From Personalization to Adaptivity: Creating Immersive Visits through Interactive Digital Storytelling at the Acropolis Museum

Storytelling has recently become a popular way to guide museum visitors, replacing traditional exhibit-centric descriptions by story-centric cohesive narrations with references to the exhibits and multimedia content. This work presents the fundamental elements of the CHESS project approach, the goal of which is to provide adaptive, personalized, interactive storytelling for museum visits. We shortly present the CHESS project and its background, we detail the proposed storytelling and user models, we describe the provided functionality and we outline the main tools and mechanisms employed. Finally, we present the preliminary results of a recent evaluation study that are informing several directions for future work

Using Semantic-Based User Profile Modeling for Context-Aware Personalised Place Recommendations

Place Recommendation Systems (PRS's) are used to recommend places to visit to World Wide Web users. Existing PRS's are still limited by several problems, some of which are the problem of recommending similar set of places to different users (Lack of Personalization) and no diversity in the set of recommended items (Content Overspecialization). One of the main objectives in the PRS's or Contextual suggestion systems is to fill the semantic gap among the queries and suggestions and going beyond keywords matching. To address these issues, in this study we attempt to build a personalized context-aware place recommender system using semantic-based user profile modeling to address the limitations of current user profile building techniques and to improve the retrieval performance of personalized place recommender system. This approach consists of building a place ontology based on the Open Directory Project (ODP), a hierarchical ontology scheme for organizing websites. We model a semantic user profile from the place concepts extracted from place ontology and weighted according to their semantic relatedness to user interests. The semantic user profile is then exploited to devise a personalized recommendation by re-ranking process of initial search results for improving retrieval performance. We evaluate this approach on dataset obtained using Google Paces API. Results show that our proposed approach significantly improves the retrieval performance compare to classic keyword-based place recommendation model

End user preference of customisable features within a course management system

Customisation is the means by which people alter and change various elements of their environment with the purpose of making it more suited to their immediate needs. This aids in creating a more personalised experience. People are extremely diverse in terms of age, gender, nationality, and with the dominant presence of technology people also have various levels of computer skills and experience. In the context of computer environments, customisation provides the ability to cater for a diverse user group, providing tools and options that assist with specific tasks, improve accessibility and achieve greater user satisfaction. Carter, MacLean, Lovstard & Moran (1990) claim that allowing a user to customise their system to match their personal work practices proves to be a useful technique. Various educational institutions are employing course management systems (CMS) to streamline and help carry out tasks involved in managing a large course. Students are also required to utilise the CMS in order to carry out various tasks associated with the study demands of their course. There is a variety of literature that discusses the types of customisable features that could be employed in a CMS; however there is no recommendation as to which of these features should be implemented. An analysis of end user preference toward customisable features offered a deeper understanding of the diversity of end user needs and the discovery of specific customisable features that are preferred by the student end user population

A Multi-Modal Latent-Features based Service Recommendation System for the Social Internet of Things

The Social Internet of Things (SIoT), is revolutionizing how we interact with our everyday lives. By adding the social dimension to connecting devices, the SIoT has the potential to drastically change the way we interact with smart devices. This connected infrastructure allows for unprecedented levels of convenience, automation, and access to information, allowing us to do more with less effort. However, this revolutionary new technology also brings an eager need for service recommendation systems. As the SIoT grows in scope and complexity, it becomes increasingly important for businesses and individuals, and SIoT objects alike to have reliable sources for products, services, and information that are tailored to their specific needs. Few works have been proposed to provide service recommendations for SIoT environments. However, these efforts have been confined to only focusing on modeling user-item interactions using contextual information, devices' SIoT relationships, and correlation social groups but these schemes do not account for latent semantic item-item structures underlying the sparse multi-modal contents in SIoT environment. In this paper, we propose a latent-based SIoT recommendation system that learns item-item structures and aggregates multiple modalities to obtain latent item graphs which are then used in graph convolutions to inject high-order affinities into item representations. Experiments showed that the proposed recommendation system outperformed state-of-the-art SIoT recommendation methods and validated its efficacy at mining latent relationships from multi-modal features

