Ubiquitous Computing

The aim of this book is to give a treatment of the actively developed domain of Ubiquitous computing. Originally proposed by Mark D. Weiser, the concept of Ubiquitous computing enables a real-time global sensing, context-aware informational retrieval, multi-modal interaction with the user and enhanced visualization capabilities. In effect, Ubiquitous computing environments give extremely new and futuristic abilities to look at and interact with our habitat at any time and from anywhere. In that domain, researchers are confronted with many foundational, technological and engineering issues which were not known before. Detailed cross-disciplinary coverage of these issues is really needed today for further progress and widening of application range. This book collects twelve original works of researchers from eleven countries, which are clustered into four sections: Foundations, Security and Privacy, Integration and Middleware, Practical Applications

Mothers\u27 Adaptation to Caring for a New Baby

To date, most research on parents\u27 adjustment after adding a new baby to their family unit has focused on mothers\u27 initial transition to parenthood. This past research has examined changes in mothers\u27 marital satisfaction and perceived well-being across the transition, and has compared their prenatal expectations to their postnatal experiences. This project assessed first-time and experienced mothers\u27 stress and satisfaction associated with parenting, their adjustment to competing demands, and their perceived well-being longitudinally before and after the birth of a baby. Additionally, how maternal and child-related variables influenced the trajectory of mothers\u27 postnatal adaptation was assessed. These variables included mothers\u27 age, their education level, their prenatal expectations and postnatal experiences concerning shared infant care, their satisfaction with the division of infant caregiving, and their perceptions of their infant\u27s temperament. Mothers (N = 136) completed an online survey during their third trimester and additional online surveys when their baby was approximately 2, 4, 6, and 8 weeks old.;First-time mothers prenatally expected a more equal division of infant caregiving between themselves and their partners than did experienced mothers. Both first-time and experienced mothers reported less assistance from their partners than they had prenatally expected. Additionally, they experienced almost twice as many violated expectations than met expectations. Growth curve modeling revealed that a cubic function of time best fit the trajectory of mothers\u27 postnatal parenting satisfaction. Mothers reported less parenting satisfaction at 4 weeks, compared to 2 and 6 weeks, and reported stability in their satisfaction between 6 and 8 weeks. A quadratic function of time best fit the trajectories of mothers\u27 postnatal parenting stress and adjustment to the demands of their baby. Mothers reported more stress and difficulty adjusting to their baby\u27s demands at 4 and 6 weeks, compared to 2 and 8 weeks. A linear function of time best fit the trajectories of mothers\u27 adjustment to home demands, generalized state anxiety, and depressive symptoms. Mothers reported less difficulty meeting home demands, less generalized anxiety, and fewer depressive symptoms across the postnatal period. Mothers\u27 violated expectations were associated with level differences in all aspects of mothers\u27 postnatal adaptation except their adjustment to home demands. Specifically, more violated expectations, in number or in magnitude, were associated with poorer postnatal adaptation. Mothers\u27 violated expectations were not associated with the slope of mothers\u27 postnatal adaptation trajectories. Exploratory models revealed that other maternal and child-related variables also impacted the level and slope of mothers\u27 postnatal adaptation.;Overall, first-time and experienced mothers were more similar than different in regards to their postnatal adaptation. This study suggests that prior findings concerning adults\u27 initial transition to parenthood may also apply to adults during each addition of a new baby into the family unit. Additionally, mothers who reported less of a mismatch between their expectations and experiences concerning shared infant care had fewer issues adapting the postnatal period. Thus, methods to increase the assistance mothers receive from their partner should be sought. Limitations of this study and suggestions for future research are also discussed

