bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparing user-dependent and user-independent training of CNN for SSVEP BCI
Authors: Ravi Aravind , Beni Nargess Heydari , Manuel Jacob , Jiang Ning 
Published: January 2020

Abstract

Objective. We presented a comparative study on the training methodologies of a convolutional neural network (CNN) for the detection of steady-state visually evoked potentials (SSVEP). Two training scenarios were also compared: user-independent (UI) training and user-dependent (UD) training. Approach. The CNN was trained in both UD and UI scenarios on two types of features for SSVEP classification: magnitude spectrum features (M-CNN) and complex spectrum features (C-CNN). The canonical correlation analysis (CCA), widely used in SSVEP processing, was used as the baseline. Additional comparisons were performed with task-related components analysis (TRCA) and filter-bank canonical correlation analysis (FBCCA). The performance of the proposed CNN pipelines, CCA, FBCCA and TRCA were evaluated with two datasets: a seven-class SSVEP dataset collected on 21 healthy participants and a twelve-class publicly available SSVEP dataset collected on ten healthy participants. Main results. The UD based training methods consistently outperformed the UI methods when all other conditions were the same, as one would expect. However, the proposed UI-C-CNN approach performed similarly to the UD-M-CNN across all cases investigated on both datasets. On Dataset 1, the average accuracies of the different methods for 1 s window length were: CCA: 69.1%  ±  10.8%, TRCA: 13.4%  ±  1.5%, FBCCA: 64.8%  ±  15.6%, UI-M-CNN: 73.5%  ±  16.1%, UI-C-CNN: 81.6%  ±  12.3%, UD-M-CNN: 87.8%  ±  7.6% and UD-C-CNN: 92.5%  ±  5%. On Dataset 2, the average accuracies of the different methods for data length of 1 s were: UD-C-CNN: 92.33%  ±  11.1%, UD-M-CNN: 82.77%  ±  16.7%, UI-C-CNN: 81.6%  ±  18%, UI-M-CNN: 70.5%  ±  22%, FBCCA: 67.1%  ±  21%, CCA: 62.7%  ±  21.5%, TRCA: 40.4%  ±  14%. Using t-SNE, visualizing the features extracted by the CNN pipelines further revealed that the C-CNN method likely learned both the amplitude and phase related information from the SSVEP data for classification, resulting in superior performance than the M-CNN methods. The results suggested that UI-C-CNN method proposed in this study offers a good balance between performance and cost of training data. Significance. The proposed C-CNN based method is a suitable candidate for SSVEP-based BCIs and provides an improved performance in both UD and UI training scenarios.
Performance Evaluation of Channel Decoder based on Recurrent Neural Network
Authors: Meng Saisi , Jiang Xue-Qin , Gao Yongbin , Hai Han , Hou Jia 
Published: January 2020

Abstract
Recently, recurrent neural network (RNN) has demonstrated superior performance for channel decoder, which motivates us to explore which RNN decoder can be more efficient. In this paper, we propose three kinds of RNN decoders, which are built upon long short term memory (LSTM), gated recurrent unit (GRU) and bidirectional gated recurrent units (Bi-GRU), respectively. The performance of these three RNN decoders are evaluated through lots of simulations, which indicate that the GRU decoder with simplest structure and least computational time, has similar bit error rate (BER) performance as that of the LSTM decoder. The Bi-GRU decoder has the best BER performance at the expenses of more computational time. However, it is prone to overfitting. Furthermore, we find that the BER performance of RNN decoders without dropout is better than that of the decoders with dropout when decoding models are underfitting, while the RNN decoders are better to dropout when decoding models are overfitting.
Detect Black Box Signals with Enhanced Spectrum and Support Vector Classifier
Authors: Lou Chenlu , Pan Xiang 
Published: January 2020

Abstract
When the plane crash into the sea and the search and rescue team search the location black box, we need to detect a specific acoustic signal in the ocean. The underwater acoustic channel has several distinct features: multipath propagation, high attenuation, and sound velocity varying as a function of depth and temperature, which make the detection of underwater signals very difficult. This paper proposes a new method in the marine signal detection and discrimination based on adaptive line enhancer and support vector machine classifier, which can provide a new idea for marine black box search. The adaptive line enhancer of the least mean square algorithm can detect narrowband signals hidden in wideband noise, while the support vector machine classifier maps signals to high-dimensional space and screens out hyperplanes separating different signals from machine data through previous data. Experimental data show that this method can bring a very high recognition rate.
The Design of a Low-Cost Tunable Laser Based on Reconstructed Equivalent Chirp
Authors: Ziyu Liu 
Published: January 2020

