plantar pressure at 100 Hz. The ankle joint torque was calculated by a standard lower extremity inverse dynamic calculation procedure with the data obtained by a motion capture system (VICON, UK) and a force plate (AMTI, USA), and was presented in a supination/pronation plane with an oblique axis of rotation at the ankle joint. Stepwise linear regression analysis suggested that pressure data at three locations beneath the foot were essential for reconstructing the ankle supination torque. Another group of five male subjects participated in a validation test with the same procedure, but with the pressure insoles replaced by the 3PS system. Estimated ankle supination torque was calculated from the equation developed by the regression analysis. Results suggested that the correlation between the standard and estimated data was high (R=0.938). The overall root mean square error was 6.91 N m, which was about 6% of the peak values recorded in the five sport motions (113 N m). With the good estimation accuracy, tiny size and inexpensive cost, the 3PS system is readily available to be implanted in sport shoe for the estimation and monitoring of ankle supination torque during dynamic sport motions

On State-Level Architecture of Digital Government Ecosystems: From ICT-Driven to Data-Centric

The \digital transformation" is perceived as the key enabler for increasing wealth and well-being by politics, media and the citizens alike. In the same vein, digital government steadily receives more and more attention. Digital government gives rise to complex, large-scale state-level system landscapes consisting of many players and technological systems { and we call such system landscapes digital government ecosystems. In this paper, we systematically approach the state-level architecture of digital government ecosystems.We will discover the primacy of the state's institutional design in the architecture of digital government ecosystems, where Williamson's institutional analysis framework supports our considerations as theoretical background. Based on that insight, we will establish the notion of data governance architecture, which links data assets with accountable organizations. Our investigation results into a digital government architecture framework that can help in large-scale digital government design e_orts through (i) separation of concerns in terms of appropriate categories, and (ii) a better assessment of the feasibility of envisioned digital transformations. With its focus on data, the proposed framework perfectly _ts the current discussion on moving from ICT-driven to data-centric digital government

Quantitatively measuring privacy in interactive query settings within RDBMS framework

Little attention has been paid to the measurement of risk to privacy in Database Management Systems, despite their prevalence as a modality of data access. This paper proposes PriDe, a quantitative privacy metric that provides a measure (privacy score) of privacy risk when executing queries in relational database management systems. PriDe measures the degree to which attribute values, retrieved by a principal (user) engaging in an interactive query session, represent a reduction of privacy with respect to the attribute values previously retrieved by the principal. It can be deployed in interactive query settings where the user sends SQL queries to the database and gets results at run-time and provides privacy-conscious organizations with a way to monitor the usage of the application data made available to third parties in terms of privacy. The proposed approach, without loss of generality, is applicable to BigSQL-style technologies. Additionally, the paper proposes a privacy equivalence relation that facilitates the computation of the privacy score

A User-driven Annotation Framework for Scientific Data

Annotations play an increasingly crucial role in scientific exploration and discovery, as the amount of data and the level of collaboration among scientists increases. There are many systems today focusing on annotation management, querying, and propagation. Although all such systems are implemented to take user input (i.e., the annotations themselves), very few systems are user-driven, taking into account user preferences on how annotations should be propagated and applied over data. In this thesis, we propose to treat annotations as first-class citizens for scientific data by introducing a user-driven, view-based annotation framework. Under this framework, we try to resolve two critical questions: Firstly, how do we support annotations that are scalable both from a system point of view and also from a user point of view? Secondly, how do we support annotation queries both from an annotator point of view and a user point of view, in an efficient and accurate way? To address these challenges, we propose the VIew-base annotation Propagation (ViP) framework to empower users to express their preferences over the time semantics of annotations and over the network semantics of annotations, and define three query types for annotations. To efficiently support such novel functionality, ViP utilizes database views and introduces new annotation caching techniques. The use of views also brings a more compact representation of annotations, making our system easier to scale. Through an extensive experimental study on a real system (with both synthetic and real data), we show that the ViP framework can seamlessly introduce user-driven annotation propagation semantics while at the same time significantly improving the performance (in terms of query execution time) over the current state of the art

Recommendation of points of interest based on context, geolocation and implicit feedback

