Dimpler Detection Using Facial Landmarks in Videos

Dimpler is one of the body languages that contributes to the emotion contempt when the action appears unilaterally, and to boredom. It is one of the subtle expressions that people did in everyday life. Although the universal seven microexpressions are clear signs of concealed emotions, subtle expressions such as dimple probably occur much more frequently than universal expressions. The dimpler muscle pulls the lip corners to the side and creates a dimple in the cheek. This study developed a dimpler detection model using 2D facial landmarks. 3 videos of totally 6 minutes were recorded while each video involved dimple and non-dimple expressions. Cheek and lip landmark were detected from each frame of the video using the Face-Alignment facial landmark detector. Features such as horizontal lip distance, vertical lip distance and lip ratio served as inputs to a linear Support Vector Machine (SVM) model. The SVM approach achieved a performance of accuracy 82.37%, sensitivity 86.58% and specificity of 84.29%.The results suggest that horizontal lip distance, vertical lip distance and lip ratio are useful features for the detection of dimpler in videos

Data selection in EEG signals classification

The alcoholism can be detected by analyzing electroencephalogram (EEG) signals. However, analyzing multi-channel EEG signals is a challenging task, which often requires complicated calculations and long execution time. This paper proposes three data selection methods to extract representative data from the EEG signals of alcoholics. The methods are the principal component analysis based on graph entropy (PCA-GE), the channel selection based on graph entropy (GE) difference, and the mathematic combinations channel selection, respectively. For comparison purposes, the selected data from the three methods are then classified by three classifiers: the J48 decision tree, the K-nearest neighbor and the Kstar, separately. The experimental results show that the proposed methods are successful in selecting data without compromising the classification accuracy in discriminating the EEG signals from alcoholics and non-alcoholics. Among them, the proposed PCA-GE method uses only 29.69 % of the whole data and 29.5 % of the computation time but achieves a 94.5 % classification accuracy. The channel selection method based on the GE difference also gains a 91.67 % classification accuracy by using only 29.69 % of the full size of the original data. Using as little data as possible without sacrificing the final classification accuracy is useful for online EEG analysis and classification application design

A Novel Completely Local Repairable Code Algorithm Based on Erasure Code

Hadoop Distributed File System (HDFS) is widely used in massive data storage. Because of the disadvantage of the multi-copy strategy, the hardware expansion of HDFS cannot keep up with the continuous volume of big data. Now, the traditional data replication strategy has been gradually replaced by Erasure Code due to its smaller redundancy rate and storage overhead. However, compared with replicas, Erasure Code needs to read a certain amount of data blocks during the process of data recovery, resulting in a large amount of overhead for I/O and network. Based on the Reed-Solomon (RS) algorithm, we propose a novel Completely Local Repairable Code (CLRC) algorithm. By grouping RS coded blocks and generating local check blocks, CLRC algorithm can optimize the locality of the RS algorithm, which can reduce the cost of data recovery. Evaluations show that the CLRC algorithm can reduce the bandwidth and I/O consumption during the process of data recovery when a single block is damaged. What\u27s more, the cost of decoding time is only 59% of the RS algorithm

Analysis of epileptic EEG signals with simple random sampling J48 algorithm

This paper describes the application of a Simple Random Sampling J48 (SRS-J48) model for classification of electroencephalogram (EEG) signals. Decision making is performed in two stages: feature extraction and classification. Eight statistical features are extracted from a two-level sample set model based on SRS technique and then classified by the J48 decision tree algorithm in Weka. The classification accuracy of the SRS-J48 is 16.35% higher than that of J48 according to the five groups of experiment with only 13% execution time on average. Besides, the proposed SRS-J48 algorithm has competitive or even better results on some of the experimental groups than Siuly’s Simple Random Sampling-Least Square-Support Vector Machine (SRS-LS-SVM)

Dimpler Detection Using Facial Landmarks in Videos

Dimpler is one of the body languages that contributes to the emotion contempt when the action appears unilaterally, and to boredom. It is one of the subtle expressions that people did in everyday life. Although the universal seven microexpressions are clear signs of concealed emotions, subtle expressions such as dimple probably occur much more frequently than universal expressions. The dimpler muscle pulls the lip corners to the side and creates a dimple in the cheek. This study developed a dimpler detection model using 2D facial landmarks. 3 videos of totally 6 minutes were recorded while each video involved dimple and non-dimple expressions. Cheek and lip landmark were detected from each frame of the video using the Face-Alignment facial landmark detector. Features such as horizontal lip distance, vertical lip distance and lip ratio served as inputs to a linear Support Vector Machine (SVM) model. The SVM approach achieved a performance of accuracy 82.37%, sensitivity 86.58% and specificity of 84.29%.The results suggest that horizontal lip distance, vertical lip distance and lip ratio are useful features for the detection of dimpler in videos

