nils-herrmann/clean_scirepeval huggingface dataset:
EEG analysis for acoustic quality evaluation using PCA and FDA

In this study, we propose the quantitative evaluation method of acoustic quality by using electroencephalogram (EEG). We analyze the EEG observed when subjects are listening to music by 3 kinds of stereo speaker system with different acoustic property. In this paper, as the first step of quantification, we analyze whether the difference of acoustic quality appear EEG by principal component analysis, analysis of variance and Fisher discriminant analysis. As a result, we confirmed that 1st principal component of EEG contains the imformation of acoustic quality evaluation which we can not obtain from questionnaires, and the possibility that we can use EEG for quantitative evaluation of acoustic quality.
A feature extraction of the EEG during listening to the music using the factor analysis and neural networks

Recently in the world, the research of the electroencephalogram (EEG) interface is done, because it has the possibility to realize an interface that can be operated without special knowledge and technology by using the EEG as a means of the interface. As one of the EEG interface, as for a goal for the final of this research, the EEG control system by any music is constructed. However, the EEG control by music is very difficult because it does not know the music and the causal relation of the EEG clearly. Therefore, the EEG analysis and music analysis is absolutely imperative in this system. In this paper, the EEG analysis method by using the FA and the NN is proposed. The FA is used for extracting the characteristics data of the EEG. The NN is used for estimating extracted the characteristics data of the EEG. Moreover teacher signal data of the NN uses the data of the characteristics data of the music. The characteristics data of music is extracted by using the Bark scale analysis. Finally, in order to show the effectiveness of the proposed method, classifying the EEG pattern is done computer simulations. The EEG pattern is 4 conditions, which are listening to Rock music, Schmaltzy Japanese ballad music, Healing music, and Classical music.
Face information processing by fast statistical learning algorithm

In this paper , we propose a new statistical learning algorithm. This study quantitatively verifies the effectiveness of its feature extraction performance for face information processing. Simple-FLDA is an algorithm based on a geometrical analysis of the Fisher linear discriminant analysis. As a high-speed feature extraction method, the present algorithm in this paper is the improved version of Simple-FLDA. First of all, the approximated principal component analysis (learning by Simple-PCA) that uses the mean vector of each class is calculated. Next, in order to adjust within-class variance in each class to 0, the vectors in the class are removed. By this processing, it becomes high-speed feature extraction method than Simple-FLDA. The effectiveness is verified by computer simulations using face images.
Drift ice recognition using remote sensing data by neural networks

In recent years, observation of a wide variety in the Earth's surface can be done by improvement of remote sensing technology. The purpose of the paper is to recognize a drift ice as thick ice, thin ice, and sea using synthetic aperture radar (SAR) images. The recognition of the drift ice is achieved by using neural networks (NN). The neural network applies two methods, a BP trained neural network and a self-organizing map. Training data are image features extracted from SAR images. There are three methods for extracting the features: Fourier transform, high-order autocorrelation function (HACF), and image features based on a run length method. We carry out a comparative experiment, and demonstrate their effectiveness by means of computer simulation.
Study on association between user's personality and individual characteristic of left prefrontal pole EEG activity

This paper introduces a method for discussing the association between human personality based on egogram scores and the results of classifying the electroencephalogram (EEG) patterns while listening to the music. The egogram based on psychological testing is used for analyzing his/her personality. The frequencies of the EEG analyzed are the components that contain significant and immaterial information and have different importance. We express the different importance through the weight value on frequencies using real-coded genetic algorithm. Then, the EEG patterns, which are determined based on the evaluation of the impression on the music, are classified using the k-nearest neighbor method. Finally, we discuss the association between his/her personality and the individual characteristic in the EEG analysis. An interesting tendency was that the false-detection accuracy of the EEG pattern, meaning the response on negative stimuli, did not become low when the score and level on ego states of Adult was extreme.
An approximation approach for rendering visual flickers in SSVEP-based BCI using monitor refresh rate

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to realize a direct communication between the human brain and the external environment in practical situations. In the conventional stimulus presentation approach, which requires a constant period of stimulation, the number of frequencies that can be presented on a computer monitor is always limited by the refresh rate of a monitor. Although an alternative approach that uses a variable on/off frame number to approximate a target flickering stimulus has been proposed in our recent study, a direct comparison between SSVEPs elicited by the conventional constant period approach and the approximation approach is still missing. This study aims to compare the amplitude, signal-to-noise ratio (SNR) and target identification accuracy of SSVEPs elicited using these two approaches with a monitor at two refresh rates (75Hz and 120Hz). Results of this study suggest that the SSVEPs elicited by the approximation approach are mostly comparable with those elicited by the constant period approach.
GA based optimisation of a multi-agent soft computing model for segmentation and classification of unstained mammalian cell images

