Deep neural networks are powerful, but they also have short-comings such as their sensitivity to adversarial examples, noise, blur, occlusion, etc. Moreover, ensuring the reliability and robustness of deep neural netw...
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Grid technology is the collection of tools and services that integration widely distributed, heterogeneous, and multi-organizational resource. For better understanding grid’s structure, an object-oriented framework o...
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The existing deep learning based state-of-theart scene text detection methods treat scene texts a type of general objects, or segment text regions directly. The latter category achieves remarkable detection results on...
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Applying SRGMs (software Reliability Growth Models) to real projects is a major concern in software reliability. Sometimes, it is hard to decide the best model for a specific project. Researchers have made a first ste...
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Elastic scaling in response to changes on demand is a main benefit of serverless computing. When bursty workloads arrive, a serverless platform launches many new containers and initializes function environments (known...
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Cloud computing is a successful business model and utility paradigm which enables people to use computing power via Internet at anytime anywhere. How to schedule a task to cloud computing is an important issue. In clo...
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Cloud computing is a successful business model and utility paradigm which enables people to use computing power via Internet at anytime anywhere. How to schedule a task to cloud computing is an important issue. In cloud computing some users focus execution time the others maybe expense on tasks execution, so how to satisfy the users and achieve the balance between execution time and expense is very vital. Based on classic Min-Min algorithm, an efficient task scheduling algorithm was proposed where Cobb-Douglas utility function was employed to express user.s preference to time and cost, and maximized user.s utility. Multi-objective optimal solution has been obtained via the utility function. Experimental results evaluating such a mechanism show that the algorithm validate vividly.
Most present research into facial expression recognition focuses on the visible spectrum, which is sen- sitive to illumination change. In this paper, we focus on in- tegrating thermal infrared data with visible spectr...
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Most present research into facial expression recognition focuses on the visible spectrum, which is sen- sitive to illumination change. In this paper, we focus on in- tegrating thermal infrared data with visible spectrum images for spontaneous facial expression recognition. First, the ac- tive appearance model AAM parameters and three defined head motion features are extracted from visible spectrum im- ages, and several thermal statistical features are extracted from infrared (IR) images. Second, feature selection is per- formed using the F-test statistic. Third, Bayesian networks BNs and support vector machines SVMs are proposed for both decision-level and feature-level fusion. Experiments on the natural visible and infrared facial expression (NVIE) spontaneous database show the effectiveness of the proposed methods, and demonstrate thermal 1R images' supplementary role for visible facial expression recognition.
software product line (SPL) engineering is increasingly being adopted in safety-critical systems. It is highly desirable to rigorously show that these systems are designed correctly. However, formal analysis for SPL...
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software product line (SPL) engineering is increasingly being adopted in safety-critical systems. It is highly desirable to rigorously show that these systems are designed correctly. However, formal analysis for SPLs is more difficult than for single systems because an SPL may contain a large number of individual systems. In this paper, we propose an efficient model-checking technique for SPLs using induction and a SAT (Boolean satisfiability problem) solver. We show how an induction-based verification method can be adapted to the SPLs, with the help of a SAT solver. To combat the state space explosion problem, a novel technique that exploits the distinguishing characteristics of SPLs, called feature cube enlargement, is proposed to reduce the verification efforts. The incremental SAT mechanism is applied to further improve the efficiency. The correctness of our technique is proved. Experimental results show dramatic improvement of our technique over the existing binary decision diagram (BDD)-based techniques.
The availability of relevance feedback is held back by the problem of the imbalance and limited size of labeled training data, as well as the real-time requirement of online interaction demands. In this paper, we prop...
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ISBN:
(纸本)9780889867178
The availability of relevance feedback is held back by the problem of the imbalance and limited size of labeled training data, as well as the real-time requirement of online interaction demands. In this paper, we propose a relevance feedback algorithm called active biased SVM (BSVM) learning, in which biased classification and active learning are employed to address these difficulties. The algorithm is applied to content-based sketch retrieval (CBSR), and the experiments prove both the effectiveness and efficiency of the proposed approach.
Multilevel security policies aim at only confidentiality assurance, with less consideration on integrity assurance and weakness in expressing channel control policies. Besides, the trusted subjects it introduces to ha...
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