Reviewerly/exHarmony huggingface dataset:
Dreamlet: A New Representation and Migration of Seismic Wavefield in Full Local Domains

Seismic events have limited time duration, vary with space/traveltime and interact with the local subsurface medium during propagation.Partitioning is a valuable strategy for nonstationary seismic data analysis, processing and wave propagation.It has the potential for sparse data representation, flexible data operation and highly accurate local wave propagation.Various local transforms are powerful tools for seismic data segmentation and representation.In this paper, a detailed description of a multi-dimensional local harmonic transformed domain wave propagation and imaging method is given.Using a tensor product of a Local Exponential Frame (LEF) vector as the time-frequency atom (a drumbeat) and a Local Cosine Basis (LCB) function as the space-wavenumber atom (a beamlet), we construct a time-frequencyspace-wavenumber local atom-dreamlet, which is a combination of drumbeat and beamlet.The dreamlet atoms have limited spatial extension and temporal duration and constitute a complete set of frames, termed as dreamlet frames, to decompose and represent the wavefield.The dreamlet transform first partitions the wavefields using time-space supporting functions and then the data in each time-space blocks is represented by local harmonic bases.The transformed wavefield is downward-continued by the dreamlet propagator, which is the dreamlet atom evolution weightings deduced from the phase-shift one-way propagator.The dreamlet imaging method is formulated with a local background propagator for large-scale medium propagation and combined with a local phase-screen correction for small-scale perturbations.The features of dreamlet migration and imaging include sparse seismic data representation, accurate wave propagation and the flexibility of localized time operations during migration.Numerical tests using Sigsbee 2A synthetic data set and real marine seismic data demonstrate the validity and accuracy of this method.With time-domain localization being involved, the dreamlet method can also be applied effectively to target-oriented migration and imaging.
Teachers’ Instructional Components of Warm-up Rehearsal in Elementary School Chorus in South Korea

The preliminary purpose of the study is to find out the instructional components of choral warm-up rehearsal for teachers in Korean elementary school. For the study, Focused Group Interview Method was used, and the participants were eleven Korean primary school teachers who have had school chorus conducting or instructing. This survey shows that the teachers in primary schools who have experienced instructing a school chorus do not feel satisfied with warm-up techniques in their rehearsal. The reason why the teachers are not happy or confident about warm-up routine was various, but mostly they are supposed to teach all subjects, including school chorus. They have not enough time to prepare or practice for warm-up contents. They feel something to be equipped with proper skills of the warm-up in the rehearsal to help their students sing in more enjoyable ways in school chorus. Through the study, it suggests that the regional education office must provide a proper and practical training course to the teachers who are scheduled to lead a school chorus. Besides, it suggests that several elements to consider for establishing a warm-up model and its necessary skills, which can be applied and improve musical aptitude for students who participate in the school chorus. https://doi.org/10.26803/ijlter.19.4.5
Character-Level Japanese Text Generation with Attention Mechanism for Chest Radiography Diagnosis

Chest radiography is a general method for diagnosing a patient’s condition and identifying important information. Therefore, a large amount of chest radiographs have been taken. In order to reduce the burden on medical professionals, methods for generating findings have been proposed. However, the study of generating chest radiograph findings has primarily focused on the English language, and to the best of our knowledge, no studies have studied Japanese data on this subject. The difficult points of the Japanese language are that the boundaries of words are not clear and that there are numerous orthographic variants. For deal with two problems, we proposed an end-to-end attention-based model that generates Japanese findings at the character-level from chest radiographs. We evaluated the method using a public dataset of Japanese chest radiograph findings. Furthermore, we confirmed via visual inspection that the attention mechanism captures the features and positional information of radiographs.
Chordiogram image descriptor based on visual attention model for image retrieval

A novel shape-based image retrieval is presented in this study. The foreground and background contents of images are strongly concealed, so they are represented individually to reduce their influence on each other in the proposed approach. The Otsu method is employed for segmenting the foreground from the background, and the saliency map and edge map are then clearly identified. Saliency reduces the time cost for feature computation, so salient edges are computed for the foreground and background images based on the selective visual attention model. Autocorrelation-based chordiogram image descriptors are computed separately for the foreground and background images, which are then combined in a hierarchical manner to form the proposed new descriptor. This approach avoids the concealment of foreground and background information, and the new descriptor is rich in geometric and its underlying texture, structure and spatial information. The proposed novel shape-based descriptor performs considerably better than conventional descriptors at content-based image retrieval. The proposed shape descriptor were extensively tested at image retrieval based on the Gardens Point Walking, St Lucia, University of Alberta Campus, Corel 10 ​k, and self-photographed image data sets. The precision and recall values were compared for the proposed and state-of-the-art-approaches when applied for shape-based image retrieval from these databases. The proposed shape descriptor provided satisfactory retrieval results in the experiments.
An Implementation of Non-Local Means Algorithm on FPGA

