Networks-on-Chip (NoC) enable scalability for future manycore architectures, facilitating parallel communication between multiple cores. Applications running in parallel on a NoC-based architecture can affect each oth...
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In the traffic video scene, the existence of shadows might generate negative effect on pattern analysis. This paper proposes a novel approach which adequately considers color space information to detect moving cast sh...
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In some real-world applications, data cannot be measured accurately. Uncertain graphs emerge when this kind of data is modeled by graph data structures. When the graph database is uncertain, our query is highly possib...
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A common problem of freely annotated or user contributed audio databases is the high variability of the labels, related to homonyms, synonyms, plurals, etc. Automatically re-labeling audio data based on audio similari...
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ISBN:
(纸本)9781479903573
A common problem of freely annotated or user contributed audio databases is the high variability of the labels, related to homonyms, synonyms, plurals, etc. Automatically re-labeling audio data based on audio similarity could offer a solution to this problem. This paper studies the relationship between audio and labels in a sound event database, by evaluating semantic similarity of labels of acoustically similar sound event instances. The assumption behind the study is that acoustically similar events are annotated with semantically similar labels. Indeed, for 43% of the tested data, there was at least one in ten acoustically nearest neighbors having a synonym as label, while the closest related term is on average one level higher or lower in the semantic hierarchy.
Web service selection based on quality of service (QoS) has been one of research focuses in service computing field. Current methods of service selection usually focus on a single service request or multiple service r...
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Web service selection based on quality of service (QoS) has been one of research focuses in service computing field. Current methods of service selection usually focus on a single service request or multiple service requests for co-selecting a shared service at a time, not considering the competitiveness among multiple independent service requests for the same functional Web services. A global optimal service selection model for multiple service requests, according to the matching degree (between service requests and Web services) and 0-1 integral programming, is proposed to solve the conflicts among service requests. A universal and feasible optimal service selection algorithm, named global optimal service selection for multiple requests (GOSSMR), is proposed to solve the model. Under the condition of meeting QoS requirements of service requests, too many requests selecting the same Web service at the same time can be avoided, thereby optimizing the service resources, avoiding the overload, and improving the performance of the system. The feasibility and effectiveness of the model and algorithm are verified by simulations in our work.
By introducing partial divided differences and partial inverse differences, bivariate symmetry associated continued fractions blending rational interpolation is constructed. We discuss the recursive algorithm, interpo...
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We present the 1.0 release of our paraphrase database, PPDB. Its English portion, PPDB:Eng, contains over 220 million paraphrase pairs, consisting of 73 million phrasal and 8 million lexical paraphrases, as well as 14...
We present the 1.0 release of our paraphrase database, PPDB. Its English portion, PPDB:Eng, contains over 220 million paraphrase pairs, consisting of 73 million phrasal and 8 million lexical paraphrases, as well as 14...
On-line portfolio selection has been attracting increasing interests from artificial intelligence community in recent decades. Mean reversion, as one most frequent pattern in financial markets, plays an important role...
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ISBN:
(纸本)9781577356332
On-line portfolio selection has been attracting increasing interests from artificial intelligence community in recent decades. Mean reversion, as one most frequent pattern in financial markets, plays an important role in some state-of-the-art strategies. Though successful in certain datasets, existing mean reversion strategies do not fully consider noises and outliers in the data, leading to estimation error and thus non-optimal portfolios, which results in poor performance in practice. To overcome the limitation, we propose to exploit the reversion phenomenon by robust L1-median estimator, and design a novel on-line portfolio selection strategy named "Robust Median Reversion" (RMR), which makes optimal portfolios based on the improved reversion estimation. Empirical results on various real markets show that RMR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results. Finally, RMR runs in linear time, and thus is suitable for large-scale trading applications.
This work presents a study for chemical leaching of sphalerite concentrate under various constant Fe3+ concentrations and redox potential conditions. The effects of Fe3+ concentration and redox potential on chemical l...
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This work presents a study for chemical leaching of sphalerite concentrate under various constant Fe3+ concentrations and redox potential conditions. The effects of Fe3+ concentration and redox potential on chemical leaching of sphalerite were investigated. The shrinking core model was applied to analyze the experimental results. It was found that both the Fe3+ concentration and the redox potential controlled the chemical leaching rate of sphalerite. A new kinetic model was developed, in which the chemical leaching rate of sphalerite was proportional to Fe3+ concentration and Fe3+ /Fe2+ ratio. All the model parameters were evaluated from the experimental data. The model predictions fit well with the experimental observed values.
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