An optimized context-aware mobile computing model to filter inappropriate incoming calls in smartphone

Requests for communication via mobile devices can be disruptive to the receiver in certain social situation. For example, unsuitable incoming calls may put the receiver in a dangerous condition, as in the case of receiving calls while driving. Therefore, designers of mobile computing interfaces require plans for minimizing annoying calls. To reduce the frequency of these calls, one promising approach is to provide an intelligent and accurate system, based on context awareness with cues of a callee's context allowing informed decisions of when to answer a call. The processing capabilities and advantages of mobile devices equipped with portable sensors provide the basis for new context-awareness services and applications. However, contextawareness mobile computing systems are needed to manage the difficulty of multiple sources of context that affects the accuracy of the systems, and the challenge of energy hungry GPS sensor that affects the battery consumption of mobile phone. Hence, reducing the cost of GPS sensor and increasing the accuracy of current contextawareness call filtering systems are two main motivations of this study. Therefore, this study proposes a new localization mechanism named Improved Battery Life in Context Awareness System (IBCS) to deal with the energy-hungry GPS sensor and optimize the battery consumption of GPS sensor in smartphone for more than four hours. Finally, this study investigates the context-awareness models in smartphone and develops an alternative intelligent model structure to improve the accuracy rate. Hence, a new optimized context-awareness mobile computing model named Optimized Context Filtering (OCF) is developed to filter unsuitable incoming calls based on context information of call receiver. In this regard, a new extended Naive Bayesian classifier was proposed based on the Naive Bayesian classifier by combining the incremental learning strategy with appropriate weight on the new training data. This new classifier is utilized as an inference engine to the proposed model to increase its accuracy rate. The results indicated that 7% improvement was seen in the accuracy rate of the proposed extended naive Bayesian classifier. On the other hand, the proposed model result showed that the OCF model improved the accuracy rate by 14%. These results indicated that the proposed model is a hopeful approach to provide an intelligent call filtering system based on context information for smartphones

Evolving extended naive Bayes classifiers

Naive Bayes classifiers are a very simple, but often effective tool for classification problems, although they are based on independence assumptions that do not hold in most cases. Extended naive Bayes classifiers also rely on independence assumptions, but break them down to artificial subclasses, in this way becoming more powerful than ordinary naive Bayes classifiers. Since the involved computations for Bayes classifiers are basically generalised mean value calculations, they easily render themselves to incremental and online learning. However, for the extended naive Bayes classifiers it is necessary, to choose and construct the subclasses, a problem whose answer is not obvious, especially in the case of online learning. In this paper we propose an evolving extended naive Bayes classifier that can learn and evolve in an online manner (c) IEEE Pres

Human robot interaction in a crowded environment

Human Robot Interaction (HRI) is the primary means of establishing natural and affective communication between humans and robots. HRI enables robots to act in a way similar to humans in order to assist in activities that are considered to be laborious, unsafe, or repetitive. Vision based human robot interaction is a major component of HRI, with which visual information is used to interpret how human interaction takes place. Common tasks of HRI include finding pre-trained static or dynamic gestures in an image, which involves localising different key parts of the human body such as the face and hands. This information is subsequently used to extract different gestures. After the initial detection process, the robot is required to comprehend the underlying meaning of these gestures [3]. Thus far, most gesture recognition systems can only detect gestures and identify a person in relatively static environments. This is not realistic for practical applications as difficulties may arise from people‟s movements and changing illumination conditions. Another issue to consider is that of identifying the commanding person in a crowded scene, which is important for interpreting the navigation commands. To this end, it is necessary to associate the gesture to the correct person and automatic reasoning is required to extract the most probable location of the person who has initiated the gesture. In this thesis, we have proposed a practical framework for addressing the above issues. It attempts to achieve a coarse level understanding about a given environment before engaging in active communication. This includes recognizing human robot interaction, where a person has the intention to communicate with the robot. In this regard, it is necessary to differentiate if people present are engaged with each other or their surrounding environment. The basic task is to detect and reason about the environmental context and different interactions so as to respond accordingly. For example, if individuals are engaged in conversation, the robot should realize it is best not to disturb or, if an individual is receptive to the robot‟s interaction, it may approach the person. Finally, if the user is moving in the environment, it can analyse further to understand if any help can be offered in assisting this user. The method proposed in this thesis combines multiple visual cues in a Bayesian framework to identify people in a scene and determine potential intentions. For improving system performance, contextual feedback is used, which allows the Bayesian network to evolve and adjust itself according to the surrounding environment. The results achieved demonstrate the effectiveness of the technique in dealing with human-robot interaction in a relatively crowded environment [7]