Non-Local means (NL-means) algorithm is a robust image denoising algorithm. Its computational complexity is, however, higher than other algorithms, and its availability is limited. In this paper, we propose an implementation method of the NL-means algorithm on FPGA. In the NL-means, the cross correlations between the small windows are repeatedly calculated, and a large number of intermediate data have to be held temporarily to reduce the amount of its computation. In our approach, the scan direction of the image is changed in the zigzag way. This zigzag scan increases the computation time because of the recalculation on the scan borders, but the required memory size can be drastically reduced. We have implemented the circuit on a Xilinx FPGA, and showed that with a small size FPGA, its real-time processing is possible.
Comparison of VR Movies and Traditional 2D Movies: A Study on Brain Waves

This paper conducted a study focusing on virtual reality and ergonomics, comparing the differences in brain wave patterns when watching 2D and VR Movies to examine human-computer interaction to understand the characteristics of VR and influences. This paper collects 30 participants' EEG signals when watching 2D and VR Movies, and performs statistical analysis on the obtained frequency, power and other data. The results show that there are statistical differences in the alpha and beta waves of watching 2D videos and VR Movies. VR Movie will bring stronger alpha and beta wave activity.
Assessing the Multi-labelness of Multi-label Data

Before constructing a classifier, we should examine the data to gain an understanding of the relationships between the variables, to assist with the design of the classifier. Using multi-label data requires us to examine the association between labels: its multi-labelness. We cannot directly measure association between two labels, since the labels’ relationships are confounded with the set of observation variables. A better approach is to fit an analytical model to a label with respect to the observations and remaining labels, but this might present false relationships due to the problem of multicollinearity between the observations and labels. In this article, we examine the utility of regularised logistic regression and a new form of split logistic regression for assessing the multi-labelness of data. We find that a split analytical model using regularisation is able to provide fewer label relationships when no relationships exist, or if the labels can be partitioned. We also find that if label relationships do exist, logistic regression with $$l_1$$ regularisation provides the better measurement of multi-labelness.
Fuzzy Neural Network Models for Classification

In this paper, we combine neural networks with fuzzy logic techniques. We propose a fuzzy-neural network model for pattern recognition. The model consists of three layers. The first layer is an input layer. The second layer maps input features to the corresponding fuzzy membership values, and the third layer implements the inference engine. The learning process consists of two phases. During the first phase weights between the last two layers are updated using the gradient descent procedure, and during the second phase membership functions are updated or tuned. As an illustration the model is used to classify samples from a multispectral satellite image, a data set representing fruits, and Iris data set.
PCAS: Pruning Channels with Attention Statistics for Deep Network Compression.

Compression techniques for deep neural networks are important for implementing them on small embedded devices. In particular, channel-pruning is a useful technique for realizing compact networks. However, many conventional methods require manual setting of compression ratios in each layer. It is difficult to analyze the relationships between all layers, especially for deeper models. To address these issues, we propose a simple channel-pruning technique based on attention statistics that enables to evaluate the importance of channels. We improved the method by means of a criterion for automatic channel selection, using a single compression ratio for the entire model in place of per-layer model analysis. The proposed approach achieved superior performance over conventional methods with respect to accuracy and the computational costs for various models and datasets. We provide analysis results for behavior of the proposed criterion on different datasets to demonstrate its favorable properties for channel pruning.
An Empirical Investigation of Sentiment Analysis of the Bug Tracking Process in Libre Office Open Source Software

In this work we are studying the sentiment in Open Source Software projects and more specifically in the process of bug reporting, to investigate the human factor, namely, the feedback from the community (end-users, developers, testers, etc.). One of the characteristics for which Open Source Software has gained attention, over the years, is the fact that it is continuously being tested and maintained by its community of volunteers. Sentiment analysis, a rapidly growing field, can enrich software evaluation with a social aspect. Results suggest that FLOSS projects’ bug reports can potentially constitute a rich emotionally - imbued information source.
Bifurcation and Chaos from DTC Induction Motor Drive System