Testes padronizados para configurações de segurança definidas pelo usuário para dispositivos Android

Orientadores: Eliane Martins, Marco VieiraTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A ampla disseminação de dispositivos móveis, como smartphones e tablets, e a sua vasta capacidade de uso, que vai desde tirar fotos ao acesso a contas bancárias, torna-os um alvo atraente para os atacantes. Isso, juntamente com o fato de que os usuários frequentemente armazenam informações pessoais em tais dispositivos, e que muitas organizações atualmente implementam a política "Bring Your Own Device" (BYOD), que permite aos funcionários usarem seus dispositivos pessoais para acessarem a infraestrutura de informação corporativa e aplicações, tornando a avaliação da segurança de dispositivos móveis uma questão fundamental. Este trabalho apresenta uma abordagem de testes padronizados para avaliar as configurações de segurança definidas pelos usuários, considerando o risco, que cada configuração possui, de prejudicar o dono do dispositivo. Esta abordagem fornece informações valiosas para aqueles que precisam avaliar, comparar ou controlar as configurações de segurança em dispositivos móveis. Na prática, para cada configuração definida pelo usuário, uma intensidade de risco é calculada com base na análise da percepção de um conjunto de especialistas de segurança. A intensidade do risco das diferentes configurações são agregadas para avaliar a segurança geral, com base na análise das configurações de um determinado dispositivo. Em suma, as principais contribuições deste trabalho são: 1) uma ferramenta que permite avaliar a segurança de dispositivos Android com base nas configurações definidas pelo usuário; 2) uma análise das configurações de segurança definidas pelo usuário de dispositivos Android, a fim de entender os problemas mais comuns relacionados às configurações de segurança; 3) uma abordagem de análise de risco para qualificar / quantificar a segurança de dispositivos móveis tomando como base as configurações de segurança definidas pelo usuário; e 4) a definição de testes padronizados benchmark de configuração de segurança para dispositivos móveis, usando o Android como estudo de caso. A plataforma Android é amplamente usada e representativa com relação ao estado da arte em computação móvel. Os resultados, com base na análise de dados coletados de 561 dispositivos, mostram que os usuários do sistema Android negligenciam importantes recomendações de segurança ao configurarem seus dispositivos e que o processo de benchmarking é realmente uma boa maneira de identificar a configuração mais segura definida pelo usuárioAbstract: The wide spreading of mobile devices, such as smartphones and tablets, and their always-advancing capabilities, ranging from taking photos to accessing banking accounts, makes them an attractive target for attackers. This, together with the fact that users frequently store critical personal information in such devices and that many organizations currently implement a "bring your own device" (BYOD) policy that allows employees to use their personal devices to access the enterprise information infrastructure and applications, makes the assessment of the security of mobile devices a key issue. This work presents an approach to benchmark the user-defined security configurations of mobile devices considering the risk that each configuration has to harm or cause any type of loss to the device owner. This approach provides valuable information for those who need to evaluate, assess, compare or control the security configurations of mobile devices. In practice, for each user-defined configuration, an intensity of severity is calculated based on the analysis of the perception of a set of security experts. The intensity of severity of the different configurations are aggregated to assess overall security, based on the analysis of the concrete settings of a given device. In short, the main contributions of this work are: 1) a tool that allows assessing the security of Android devices based on the user-defined configurations; 2) a security analysis of the user-defined security configurations of Android devices in order to understand the common misconfiguration problems; 3) a risk analysis approach to qualify/quantify the security of mobile devices concerning the user-defined security configurations; and 4) a security configuration benchmark for mobile devices, using Android as case study. The Android platform is widely use and representative with respect to the state-of-the-art in mobile computing. The results presented, based on the analysis of data collected from 561 devices, show that Android users neglect important security recommendations while configuring their devices and that benchmarking is indeed a practical way to assess and compare the security of mobile devices with regard to user-defined configurations. In fact, the results can be used by both manufacturers and users to enhance the security level of their devicesDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação0495/15-8CAPESBE

