8,198 research outputs found

    Mobile recommender apps with privacy management for accessible and usable technologies

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    The paper presents the preliminary results of an ongoing survey of the use of computers and mobile devices, interest in recommender apps and knowledge and concerns about privacy issues amongst English and Italian speaking disabled people. Participants were found to be regular users of computers and mobile devices for a range of applications. They were interested in recommender apps for household items, computer software and apps that met their accessibility and other requirements. They showed greater concerns about controlling access to personal data of different types than this data being retained by the computer or mobile device. They were also willing to make tradeoffs to improve device performance

    Context Aware Computing for The Internet of Things: A Survey

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    As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

    Contributions to reasoning on imprecise data

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    This thesis contains four contributions which advocate cautious statistical modelling and inference. They achieve it by taking sets of models into account, either directly or indirectly by looking at compatible data situations. Special care is taken to avoid assumptions which are technically convenient, but reduce the uncertainty involved in an unjustified manner. This thesis provides methods for cautious statistical modelling and inference, which are able to exhaust the potential of precise and vague data, motivated by different fields of application, ranging from political science to official statistics. At first, the inherently imprecise Nonparametric Predictive Inference model is involved in the cautious selection of splitting variables in the construction of imprecise classification trees, which are able to describe a structure and allow for a reasonably high predictive power. Dependent on the interpretation of vagueness, different strategies for vague data are then discussed in terms of finite random closed sets: On the one hand, the data to be analysed are regarded as set-valued answers of an item in a questionnaire, where each possible answer corresponding to a subset of the sample space is interpreted as a separate entity. By this the finite random set is reduced to an (ordinary) random variable on a transformed sample space. The context of application is the analysis of voting intentions, where it is shown that the presented approach is able to characterise the undecided in a more detailed way, which common approaches are not able to. Altough the presented analysis, regarded as a first step, is carried out on set-valued data, which are suitably self-constructed with respect to the scientific research question, it still clearly demonstrates that the full potential of this quite general framework is not exhausted. It is capable of dealing with more complex applications. On the other hand, the vague data are produced by set-valued single imputation (imprecise imputation) where the finite random sets are interpreted as being the result of some (unspecified) coarsening. The approach is presented within the context of statistical matching, which is used to gain joint knowledge on features that were not jointly collected in the initial data production. This is especially relevant in data production, e.g. in official statistics, as it allows to fuse the information of already accessible data sets into a new one, without the requirement of actual data collection in the field. Finally, in order to share data, they need to be suitably anonymised. For the specific class of anonymisation techniques of microaggregation, its ability to infer on generalised linear regression models is evaluated. Therefore, the microaggregated data are regarded as a set of compatible, unobserved underlying data situations. Two strategies to follow are proposed. At first, a maximax-like optimisation strategy is pursued, in which the underlying unobserved data are incorporated into the regression model as nuisance parameters, providing a concise yet over-optimistic estimation of the regression coefficients. Secondly, an approach in terms of partial identification, which is inherently more cautious than the previous one, is applied to estimate the set of all regression coefficients that are obtained by performing the estimation on each compatible data situation. Vague data are deemed favourable to precise data as they additionally encompass the uncertainty of the individual observation, and therefore they have a higher informational value. However, to the present day, there are few (credible) statistical models that are able to deal with vague or set-valued data. For this reason, the collection of such data is neglected in data production, disallowing such models to exhaust their full potential. This in turn prevents a throughout evaluation, negatively affecting the (further) development of such models. This situation is a variant of the chicken