nils-herrmann/clean_scirepeval huggingface dataset:
A Design of Gender and Age Estimation System Based on Facial Knowledge

The purpose of this paper is to propose a method of gender and age estimation which is robust for environmental changing. We propose a feature-point detection method which is the advanced retinal sampling method (ARSM), and a feature extraction method. As features for the gender and age estimation, facial shape, skin texture, hue and Gabor-feature are used. In order to show the effectiveness of proposed method, not only real-age database of facial image but also appearance-age database is employed. We also analyze the facial features characteristic to each age category and gender, and examine the difference feature of between the real-age and appearance-age in a facial area. Moreover, we examined the left-right symmetric property of the face concerning gender and age estimation by the proposed method.
Enhancing unsupervised canonical correlation analysis-based frequency detection of SSVEPs by incorporating background EEG

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to provide a fast communication channel between human brain and external devices. In SSVEP-based BCIs, Canonical Correlation Analysis (CCA) has been widely used to detect frequency-coded SSVEPs due to its high efficiency and robustness. However, the detectability of SSVEPs differs among frequencies due to a power-law distribution of the power spectra of spontaneous electroencephalogram (EEG) signals. This study proposed a new method based on the fact that changes of canonical correlation coefficients for SSVEPs and background EEG signals follow the same trend along frequency. The proposed method defined a normalized canonical correlation coefficient, the ratio of the canonical correlation coefficient for SSVEPs to the mean of the canonical correlation coefficients for background EEG signals, to enhance the frequency detection of SSVEPs. An SSVEP dataset from 13 subjects was used for comparing classification performance between the proposed method and the standard CCA method. Classification accuracy and simulated information transfer rates (ITR) suggest that, in an unsupervised way, the proposed method could considerably improve the frequency detection accuracy of SSVEPs with little computational effort.
Intelligent packet shaper to avoid network congestion for improved streaming video quality at clients

This paper proposes a traffic shaping algorithm based on neural network, which adapts to a network over which streaming video is being transmitted. The purpose of this intelligent shaper is to eradicate all traffic congestions and improve the end-users video quality. It possesses the capability to predict, to a very high level of accuracy, a state of congestion based upon the training data collected about the networks behavior. Initially, the current traffic shaping technologies are discussed and later a simulation in a controlled environment is illustrated to exhibit the effects of this intelligent traffic-shaping algorithm on the underlying networks real time packet traffic and the eradication of unwanted abruptions in the streaming videos quality. This paper concluded from the results of the simulation that neural networks are a very superior means of modeling real-time traffic and that it can be applied as an appropriate solution to network congestion.
Design and evaluation of neural networks for coin recognition by using GA and SA

In this paper, we propose a method to design a neural network (NN) by using a genetic algorithm (GA) and simulated annealing (SA). And also, in order to demonstrate the effectiveness of the proposed scheme, we apply the proposed scheme to a coin recognition example. In general, as a problem becomes complex and large-scale, the number of operations increases and hardware implementation to real systems (coin recognition machines) using NNs becomes difficult. Therefore, we propose the method which makes a small-sized NN system to achieve a cost reduction and to simplify hardware implementation to the real machines. The coin images used in this paper were taken by a cheap scanner. Then they are not perfect, but a part of the coin image could be used in computer simulations. Input signals, which are Fourier spectra, are learned by a three-layered NN. The inputs to NN are selected by using GA with SA to make a small-sized NN. Simulation results show that the proposed scheme is effective to find a small number of input signals for coin recognition.
Designing of the Color KANSEI Information Map Considering the Individual Characteristic by Using Multivariable Analysis

The color design is one of the most important elements that influence the impression of products, therefore the technology which understands and reflects the consumer's sensibility is needed in the color design. Especially, the color coordination system that connects colors with impressions is expected as the system supporting the color design. Therefore, in the field of the KANSEI engineering, researches that model the correlation of colors (the arrangement of the color) and impression words have been done. However, the impression received from the color is various by individual, therefore, it is necessary to model the KANSEI information to each individual. In this paper, we analyze the data of the evaluation questionnaire of the color for the subject, and propose the method of extracting the KANSEI information of the subject. Concretely, by using the multivariable analysis, the correlation with an amount of the sensibility and the color feature is found. As a result, it is thought that the tendency of impressions to colors can be expressed in the sight
Incremental learning method in wrist EMG recognition system