Enhancing Online Security with Image-based Captchas

Given the data loss, productivity, and financial risks posed by security breaches, there is a great need to protect online systems from automated attacks. Completely Automated Public Turing Tests to Tell Computers and Humans Apart, known as CAPTCHAs, are commonly used as one layer in providing online security. These tests are intended to be easily solvable by legitimate human users while being challenging for automated attackers to successfully complete. Traditionally, CAPTCHAs have asked users to perform tasks based on text recognition or categorization of discrete images to prove whether or not they are legitimate human users. Over time, the efficacy of these CAPTCHAs has been eroded by improved optical character recognition, image classification, and machine learning techniques that can accurately solve many CAPTCHAs at rates approaching those of humans. These CAPTCHAs can also be difficult to complete using the touch-based input methods found on widely used tablets and smartphones.;This research proposes the design of CAPTCHAs that address the shortcomings of existing implementations. These CAPTCHAs require users to perform different image-based tasks including face detection, face recognition, multimodal biometrics recognition, and object recognition to prove they are human. These are tasks that humans excel at but which remain difficult for computers to complete successfully. They can also be readily performed using click- or touch-based input methods, facilitating their use on both traditional computers and mobile devices.;Several strategies are utilized by the CAPTCHAs developed in this research to enable high human success rates while ensuring negligible automated attack success rates. One such technique, used by fgCAPTCHA, employs image quality metrics and face detection algorithms to calculate a fitness value representing the simulated performance of human users and automated attackers, respectively, at solving each generated CAPTCHA image. A genetic learning algorithm uses these fitness values to determine customized generation parameters for each CAPTCHA image. Other approaches, including gradient descent learning, artificial immune systems, and multi-stage performance-based filtering processes, are also proposed in this research to optimize the generated CAPTCHA images.;An extensive RESTful web service-based evaluation platform was developed to facilitate the testing and analysis of the CAPTCHAs developed in this research. Users recorded over 180,000 attempts at solving these CAPTCHAs using a variety of devices. The results show the designs created in this research offer high human success rates, up to 94.6\% in the case of aiCAPTCHA, while ensuring resilience against automated attacks

Model driven design and data integration in semantic web information systems

The Web is quickly evolving in many ways. It has evolved from a Web of documents into a Web of applications in which a growing number of designers offer new and interactive Web applications with people all over the world. However, application design and implementation remain complex, error-prone and laborious. In parallel there is also an evolution from a Web of documents into a Web of `knowledge' as a growing number of data owners are sharing their data sources with a growing audience. This brings the potential new applications for these data sources, including scenarios in which these datasets are reused and integrated with other existing and new data sources. However, the heterogeneity of these data sources in syntax, semantics and structure represents a great challenge for application designers. The Semantic Web is a collection of standards and technologies that offer solutions for at least the syntactic and some structural issues. If offers semantic freedom and flexibility, but this leaves the issue of semantic interoperability. In this thesis we present Hera-S, an evolution of the Model Driven Web Engineering (MDWE) method Hera. MDWEs allow designers to create data centric applications using models instead of programming. Hera-S especially targets Semantic Web sources and provides a flexible method for designing personalized adaptive Web applications. Hera-S defines several models that together define the target Web application. Moreover we implemented a framework called Hydragen, which is able to execute the Hera-S models to run the desired Web application. Hera-S' core is the Application Model (AM) in which the main logic of the application is defined, i.e. defining the groups of data elements that form logical units or subunits, the personalization conditions, and the relationships between the units. Hera-S also uses a so-called Domain Model (DM) that describes the content and its structure. However, this DM is not Hera-S specific, but instead allows any Semantic Web source representation as its DM, as long as its content can be queried by the standardized Semantic Web query language SPARQL. The same holds for the User Model (UM). The UM can be used for personalization conditions, but also as a source of user-related content if necessary. In fact, the difference between DM and UM is conceptual as their implementation within Hydragen is the same. Hera-S also defines a presentation model (PM) which defines presentation details of elements like order and style. In order to help designers with building their Web applications we have introduced a toolset, Hera Studio, which allows to build the different models graphically. Hera Studio also provides some additional functionality like model checking and deployment of the models in Hydragen. Both Hera-S and its implementation Hydragen are designed to be flexible regarding the user of models. In order to achieve this Hydragen is a stateless engine that queries for relevant information from the models at every page request. This allows the models and data to be changed in the datastore during runtime. We show that one way to exploit this flexibility is by applying aspect-orientation to the AM. Aspect-orientation allows us to dynamically inject functionality that pervades the entire application. Another way to exploit Hera-S' flexibility is in reusing specialized components, e.g. for presentation generation. We present a configuration of Hydragen in which we replace our native presentation generation functionality by the AMACONT engine. AMACONT provides more extensive multi-level presentation generation and adaptation capabilities as well aspect-orientation and a form of semantic based adaptation. Hera-S was designed to allow the (re-)use of any (Semantic) Web datasource. It even opens up the possibility for data integration at the back end, by using an extendible storage layer in our database of choice Sesame. However, even though theoretically possible it still leaves much of the actual data integration issue. As this is a recurring issue in many domains, a broader challenge than for Hera-S design only, we decided to look at this issue in isolation. We present a framework called Relco which provides a language to express data transformation operations as well as a collection of techniques that can be used to (semi-)automatically find relationships between concepts in different ontologies. This is done with a combination of syntactic, semantic and collaboration techniques, which together provide strong clues for which concepts are most likely related. In order to prove the applicability of Relco we explore five application scenarios in different domains for which data integration is a central aspect. This includes a cultural heritage portal, Explorer, for which data from several datasources was integrated and was made available by a mapview, a timeline and a graph view. Explorer also allows users to provide metadata for objects via a tagging mechanism. Another application is SenSee: an electronic TV-guide and recommender. TV-guide data was integrated and enriched with semantically structured data from several sources. Recommendations are computed by exploiting the underlying semantic structure. ViTa was a project in which several techniques for tagging and searching educational videos were evaluated. This includes scenarios in which user tags are related with an ontology, or other tags, using the Relco framework. The MobiLife project targeted the facilitation of a new generation of mobile applications that would use context-based personalization. This can be done using a context-based user profiling platform that can also be used for user model data exchange between mobile applications using technologies like Relco. The final application scenario that is shown is from the GRAPPLE project which targeted the integration of adaptive technology into current learning management systems. A large part of this integration is achieved by using a user modeling component framework in which any application can store user model information, but which can also be used for the exchange of user model data