or egg dilemma. The ambition of this thesis is to break this cycle by providing actual methods for dealing with vague data in relevant situations in practice, to stimulate the required data production.Diese Schrift setzt sich in vier Beiträgen für eine vorsichtige statistische Modellierung und Inferenz ein. Dieses wird erreicht, indem man Mengen von Modellen betrachtet, entweder direkt oder indirekt über die Interpretation der Daten als Menge zugrunde liegender Datensituationen. Besonderer Wert wird dabei darauf gelegt, Annahmen zu vermeiden, die zwar technisch bequem sind, aber die zugrunde liegende Unsicherheit der Daten in ungerechtfertigter Weise reduzieren. In dieser Schrift werden verschiedene Methoden der vorsichtigen Modellierung und Inferenz vorgeschlagen, die das Potential von präzisen und unscharfen Daten ausschöpfen können, angeregt von unterschiedlichen Anwendungsbereichen, die von Politikwissenschaften bis zur amtlichen Statistik reichen. Zuerst wird das Modell der Nonparametrischen Prädiktiven Inferenz, welches per se unscharf ist, in der vorsichtigen Auswahl von Split-Variablen bei der Erstellung von Klassifikationsbäumen verwendet, die auf Methoden der Imprecise Probabilities fußen. Diese Bäume zeichnen sich dadurch aus, dass sie sowohl eine Struktur beschreiben, als auch eine annehmbar hohe Prädiktionsgüte aufweisen. In Abhängigkeit von der Interpretation der Unschärfe, werden dann verschiedene Strategien für den Umgang mit unscharfen Daten im Rahmen von finiten Random Sets erörtert. Einerseits werden die zu analysierenden Daten als mengenwertige Antwort auf eine Frage in einer Fragebogen aufgefasst. Hierbei wird jede mögliche (multiple) Antwort, die eine Teilmenge des Stichprobenraumes darstellt, als eigenständige Entität betrachtet. Somit werden die finiten Random Sets auf (gewöhnliche) Zufallsvariablen reduziert, die nun in einen transformierten Raum abbilden. Im Rahmen einer Analyse von Wahlabsichten hat der vorgeschlagene Ansatz gezeigt, dass die Unentschlossenen mit ihm genauer charakterisiert werden können, als es mit den gängigen Methoden möglich ist. Obwohl die vorgestellte Analyse, betrachtet als ein erster Schritt, auf mengenwertige Daten angewendet wird, die vor dem Hintergrund der wissenschaftlichen Forschungsfrage in geeigneter Weise selbst konstruiert worden sind, zeigt diese dennoch klar, dass die Möglichkeiten dieses generellen Ansatzes nicht ausgeschöpft sind, so dass er auch in komplexeren Situationen angewendet werden kann. Andererseits werden unscharfe Daten durch eine mengenwertige Einfachimputation (imprecise imputation) erzeugt. Hier werden die finiten Random Sets als Ergebnis einer (unspezifizierten) Vergröberung interpretiert. Der Ansatz wird im Rahmen des Statistischen Matchings vorgeschlagen, das verwendet wird, um gemeinsame Informationen über ursprünglich nicht zusammen erhobene Merkmale zur erhalten. Dieses ist insbesondere relevant bei der Datenproduktion, beispielsweise in der amtlichen Statistik, weil es erlaubt, die verschiedenartigen Informationen aus unterschiedlichen bereits vorhandenen Datensätzen zu einen neuen Datensatz zu verschmelzen, ohne dass dafür tatsächlich Daten neu erhoben werden müssen. Zudem müssen die Daten für den Datenaustausch in geeigneter Weise anonymisiert sein. Für die spezielle Klasse der Anonymisierungstechnik der Mikroaggregation wird ihre Eignung im Hinblick auf die Verwendbarkeit in generalisierten linearen Regressionsmodellen geprüft. Hierfür werden die mikroaggregierten Daten als eine Menge von möglichen, unbeobachtbaren zu Grunde liegenden Datensituationen aufgefasst. Es werden zwei Herangehensweisen präsentiert: Als Erstes wird eine maximax-ähnliche Optimisierungsstrategie verfolgt, dabei werden die zu Grunde liegenden unbeobachtbaren Daten als Nuisance Parameter in das Regressionsmodell aufgenommen, was eine enge, aber auch über-optimistische Schätzung der Regressionskoeffizienten liefert. Zweitens wird ein Ansatz im Sinne der partiellen Identifikation angewendet, der per se schon vorsichtiger ist (als der vorherige), indem er nur die Menge aller möglichen Regressionskoeffizienten schätzt, die erhalten werden können, wenn die Schätzung auf jeder zu Grunde liegenden Datensituation durchgeführt wird. Unscharfe Daten haben gegenüber präzisen Daten den Vorteil, dass sie zusätzlich die Unsicherheit der einzelnen Beobachtungseinheit umfassen. Damit besitzen sie einen höheren Informationsgehalt. Allerdings gibt es zur Zeit nur wenige glaubwürdige statistische Modelle, die mit unscharfen Daten umgehen können. Von daher wird die Erhebung solcher Daten bei der Datenproduktion vernachlässigt, was dazu führt, dass entsprechende statistische Modelle ihr volles Potential nicht ausschöpfen können. Dies verhindert eine vollumfängliche Bewertung, wodurch wiederum die (Weiter-)Entwicklung jener Modelle gehemmt wird. Dies ist eine Variante des Henne-Ei-Problems. Diese Schrift will durch Vorschlag konkreter Methoden hinsichtlich des Umgangs mit unscharfen Daten in relevanten Anwendungssituationen Lösungswege aus der beschriebenen Situation aufzeigen und damit die entsprechende Datenproduktion anregen