Most existing approaches for determining serious pathological conditions involve analysis of stained images of human tissue. Recently, unstained methods have been used for classification and analysis of cells in human and mammalian tissues. The classifications accuracies have been have been quite poor. We describe a novel application of genetic algorithms for significantly improving the segmentation and classification of cells in unstained Chinese hamster ovarian image samples. The multiagent soft computing model represents a symbiotic relationship between soft computing agents like genetic algorithms, neural networks and water immersion and morphological agents for segmentation and classification of cells in unstained Chinese hamster ovarian image samples.
Mobile Robot Photographer

In this study, we show the mobile photographing robot which moves around entertainment facilities, and automatically takes a picture of people who desire a commemorative picture. Our robot approaches target human by detecting his/her face from the image captured from monocular camera attached on the robot. In our method, the robot behaviors are controlled by using the face detection results. In order to validate the usefulness of the proposed method, the performance of the mobile photographing robot is evaluated. From the experimental results, it was shown that the robot could approach a human and take a picture automatically without operator's intervention from human approaching to photographing.
The Proposal of the EEG Characteristics Extraction Method in Weighted Principal Frequency Components Using the RGA

An EEG has frequency components which can describe most of the significant features. These combinations are often unique like individual human beings and yet they have underlying basic features. These frequency components are contained the important and/or not so important components, and then each importance of these frequency components are different. The real-coded genetic algorithm (: RGA) is used for selecting and being weighted the principal characteristic frequency components. We attempt to construct mental change appearance model (: MCAM) of only one measurement point. In order to show the effectiveness of the proposed method, computer simulations are carried out by using real data
DOA estimation by 3-D microphone array in the presence of spatial aliasing

This study proposes a method to estimate the direction of arrival (DOA) for multiple sources by using microphone array under aliasing condition. Many conventional DOA estimation methods based on the sparseness of time-frequency (T-F) components of speech signals have to limit the sensor distance because of avoiding spatial aliasing due to the use of phase difference. Here we propose a 3-dimentional DOA or both azimuth and elevation angles estimation method capable to treat even in the presence of spatial aliasing. To cope with this problem, a reliable T-F cell selection and new bandwidth control in the kernel density estimation. In order to verify the effectiveness of the proposed method, experimental simulations have been performed. The simulation results show that the proposed DOA estimation gives accurate estimation even for spatial aliasing cases.
A proposal of adaptive graininess suppression method

Previous studies of image restoration for noise image were based on mask processing. These conventional noise removal methods represented from mask processing have issue of definition degradation to accompany spacial processing. In this paper, we propose a graininess suppression method based on edge shape. In this method, we detect edges from a noise image and perform graininess suppression for this image based on edge information. On the edge detection, we execute an image transformation for an image that enables us to extract edge by making principal component image. Moreover, we use the canny edge detection operator that can detect a weak edge that relates to a real edge, and do not detect a lie edge. In the suppression process, we use Wiener filter that can restore an noise image without making a complete edge map and the original signal map. We have that the present method for the noise added images to verify effectiveness and have confirmed this.
An Analysis of the Influence of Facial Feature for Apparent Age Estimation

The purpose of this paper is to analyze apparent age feature based on age perception. In order to construct the apparent age database, age estimation experiment is performed to the HOIP facial database by 60 subjects. We defined 46 age features which contribute to the age perception from facial image. This process is performed based on research on age acknowledgment in field psychology and technological conventional method about real-age estimation from facial image. The apparent age feature used potentially when human performs age estimation is decided from 46 defined features by using the AIC and the multiple linear regression analysis. By using the proposed method, facial features that influenced on the apparent age in each gender was confirmed.
Invisible Calibration Pattern Based on Human Visual Perception Characteristics

In the print-type steganographic system and watermark, a calibration pattern is arranged around contents where invisible data is embedded, as plural feature points corresponding to between an original image and the scanned image for normalization of the scanned image. However, it is clear that conventional methods interfere with page layout and artwork of contents. In addition, visible calibration patterns are not suitable for security service. In this paper, we propose an arrangement and detection method of an invisible calibration pattern based on characteristics of human visual perception. The calibration pattern is embedded to blue intensity in an original image by adding high frequency component.
Robust face detection for direction changing using evolutionary algorithms