Abstract
With the development of optical fiber communication, tunable laser design has become an international research hot spot. In this paper, a new type distributed feedback (DFB) tunable laser is introduced. It uses the Reconstruction-Equivalent-Chirp (REC) technology, combining the stepped chirp sampling structure and superstructure fiber Bragg grating structure. We can obtain continuous tunable multi-channel laser and achieve the purpose of eliminating chirp of the chirp pulse and compressing pulse width at the same time. Through the simulation calculation, it can be known that the output power of each laser’s channel are roughly equal. The laser can realize the tuning between 1530 nm to 1560 nm. And for the initial pulse with the conditions of Tin = 20 ps, the chirp coefficient C = 5 and time-bandwidth product is 2.25, time-bandwidth product will change to 0.458 after REC compression. The compression ratio is about 5:1, which means the pulse frequency chirp can be basically eliminated. The structure only needs to be manufactured by traditional lithography. Its low cost and excellent performance leads to a good applied prospect.
Visualization of Learning Outcome Structure for Self-Learning
Authors: Nitchot Athitaya , Gilbert Lester 
Published: January 2020

Abstract
The success and quality of an educational or training system is assessed by its match to the competences it claims to develop. In this research, we propose a conceptual model of contextualized competence (or learning outcome), being a learner’s capability with respect to some subject matter. We also suggest a controlled vocabulary of capability verbs, based upon Merrill’s theory insight that a particular capability should be associated with a particular type of subject matter. At this stage, the model is deployed as a subject matter graph without contextualization in the domain of programming fundamentals. The implemented tool associates learning resources (mainly html links) with the domain knowledge structures, offering graph creation and visualization, resource suggestions, and learning paths. Future work will include learning outcomes and related context, and will evaluate user satisfaction and accessibility.
Segmentation of Lung Images Using Textural Features
Authors: Ilyasova N Yu , Shirokanev A S , Demin N S 
Published: January 2020

Abstract
This paper proposes a technology for lung image segmentation using textural features. To analyze the informativeness of the features, the K-Means method was used. The segmentation result can be used to describe the characteristics of the patient: age, gender, physique, etc. The study was conducted on a large set of fragmented images. X-ray clustering errors for the 12×12, 24×24, and 36×36 fragmentation windows were presented. Pre-processing of images was also used to estimate the quality. The study showed that the technology provided key objects selecting error at little more than 25%. However, the alignment procedure reduced this error to 13%. When using the k-means method, a link was found between the segmentation error and the size of the fragmentation window. The selection of the range of interest on the lungs x-ray in accordance with the equalization was considered preferable. The high error of the first clustering method was associated with the choice of feature space; in our case, image processing was conducted for all features calculated using MaZda software.
Analysis of Convolutional Neural Network for Fundus Image Segmentation
Authors: Shirokanev A S , Ilyasova N Yu , Demin N S 
Published: January 2020

Abstract
In this paper the study of fundus image segmentation using convolutional neural networks is carried out. A neural network architecture was made to classify four classes of images, which are made up of thick and thin blood vessels, healthy areas, and exudate areas. The CNN architecture was constructed empirically so as the required accuracy of no less than 96 % is ensured. The segmentation error was calculated on the exudates class, which is key for laser coagulation surgery. In the paper we utilized the HSL color model because it renders color characteristics of eye blood vessels and exudates most adequately. We have demonstrated the H channel to be most informative. We have investigated the robustness of technology to various noises. Experimental studies have shown the instability of the convolutional neural network to Gaussian white noise and resistance to impulse noise.
Face Alignment by Supervised Descent Method with Head Pose Estimation
Authors: Zheng Zheng 
Published: January 2020

Abstract
Face alignment, which aims at locating facial key points automatically, is an important topic in computer vision community. And many works have been done to solve this problem. The most well-known solution is Supervised Decent Method(SDM). However, SDM has been designed to use mean shape as initial shape, which is vulnerable to large pose variation. In this paper, we present a novel approach for detection of the facial key points, getting initial shape from a special shape according to the head pose of the data. Experiments show that our approach achieves significant improvement. In both 21 points and 68 points detection cases, our method achieves nearly 50% improvement on challenging dataset IBUG, and about 1% improvement in HELEN and LFPW test set.
The Method of Vehicle Type Identification Based on Improved Corner Feature
Authors: Tong ZHANG , Bochong WEN 
Published: January 2020

Abstract
This paper present a method of vehicle type identification using improved corner points as key points for feature description. The corner points can reflect the information of different type. Therefore, we adopt the features of corner points to recognize which type the vehicle belongs to. The method of Harris corner detection was improved, and the ratio of eigenvalues of M matrix in different directions was added to the corner response. The corner points were detected by improved corner response and results of test showed that the count of the detected corner points was increased. Considering rotation invariance and scale invariance, feature description is carried out around detected corner points. The vector of feature is determined by the Harr wavelet response of the pixels around the corner point, but the direction is only one of the eight values, and the length of the vector is 16. The matching rate of feature vectors between different types is used to determine which type the vehicle belongs to. The results of experiment confirmed the validity of the method.
Application of Mesh-Less Algorithms in Image Processing
Authors: Yan Zhang , Heng Zhang 
Published: January 2020