In a field of pattern recognition, researches of feature extraction and dimension reduction using the Simple-PCA that is an approximation algorithm of the principal component analysis (PCA) are actively conducted. In such a statistical method, a lot of algorithms that perform incremental learning by using new incremental data exist. For example, there is an algorithm named Incremental PCA for PCA. However, the algorithm capable of performing incremental learning to the Simple-PCA has not been presented yet. In this paper, we propose Incremental Simple-PCA that introduces an incremental learning function to the Simple-PCA. Moreover, the effectiveness of this algorithm is verified in EMG pattern recognition.
Influence on electroencephalogram at the prefrontal cortex due to tinnitus and sounds

The purpose of this paper is to investigate the influence on electroencephalogram (EEG) at the prefrontal cortex due to tinnitus and sounds. Tinnitus is a phenomenon that a person hears sounds even though there is no sound source. Conventional methods of investigating tinnitus are subjective and need long time. In this paper, EEG at the prefrontal cortex was used because the relationship between tinnitus and prefrontal cortex has been revealed. Tinnitus patients and healthy controls were classified and the distress level was estimated using EEG in researches so far, and identified the necessary features for these analyses. However, there is a problem that it is not certain whether features used in these analyses arise similarly in tinnitus and other sounds. The purpose of this paper is to verify which EEG features identified in previous researches arise from tinnitus or sounds. EEG data of tinnitus patients and healthy controls were measured and a questionnaire was conducted on tinnitus distress to the tinnitus patients. EEG measurements of healthy controls at resting with no stimulation and at listening to sounds which simulated tinnitus were performed and a questionnaire was conducted to see which sound they felt distress. Differences of EEG between tinnitus patients and healthy controls and differences of EEG between controls at resting with no stimulation and at listening to sound were different. It is thought that EEG changes of tinnitus patients at the prefrontal cortex arise from only tinnitus.
Iterative trajectory generation for mobile robot to pass through the waypoints

This paper proposes a trajectory generation method for mobile robots based on trajectory segment extension. The trajectory for mobile robot must be generated by depending on the conditions during the robot traveling. Such conditions to design the trajectory are given when the robot comes around those locations and are not provided before arriving around there. From this point of view, we have started to develop an on-line trajectory generation method based on extending the trajectory segment that is the low-order polynomial curve. We already proposed a trajectory extension approach calculated with sequentially designated waypoints in the absolute time. In this paper, we improved the method by utilizing the relative time expression for extension of the trajectory segment. Simulations with proposed method were done and the results were shown in this paper.
Evaluation of stress accumulation using the EEG and NIRS

In the recent research, accumulation of stress has been shown to induce lifestyle diseases and mental / nervous diseases. In this research, visual search task was chosen to stress subjects, and the brain activity was measured by using near infrared spectroscopy (NIRS) and an electroencephalograph (EEG) was measured. Advantages of using these in combination are that it is relatively simple and less burdensome to the subject and it can be measured from the two viewpoints of neural activity and hemodynamics for the same brain region. As prior research, there are studies investigating the presence of stress by using brain activity, but there are no researches measuring the change in brain activity under continuous stress. The aim of this research is to extract features of active sites of brain function and electroencephalograms in stress accumulation. This research conducted experiments and analysis focusing on the two poles of Fp1 and Fp2 in international 10–20 method. In NIRS, when every time stress was accumulated, the difference was confirmed between right and left in brain activity. In EEG, there was no common feature between data in frequency and phase. From the above, it is possible to objectively and quantitatively evaluate the degree of stress accumulation by combining near-infrared spectroscopy and electroencephalograph by extracting a feature amount that changes every time stress accumulates in the EEG signal. As future tasks, the number of subjects should be increased. Also, different types of EEG analysis and the number of measurement points should be considered.
Measurement of skin texture using genetic image analysis

A binarization method of images for presumption of skin age is proposed using genetic programming (GP) which is an evolutionary computing method. Skin age can be estimated by skin texture, but it is difficult to binarize skin images owing to individual differences. Therefore, results obtained by genetic image processing were achieved with consideration of individual differences, and good results were obtained. The fast Fourier transform (FFT) was performed on skin images and a classification experiment was carried out by a neural network (NN) as a comparative experiment. However it is demonstrated that an effective classification result was not obtained by FFT and NN compared to our approach.
Identifying the dominant species of tropical wood species using histogram intersection method

