This paper primarily designates the isolated words transfiguration in the Kannada language based on a few techniques like Mel Frequency Cepstral Coefficients (MFCCs), Euclidean distance measure, Wavelet Transforms and...
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In face-To-face conversations, facial expressions help humans communicate nonverbally. Since the early 1990s, academics' focus on automatic facial expression detection, which is essential for human-machine interfa...
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In the realm of defect prediction in complex systems, the integration of advanced machine learning techniques holds promise for enhancing accuracy and efficiency. This research introduces a novel approach by combining...
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Air pollution, a major global concern resulting in numerous annual fatalities, has been associated with various health disorders. This study focuses on understanding the impact of residing in heavily polluted cities o...
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As users are interacting with a large of mobile apps under various usage contexts, user involvements in an app design process has become a critical issue. Despite this fact, existing apps or app store platforms only p...
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ISBN:
(纸本)9781450318990
As users are interacting with a large of mobile apps under various usage contexts, user involvements in an app design process has become a critical issue. Despite this fact, existing apps or app store platforms only provide a limited form of user involvements such as posting app reviews and sending email reports. While building a unified platform for facilitating user involvements with various apps is our ultimate goal, we present our preliminary work on handling developers’ information overload attributed to a large number of app comments. To address this issue, we first perform a simple content analysis on app reviews from the developer’s standpoint. We then propose an algorithm that automatically identifies informative reviews reflecting user involvements. The preliminary evaluation results document the efficiency of our algorithm.
Within the scope of the course computerscience in Mechanical engineering, a blended learning concept is used in order to teach students of mechanical engineering the basic principles of computerscience. This concept...
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Stream processing applications continuously process large amounts of online streaming data in real time or nearreal time. They have strict latency constraints. However, the continuous processing makes them vulnerable ...
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Stream processing applications continuously process large amounts of online streaming data in real time or nearreal time. They have strict latency constraints. However, the continuous processing makes them vulnerable to any failures,and the recoveries may slow down the entire processing pipeline and break latency constraints. The upstream backupscheme is one of the most widely applied fault-tolerant schemes for stream processing systems. It introduces complexbackup dependencies to tasks, which increases the difficulty of controlling recovery latencies. Moreover, when dependenttasks are located on the same processor, they fail at the same time in processor-level failures, bringing extra recovery latencies that increase the impacts of failures. This paper studies the relationship between the task allocation and therecovery latency of a stream processing application. We present a correlated failure effect model to describe the recoverylatency of a stream topology in processor-level failures under a task allocation plan. We introduce a recovery-latency awaretask allocation problem （RTAP） that seeks task allocation plans for stream topologies that will achieve guaranteed recoverylatencies. We discuss the difference between RTAP and classic task allocation problems and present a heuristic algorithmwith a computational complexity of O（n log2 n） to solve the problem. Extensive experiments were conducted to verify thecorrectness and effectiveness of our approach. It improves the resource usage by 15%-20% on average.
Here we present the SimCon tool to enable evaluators of pervasive applications to rapidly place and configure context sources within a Virtual Reality Environment to conduct repeatable evaluations early in the develop...
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We propose a semi-supervised framework to handle diverse data formats or data with mixed-type attributes. Our preliminary results in clustering data with mixed numerical and categorical attributes show that the propos...
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