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This paper introduces Aicyber's system for IALP 2016 shared task, the Dimensional Sentiment Analysis of Chinese Words. The system is an ensemble of several boosted one layer neural networks, each one is trained on a different type of Chinese word vector. Our best system mainly use position-based character-enhanced word embedding and FastText as word vectors and achieve Mean Absolute Error 0.577(1st)...
We consider the problem of estimating of the number of components that are correlated between two sets of high-dimensional data. In many applications the number of available samples is very small, in which case conventional techniques do not accurately determine the model order. Recent approaches for the sample-poor scenario are based on a combined PCA-CCA (principal component analysis-canonical correlation...
Feature extraction addresses the problem of finding the most compact and informative set of features. To maximize the effectiveness of each single feature extraction algorithm and to develop an efficient intrusion detection system, an ensemble of Linear Discriminant Analysis and Principle Component Analysis feature extraction algorithms is implemented. The experiments demonstrate that the ensemble...
Brain-Computer Interface (BCI) can be realized by translating user's thoughts into control commands to assist paralyzed persons to communicate and control electronic devices. In this work, Electroencephalographic (EEG) signals were recorded from four subjects while they perform different mental states. We present an Artificial-Neural-Network-based approach for the purpose of classifying Electroencephalographic...
This work presents a detector for a pilot contamination attack (PCA) in multi-user time-division duplex (TDD) wireless networks. We consider a scenario in which an active eavesdropper with multiple antennas attempts a PCA to a Multiple-Input-Single-Output (MISO) target system. To fend off the PCA, we propose a PCA detector which exploits an imbalance between estimated channels in two-way training...
According to a recent study poor sitting posture of the spine has been shown to lead to a variety of spinal disorders. For this reason, it is important to measure the sitting posture. In this study, we proposed a system that sitting posture classify using 3-axis accelerometer. We retrieved acceleration data from single tri-axial accelerometer attached on the back of the subject's neck in 5-types of...
Structural health monitoring, which is the process of assessing the health of instrumented structures, is becoming increasingly important as much of the world's infrastructure ages and deteriorates. Wireless sensor networks (WSNs) have the potential to deliver continuous, highly accurate structural health monitoring at a low cost. In this paper, we present a novel WSN system that monitors ambient...
In recent years, e-rehabilitation has become an emerging topic, firstly because of an increasing demand, secondly because of improved sensor systems and higher computational performance. Furthermore, due to the lack of therapists in Germany, an adequate supervision of the therapy is often impossible. A tracking of physiological parameters, such as the heart rate, can contribute to an improved evaluation...
In image classification tasks, the image is rarely represented as only a collection of raw pixels. Myriad alternative representations, from Gaussian kernels to bags-of-words to layers of a convolutional neural network, have been proposed both to decrease the dimensionality of the task and, more importantly, to move into a space which better facilitates classification. This work explores several methods...
Hand shape recognition is a challenging task because hands are deformable objects. Some techniques for hand shape recognition using Computer Vision have been proposed. The key problem is how to make hand gestures understood by computers/mobile devices. In this paper we present a study about Principal Component Analysis (PCA) used to reduce the dimensionality and extract features of images of the human...
Classifier allows the user to classify between different classes based on the features acquired. The goals and applications of different classifiers are different. As the feature selection is one of the important criteria. In this paper we introduce a method of ranking the features of one class with respect to another and it tells the user that in the training set which feature has higher ranking...
Software organizations and educational institutions rely heavily upon e-learning technologies nowadays. There is a requirement to know how numerous interventions can influence the recognized determinants of IT design, acceptance and usage. The statistical meta-analysis of several prototypes, particularly the technology acceptance model (TAM) studies have shown that it is a valid and robust model that...
Face recognition has an important role in the age of information technology to improve comfort and security. Various face recognition algorithms are proven to be effective if done under a controlled environment. However, recognition performance will decrease significantly in the uncontrolled environment, for example caused by illumination variation. This paper discusses the approach to deal with varying...
Feature extraction addresses the problem of finding the most compact and informative set of features. To maximize the effectiveness of each single feature extraction algorithm and to develop an efficient intrusion detection system, an ensemble of Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) feature extraction algorithms is implemented. This ensemble PCA-LDA method has...
A new temporal sampling method for human action recognition using depth maps is proposed. The depth map frames are projected onto three orthogonal Cartesian planes respectively and the absolute difference between two successive projected ones is stacked to form the motion energy. Three sub-action frame sequences are selected accordingly, which constructs Adaptive Temporal Sampling (ATS) descriptor,...
The effect of using autoencoders for dimensionality reduction of a medical data set is investigated. A stack of two autoencoders has been trained for popular benchmark medical data set for dermatological disease diagnosis. The improvement of the presented approach has been visualized by the Principal Component Analysis method. Results shows that the use of a autoencoders significantly improves the...
This paper develops a Linear Discriminant Analysis based face recognition system in the Discrete Cosine Transform (DCT) domain as a departure from the traditional analysis in the spatial domain. In the training mode, the truncated DCT coefficients are used to find discriminating features for all the subjects in the image database. The compact representation of the truncated DCT coefficients leads...
Image classification is a preferred method for agriculture product identification since the product is intact from the process. Many published works have adopted image classification techniques for identifying rice seed varieties. Based on a classification algorithm and given sample features vectors, a classification model is built from a set of optimal parameters and operators. However, the obtained...
In order to identify a large number of very similar objects, a novel recognition approach is proposed by mean of combination of two dynamic grouping algorithms, the visual processing mechanism, PCA and multi-pathway SVM. The samples have been segmented to appropriate groups by grouping features, and then features with rotation invariance and translation invariance of each group are extracted. Finally,...
Recent learning-based face super-resolution methods, such as Yang's Sparse Coding Super-Resolution (SCSR) are promising with sharp edges visually. But it also leads to obvious artifacts. In order to eliminate the artifacts, Online Dictionary Learning (ODL) algorithm is introduced in the dictionary learning phase to generate accurate overcomplete dictionary. On the other hand, the reconstruction regularization...
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