The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Improving the accuracy, reducing the time to authenticate users and preserving privacy are some of the pivotal issues in smartphone security. A majority of published owner identification methods have concentrated on improving accuracy, emphasizing less on response time. Usage pattern of smartphone apps by the owner may be used as an important signature to differentiate between the legitimate user...
The huge training overhead for obtaining channel state information (CSI) at the BS has been recognized as a major challenge in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) cellular networks. To solve this problem, we propose an angular domain pilot design and channel estimation scheme to reduce the required overhead by exploiting the angle domain channel sparsity....
In P300 speller brain-computer interface (BCI), the stimulus sequence is presented to subject for several rounds to achieve reliable P300 detection. Traditionally, the number of rounds is fixed and relatively large (e.g., 15 in the Wadsworth Dataset of BCI Competition 2005), which results in low information transfer rate. In order to improve the speed of character recognition without affecting the...
The algorithm of expert evaluation of software tools to create distance courses using the Analytic Hierarchy Process is proposed, a system of criteria is developed and a hierarchical model of choice of optimal solution that improve the quality of the educational process through the use of innovative methods and technologies.
This paper investigates whether advanced neural network techniques can be applied to the detection and identification of typical targets in the context of land warfare. We collected 13 typical targets and built a detection data set. Based on the Faster R-CNN framework, we improve the detection accuracy by two ways. First, we design a neural network model with strong local modeling capabilities. Second,...
This paper proposes the use of Stacked Denoising Autoencoder to predict the direction of movement of stock indexes based on the historical and volume data of the underlying stocks. The Stacked Denoising Autoencoder is a deep learning method widely used in the field of computer vision which is capable of learning a compact feature representation of the data for stock index prediction. The Hybrid Gravitational...
API documentation is useful for developers to better understand how tocorrectly use the libraries. However, not all libraries provide gooddocumentation on API usages. To provide better documentation, existingtechniques have been proposed including program analysis-based anddata mining-based approaches. In this work, instead of mining, we aimto generate behavioral exception documentation for any given...
The Semi-Supervised Support Vector Machine (S3VM) solves a non-convex, Mixed-Integer Program (MIP). Due to difficulty in solving the problem, convex approximations have typically been used. However, existing approaches suffer from poor scalability and struggle on certain datasets, compared to graph based counterparts. The poor predictive performance suggests that for some datasets, convex approximations...
For constructing a span-lateral inhibition neural network (S-LINN) with optimal architecture and parameters for actual application, a self-organizing optimization approach is proposed in this paper to tune the architecture and parameters simultaneously. This self-organization pruning algorithm is to build a modified significant index function to evaluate the significance of hidden neurons. A preprocessing...
The academic mobility is one of key factors that enable the globalization of research and education. In this paper we study the network of ERASMUS staff and student exchange agreements between academic institutions involved in FETCH - a big European project oriented towards future education and training in computer science. The structure of the network was investigated relying on standard metrics...
Development of fast watermarking schemes for all multimedia objects is crucial to the present day research in information security. Besides speed of execution minimizing the trade-off between visual quality and robustness is another important requirement of this research domain. In view of this, a newly developed single layer feedforward network (SLFN) commonly known as Bidirectional Extreme Learning...
The complex mechanical structure and working characteristics of crane determine it is a kind of construction machinery with larger risk factors. In order to ensure the safety and reliability of the crane during operation process, also avoid serious failure which affects the efficiency and progress of the engineering project, this paper uses discrete Hopfield neural network approach to evaluate and...
Link prediction is to calculate the probability of a potential link between a pair of unlinked nodes in the future. It has significance value in both theoretical and practical. The similarity of two nodes in the networks is an essential factor to determine the probability of a potential link between them. One of the important methods with the similarity of two nodes is to consider common neighbors...
NERC Critical Infrastructure Protection (CIP) Reliability Standards apply to utilities that support the bulk electrical grid in North America and are meant to protect the Grid from cyber-attack. While Industrial facilities are not bound by the regulations outlined in NERC CIP a review of those regulations can help industrial facilities protect their infrastructure from cyber-attack. This paper reviews...
Support vector machines (SVMs) are promising methods for the prediction of the financial time-series because they use a risk function, consisting of an empirical error and a regularized term, which is derived from the structural risk minimization principle. This study applies SVM for predicting the stock price index. In addition, this study examines the feasibility of the applying SVM in financial...
Patients with major depressive disorder (MDD) who do not achieve full symptomatic recovery after antidepressant treatment have a higher risk of relapse. Compared to pharmacotherapies, electroconvulsive therapy (ECT) more rapidly produces a greater extent of response in severely depressed patients. However, prediction of which candidates are most likely to improve after ECT remains challenging. Using...
This paper addresses the estimation of pairwise supervoxel correspondences toward automatic semi-dense medical image registration. Supervoxel matching is performed through random forests (RF) with supervoxel indexes as label entities to predict matching areas in another target image. Ensuring accurate supervoxel boundary adherence requires a fine supervoxel decomposition which highly increases learning...
The increase in the amount of data acquired from the monitoring of power system components has motivated utilities to employ effective strategies for processing the information collected. Hence, salient features can be identified and efficient decisions is made. An important component of any power system is power transformers, which have the single highest value of the equipment installed in high-voltage...
In this paper, we formed prediction intervals using historical similarities, found through the direct correlation. At first, a string of 5 to 20 recent samples is correlated with a long training string of samples. Then, the highest normalized correlation values and corresponding indexes are picked. After that, the amplitudes of the matched samples are adjusted by multiplying the value with the amplitude...
Automated pose estimation is a fundamental task in computer vision. In this paper, we investigate the generic framework of Cascaded Pose Regression (CPR), which demonstrates practical effectiveness in pose estimation on deformable and articulated objects. In particular, we focus on the use of CPR for face alignment by exploring existing techniques and verifying their performances on different public...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.