Threshold selection in multi-value images is performed based on their color information. When the threshold in an image is fixed it lacks versatility for the others. Because the color information varies under the influence of light conditions. In this paper, a Genetic Algorithm (GA) is used to select the most likely values of lips and skin colors in a light condition. It is possible to extract objects from the multi-value image only with the color information. In this paper, the objects of extraction are chosen to be the human lips and skin colors.
Detection of latent emotion using TV commercial evaluation

In this study, we detect latent emotions by evaluating Japanese TV commercials. Since TV commercials have penetrated deeply in our lives, they have given significant impacts on consumers' willingness to buy as well as on enhancement of brand image. Evaluating TV commercials and comprehending psychology of consumers are cited as one of key business issues. Therefore, we focus on consumers' latent emotions while watching TV commercials that they favorably remembered afterward, by using an electroencephalogram (EEG) and a questionnaire. In our experiment, we deal with award-winning and non-award-winning TV commercials in Japan. Moreover, we examine the effects of the favorable TV commercials on consumers' memories with time. The results showed that different kinds of latent emotions were interacted depending on characteristics of TV commercials: the presence of award and viewing time. In particular, consumers experienced the same kinds of emotions while watching the award-winning TV commercials that left favorable impressions on them not only soon after watching but also a month later. This tendency did not apply to the non-award-winning TV commercials. In this way, we investigate how consumers' favorable TV commercials actually impact on their latent emotions and their memories.
Augmented classification of Japanese visemes and hierarchical weighted discrimination for visual speech recognition

For the purpose of automatic speech recognition and speech animation synthesis, speaker verification and so on, there have been studies on `viseme'. Viseme is a visually identifiable unit of utterance or the equivalent unit in the visual domain of the phoneme in audio domain. The classification and the discrimination method of visemes are still important topics. This paper focuses on the number of classification units and a discrimination procedure of Japanese visemes: We extend the number of visemes from 6 to 9 to expanse the word representation by their series, then propose the hierarchical weighted discrimination using multiple discriminative analysis (MDA) to enhance the discriminative ability. In order to verify and discuss the availability of our proposals, visemes discrimination and word recognition experiments were conducted. From these results, the validity of the proposed methods was confirmed.
Fast face detection system using the GA-based threshold method

Threshold selection in multi-value images is performed based on their color information. When the threshold in an image is fixed it lacks versatility for the others. Because the color information varies under the influence of light conditions. In this paper, a genetic algorithm (GA) is used to select the most likely values of lips and skin colors in a light condition. It is possible to extract objects from the multi-value image only with the color information. In this paper, the objects of extraction are chosen to be the human lips and skin colors.
Aligning the real space with the model on see-through typed HMD for mixed reality

In this paper, we propose an approach to align the real space with the three-dimensional (3D) model for outdoor wearable mixed reality (MR) system. In our approach, we use a monocular see-through typed head-mounted display (ST-HMD) and a virtual reality (VR) sensor. We can measure six degree-of-freedom (6DOF) using this sensor. In the default setting, it is difficult and burden for people to handle the 3D model using air mouse. Therefore, we reduce the burden by automating the default setting. Moreover, when the user changes the viewpoint, we need to change the computer graphics (CG) model on ST-HMD with corresponding to the real objects of the difference in vision. We obtain the translation and rotation data from VR sensor to reflect the CG model. Then, we structured the alignment system in this system. Furthermore, in order to evaluate the proposed alignment for three-dimensional (3D) model, we show some results of the wearable aligning system using the 3D model.
Face detection and emotional extraction system using double structure neural network

In this paper, we propose a new method to examine whether or not human faces are included in color images by using a lip detection neural network (LDNN) and a skin distinction neural network (SDNN). In conventional methods, if there is the same color as the skin color in scenes, the domain, which is accepted as not only the skin color but any other color, can be searched. However, first, the lip is detected by LDNN in the proposed method. Next, SDNN is utilized to distinguish the skin color from the others. The proposed method can obtain relatively high recognition accuracy, since it has the double recognition structure of LDNN and SDNN. Finally, in order to demonstrate the effectiveness of the proposed scheme, computer simulations were performed. First 100 lip color, 100 skin color and 100 background pictures, which are changed into 10 /spl times/ 10 pixel, are prepared for training. The validity was verified by testing images containing several faces.
Feature Generation by Simple-FLDA for Pattern Recognition