This research is addresses to determine the dominant species that located in the overlapped clusters produced by the Kohonen Self-Organizing Map (KSOM). Before, KSOM algorithm able to cluster the tropical wood species data set effectively and accurately according to the wood features, which is wood pores sizes. Unfortunately, there are seven overlapped clusters in the clustering result and this is due to similarity features among the wood species. This problem has caused difficulty in determining the separation boundary amongst clusters, where the most dominant species for every overlapped cluster is difficult to identify. As for a solution, the Histogram Intersection (HI) method is proposed in this research to solve this problem. From the experiments, the HI has proved that it can determine the dominant species of the overlapped clusters effectively resulting in an improvement of 1.12% of the classification accuracy, compared with the clustering result without HI technique. It has shown the implementation of this method has successfully solved the problem occurred.
Rule extraction from a trained neural network for image keywords extraction

This paper presents a rule extraction method from a trained neural network (NN), which is used for keywords extraction from images. In our approach, first, a bit map image in the RGB color space is transformed into that in the L*a*b* color space. Next, it clusters image pixels using the fuzzy c-means method and domains are extracted through a labeling process. Features, such as area of obtained domains, color information, and coordinates of the center of gravity, are then calculated, which are used as input attributes to NN. NN is then trained using such features. After NN learning, rule extraction is carried out using binarized output values in the hidden layer for each keyword. The rules extracted in this paper are If-then rules, which include logical functions. The methods of generating keywords using NN and the rules are presented and their comparative experiments are performed. Finally the validity of these methods was verified by means of computer simulations.
Color image modification based on visual saliency for guiding visual attention

In this paper, we propose a modification method of color image, for a given region in the image, to obtain the image in which the region shows the highest saliency. Generally, it is thought that the most important part of an information system that assists human activities is a natural interface with human. In addition, eye-gaze information strongly reflects the human interest or their attention. Therefore, a gaze-based interface is promising for future practical applications. In particular, if we can smoothly guide the user's visual attention toward a target without interrupting their current visual attention, the usefulness of the gaze-based interface will be highly enhanced. In order to realize a novel interface based on visual attention, we propose a modification method for color image based on visual saliency map. The proposed method iteratively modifies the intensity and color so that the saliency inside the specified region increases while that outside the region decreases. This iteration is carried out until we obtain the image in which the specified region is most salient over the entire image. With the image obtained in this way, we can smoothly attract the user's visual attention to the specified region, without any interruption to the user's gaze. We evaluated the proposed method by a gaze measurement system and subjective experiments. As a result, we confirmed that the user's visual attention is indeed attracted toward our specified region without uncomfortable feeling.
Hippocampal activity during serotonergic neuronal activity manipulation using optogenetics

In order to improve brain dysfunction such as mental disease, it is necessary to clarify the mechanism of activity in the brain. To clarify the relationship between the brain region and the neurotransmitter is important for elucidating the brain. Especially investigating the relationship between serotonin and hippocampus leads to elucidate of the mechanism of depression and sleep. The technique called optogenetics enabled only specific cells to be manipulated by light stimulation in recent years. This technique is a useful technique for clarifying the function of the brain. Because instead of stimulating multiple cells such as electrical stimulation, optogenetics can stimulate only specific cells, so that its function can be clarified. This study focused on neurotransmitter serotonin and the brain region called the hippocampus. For these purpose we can clarify the relationship between serotonin and hippocampus by manipulating serotonin neural activity using optogenetics while invasively measuring the ventral hippocampus of the mouse. In addition, identification of pyramidal cell layer is necessary to clarify hippocampal activity. Currently, visual estimation is performed based on the histology that dissected the brain, it still has a problem. Now this technique can't estimate ambiguous of the brain part and measure in an online manner. For this problem, we focus on the clarity of the brain part estimation when we used optogenetics. Therefore, we could estimate the pyramidal cell layer of the hippocampus by analyzing nerve activity and thought that the effect of serotonin activation or suppression on the hippocampus could be clarified. As a result, we showed that pyramidal cell layer can be identified by using principal component analysis, and could clarify the variation of nerve activity in the pyramidal cell layer of hippocampus during serotonin operation.
Association between ego scores and individual characteristics in EEG analysis: Basic study on individual brain activity

