13925823
Context inference of users' social relationships and distributed policy management
Inference of high-level context is becoming crucial in development of context-aware applications. An example is social context inference - i.e., deriving social relations based upon the user's daily communication with other people. The efficiency of this mechanism mainly depends on the method(s) used to draw inferences based on existing evidence and sample information, such as a training data. Our approach uses rule-based data mining, Bayesian network inference, and user feedback to compute the probabilities of another user being in the specific social relationship with a user whose daily communication is logged by a mobile phone. In addition, a privacy mechanism is required to ensure the user's personal integrity and privacy when sharing this user's sensitive context data. Therefore, the derived social relations are used to define a user's policies for context access control, which grant the restricted context information scope depending on the user's current context. Finally, we propose a distributed architecture capable of managing this context information based upon these context access policies.
14005418
A Comprehensive Context Modeling Framework for Pervasive Computing Systems
Context management in pervasive computing environments must reflect the specific characteristics of these environments, e.g. distribution, mobility, resource-constrained devices, or heterogeneity of context sources. Although a number of context models have been presented in the literature, none of them supports all of these requirements to a sufficient extent at the same time. In this paper, we present a comprehensive and integrated approach for context modeling in pervasive computing environments. It combines the advantages of existing approaches and addresses the need for supporting effective software development. The proposed context model follows an ontology-based approach and has three layers of abstraction, i.e. conceptual layer, exchange layer, and functional layer. This layered approach facilitates a model-driven development of context-aware applications. Throughout the paper we compare our solution with the related work in order to clearly demonstrate why we needed to develop a new context management framework and where we have adopted existing ideas.
14972044
Service-Oriented Adaptation in Ubiquitous Computing Environments
Ubiquitous computing environments may offer dynamically discoverable services that can be utilized by adaptive applications to improve their functionality and quality of service. We have developed a general software development framework for context-aware adaptive applications. In this paper we present our novel modeling concepts that extend an existing component-based modeling approach by service-oriented adaptation concepts. With our new extensions a dynamically discovered service may replace an application component if it increases the overall utility of the application. We discuss open questions and point to potential solution approaches.
209498655
Preface
The title of this book ‘One-Dimensional Finite Elements: An Introduction to the FE Method’ stands for content and focus. Nowadays, much literature regarding the topic of the finite element method exists. The different works reflect the multifaceted perceptions and application possibilities. The basic idea of this introduction into the finite element method relies on the concept of explaining the complex method with the help of one-dimensional elements. It is the goal to introduce the manifold aspects of the finite element method and to enable the reader to get a methodical understanding of important subject areas. The reader learns to understand the assumptions and derivations at different physical problems in structural mechanics. Furthermore, he/she learns to critically evaluate possibilities and limitations of the finite element method. Additional comprehensive mathematical descriptions, which solely result from advanced illustrations for twoor three-dimensional problems, are omitted. Hence, the mathematical description largely remains simple and clear. The focus on one-dimensional elements, however, is not just a pure limitation on a simpler and clearer formal illustration of the necessary equations. Within structural engineering, there are various structures— for example bridges or high transmission towers—which are usually modeled via one-dimensional elements. Therefore, this work also contains a ‘set of tools’, which can also be applied in practice. The concentration on one-dimensional elements is new for a textbook and allows the treatment of various basic and demanding physical questions of structural mechanics within one single textbook. This new concept, therefore, allows a methodical understanding of important subject areas (for example plasticity or composite materials), which occur to a prospective engineer during professional work, which however are seldom treated in this way at universities. Consequently, simple access is possible, also in supplementary areas of application of the finite element method. This book originates from a collection of lecture notes which were developed as written material for lectures and training documents for specialized courses on the finite element method. Especially, the calculated examples and the supplementary
14497648
An Adaptation Reasoning Approach for Large Scale Component-based Applications
There is a growing demand for context-aware applications that can dynamically adapt to their run-time environment. An application offers a collection of functionalities that can be realized through a composition of software components and/or services that are made available at runtime. With the availability of alternative variants of such components and/or services that provide the basic functionalities, while differ in extra- functional characteristics, characterized by quality of services (QoS), an unforeseen number of application variants can be created. The variant that best fits the current context is selected through adaptation reasoning, which can suffer from the processing capabilities of resource-scarce mobile devices, especially when a huge number of application variants needs to be reason about. In this paper, we present a reasoning approach, which provides a meaningful adaptation decision for adaptive applications having a large number of variants within a reasonable time frame. The approach is validated through two arbitrary applications with large number of variants.