In this paper, a new feature generation method for pattern recognition is proposed, which is approximately derived from geometrical interpretation of the Fisher linear discriminant analysis (FLDA). In a field of pattern recognition or signal processing, the principal component analysis (PCA) is popular for data compression and feature extraction. Furthermore, iterative learning algorithms for obtaining eigenvectors in PCA have been presented in such fields, including neural networks. Their effectiveness has been demonstrated in many applications. However, recently the FLDA has been used in many fields, especially face image analysis. The drawback of FLDA is a long computational time based on a large-sized covariance matrix and the issue that the within-class covariance matrix is usually singular. Generally FLDA has to carry out minimization of a within-class variance. However in this case the inverse matrix of the within-class covariance matrix cannot be obtained, since data dimension is generally higher than the number of data and then it includes many zero eigenvalues. In order to overcome this difficulty, a new iterative feature generation method, a simple FLDA is introduced and its effectiveness is demonstrated for pattern recognition problems
Appearance-age feature extraction from facial image based on age perception

An age is relatively important information in the various features which are shown human from face. It is possible to apply it to an amusement and an aesthetic surgery by analyzing the appearance-age feature which is used for age perception in a facial region. In this paper, we analyze facial feature of appearance-age based on age perception. In order to construct the appearance-age database, age estimation experiment is performed to the HOIP facial database by 15 subjects. The appearance-age feature used potentially when human performs age estimation is decided by using the genetic algorithms and the neural network. By using the proposed method, facial features that influenced on the appearance-age in each gender was confirmed. Moreover, it is suggested that that appearance-age feature which is used for age estimation is different in each generation when human performs age estimation.
A system identification method using a hybrid-type genetic algorithm

In this paper, we propose a new system identification method by using a genetic algorithm (GA) which has a hybrid structure. The hybrid structure means that a GA has 2 structures. One is the most popular chromosome type GA. That is, chromosomes have binary type genes. The other one is real coded GA. The former is used for determining a function type automatically. The latter is used for determining the coefficient of the function, time delay in the system and combination of the functions automatically. Finally, in order to show the effectiveness of the proposed method, computer simulations were done. Furthermore, in the computer simulations, 2-kinds of systems are identified. One is the hammer stain model. The other is a complex model. From these simulation results, the effectiveness of the proposed method is cleared.
Automatic Extraction of Heart Contour Using SNAKES

Recently, as sophisticated medical instruments have been developed, the internal state of a human body has become well-known. In particular, in the examination of heart, non-surgical and non-intrusion ultrasonic diagnostics are generally utilized. However, the diagnosis depends on the doctor's subjectivity and experience. Furthermore, the doctor's burden increases because the contour of the heart is extracted by manual operations. Therefore, the development of a system that offers objective information in addition to a subjective judgment of the doctor by extracting and analyzing quantitative information from image analysis by computers is necessary. In this paper, the contour of the chamber wall is extracted by using an active contour model SNAKES as a preprocessing of the heart disease detection. The contour has been extracted by using SNAKES of the expansion model from the inner surface of heart
Detection of the human-activity using the FCM

In this paper, we propose the detection system of the human activity by using the electroencephalograms (EEG). First, we measure the EEG data for subjects. In most of all conventional studies, the EEG having a lot of sensors is used. Therefore, subjects must eat or smoke while using the EEG interface. However, this situation is not practical for subjects. In this study, taking account of the burden of subjects, we use only one measurement point 'FPI'. First, we measure the EEG data and the EMG data for subjects. Then, the EEG feature is extracted by using the singular value decomposition (SVD). From the result, we classify the EEG pattern by the fuzzy c-means (FCM). If we cannot classify the EEG pattern into each activity, the discriminant analysis (DA) is used. We consider the EEG features of activities. Then, in order to show the effectiveness of the proposed method, computer simulations are done.
Wheelchair control system by using electrooculogram signal processing

This study aims to propose electrooculogram signal processing method for voluntary eye blink detection and apply it to wheelchair control system. In this study, we defined double blink and wink as a voluntary eye blink, and normal blink as an involuntary blink. The proposed method can detect voluntary eye blinks in distinction from involuntary eye blinks with 98.28 percent accuracy. Additionally, we showed the effectiveness of wheelchair control system.