User-centred video abstraction

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe rapid growth of digital video content in recent years has imposed the need for the development of technologies with the capability to produce condensed but semantically rich versions of the input video stream in an effective manner. Consequently, the topic of Video Summarisation is becoming increasingly popular in multimedia community and numerous video abstraction approaches have been proposed accordingly. These recommended techniques can be divided into two major categories of automatic and semi-automatic in accordance with the required level of human intervention in summarisation process. The fully-automated methods mainly adopt the low-level visual, aural and textual features alongside the mathematical and statistical algorithms in furtherance to extract the most significant segments of original video. However, the effectiveness of this type of techniques is restricted by a number of factors such as domain-dependency, computational expenses and the inability to understand the semantics of videos from low-level features. The second category of techniques however, attempts to alleviate the quality of summaries by involving humans in the abstraction process to bridge the semantic gap. Nonetheless, a single user’s subjectivity and other external contributing factors such as distraction will potentially deteriorate the performance of this group of approaches. Accordingly, in this thesis we have focused on the development of three user-centred effective video summarisation techniques that could be applied to different video categories and generate satisfactory results. According to our first proposed approach, a novel mechanism for a user-centred video summarisation has been presented for the scenarios in which multiple actors are employed in the video summarisation process in order to minimise the negative effects of sole user adoption. Based on our recommended algorithm, the video frames were initially scored by a group of video annotators ‘on the fly’. This was followed by averaging these assigned scores in order to generate a singular saliency score for each video frame and, finally, the highest scored video frames alongside the corresponding audio and textual contents were extracted to be included into the final summary. The effectiveness of our approach has been assessed by comparing the video summaries generated based on our approach against the results obtained from three existing automatic summarisation tools that adopt different modalities for abstraction purposes. The experimental results indicated that our proposed method is capable of delivering remarkable outcomes in terms of Overall Satisfaction and Precision with an acceptable Recall rate, indicating the usefulness of involving user input in the video summarisation process. In an attempt to provide a better user experience, we have proposed our personalised video summarisation method with an ability to customise the generated summaries in accordance with the viewers’ preferences. Accordingly, the end-user’s priority levels towards different video scenes were captured and utilised for updating the average scores previously assigned by the video annotators. Finally, our earlier proposed summarisation method was adopted to extract the most significant audio-visual content of the video. Experimental results indicated the capability of this approach to deliver superior outcomes compared with our previously proposed method and the three other automatic summarisation tools. Finally, we have attempted to reduce the required level of audience involvement for personalisation purposes by proposing a new method for producing personalised video summaries. Accordingly, SIFT visual features were adopted to identify the video scenes’ semantic categories. Fusing this retrieved data with pre-built users’ profiles, personalised video abstracts can be created. Experimental results showed the effectiveness of this method in delivering superior outcomes comparing to our previously recommended algorithm and the three other automatic summarisation techniques