Mestrado em Engenharia de Computadores e TelemáticaAtualmente, a quantidade de informação existente dificulta o processo de seleção de informação relevante para o utilizador, devido à falta de conhecimento ou tempo por parte do mesmo. Deste modo foram desenvolvidos sistemas de recomendação com o intuito de auxiliar o utilizador na descoberta de informação útil. Existem diversas técnicas de recomendação que podem ser aplicadas dependendo do domínio em que o sistema se encontra inserido, dos objetivos do utilizador e da opinião fornecida por este. Os sistemas de recomendação tradicionais apenas consideram as entidades utilizador e item, ignorando a informação contextual, sendo que esta desempenha um importante papel na tomada de decisão dos utilizadores. Nesta dissertação são desenvolvidos dois métodos de recomendação orientados ao contexto, sendo um primeiro baseado na frequência contextual e o segundo na personalização. Para isto foram analisadas as técnicas de recomendação existentes, formas de inclusão do contexto no processo de recomendação e métodos de avaliação destes sistemas. Estes dois métodos foram instanciados e avaliados recorrendo à análise de um conjunto de dados referentes a visitas efetuadas por utilizadores a pontos de interesse na cidade de Nova Iorque (recolhidos no foursquare). Os dois métodos de recomendação relacionam a situação contextual no momento da recomendação com a frequência de visitas efetuadas aos pontos de interesse. No primeiro método são consideradas as visitas efetuadas por todos os utilizadores, já no segundo método apenas são consideradas as visitas efetuadas pelo utilizador que requer a recomendação. Para avaliação dos métodos propostos, foi usada a métrica de hit-rate e a métrica baseada no fator de decréscimo. Para além disto é efetuada uma comparação entre métodos utilizados e o método de dominância contextual desenvolvido em trabalhos anteriores. O método de dominância contextual supera a frequência contextual no caso em que a situação contextual é constituída por todas as dimensões, apresentando um erro menor e uma maior cobertura. O método baseado na frequência contextual supera o método baseado na personalização em termos de cobertura, contudo o erro é maior. Verifica-se também, que quanto maior for o nível de generalização dos pontos de interesse usado nos métodos de recomendação, maior é o erro e a cobertura. Os resultados obtidos através desta avaliação permitem obter a influência de cada dimensão contextual e o impacto do uso dos níveis hierárquicos nos métodos de recomendaçãoNowadays, the large amount of existing data makes the selection of relevant information difficult for the users. This way, recommendation systems were developed to assist users in finding useful information. There are several recommendation techniques that can be applied depending on the application domains, user’s goals and preferences. Traditional recommendation systems only consider the data about users and items, ignoring context information that also plays a relevant role in decision making. In this work two contextoriented methods of recommendation are developed, one based on contextual frequency and a second one based on personalization. Existing recommendation techniques were analyzed, as well as how to consider context in the recommendation process and methods of evaluation of these systems. The two methods were instantiated and evaluated using datasets about visits of users to points of interest in New York City collected on foursquare. These methods rely on the relationship between the contextual status at the time of the recommendation and the frequency of visits made to the points of interest in similar contextual conditions. The first method considers the visits made by all users, and in the second one only uses the visits made by the user that requires the recommendation. The proposed methods are evaluated using a hit-rate metric and a metric based on a decreasing factor. The performance of these methods is compared with the performance of a method based on contextual dominance proposed in previous work. The performance of the method based on contextual dominance is better than the performance of the method based on contextual frequency when all dimensions defining the contextual status are used, presenting a smaller error and a greater coverage. The method based on the contextual frequency exceeds the performance method based on personalization in terms of coverage, but the error is greater. The results show that errors and coverage increase with the level of generalization of the points of interest used in the recommendation methods. The results obtained through this evaluation allow us to infer about the influence of each contextual dimension and the impact of the use of the hierarchical levels in the recommendation methods

Personalised video retrieval: application of implicit feedback and semantic user profiles