A theoretical and computational basis for CATNETS

The main content of this report is the identification and definition of market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. These build the theoretical foundation for the work within the following two years of the CATNETS project. --Grid Computing

Weighting Waiting: Evaluating the Perception of In-Vehicle Travel Time Under Moving and Stopped Conditions

This paper describes experiments comparing traditional computer administered stated preference with virtual experience stated preference to ascertain how people value stopped delay compared with stop-and- go or freeflow traffic. The virtual experience stated preference experiments were conducted using a wrap around driving simulator. The two methods produced two different results, with the traditional computer assisted stated preference suggesting that ramp delay is 1.6 Ð 1.7 times more onerous than freeway time, while the driving simulator based virtual experience stated preference suggested that freeway delay is more onerous than ramp delay. Several reasons are hypothesized to explain the differences, including recency, simultaneous versus sequential comparison, awareness of public opinion, the intensity of the stop-and-go traffic, and the fact that driving in the real-world is a goal directed activity. However without further research, which, if any, of these will eventually prove to be the reason is unclear. What is clear is that a comparison of the computer administered stated preference with virtual experience stated preference produces different results, even though both procedures strive to find the same answers in nominally identical sets of conditions. Because people experience the world subjectively, and make decisions based on those subjective experiences, future research should be aimed at better understanding the differences between these subjective methodologies.transportation, travel behavior, driving simulator, ramp meters

Clarity of View: An Analytic Hierarchy Process (AHP)-Based Multi-Factor Evaluation Framework for Driver Awareness Systems in Heavy Vehicles

Several emerging technologies hold great promise to improve the situational awareness of the heavy vehicle driver. However, current industry-standard evaluation methods do not measure all the comprehensive factors contributing to the overall effectiveness of such systems. The average commercial vehicle driver in the USA is 54 years old with many drivers continuing past retirement age. Current methods for evaluating visibility systems only consider field of view and do not incorporate measures of the cognitive elements critical to drivers, especially the older demographic. As a result, industry is challenged to evaluate new technologies in a way that provides enough information to make informed selection and purchase decisions. To address this problem, we introduce a new multi-factor evaluation framework, “Clarity of View,” that incorporates several important factors for visibility systems including: field of view, image detection time, distortion, glare discomfort, cost, reliability, and gap acceptance accuracy. It employs a unique application of the Analytic Hierarchy Process (AHP) that involves both expert participants acting in a Supra-Decision Maker role alongside driver-level participants giving both actual performance data as well as subjective preference feedback. Both subjective and objective measures have been incorporated into this multi-factor decision-making model that will help industry make better technology selections involving complex variables. A series of experiments have been performed to illustrate the usefulness of this framework that can be expanded to many types of automotive user-interface technology selection challenges. A unique commercial-vehicle driving simulator apparatus was developed that provides a dynamic, 360-degree, naturalistic driving environment for the evaluation of rearview visibility systems. Evaluations were performed both in the simulator and on the track. Test participants included trucking industry leadership and commercially licensed drivers with experience ranging from 1 to 40 years. Conclusions indicated that aspheric style mirrors have significant viability in the commercial vehicle market. Prior research on aspheric mirrors left questions regarding potential user adaptation, and the Clarity of View framework provides the necessary tools to reconcile that gap. Results obtained using the new Clarity of View framework were significantly different than that which would have previously been available using current industry status-quo published test methods. Additional conclusions indicated that middle-aged drivers performed better in terms of image detection time than young and elderly age categories. Experienced drivers performed better than inexperienced drivers, regardless of age. This is an important conclusion given the demographic challenges faced by the commercial vehicle industry today that is suffering a shortage of new drivers and may be seeking ways to retain its aging driver workforce. The Clarity of View evaluation framework aggregates multiple factors critical to driver visibility system effectiveness into a single selection framework that is useful for industry. It is unique both in its multi-factor approach and custom-developed apparatus, but also in its novel approach to the application of the AHP methodology. It has shown significance in ability to discern more well-informed technology selections and is flexible to expand its application toward many different types of driver interface evaluations