Intelligent Computing: The Latest Advances, Challenges and Future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy and an abundance of innovations in the theories, systems, and applications of intelligent computing are expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners

Literacy for digital futures : Mind, body, text

The unprecedented rate of global, technological, and societal change calls for a radical, new understanding of literacy. This book offers a nuanced framework for making sense of literacy by addressing knowledge as contextualised, embodied, multimodal, and digitally mediated. In today’s world of technological breakthroughs, social shifts, and rapid changes to the educational landscape, literacy can no longer be understood through established curriculum and static text structures. To prepare teachers, scholars, and researchers for the digital future, the book is organised around three themes – Mind and Materiality; Body and Senses; and Texts and Digital Semiotics – to shape readers’ understanding of literacy. Opening up new interdisciplinary themes, Mills, Unsworth, and Scholes confront emerging issues for next-generation digital literacy practices. The volume helps new and established researchers rethink dynamic changes in the materiality of texts and their implications for the mind and body, and features recommendations for educational and professional practice

Componentes e pontos de quebra em séries temporais na análise de imagens de sensoriamento remoto

Orientador: Ricardo da Silva TorresDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A detecção e caracterização de mudanças temporais são indicadores cruciais no processo de compreensão da maneira como mecanismos complexos funcionam e evoluem. Técnicas e imagens de sensoriamento remoto têm sido amplamente empregadas nas últimas décadas com objetivo de detectar e investigar mudanças temporais na superfície terrestre. Tal detecção em dados de séries temporais é passível de ser refinada ainda mais isolando-se as componentes aditivas de tendência e sazonalidade do ruído subjacente. Este trabalho investiga, em particular, o método Breaks For Additive Season and Trend (BFAST) para a análise, decomposição e detecção de pontos de quebra em séries temporais associadas a dados de sensoriamento remoto. Os outputs do método são, então, utilizados em três distintas ¿ mas altamente interconectadas ¿ linhas de pesquisa: em uma melhor compreensão de fenômenos climáticos; na correlação com dados de distúrbios antropológicos; e em problemas de classificação usando funções de dissimilaridade descobertas por um framework evolucionário baseado em Programação Genética (GP). Experimentos realizados demonstram que a decomposição e pontos de quebra produziram resultados efetivos quando aplicados aos estudos com dados ecológicos, mas não foram capazes de melhorar os resultados de classificação quando comparados ao uso das séries brutas. As realizações nesses três contextos também culminaram na criação de duas ferramentas de análise de séries temporais com código aberto baseadas na web, sendo que uma delas foi tão bem aceita pela comunidade-alvo, que atualmente encontra-se integrada em uma plataforma privada de computação em nuvemAbstract: Detecting and characterizing temporal changes are crucial indicators in the process of understanding how complex mechanisms work and evolve. The use of remote sensing images and techniques has been broadly employed over the past decades in order to detect and investigate temporal changes on the Earth surface. Such change detection in time series data may be even further refined by isolating the additive long-term (trend) and cyclical (seasonal) components from the underlying noise. This work investigates the particular Breaks For Additive Season and Trend (BFAST) method for the analysis, decomposition, and breakpoint detection of time series associated with remote sensing data. The derived outputs from that method are, then, used in three distinct ¿ but highly interconnected ¿ research venues: in a better comprehension of climatic phenomena; in the correlation to human-induced disturbances data; and in data classification problems using time series dissimilarity functions discovered by a Genetic-Programming-(GP)-based evolutionary framework. Performed experiments show that decomposition and breakpoints produced insightful and effective results when applied to the ecological data studies, but could not further improve the classification results when compared to its raw time series counterpart. The achievements in those three contexts also led to the creation of two open-source web-based time series analysis tools. One of those tools was so well received by the target community, that it is currently integrated into a private cloud computing platformMestradoCiência da ComputaçãoMestre em Ciência da Computação132847/2015-92015/02105-0CNPQFAPES