The impact of a training program in phonological awareness on children's early writing

This study examined the effects that a training program in phonological awareness had on the early writing skills of children in a Grade One class in the Lincoln County Separate school system. The intent of the training program was to provide consistent and systematic practice in the manipulation of the phonological structure of language. The games and activities of the training program were related to a framework of developmental phonological skills and practised in a group setting during an unstructured period of the regular classroom schedule. The training program operated three days in a six-day cycle for approximately twenty minutes a day, from November until mid-March. All children were tested at the outset and conclusion of the study to determine level of functioning in letter identification, word recognition, verbal intelligence, phonological awareness and spelling. Results of the pre-tests and post-tests were compared to determine differences between the experimental and control groups over time. In addition, a systematic analysis of the children's writing looked at the development of the spelling of regular and irregular words. The results of this study provided strong support for the hypothesis that the treatment group would progress through the stages of early writing development more quickly than children without such training. On the basis of differences between the groups over time, it was evident that training in phonological awareness had a direct positive effect on the spelling of regular words for children during the early stages of writing. The training program did not have a significant effect on the spelling of irregular words. Test results evaluating phonological awareness indicated a significant difference within each group over time but no significance between the groups during the experimental period. It would appear that the results of these tests reflect maturational changes in the child rather than causal effects of the training program. Nor did the effects of the training program transfer significantly to other aspects of language. Although some of the hypotheses considered were not supported by the study, the results do indicate that children during the early stages of writing development can benefit from a training program in phonological awareness. The theoretical direction for effective programming as a result of this study is discussed. The educational implications of training phonological awareness concurrent to beginning efforts in writing are considered.

User Profile Management and Selection in Context-Aware Service Platforms for Networks Beyond 3G

In recent years, progress in the area of mobile telecommunications has changed our way of life, in the private as well as the business domain. Mobile and wireless networks have ever increasing bit rates, mobile network operators provide more and more services, and at the same time costs for the usage of mobile services and bit rates are decreasing. However, mobile services today still lack functions that seamlessly integrate into users’ everyday life. That is, service attributes such as context-awareness and personalisation are often either proprietary, limited or not available at all. In order to overcome this deficiency, telecommunications companies are heavily engaged in the research and development of service platforms for networks beyond 3G for the provisioning of innovative mobile services. These service platforms are to support such service attributes. Service platforms are to provide basic service-independent functions such as billing, identity management, context management, user profile management, etc. Instead of developing own solutions, developers of end-user services such as innovative messaging services or location-based services can utilise the platform-side functions for their own purposes. In doing so, the platform-side support for such functions takes away complexity, development time and development costs from service developers. Context-awareness and personalisation are two of the most important aspects of service platforms in telecommunications environments. The combination of context-awareness and personalisation features can also be described as situation-dependent personalisation of services. The support for this feature requires several processing steps. The focus of this doctoral thesis is on the processing step, in which the user’s current context is matched against situation-dependent user preferences to find the matching user preferences for the current user’s situation. However, to achieve this, a user profile management system and corresponding functionality is required. These parts are also covered by this thesis. Altogether, this thesis provides the following contributions: The first part of the contribution is mainly architecture-oriented. First and foremost, we provide a user profile management system that addresses the specific requirements of service platforms in telecommunications environments. In particular, the user profile management system has to deal with situation-specific user preferences and with user information for various services. In order to structure the user information, we also propose a user profile structure and the corresponding user profile ontology as part of an ontology infrastructure in a service platform. The second part of the contribution is the selection mechanism for finding matching situation-dependent user preferences for the personalisation of services. This functionality is provided as a sub-module of the user profile management system. Contrary to existing solutions, our selection mechanism is based on ontology reasoning. This mechanism is evaluated in terms of runtime performance and in terms of supported functionality compared to other approaches. The results of the evaluation show the benefits and the drawbacks of ontology modelling and ontology reasoning in practical applications.

Information Exchange and Fusion in Dynamic and Heterogeneous Distributed Environments

Context awareness, dynamic reconfiguration at runtime and heterogeneity are key characteristics of future distributed systems, particularly in ubiquitous and mobile computing scenarios. The main contributions of this dissertation are theoretical as well as architectural concepts facilitating information exchange and fusion in heterogeneous and dynamic distributed environments. Our main focus is on bridging the heterogeneity issues and, at the same time, considering uncertain, imprecise and unreliable sensor information in information fusion and reasoning approaches. A domain ontology is used to establish a common vocabulary for the exchanged information. We thereby explicitly support different representations for the same kind of information and provide Inter-Representation Operations that convert between them. Special account is taken of the conversion of associated meta-data that express uncertainty and impreciseness. The Unscented Transformation, for example, is applied to propagate Gaussian normal distributions across highly non-linear Inter-Representation Operations. Uncertain sensor information is fused using the Dempster-Shafer Theory of Evidence as it allows explicit modelling of partial and complete ignorance. We also show how to incorporate the Dempster-Shafer Theory of Evidence into probabilistic reasoning schemes such as Hidden Markov Models in order to be able to consider the uncertainty of sensor information when deriving high-level information from low-level data. For all these concepts we provide architectural support as a guideline for developers of innovative information exchange and fusion infrastructures that are particularly targeted at heterogeneous dynamic environments. Two case studies serve as proof of concept. The first case study focuses on heterogeneous autonomous robots that have to spontaneously form a cooperative team in order to achieve a common goal. The second case study is concerned with an approach for user activity recognition which serves as baseline for a context-aware adaptive application. Both case studies demonstrate the viability and strengths of the proposed solution and emphasize that the Dempster-Shafer Theory of Evidence should be preferred to pure probability theory in applications involving non-linear Inter-Representation Operations.