This paper introduces a method for discussing an association between ego scores and individual characteristics in EEG analysis. The ego scores express one of the personality and/or nature on a human. The EEG analysis calculates the power spectra of the frequency weighted of the EEG signal, and evaluates through detecting the EEG patterns while listening to the music. A k-nearest neighbor classifier is used to detect the EEG patterns. The specified weight value to weight frequency of EEG and the results of EEG pattern detection express the individual characteristics in EEG analysis. Finally, we do experiment using a real EEG data for discussing the association the ego scores and the individual characteristics in EEG analysis. An interesting tendency was that the subjects who were the combined ego-type tended to have a powerful response to negative stimuli more than positive stimuli. They had the stable filter weighted frequency.
Facial expression estimation using simplified head model based on particle filtering

We propose a method for real time estimation of head poses and facial expressions without using a complex 3D head model. Most conventional methods for head pose and facial expression estimation required preliminary estimation using a complex 3D head model. In the proposed method, however, a large number of feature points and a whole facial image are not required in order to estimate facial expressions. The proposed method estimates the head pose and facial expression based on a simplified 3D deformable facial shape model approximated by a cylindrical model. In order to show the effectiveness of the proposed method, we performed facial expression estimation experiments.
Proposed blink artifacts rejection method mixing with electroencephalographic recordings using single-channel electroencephalographic recordings and non-negative matrix factorization

Recently, a lot of research concerning biological signal analysis has been proceeded in various field. Especially, research about electroencephalographic (EEG) signals has attracted attention since the signal contains a mixture of endogenous brain activities. However, the EEG signal is often mixed physiological artifacts such as blink, oculogyration, heart beat or muscle activities. Therefore, it is important to remove these artifacts from the EEG recordings when researchers attempt to analyze brain activities accurately. An independent component analysis (ICA) has been used for removing blink artifacts with over 90 percentage of the repeatability for original signals. However, a common major drawback to the ICA is that this method can only manage overdetermined mixtures, which entails using some EEG electrodes. There is no rejective method for single-channel EEG signals. Therefore, in this paper, we proposed an artifactitious rejection method using single-channel EEG recordings and 2 step non-negative matrix factorization (NMF). We acquired the multi-channel EEG recordings from 3 subjects who blink every 5 seconds according to metronomic sound. Furthermore, we applied the NMF using single-channel EEG (Fp1) recordings to reject blink artifacts, and the ICA using multi-channel EEG recordings to obtain clean Fp1 signals. High SNR between reconstructed signals by the proposed method and the ICA was showed. Moreover, over 99 percentage of the repeatability for original EEG signals was indicated by the proposed method.
Feature extraction from EEG patterns in music listening

Various illnesses are caused by stress, and stress release is being carried out by musical therapy. A variety of music is used in the musical therapy, and it takes a long time for patient and music therapist to select the music. Generally, time selecting music can be reduced and the musical therapy can be done more easily if effective music for the purpose it is easily found. For this purpose, we measure and extract an EEG (electroencephalogram) difference between music genres as characteristic data in this paper. Our method produces data based on frequency appearance rate, extracts features by principal component analysis, and then analyzes them by using a neural network. Finally in order to show the effectiveness of the proposed method, we carried out computer simulations by using the real data.
Feature Extraction in Listen'ng to Music Using Statistical Analystis of the EEG

In order to solve stress problems, researchers have studied healing, especially the music therapy. It is mentioned that objective evaluation of the music therapy is an important assignment, and some researchers have tried objective measurement based on physiological change. In this paper, the purpose is extraction of features that may be influenced by the music. We pay attention to EEG (electroencephalogram) as an objective and absolute scale. This paper proposes a method that extracts features of the EEG by the CDA (canonical discriminant analysis). From the result of the experiment, it is suggested that the CDA extracts the features influenced by the individual and the music type
Iterative polynomial-based trajectory extension for mobile robot

This paper proposes a trajectory generation method for mobile robot by extending the trajectory segment iteratively. Considering the practical uses of the mobile robot like service purpose in the real world, it requires that the trajectory planning is done adaptively by depending on the situation changes. For that, designated waypoints for future motion are given sequentially from the environment recognition process and thus trajectory generation should be processed in dynamical situation. In this paper, we described an iterative extension of the polynomial-based trajectory by considering the designated waypoint, the continuity with the existing trajectory segment and the stoppage constraint at the end of each trajectory segment. Proposed method provides the next trajectory segment for a certain period by referring to these conditions. Resultantly, iteratively generated trajectory segments connect each other smoothly and organize a single continuous trajectory. Computer simulations with proposed method were conducted and the results were shown. They showed proposed method realizes trajectory generation based on sequential extension of the trajectory segment.