Abstract
Mesh-less method is a discrete governing equation which does not need to generate grids in numerical calculation and constructs interpolation functions according to some arbitrarily distributed coordinate points. This method can easily simulate flow fields of various complex shapes. In this paper, mesh-less method is applied to phase unwrapping in image processing of InSAR. This method can forbid error accumulate to the whole image in the sense of least squares. The precision of this method are can showed by real data.
A Tomato Quality Identification Method Based on Raman Spectroscopy and Convolutional Neural Network
Authors: Yin Wu , He Chenying , Wu Qibao 
Published: January 2020

Abstract
in recent years, more and more technologies have been applied in monitoring growth and production efficiency of plants, i.e. agricultural Internet of Things (IOT) and new information-aware technologies. The architecture of the IOT is divided into four layers, i.e., the sensing layer, network layer, processing layer and application layer[1-4]. Among them, the perception layer is the facial features and the skin of the IOT, which is the basis of the IOT [5]. Raman spectroscopy technology has the advantages of fast, simplicity, accuracy, non-destructive and automatic identification, which has become a powerful analytical verifying method. The method of tomato quality identification that based on the Raman spectroscopy combined with convolutional neural network (CNN)[6]was explored. The Raman spectrum of tomato was collected by Raman sensor to construct a neural network with deep network structure. Through repeatedly learning and training in Raman map, we can determine the map recognition model of high quality tomatoes and use matplotlib to realize the identification simulation.
Attention Computation in the Processing of L2 Cognition: Revisiting Learning Models
Authors: Lan Yizhou , Li Will Xiangyu 
Published: January 2020

Abstract
Studies show that the way our brain processes incoming speech sounds has a lower-level grounding derived of acoustic similarity. Previous theoretical models of speech sound processing posit that higher-level cognitive process plays little role in perception and in successful and complete processing of speech sounds. The present study investigates if such models may be effectively extended to incorporate influences from higher level cognitive cues, such as voluntary attention, to certain acoustic dimensions of the speech sound stimuli. In this paper, we investigate the relationship in a qualitative way between the efficiency of the language processing and high-level perceptual mechanism through computational simulation of speech perception, and accuracy and reaction-time measurements. The results of experiments lead to an enhancement of existing statistical signal processing and perception models’ predictions. Our findings revealed that acoustic similarity in speech sound signals merely does not accurately predict the acquisition outcome, and the enhancement of natural language learning can be achieved by effectively mining out the auxiliary cognitive cues in these signal processing activities.
Analysis of prospective chemistry teachers’ understanding about rate of reaction concept
Authors: Wardah A C , Wiyarsi A , Prodjosantoso A K 
Published: January 2020

AbstractThis research was carried out to reveal prospective chemistry teachers’ understanding in rate of reaction concept. A survey with a quantitative approach study was conducted toward 61 of prospective chemistry teachers who were taking a basic chemistry course. Data were obtained using a true and false test which containing 30 questions related to the rate of reaction topic. They were categorized into five concepts covering rate of reaction basic concept, rate of reaction law, collision theory, factors that affect the rate of reaction, and reaction mechanism concept are 3, 4, 3, 14, and 6 questions respectively. The results proved that most of them had a low understanding in the rate of reaction concept, especially on the first concept but in the concept of collision theory they had a relatively high understanding. The efforts are needed by them as prospective chemistry teachers to increase their understanding of the rate of reaction concept. However, this research did not explore their description in explaining about the concept. Therefore, there is a need for further research to discuss the mental models of prospective chemistry teachers in the rate of reaction concept understanding.
Chemistry teaching self-efficacy: A scale development
Authors: Tyas A S , Senam  , Wiyarsi A , Laksono E W 
Published: January 2020

Abstract
This study was aimed to develop the Chemistry Teaching Self-efficacy (CT-SE) instrument. The method of this study is an applied survey method. The data of pre-service teachers’ CT-SE was obtained through a questionnaire. Three dimensions of self-efficacy were examined in this study, Personal Science Teaching Efficacy belief (PSTE), knowledge efficacy, and teaching efficacy. A group expert confirmed the content validity of the CT-SE with 50 items that had been administered to 137 pre-service teachers. The data were analyzed using the Rasch model that the met criteria of the unidimensionality, item fit, difficulty/ability estimation, reliability, and information function. The results of the Rasch model showed that there are 39 items CT-SE that fit with the Rash model and high reliability for the person’s reliability and excellent reliability for the item reliability. The chemistry teaching self-efficacy is acceptable as a good instrument to collect the data. This study suggests that chemistry teaching self-efficacy will prove to be a useful instrument for the measure of pre-service teacher self-efficacy for acid-base topics in chemistry learning.
Analysis of misconceptions by four tier tests in electrochemistry, case study on students of the chemistry education study program UIN Antasari Banjarmasin
Authors: Murniningsih  , Muna K , Irawati R K 
Published: January 2020

