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Motor imagery (MI) based on brain computer interfaces (BCIs) have been widely applied for upper limb motor rehabilitation. Due to the fact that a large number of disabled people need to restore or improve walking ability, it is also important to investigate the use of MI-based BCIs for lower limb motor rehabilitation. The brain activity of lower limb MI is more difficult to detect because of low reliability...
Static biometrie images are not private and may be copied to make a physical and digital stimulant without an owner being aware of it, therefore the search for efficient solutions of personal authentication via dynamic biometric characteristics is still in process. A series of computational experiments based on biometric data obtained through a handwritten signature and keystroke dynamics is carried...
Anomaly-based Network Intrusion Detection Systems (NIDSs) are a common security defense for modern networks. The success of their operation depends upon vast quantities of training data. However, one major limitation is the inability of NIDS to be reliably trained using imbalanced datasets. Network observations are naturally imbalanced, yet without substantial data pre-processing, NIDS accuracy can...
The goal of complex event detection is to automatically detect whether an event of interest happens in temporally untrimmed long videos which usually consist of multiple video shots. Observing some video shots in positive (resp. negative) videos are irrelevant (resp. relevant) to the given event class, we formulate this task as a multi-instance learning (MIL) problem by taking each video as a bag...
The evolution of convolutional neural networks (CNNs) into more complex forms of organization, with additional layers, larger convolutions and increasing connections, established the state-of-the-art in terms of accuracy errors for detection and classification challenges in images. Moreover, as they evolved to a point where Gigabytes of memory are required for their operation, we have reached a stage...
Image enhancement is a common pre-processing step before the extraction of biometric features from a fingerprint sample. This can be essential especially for images of low image quality. An ideal fingerprint image enhancement should intend to improve the end-to-end biometric performance, i.e. the performance achieved on biometric features extracted from enhanced fingerprint samples. We use a model...
This paper presents a new method for the reconstruction of images from samples located at non-integer mesh positions. This is a common scenario for many image processing applications such as multi-image super-resolution, frame-rate up-conversion, or virtual view synthesis in multi-camera systems. The proposed method consists of an iterative procedure that employs adaptive denoising in order to reduce...
In this paper, we propose a real-time detection algorithm using a MCT AdaBoost classifier which detects two-wheeler in a blind spot. The proposed algorithm uses a cascade classifier generated by AdaBoost learning based on the MCT feature vector. The MCT AdaBoost classifier is composed of weak classifiers as many as the number of pixels of the detection window, and each pixel becomes a weak classifier...
Machine learning has become one of the go-to methods for solving problems in the field of networking. This development is driven by data availability in large-scale networks and the commodification of machine learning frameworks. While this makes it easier for researchers to implement and deploy machine learning solutions on networks quickly, there are a number of vital factors to account for when...
Argumentation mining aims at automatically extracting the premises-claim discourse structures in natural language texts. There is a great demand for argumentation corpora for customer reviews. However, due to the controversial nature of the argumentation annotation task, there exist very few large-scale argumentation corpora for customer reviews. In this work, we novelly use the crowdsourcing technique...
Fault detection method using k nearest neighbor rule has shown its advantages in dealing with nonlinear, multi-mode, and nonGaussian distributed data. Once a fault is detected in industrial processes, recognizing fault variables becomes the crucial task subsequently. Recently, the method of fault variables recognition using k nearest neighbor reconstruction (FVR-kNN) has been reported. However, the...
In light of the powerful learning capability of deep neural networks (DNNs), deep (convolutional) models have been built in recent years to address the task of salient object detection. Although training such deep saliency models can significantly improve the detection performance, it requires large-scale manual supervision in the form of pixel-level human annotation, which is highly labor-intensive...
Storage reliability of the ammunition dominates the efforts in achieving the mission reliability goal. Prediction of storage reliability is important in practice to monitor the ammunition quality. In this paper we provided an integrated method where particle swarm optimization (PSO) algorithm is applied to adjust and optimize the BP neural network global parameters (weights and thresholds). The experiment...
Human pose analysis is presently dominated by deep convolutional networks trained with extensive manual annotations of joint locations and beyond. To avoid the need for expensive labeling, we exploit spatiotemporal relations in training videos for self-supervised learning of pose embeddings. The key idea is to combine temporal ordering and spatial placement estimation as auxiliary tasks for learning...
Semi-supervised learning (SSL) is an import paradigm to make full use of a large amount of unlabeled data in machine learning. A bottleneck of SSL is the overfitting problem when training over the limited labeled data, especially on a complex model like a deep neural network. To get around this bottleneck, we propose a bio-inspired SSL framework on deep neural network, namely Deep Growing Learning...
This paper proposes a novel approach for segmenting primary video objects by using Complementary Convolutional Neural Networks (CCNN) and neighborhood reversible flow. The proposed approach first pre-trains CCNN on massive images with manually annotated salient objects in an end-to-end manner, and the trained CCNN has two separate branches that simultaneously handle two complementary tasks, i.e.,...
In this paper, we propose a novel Superimposed Training (ST) technique for Orthogonal Frequency Division Multiplexing (OFDM) systems, where data and training signals are divided in orthogonal code domains in order to mitigate the interference between them. The data signal is partitioned into disjoint bins, which are spread using orthogonal codes and multiplexed in code domain. Then, the new data signal...
During the last decade, several Internet of Things (IoT) applications has been developed to facilitate machine-to-human and machine-to-machine communication with the physical world by integrating both digital and physical entities through the internet. However, multiple important challenges need to be addressed in order to take the full advantage of these applications. One of the most important of...
A great deal of research has been done to develop data-driven soft-sensors for quality estimation and control. However, a soft-sensor does not always function well. If estimates of the soft-sensor are blindly believed and used in a control system, the product quality and process performance will be deteriorated. To solve this issue, an on-line reliability evaluation method of soft-sensors using the...
Convolutional neural network has made major progress in classification problems of general object recognition. Classification of facial images is one of them. However, expression of the networks for classification depends on datasets and the network model, which is vulnerable to changes in the adaptation range. We propose the network that has the two convolutional parts of pre-trained CNN by transfer...
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