    The Right Balance

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    Evaluating e-commerce trust using fuzzy logic [article]

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    Trust is widely recognized as an essential factor for the continual development of business to customer electronic commerce (B2C EC). Many trust models have been developed, however, most are subjective and do not take into account the vagueness and ambiguity of EC trust and the customers’ intuitions and experience when conducting online transactions. In this article, we develop a fuzzy trust model using fuzzy reasoning to evaluate EC trust. This trust model is based on the information customers expect to find on an EC Website and is shown to increase customers trust towards online merchants. We argue that fuzzy logic is suitable for trust evaluation as it takes into account the uncertainties within e-commerce data and like human relationships; it is often expressed by linguistics terms rather then numerical values. The evaluation of the proposed model will be illustrated using two case studies and a comparison with two evaluation models was conducted to emphasise the importance of usin fuzzy logic

    Sex By Deception

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    In this paper I will use sex by deception as a case study for highlighting some of the most tricky concepts around sexuality and moral psychology, including rape, consensual sex, sexual rights, sexual autonomy, sexual individuality, and disrespectful sex. I begin with a discussion of morally wrong sex as rooted in the breach of five sexual liberty rights that are derived from our fundamental human liberty rights: sexual self-possession, sexual autonomy, sexual individuality, sexual dignity and sexual privacy. I then argue (against the standard interpretation) that experimental findings in moral psychology show that the principle of respect for persons—a principle that grounds our human liberty rights—drives our intuitive moral judgments. In light of this discussion, I examine a puzzle about sex by deception—a puzzle which at first may seem to compel us to define 'rape' strictly in terms of force rather than sexual autonomy. I proceed by presenting an argument against the view that, as a rule, sex by deception undermines consent—a position held by prominent thinkers such as Philippe Patry (2001), Onora O’Neill (2003), Rubenfeld (2012), Tom Dougherty (2013a, 2013b), Joyce M. Short (2013), and Danielle Bromwich and Joseph Millum (2013, 2018). As we will see, sex following deception to increase your chances does not always constitute rape. Lying about your age, education, job, family background, marital status, or interest in a relationship, for example, does not make your sex partner incapable of consenting, which is to say that sex by deception need not be rape. I even go so far as to say that sex with another person that is facilitated by withholding information about having a venereal disease shouldn't be classified as rape. Although sex by deception doesn't compromise consent, it nonetheless violates the principle of respect for persons, not by vitiating sexual autonomy and compromising consent, but by failing to respect other sexual rights, such as our rights to sexual dignity, individuality, and privacy

    Class Problem!: Why the Inconsistent Application of Rule 23\u27s Class Certification Requirements During Overbreadth Analysis is a Threat to Litigant Certainty

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    Rule 23 of the Federal Rules of Civil Procedure is home to the class action device. It is well-documented that this rule significantly impacts our legal system. As a result, the need for its effective utilization has been apparent since its introduction. Despite this, federal courts have inconsistently applied the rule during their analyses of overbroad class definitions at the class certification stage. Consequently, parties involved in such litigation have been exposed to unnecessary costs and the potential for forum shopping. Nonetheless, this judicial inconsistency has gone largely unrecognized because it does not implicate the results of class certification. Hence, courts here must first recognize the general need for uniformity before a precise standard for overbreadth analysis may be chosen. Only then, this Note argues, may the aforementioned detrimental consequences be avoided

    Inferring Complex Activities for Context-aware Systems within Smart Environments

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    The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

    Adaptive User Interfaces for Intelligent E-Learning: Issues and Trends

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    Adaptive User Interfaces have a long history rooted in the emergence of such eminent technologies as Artificial Intelligence, Soft Computing, Graphical User Interface, JAVA, Internet, and Mobile Services. More specifically, the advent and advancement of the Web and Mobile Learning Services has brought forward adaptivity as an immensely important issue for both efficacy and acceptability of such services. The success of such a learning process depends on the intelligent context-oriented presentation of the domain knowledge and its adaptivity in terms of complexity and granularity consistent to the learner’s cognitive level/progress. Researchers have always deemed adaptive user interfaces as a promising solution in this regard. However, the richness in the human behavior, technological opportunities, and contextual nature of information offers daunting challenges. These require creativity, cross-domain synergy, cross-cultural and cross-demographic understanding, and an adequate representation of mission and conception of the task. This paper provides a review of state-of-the-art in adaptive user interface research in Intelligent Multimedia Educational Systems and related areas with an emphasis on core issues and future directions
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