Statistische und Probabilistische Methoden für Data Stream Mining

The aim of this work is not only to highlight and summarize issues and challenges which arose during the mining of data streams, but also to find possible solutions to illustrated problems. Due to the streaming nature of the data, it is impossible to hold the whole data set in the main memory, i.e. efficient on-line computations are needed. For instance incremental calculations could be used in order to avoid to start the computation process from scratch each time new data arrive and to save memory. Another important aspect in data stream analysis is that the data generating process does not remain static, i.e.\ the underlying probabilistic model cannot be assumed to be stationary. The changes in the data structure may occur over time. Dealing with non-stationary data requires change detection and on-line adaptation. Furthermore real data is often contaminated with noise, this causes a specific problem for approaches dealing with the data streams. They must be able to distinguish between changes according to noise and changes of the underlying data generating process or its parameters. In this work we propose a variety of different methods, which fulfil specific requirements of data stream mining. Furthermore we carry out theoretical analysis of effects of noise and changes in data stream for sliding window based evolving system in order to illustrate the problem of suboptimal window size. In order to do the validation of an evolving system significant, we propose some simple benchmark tests that can give an idea of how much an evolving system might be misled by noise.Das Hauptziel dieser Arbeit ist es, zentrale Probleme und wichtige Aspekte im Datastream-Mining zu veranschaulichen und mögliche Lösungen zu diskutierten Problemen vorzustellen. Da die Anzahl der Daten bei Datastreams potenziell unendlich ist und die statistischen Eigenschaften der Daten sich mit der Zeit ändern können, lassen sich klassische Data-Mining- und Statistikmethoden nicht auf Data Streams direkt anwenden. Aus diesem Grund werden im Rahmen dieser Arbeit bereits existierende Ansätze an die Datastream-Problematik angepasst und neue Methoden entwickelt. Zum Beispiel werden inkrementelle oder rekursive Berechnungen statistischer Parameter und statistischer Tests vorgestellt, die nötig sind, um Berechnungen online und auf Hardware wie Steuergeräten mit teilweise recht begrenzter Rechen und Speicherkapazität ausführen zu können. Ein wesentliches Problem stellt die Unterscheidung zwischen zufälligen Schwankungen im Sinne von Rauschen und echten Änderungen in Datastreams dar. Es bietet sich an, Hypothesentests mit inkrementeller Berechnung für dieses Problem der Change Detection einzusetzen. In dieser Arbeit werden inkrementelle und auf Fenstertechnik basierende statistische Tests für Change Detection vorgestellt. Die Mehrzahl der existierenden Algorithmen zum Datastream-Mining verwenden keine expliziten Methoden zur Change Detection, sondern benutzen für die Vorhersage gleitende Fenster fester Breite. Nur wenige dieser Methoden beschäftigen sich mit der Frage wie die Fenstergröße ausgewählt werden soll und welche Effekte Veränderungen in den Daten auf die Vorhersagequalität haben. Hierzu wird eine theoretische Analyse für die optimale Fensterbreite für zwei Datenmodelle durchgeführt und gezeigt, dass eine suboptimale Fenstergröße zur drastischen Senkung der Vorhersagequalität führen kann. Außerdem können die vorgestellten Datenmodelle als Benchmark Tests für fensterbasierte Ansätze verwendet werden. Dies kann einen Eindruck vermitteln, wie stark ein sich an Datastreams automatisch anpassendes "Evolving System" durch Rauschen in den Daten negativ beeinflusst wird