Abstract
This study aims to determine misconceptions on electrochemistry on the students of Chemistry Education Study Program UIN Antasari. The qualitative descriptive approach in this study was used to identify student misconceptions using the four-tier test. There are four (4) test levels which include: (1) multiple-choice with C2-C6 level, (2) confidence rating of the answer beliefs, (3) reasons for the answers, and (4) confidence rating of the reasoning beliefs for the answers. The three criteria used in the level of misconception consist of high, medium and low criteria. The results show that the students of the Chemistry Education Study Program of UIN Antasari experience misconception with moderate criteria on all electrochemical concepts which include the concepts of species undergoing oxidation and reduction in both Voltaic and electrolysis cells. Student’s misconception about the direction of electron flow, the condition of electron flow during electrolysis, and cathodic protection of iron.
Profile of students’ self-efficacy in chemistry learning: Case study at senior high school
Authors: Zarkasyi C S , Partana C F 
Published: January 2020

Abstract
This study aims to analyse students’ self-efficacy in chemistry learning. Descriptive quantitative method was used in this research. The research respondent was 77 senior high school student in Yogyakarta Indonesia. Data collection techniques was survey. Instruments used for collecting data was questionnaire sheet. The data analysis technique used was V’Aiken equation to analyse experts judgement in validation process and likert scale to analyse self-efficacy data. The results of this study were 1) 53% of student had higher criteria of self-efficacy and 47% had lower of it, 2) the factors affecting students’ self-efficacy were motivation, personal experience, phsycology, emotional, and social environment. The more factors owned by student, the more students’ self-efficacy rose otherwise their self-efficacy will decrease.
Application of problem based learning model to improve problem solving ability of student of XI science grade in chemistry
Authors: Kurniawan E , Sofyan H 
Published: January 2020

Abstract
The purpose of this study was to determine the effect of the application of problem-based learning models compared to conventional learning models to improve students’ problem solving skills in XI IPA SMAN 2 Labuapi This research is quasi-experimental, which used nonequivalent control group design. The population in this study were all students of XI IPAN SMAN 2 Labuapi in the academic year 2015/2016. The research sample consisted of two classes, namely class XI IPA 1 as an experimental class and class XI IPA 2 as a control class. The sampling technique used saturated sampling techniques. The instrument used to measure the ability to solve problems in the form of a description test. Data analysis techniques used the Anacova test for hypothesis testing and N-Gain to see the improvement of students’ problem solving skills. Anacova test results found that there were significant differences between the variables involved in the study, while the average increase in students’ problem solving abilities for the experimental class was lower than the control class. Based on these results it can be concluded that the problem solving abilities of students taught with problem-based learning models do not have a higher increase compared to conventional learning.
Attitude toward chemistry: Student’s perception based on learning experience
Authors: Fahmidani Y , Rohaeti E 
Published: January 2020

AbstractThis research is descriptive study that explains students’ scientific attitude in learning chemistry. Based on the purpose, a questionnaire which contained several statements about students’ scientific attitude was distributed. The number of students who participated was 55 students in two high schools in Yogyakarta, Indonesia. Questionnaire data were analyzed quantitatively as a percentages, then categorized as low, medium and high. The data shows that most students still have scientific attitudes in the low and medium categories. This research is can be additional information for teachers and other researchers to improve students’ scientific attitude because scientific attitudes are also related to student achievement.
A need analysis to optimization computer laboratory in chemistry learning process according to chemistry teachers and natural science students in 3T regions
Authors: Amin D I , Ikhsan J 
Published: January 2020

Abstract
The aims of this descriptive research were identifying optimization level of chemistry teachers and students in the utilization and using computer laboratory commonly used for computer based national test, and learning style of science students in 11th grade at Senior High School while chemistry learning process in 3T regions. The participants consisted of 8 chemistry teachers and 82 natural science students in 3 Bangkalan sub-district of Bangkalan Regency, East Java Province. Data collection was conducted by giving a close-ended questionnaire which consists of 23 statements for chemistry teachers and 18 statements for science students with 4 alternative respons option (very appropriate to very inappropriate). The data was analyzed by percentage formula. The results showed that chemistry teachers have not optimized the using computer laboratories in chemistry learning. The student participants, who have visual and kinesthetic style learning, have never used computer laboratory facilities that available in schools for the benefit of learning chemistry.