20326889
Towards a Comprehensive Teamwork Model for Highly Dynamic Domains
Cooperative behaviour of agents within highly dynamic and nondeterministic domains is an active field of research. In particular establishing responsive teamwork, where agents are able to react to dynamic changes in the environment while facing unreliable communication and sensory noise, is an open problem. Unexpectedly changing situations force agents to react and adapt under tight time-constraints. Hence they often cannot communicate or agree upon their decisions before acting upon them. We present a novel model for cooperative behaviour geared towards such domains. In our approach, the agents estimate each other’s decision and correct these estimations once they receive contradictory information. We aim at a comprehensive approach for agent teamwork featuring intuitive modelling capabilities for multi-agent activities, abstractions over activities and agents, and clear operational semantics for the new model. We show experimentally that the resulting behaviour stabilises towards teamwork and can achieve a cooperative goal.
10947752
Noise Clustering-Based Speaker Verification
The normalisation method for speaker verification proposed in this paper is based on the idea of the noise clustering method in fuzzy clustering. The proposed method can reduce false acceptance errors and apply to all current normalisation scores. Experiments performed on the ANDOSL and YOHO speech corpora show better results for the proposed method.
17740711
Hierarchical data access in structured P2P-networks
Hierarchically structured organizations have clear and relatively short communication paths. However, one does not take advantage of this fact in modern peer-to-peer overlays. We show how a command and control order can be imposed on a given peer-to-peer network. This allows fast hierarchical data storage and data access. Maintaining a hierarchical organization structure upon a given distributed hashtable overlay network scales with O(logN), where N is the number of peers in the network. This approach can be used to benefit from hierarchical structure in any communication system, especially in first response scenarios.
14362967
What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks
Traditional biosurveillance algorithms detect disease outbreaks by looking for peaks in a univariate time series of health-care data. Current health-care surveillance data, however, are no longer simply univariate data streams. Instead, a wealth of spatial, temporal, demographic and symptomatic information is available. We present an early disease outbreak detection algorithm called What's Strange About Recent Events (WSARE), which uses a multivariate approach to improve its timeliness of detection. WSARE employs a rule-based technique that compares recent health-care data against data from a baseline distribution and finds subgroups of the recent data whose proportions have changed the most from the baseline data. In addition, health-care data also pose difficulties for surveillance algorithms because of inherent temporal trends such as seasonal effects and day of week variations. WSARE approaches this problem using a Bayesian network to produce a baseline distribution that accounts for these temporal trends. The algorithm itself incorporates a wide range of ideas, including association rules, Bayesian networks, hypothesis testing and permutation tests to produce a detection algorithm that is careful to evaluate the significance of the alarms that it raises.
2986922
Bayesian Network Anomaly Pattern Detection for Disease Outbreaks
Early disease outbreak detection systems typically monitor health care data for irregularities by comparing the distribution of recent data against a baseline distribution. Determining the baseline is difficult due to the presence of different trends in health care data, such as trends caused by the day of week and by seasonal variations in temperature and weather. Creating the baseline distribution without taking these trends into account can lead to unacceptably high false positive counts and slow detection times. This paper replaces the baseline method of (Wong et al., 2002) with a Bayesian network which produces the baseline distribution by taking the joint distribution of the data and conditioning on attributes that are responsible for the trends. We show that our algorithm, called WSARE 3.0, is able to detect outbreaks in simulated data with almost the earliest possible detection time while keeping a low false positive count. We also include the results of running WSARE 3.0 on real Emergency Department data
32066141
A proposed fuzzy pattern verification system
Proposes a fuzzy pattern verification system based on fuzzy if-then rules and fuzzy membership functions in fuzzy clustering. The proposed fuzzy system is applied to speaker verification and is compared with current probabilistic verification systems. Experiments performed on the ANDOSL and YOHO speech corpora show better results for the proposed system.