Security, trust and privacy supported by context-aware middleware

Driven by new network and middleware technologies such as mobile broadband, near-field communication, and context awareness the so-called ambient lifestyle will foster innovative use cases in building automation, healthcare and agriculture. In the EU project Hydra1 highlevel security, trust and privacy concerns such as loss of control, profiling and surveillance are considered at the outset. At the end of this project the Hydra middleware development platform will have been designed so as to enable developers to realise secure ambient scenarios especially in the user domains of building automation, healthcare, and agriculture. This paper gives a short introduction to the Hydra project, its user domains and its approach to ensure security by design. Based on the results of a focus group analysis of the building automation domain typical threats are evaluated and their risks are assessed. Then, specific security requirements with respect to security, privacy, and trust are derived in order to incorporate them into the Hydra Security Meta Model. How concepts such as context security, semantic security, and virtualisation support the overall Hydra approach will be introduced and illustrated on the basis of a technical building automation scenario.

Towards semantic resolution of security in ambient environments

Driven by new network and middleware technologies such as mobile broadband, near-field communication, and context awareness the so-called ambient lifestyle will foster innovative use cases in different domains. In the EU project Hydra high-level security, trust and privacy concerns such as loss of control, profiling and surveillance are considered at the outset. At the end of this project the. Hydra middleware development platform will have been designed so as to enable developers to realise secure ambient scenarios. This paper gives a short introduction to the Hydra project and its approach to ensure security by design. Based on the results of a focus group analysis of the user domain "building automation" typical threats are evaluated and their risks are assessed. Then, specific security requirements with respect to security, privacy, and trust are derived in order to incorporate them into the Hydra Security Meta-Model. How concepts such as context, semantic resolution of security, and virtualisation support the overall Hydra approach will be introduced and illustrated on the basis of it technical building automation scenario.

A new algorithm for tracking people in a pervasive mining environment

This paper analyze and study a pervasive computing system in a mining environment to track people based on RFID (radio frequency identification) technology. In first instance, we explain the RFID fundamentals and the LANDMARC (location identification based on dynamic active RFID calibration) algorithm, then we present the proposed algorithm combining LANDMARC and trilateration technique to collect the coordinates of the people inside the mine, next we generalize a pervasive computing system that can be implemented in mining, and finally we show the results and conclusions.

Prescription software for recovery and rehabilitation using Microsoft Kinect

Brain injuries, including stroke, can be debilitating incidents with potential for severe long term effects; many people stop making significant progress once leaving in-patient medical care and are unable to fully restore their quality of life when returning home. The aim of this collaborative project, between the Royal Berkshire NHS Foundation Trust and the University of Reading, is to provide a low cost portable system that supports a patient's condition and their recovery in hospital or at home. This is done by providing engaging applications with targeted gameplay that is individually tailored to the rehabilitation of the patient's symptoms. The applications are capable of real-time data capture and analysis in order to provide information to therapists on patient progress and to further improve the personalized care that an individual can receive.

Determining service trustworthiness in inter loud computing environments

Deployment of applications and scientific workflows that require resources from multiple distributed platforms are fuelling the federation of autonomous clouds to create cyber infrastructure environments. As the scope of federated cloud computing enlarges to ubiquitous and pervasive computing, there will be a need to assess and maintain the trustworthiness of the cloud computing entities. In this paper, we present a fully distributed framework that enable interested parties determine the trustworthiness of federated cloud computing entities.

Human-computer interaction in ubiquitous computing environments

Purpose – The purpose of this paper is to explore characteristics of human-computer interaction when the human body and its movements become input for interaction and interface control in pervasive computing settings.

Design/methodology/approach – The paper quantifies the performance of human movement based on Fitt's Law and discusses some of the human factors and technical considerations that arise in trying to use human body movements as an input medium.

Findings – The paper finds that new interaction technologies utilising human movements may provide more flexible, naturalistic interfaces and support the ubiquitous or pervasive computing paradigm.

Practical implications – In pervasive computing environments the challenge is to create intuitive and user-friendly interfaces. Application domains that may utilize human body movements as input are surveyed here and the paper addresses issues such as culture, privacy, security and ethics raised by movement of a user's body-based interaction styles.

Originality/value – The paper describes the utilization of human body movements as input for interaction and interface control in pervasive computing settings.

