As an alternative to question answering methods based on feature engineering, deep learning approaches such as convolutional neural networks (CNNs) and Long Short-Term Memory Models (LSTMs) have recently been proposed...
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Analyzing the crash reports recorded upon software crashes is a critical activity for software quality assurance. Predicting whether or not the fault causing the crash (crashing fault for short) resides in the stack t...
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ISBN:
(纸本)9781728149837
Analyzing the crash reports recorded upon software crashes is a critical activity for software quality assurance. Predicting whether or not the fault causing the crash (crashing fault for short) resides in the stack traces of crash reports can speed-up the program debugging process and determine the priority of the debugging efforts. Previous work mostly collected label information from bug-fixing logs, and extracted crash features from stack traces and source code to train classification models for the Identification of Crashing Fault Residence (ICFR) of newly-submitted crashes. However, labeled data are not always fully available in real applications. Hence the classifier training is not always feasible. In this work, we make the first attempt to develop a cross project ICFR model to address the data scarcity problem. This is achieved by transferring the knowledge from external projects to the current project via utilizing a state-of-the-art Balanced Distribution Adaptation (BDA) based transfer learning method. BDA not only combines both marginal distribution and conditional distribution across projects but also assigns adaptive weights to the two distributions for better adjusting specific cross project pair. The experiments on 7 software projects show that BDA is superior to 9 baseline methods in terms of 6 indicators overall.
Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CCC) together. In this paper, we dissect the interactio...
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Deep convolutional neural networks based methods have brought great breakthrough in images classification, which provides an end-to-end solution for handwritten Chinese character recognition(HCCR) problem through lear...
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When investigating the spreading of a piece of information or the diffusion of an innovation, we often lack information on the underlying propagation network. Reconstructing the hidden propagation paths based on the o...
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In many realistic scenarios,such as political election and viral marketing,two opposite opinions,i.e.,positive opinion and negative opinion,spread simultaneously in the same social networks[1,2].Consequently,to achiev...
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In many realistic scenarios,such as political election and viral marketing,two opposite opinions,i.e.,positive opinion and negative opinion,spread simultaneously in the same social networks[1,2].Consequently,to achieve good word-of-mouth effect,it is desired to maximize the spread of posi-
The problem of network function computation over a directed acyclic network is investigated in this paper. In such a network, a sink node desires to compute with zero error a target function, of which the inputs are g...
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Non-blind audio bandwidth extension is a standard technique within contemporary audio codecs to efficiently code audio signals at low bitrates. In existing methods, in most cases high frequencies signal is usually gen...
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Non-blind audio bandwidth extension is a standard technique within contemporary audio codecs to efficiently code audio signals at low bitrates. In existing methods, in most cases high frequencies signal is usually generated by a duplication of the corresponding low frequencies and some parameters of high frequencies. However, the perception quality of coding will significantly degrade if the correlation between high frequencies and low frequencies becomes weak. In this paper, we quantitatively analyse the correlation via computing mutual information value. The analysis results show the correlation also exists in low frequency signal of the context dependent frames besides the current frame. In order to improve the perception quality of coding, we propose a novel method of high frequency coarse spectrum generation to improve the conventional replication method. In the proposed method, the coarse high frequency spectrums are generated by a nonlinear mapping model using deep recurrent neural network. The experiments confirm that the proposed method shows better performance than the reference methods.
Learning to rank has been intensively studied and widely applied in information retrieval. Typically, a global ranking function is learned from a set of labeled data, which can achieve good performance on average but ...
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In this work we investigate energy complexity, a Boolean function measure related to circuit complexity. Given a circuit C over the standard basis {∨2, ∧2, ¬}, the energy complexity of C, denoted by EC(C), is t...
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