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We develop an intelligent credit rating system that can provide debtors' rating information without involving credit rating agencies. Several models are used for credit scoring in our work, including the Duffie's model, logistic regression, and random forest. We compare the performance of these models and build an in-depth understanding of the evaluation of credit rating. Furthermore, we propose a...
With the prevalence of Android-based mobile devices, automated testing for Android apps has received increasing attention. However, owing to the large variety of events that Android supports, test input generation is a challenging task. In this paper, we present a novel approach and an open source tool called EHBDroid for testing Android apps. In contrast to conventional GUI testing approaches, a...
Listening to music can reduce stress. This research is to study the development of music mood application called DeMuse. It mainly concerns with the users' favorable and recommended music genre. In addition, DeMuse will be presented in health and fitness category of mood music based mobile application. In order to complete DeMuse, it will carry out the identification of the features for the particular...
Automatic loop-invariant generation is important in program analysis and verification. In this paper, we propose to generate loop-invariants automatically through learning and verification. Given a Hoare triple of a program containing a loop, we start with randomly testing the program, collect program states at run-time and categorize them based on whether they satisfy the invariant to be discovered...
New and unseen network attacks pose a great threat to the signature-based detection systems. Consequently, machine learning-based approaches are designed to detect attacks, which rely on features extracted from network data. The problem is caused by different distribution of features in the training and testing datasets, which affects the performance of the learned models. Moreover, generating labeled...
Node.js becomes increasingly popular in building server-side JavaScript applications. It adopts an event-driven model, which supports asynchronous I/O and non-deterministic event processing. This asynchrony and non-determinism can introduce intricate concurrency bugs, and leads to unpredictable behaviors. An in-depth understanding of real world concurrency bugs in Node.js applications will significantly...
Recently, sparse representation based classifiers (SRC) and collaborative representation based classifiers (CRC) have been shown to give very good performance under controlled scenarios. However, in practical applications, face recognition often encounters variations in illumination, expression, noise and occlusion, which cause severe performance degradation (due to the outliers in testing). In this...
Based on the method of Skeletonization, the concept of influence factor is introduced in this paper. A method for trimming the fat from a Back Propagation (BP) neural network is proposed by modifying weight and influence factor alternately, and node with the least influence factor was deleted. This method is applied to modeling superheated steam temperature system of plant station. Simulation results...
This paper introduces an intelligent integrated nondestructive testing system based on active the infrared thermal wave detection. The excitation heat source of the thermal wave includes ultrasonic excitation and electromagnetic excitation and exploiting the powerful resources of computer system and LabVIEW software to develop the virtual instrument. The integrated nondestructive testing system based...
Deep Convolutional Neural Networks (CNNs) achieve substantial improvements in face detection in the wild. Classical CNN-based face detection methods simply stack successive layers of filters where an input sample should pass through all layers before reaching a face/non-face decision. Inspired by the fact that for face detection, filters in deeper layers can discriminate between difficult face/non-face...
In this paper, a new similar stock prices patterns extraction method is proposed for short-term stock trend forecasting. Different from Euclidean distance and conventional dynamic time warping (DTW) based historical features extraction, the new feature extraction method is developed based on a modified DTW approach, which allows the length of historical sub-sequences to be in a suitable range and...
With the rapid development of Internet technologies such as cloud computing and big data, the scales of distributed information systems in big companies have grown to enormous sizes. Automatic detection and diagnosis of system faults in the large-scale information systems is complicated and important in both practice and research. In this paper, we propose a Graph-based Fault Diagnosis approach in...
For solving the problems of modern intelligence algorithms such as slow convergence and low precision, a new algorithm based on bionics principle has been proposed which is inspired by the foraging behavior of seven-spot ladybirds in the nature. By analyzing the bionic principle of Seven-spot ladybird Optimization(SLO), we simulate the region search pattern of predation of seven-spot ladybirds, combining...
Conventional reliability demonstration test based on statistical method is widely used in industry as it is simple and convenient to apply. But for the products with high reliability and long life, this test method fails to satisfy the demand for short cycle and low cost, and is liable to cause the phenomenon of over-test and short-test. This paper gives a step-stress accelerated sequential probability...
Accelerated degradation testing (ADT) Bayesian optimization design can determine prior distribution of model parameters by prior information, which avoiding the uncertainty to the result of design brought by assuming parameter selection arbitrarily. However, different people may have different tendencies when choosing prior distributions through the same prior information. It may result in different...
A surface layer formation by Cs+ bombardment was observed during ultra-thin oxynitride gate dielectrics depth profiling. A significant thickness change relative to ultra-thin layer of oxynitride was noticed when testing a bombarded sample after a period of time. Cs, O and N depth profiles were examined by Dynamic Secondary Ion Mass Spectrometry (DSIMS). The bombarded sample and new sample were investigated...
As a breakthrough in artificial intelligence, deep learning allows for the automatic extraction of features without considerable prior knowledge and the determination of the complex non-linear relationship of the input parameters. Owing to these advantages, deep neural networks (DNNs) are superior to traditional artificial neural networks with shallow architectures, and are thus becoming widely used...
An interesting target detection framework with transferred deep convolutional neural network (CNN) is proposed. For CNN, many labeled samples are needed to train the multi-layer network. However, for target detection tasks, only few target spectral signatures are available, or they are unknown in anomaly detection. In this work, we employ a reference data and further generate pixel-pairs to enlarge...
The discrete time based transmission constraints are commonly used in transmission constrained unit commitment problems. However, the security operation requirements may be not satisfied by the solution of the discrete time based formulation for the instantaneous power flow may not be always within the secure limits. To address the problem, the paper proposes a way to revise the formulation of the...
This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results. A new DIVerse 2K resolution image dataset (DIV2K) was employed. The challenge had 6 competitions divided into 2 tracks with 3 magnification factors each. Track 1 employed the standard bicubic downscaling setup, while Track 2...
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