Providing navigation assistance to users is a complex task generally consisting of two phases: planning a tour (phase one) and supporting the user during the tour (phase two). In the first phase, users interface to da...
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ISBN:
(纸本)9781450307789
Providing navigation assistance to users is a complex task generally consisting of two phases: planning a tour (phase one) and supporting the user during the tour (phase two). In the first phase, users interface to databases via constrained or natural language interaction to acquire prior knowledge such as bus schedules etc. In the second phase, often unexpected external events, such as delays or accidents, happen, user preferences change, or new needs arise. This requires machine intelligence to support users in the navigation realtime task, update information and trip replanning. To provide assistance in phase two, a navigation system must monitor external events, detect anomalies of the current situation compared to the plan built in the first phase, and provide assistance when the plan has become unfeasible. In this paper we present a prototypical mobile speech-controlled navigation system that provides assistance in both phases. The system was designed based on mplications from an analysis of real user assistance needs investigated in a diary study that underlines the vital importance of assistance in phase two. Copyright 2011 ACM.
Human pose estimation from monocular image sequences is attracting increasing attention, and 2D (image-based) as well as 3D (joint-motion based) approaches have been proposed. The former is computationally fast, and w...
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Human pose estimation from monocular image sequences is attracting increasing attention, and 2D (image-based) as well as 3D (joint-motion based) approaches have been proposed. The former is computationally fast, and works also for less frequent poses, but reliability is low. The latter is computationally expensive owing to the high-dimensionality of the problem, despite attempts at dimensionality-reduction. We propose to impose temporal continuity constraints on the 2D approaches in order to improve reliability and obtain spatio-temporal descriptions of the action in the image plane. In the first stage, at each frame-level spatial localisation will be done by applying background subtraction, after that static image pose estimation is performed using CRF-based probabilistic assembly of parts, based on the approach by Ramanan [1]. Next, pose continuity is imposed via first-order Markovian constraints on the CRF search. This results in improved spatial accuracy as well as significantly reduced search. We present results from the Weizmann video dataset to demonstrate how such an approach can serve as an image-plane based alternative to full 3D modeling or manifold search.
In this paper, an algorithm of Selective Block Size Decision for Intra Prediction in Video Coding and Learning Website will be presented. The main purpose is to reduce the high complexity of video when predict in intr...
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Motion estimation is the most important part that needs about 80% of computation in the procedure of image compression coding. So there are many fast algorithms for motion vector search proposed to reduce time consume...
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Actions consist of short shape-motion fragments which recur in a seemingly unique sequence. We propose that these short fragments may constitute a concise vocabulary for actions. Models based on such “words” sometim...
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Actions consist of short shape-motion fragments which recur in a seemingly unique sequence. We propose that these short fragments may constitute a concise vocabulary for actions. Models based on such “words” sometimes use the bag of words paradigm, which ignores sequence information. Also, despite the well-known utility of Fourier and similar features for temporal modelling, Fourier models have not received due attention to model action words until recently. Hence, we employ shape-frequency features as a temporally windowed Fourier transform to capture local motion and shape information. Unsupervised clustering discovers the naturally occurring modes (words) of these features. Each labelled video can thus be represented as a sequence of cluster transitions. Though different actions share common words, we observe that the word sequences are different for different actions, enabling easy discrimination. We evaluate the model on the Weizmann action dataset [1] and achieve 96.7% classification accuracy, and show how it compares to other similar algorithms.
This paper extends a previous market microstructure model, where we used Genetic Programming (GP) as an inference engine for trading rules, and Self Organizing Maps as a clustering machine for those rules. Experiments...
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This paper extends a previous market microstructure model, where we used Genetic Programming (GP) as an inference engine for trading rules, and Self Organizing Maps as a clustering machine for those rules. Experiments...
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This paper extends a previous market microstructure model, where we used Genetic Programming (GP) as an inference engine for trading rules, and Self Organizing Maps as a clustering machine for those rules. Experiments in that work took place under a single financial market and investigated whether its behavior is non-stationary or cyclic. Results showed that the market's behavior was constantly changing and strategies that would not adapt to these changes, would become obsolete, and their performance would thus decrease over time. However, because experiments in that work were based on a specific GP algorithm, we are interested in this paper to prove that those results are independent of the choice of such algorithms. We thus repeat our previous tests under two more GP frameworks. In addition, while our previous work surveyed only a single market, in this paper we run tests under 10 markets, for generalization purposes. Finally, we deepen our analysis and investigate whether the performance of strategies, which have not co-evolved with the market, follows a continuous decrease, as it has been previously suggested in the agent-based artificial stock market literature. Results show that our previous results are not sensitive to the choice of GP. Strategies that do not co-evolve with the market, become ineffective. However, we do not find evidence for a continuous performance decrease of these strategies.
The purpose of this study is to examine the correlations between motivation and achievement of online training course of middle and elementary school teachers, as well as the impact of variables on overall learning ac...
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This study integrated concept mapping and annotation sharing of an online summarization learning environment, this approach employed concept mapping as a scaffolding, which learners grasped the key points. It helped l...
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NULL Conventional Logic (NCL) is a Delay-Insensitive (DI) clockless paradigm and is suitable for implementing asynchronous circuits. Efficient methods of analysis are required to specify and verify such DI systems. Ba...
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NULL Conventional Logic (NCL) is a Delay-Insensitive (DI) clockless paradigm and is suitable for implementing asynchronous circuits. Efficient methods of analysis are required to specify and verify such DI systems. Based on Delay Insensitive sequential Process (DISP) specification, this paper demonstrates the application of formal methods by applying Process Analysis Toolkit (PAT) to model and verify the behavior of NCL circuits. A few useful constructs are successfully modeled and verified by using PAT. The flexibility and simplicity of the coding, simulation and verification shows that PAT is effective and applicable for NCL circuit design and verification.
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