Establishing trust in hybrid cloud computing environments

Establishing trust for resource sharing and collaboration has become an important issue in distributed computing environment. In this paper, we investigate the problem of establishing trust in hybrid cloud computing environments. As the scope of federated cloud computing enlarges to ubiquitous and pervasive computing, there will be a need to assess and maintain the trustworthiness of the cloud computing entities. We present a fully distributed framework that enable trust-based cloud customer and cloud service provider interactions. The framework aids a service consumer in assigning an appropriate weight to the feedback of different raters regarding a prospective service provider. Based on the framework, we developed a mechanism for controlling falsified feedback ratings from iteratively exerting trust level contamination due to falsified feedback ratings. The experimental analysis shows that the proposed framework successfully dilutes the effects of falsified feedback ratings, thereby facilitating accurate and fair assessment of the service reputations.

Context aware privacy in visual surveillance

In this paper we present preliminary work implementing dynamic privacy in public surveillance. The aim is to maximise the privacy of those under surveillance, while giving an observer access to sufficient information to perform their duties. As these aspects are in conflict, a dynamic approach to privacy is required to balance the system's purpose with the system's privacy. Dynamic privacy is achieved by accounting for the situation, or context, within the environment. The context is determined by a number of visual features that are combined and then used to determine an appropriate level of privacy.

Dynamic privacy in a smart house environment

A smart house can be regarded as a surveillance environment in which the person being observed carries out activities that range from intimate to more public. What can be observed depends on the activity, the person observing (e.g. a carer) and policy. In assisted living smart house environments, a single privacy policy, applied throughout, would be either too invasive for an occupant, or too restrictive for an observer, due to the conflicting goals of surveillance and private environments. Hence, we propose a dynamic method for altering the level of privacy in the environment based on the context, the situation within the environment, encompassing factors relevant to ensuring the occupant's safety and privacy. The context is mapped to an appropriate level of privacy, which is implemented by controlling access to data sources (e.g. video) using data hiding techniques. The aim of this work is to decrease the invasiveness of the technology, while retaining the purpose of the system.

Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes

A fundamental task in pervasive computing is reliable acquisition of contexts from sensor data. This is crucial to the operation of smart pervasive systems and services so that they might behave efficiently and appropriately upon a given context. Simple forms of context can often be extracted directly from raw data. Equally important, or more, is the hidden context and pattern buried inside the data, which is more challenging to discover. Most of existing approaches borrow methods and techniques from machine learning, dominantly employ parametric unsupervised learning and clustering techniques. Being parametric, a severe drawback of these methods is the requirement to specify the number of latent patterns in advance. In this paper, we explore the use of Bayesian nonparametric methods, a recent data modelling framework in machine learning, to infer latent patterns from sensor data acquired in a pervasive setting. Under this formalism, nonparametric prior distributions are used for data generative process, and thus, they allow the number of latent patterns to be learned automatically and grow with the data - as more data comes in, the model complexity can grow to explain new and unseen patterns. In particular, we make use of the hierarchical Dirichlet processes (HDP) to infer atomic activities and interaction patterns from honest signals collected from sociometric badges. We show how data from these sensors can be represented and learned with HDP. We illustrate insights into atomic patterns learned by the model and use them to achieve high-performance clustering. We also demonstrate the framework on the popular Reality Mining dataset, illustrating the ability of the model to automatically infer typical social groups in this dataset. Finally, our framework is generic and applicable to a much wider range of problems in pervasive computing where one needs to infer high-level, latent patterns and contexts from sensor data.

HorusCML: context-aware domain-specific visual languages designer

The objective behind building domain-specific visual languages (DSVLs) is to provide users with the most appropriate concepts and notations that best fit with their domain and experience. However, the existing DSVL designers do not support integrating environment and user context information when modeling, editing or viewing DSVL models at different locations, permissions, devices, etc. In this paper, we introduce HorusCML, a context-aware DSVL designer, which supports DSVL experts in integrating necessary context details within their DSVLs. The resultant DSVLs can reflect different facets, layouts, and behaviours according to context it is used in. We show a case study on developing a context-aware data flow diagram DSVL tool using HorusCML.

Continuous discovery of co-location contexts from Bluetooth data

The discovery of contexts is important for context-aware applications in pervasive computing. This is a challenging problem because of the stream nature of data, the complexity and changing nature of contexts. We propose a Bayesian nonparametric model for the detection of co-location contexts from Bluetooth signals. By using an Indian buffet process as the prior distribution, the model can discover the number of contexts automatically. We introduce a novel fixed-lag particle filter that processes data incrementally. This sampling scheme is especially suitable for pervasive computing as the computational requirements remain constant in spite of growing data. We examine our model on a synthetic dataset and two real world datasets. To verify the discovered contexts, we compare them to the communities detected by the Louvain method, showing a strong correlation between the results of the two methods. Fixed-lag particle filter is compared with Gibbs sampling in terms of the normalized factorization error that shows a close performance between the two inference methods. As fixed-lag particle filter processes a small chunk of data when it comes and does not need to be restarted, its execution time is significantly shorter than that of Gibbs sampling.