Epileptogenic focus detection in intracranial EEG based on delay permutation entropy

Epileptogenic localization is a critical factor for successful epilepsy surgery. Determining the epileptogenic hippocampus with single channel intracranial electroencephalography (iEEG) recording is beneficial to decrease the risk of infection compared with that based on multi-channel iEEGs. Delay permutation entropy (DPE) methodology is presented in this study to measure iEEG with different delay lag based on focal epileptogenic zone. A total of 1600 20-s epileptic iEEG are evaluated and are used as features to classify epileptogenic and non-epileptogenic zone. Experimental results show that the DPE index of epileptogenic iEEG is significant lower than that of non-epileptogenic hemisphere when delay lag ranges from 5 to 30 (p=0.01). In addition, the accuracy of identifying epileptogenic region with the DPE index is increased when the delay lag between 5 and 25, compared to the performance of the PE index

Analyzing EEG signals using graph entropy based principle component analysis and J48 decision tree

This paper proposed a method using principle component analysis based on graph entropy (PCA-GE) and J48 decision tree on electroencephalogram (EEG) signals to predict whether a person is alcoholic or not. Analysis is performed in two stages: feature extraction and classification. The principle component analysis (PCA) chooses the optimal subset of channels based on graph entropy technique and the selected subset is classified by the J48 decision tree in Weka. K-nearest neighbor (KNN) and support vector machine (SVM) in R package are also used for comparison. Experimental results show that the proposed PCA-GE method is successful in selecting a subset of channels, which contributes to the high accuracy and efficiency in the classification of alcoholics and non-alcoholics

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Unsupervised classification of epileptic EEG signals with multi scale K-means algorithm

Most epileptic EEG classification algorithms are supervised and require large training data sets, which hinders its use in real time applications. This paper proposes an unsupervised multi-scale K-means (MSK-means) algorithm to distinguish epileptic EEG signals from normal EEGs. The random initialization of the K-means algorithm can lead to wrong clusters. Based on the characteristics of EEGs, the MSK-means algorithm initializes the coarse-scale centroid of a cluster with a suitable scale factor. In this paper, the MSK-means algorithm is proved theoretically being superior to the K-means algorithm on efficiency. In addition, three classifiers: the K-means, MSK-means and support vector machine (SVM), are used to discriminate epileptic EEGs from normal EEGs using six features extracted by the sample entropy technique. The experimental results demonstrate that the MSK-means algorithm achieves 7% higher accuracy with 88% less execution time than that of K-means, and 6% higher accuracy with 97% less execution time than that of the SVM

High-Performance Ternary NiCoMo Electrocatalyst with Three-Dimensional Nanosheets Array Structure

Oxygen evolution reaction is a key process in hydrogen production from water splitting. The development of non-noble metal electrode materials with high efficiency and low cost has become the key factor for large-scale hydrogen production. Binary NiCo-layered double hydroxide (LDH) has been used as a non-noble metal electrocatalyst for OER, but its overpotential is still large. The microstructure of the catalyst is tuned by doping Mo ions into the NiCo-LDH/NF nanowires to form ternary NiCoMo-LDH/NF nanosheet catalysts for the purpose of enhancing the active sites and reducing the initial overpotential. Only 1.5 V (vs. reversible hydrogen electrode (RHE), ≈270 mV overpotential) is required to achieve a catalytic current density of 10 mA cm−2 and a small Tafel slope of 81.46 mV dec−1 in 1 M KOH solution, which manifests the best performance of NiCo-based catalysts reported up to now. Electrochemical analysis and micro-morphology show that the high catalytic activity of NiCoMo-LDH/NF is attributable to the change of the microstructure. The interconnected nanosheet arrays have the obvious advantages of electrolyte diffusion and ion migration. Thus, the active sites of catalysts are significantly increased, which facilitates the adsorption and desorption of intermediates. We conclude that NiCoMo-LDH/NF is a promising electrode material for its low cost and excellent electrocatalytic properties