A challenging problem in the user profiling domain is to create profiles of users of retrieval systems. This problem even exacerbates in the multimedia domain. Due to the Semantic Gap, the difference between low-level data representation of videos and the higher concepts users associate with videos, it is not trivial to understand the content of multimedia documents and to find other documents that the users might be interested in. A promising approach to ease this problem is to set multimedia documents into their semantic contexts. The semantic context can lead to a better understanding of the personal interests. Knowing the context of a video is useful for recommending users videos that match their information need. By exploiting these contexts, videos can also be linked to other, contextually related videos. From a user profiling point of view, these links can be of high value to recommend semantically related videos, hence creating a semantic-based user profile. This thesis introduces a semantic user profiling approach for news video retrieval, which exploits a generic ontology to put news stories into its context. Major challenges which inhibit the creation of such semantic user profiles are the identification of user's long-term interests and the adaptation of retrieval results based on these personal interests. Most personalisation services rely on users explicitly specifying preferences, a common approach in the text retrieval domain. By giving explicit feedback, users are forced to update their need, which can be problematic when their information need is vague. Furthermore, users tend not to provide enough feedback on which to base an adaptive retrieval algorithm. Deviating from the method of explicitly asking the user to rate the relevance of retrieval results, the use of implicit feedback techniques helps by learning user interests unobtrusively. The main advantage is that users are relieved from providing feedback. A disadvantage is that information gathered using implicit techniques is less accurate than information based on explicit feedback. In this thesis, we focus on three main research questions. First of all, we study whether implicit relevance feedback, which is provided while interacting with a video retrieval system, can be employed to bridge the Semantic Gap. We therefore first identify implicit indicators of relevance by analysing representative video retrieval interfaces. Studying whether these indicators can be exploited as implicit feedback within short retrieval sessions, we recommend video documents based on implicit actions performed by a community of users. Secondly, implicit relevance feedback is studied as potential source to build user profiles and hence to identify users' long-term interests in specific topics. This includes studying the identification of different aspects of interests and storing these interests in dynamic user profiles. Finally, we study how this feedback can be exploited to adapt retrieval results or to recommend related videos that match the users' interests. We analyse our research questions by performing both simulation-based and user-centred evaluation studies. The results suggest that implicit relevance feedback can be employed in the video domain and that semantic-based user profiles have the potential to improve video exploration

Abstraction and cartographic generalization of geographic user-generated content: use-case motivated investigations for mobile users

On a daily basis, a conventional internet user queries different internet services (available on different platforms) to gather information and make decisions. In most cases, knowingly or not, this user consumes data that has been generated by other internet users about his/her topic of interest (e.g. an ideal holiday destination with a family traveling by a van for 10 days). Commercial service providers, such as search engines, travel booking websites, video-on-demand providers, food takeaway mobile apps and the like, have found it useful to rely on the data provided by other users who have commonalities with the querying user. Examples of commonalities are demography, location, interests, internet address, etc. This process has been in practice for more than a decade and helps the service providers to tailor their results based on the collective experience of the contributors. There has been also interest in the different research communities (including GIScience) to analyze and understand the data generated by internet users. The research focus of this thesis is on finding answers for real-world problems in which a user interacts with geographic information. The interactions can be in the form of exploration, querying, zooming and panning, to name but a few. We have aimed our research at investigating the potential of using geographic user-generated content to provide new ways of preparing and visualizing these data. Based on different scenarios that fulfill user needs, we have investigated the potential of finding new visual methods relevant to each scenario. The methods proposed are mainly based on pre-processing and analyzing data that has been offered by data providers (both commercial and non-profit organizations). But in all cases, the contribution of the data was done by ordinary internet users in an active way (compared to passive data collections done by sensors). The main contributions of this thesis are the proposals for new ways of abstracting geographic information based on user-generated content contributions. Addressing different use-case scenarios and based on different input parameters, data granularities and evidently geographic scales, we have provided proposals for contemporary users (with a focus on the users of location-based services, or LBS). The findings are based on different methods such as semantic analysis, density analysis and data enrichment. In the case of realization of the findings of this dissertation, LBS users will benefit from the findings by being able to explore large amounts of geographic information in more abstract and aggregated ways and get their results based on the contributions of other users. The research outcomes can be classified in the intersection between cartography, LBS and GIScience. Based on our first use case we have proposed the inclusion of an extended semantic measure directly in the classic map generalization process. In our second use case we have focused on simplifying geographic data depiction by reducing the amount of information using a density-triggered method. And finally, the third use case was focused on summarizing and visually representing relatively large amounts of information by depicting geographic objects matched to the salient topics emerged from the data

Deliverable D4.1 Specification of user profiling and contextualisation

This deliverable presents a comprehensive research of past work in the field of capturing and interpreting user preferences and context and an overview of relevant digital media-specific techniques, aiming to provide insights and ideas for innovative context-aware user preference learning and to justify the user modelling strategies considered within LinkedTV’s WP4. Based on this research and a study over the specific technical and conceptual requirements of LinkedTV, a prototypical design for profiling and contextualizing user needs in a linked media environment is specified