In this paper, three phase induction motor (IM) has been modelled in stationary reference frame and controlled by using direct torque control (DTC) method with constant V/F ratio. The obtained drive system consists of nine nonlinear first order differential equations. The numerical analysis is used to investigate the system behavior due to control parameter change. The integral gain of speed loop is used as bifurcation parameter to test the system dynamics. The simulation results show that the system has period-doubling route to chaos, period-1, period-2, period-4, and then the system gets chaotic oscillation. A specific value of the parameter range shows that the system has very strong randomness and a high degree of disturbance
Managing the Syntactic Blindness of Latent Semantic Analysis

Natural Language Processing is a sub-field of Artificial Intelligence that is used for analysing and representing human language automatically.Natural Language Processing has been employed in many applications, such as information retrieval, information processing, automated answer grading etc.Several approaches have been developed for understanding the meaning of text, commonly known as semantic analysis.Latent Semantic Analysis is a widely used corpus-based approach that evaluates similarity of text on the basis of semantic relations among words.Latent Semantic Analysis has been used successfully in different language systems for calculating the semantic similarity of texts.However, Latent Semantic Analysis ignores the structural composition of sentences and therefore this technique suffers from the syntactic blindness problem.Latent Semantic Analysis fails to distinguish between sentences that contain semantically similar words but have completely opposite meaning.Latent Semantic Analysis is also blind to the syntactic structure of a sentence and therefore cannot differentiate between sentences and lists of keywords.In such a situation, the comparison between a sentence and a list of keywords without any syntactic structure gets a high similarity score.In this research we propose an algorithmic extension to Latent Semantic Analysis which focuses on syntactic composition of a sentence to overcome Latent Semantic Analysis's syntactic blindness problems.We tested our approach on sentence pairs containing similar words but having different meaning.Our results showed that our extension provides more realistic semantic similarity scores.
THE METHOD OF TRAINING COMPETITIVE SPECIALISTS WITH THE USE OF COMPUTER TECHNOLOGY

The current situation in the national and global economy is characterized by uncertainty, a high level of risk, and significant variability in social and financial conditions. Universities and colleges  educational institutions that provide services in the field of professional education  also feel the significant impact of these transformations, which affects the level of demand for various specialists, due to the urgent needs of the formed market and aimed at improving the adaptive skills of a professional in modern conditions.Nowadays, the specialist is forced to adapt to the needs of the labor market, which are characteristic for such processes as the decline in demand in low-skilled labor force; the emergence of a special stratum of highly competent workers ensuring the proper functioning of complex and expensive machinery; a decrease in the number of persons employed in production, and expansion of personnel whose work responsibilities are related to construction, design, maintenance, promotion; encouraging employees who are able to handle the latest devices and units and speak a foreign language (usually English) within the framework of their profession; active search for managers who are able to build collective activities, use human and inanimate (material, chronological, etc.) resources with the maximum possible result; increasing attention to specialists who demonstrate a high level of professionalism at the very beginning of an adult career. The current period is characterized by intensive modernization of the educational services market, which encourages universities and colleges to apply a new strategy for training qualified personnel. The teacher should intelligently alternate teaching methods and techniques to ensure the training of competent professionals in demand on the labor market. It is expected that the variety of these ways will awake students interest in learning new things in the educational process. The function of a teacher is to strengthen the motivational foundation of students activities, encourage them to acquire and master new knowledge and skills without assistance actively, to intensify cognitive needs, to increase interest in academic discipline with the help of modern technologies.
The role of chatterbots in enhancing tourism: a case study of Penang tourism spots

Chatterbots have been widely used as a tool for conversational booking assistance mainly for hotels such as the Expedia. This paper extends the use of chatterbot beyond booking by presenting the proof of concept of a chatterbot expert system called the VIZARD. The proposed VIZARD is developed using an expert system shell called verbot. The core of Vertbot 5 is the natural language processing (NLP) engine based on pattern matching. The core Verbot 5 engine is responsible for finding matches to a given user input string and firing the appropriate rule. The findings from the user acceptance test concluded that majority of the respondents agreed that the VIZARD expert system stands at an unbiased state while being more aligned on supporting the usefulness of the system.
THE EFFECTS OF EXPLICIT SUPPLEMENTAL INSTRUCTION ON STUDENTS’ LANGUAGE COMPETENCE

This paper reports on the initial results of the project “Supplemental instruction as a tool for improving students’ language competence at the Faculty of Philology”. The project started in 2017 at the Faculty of Philology in Stip with the main objective of determining the effects of supplemental instruction on improving students’ language competence. Based on the results of the placement test for all participants in order to determine their level of language competence, the participants were divided into two groups – A and B. The supplemental instruction in the first semester was conducted with group A, which was the experimental group, using explicit teaching methods, while group B was the control group. The results of the experimental group showed that there was a significant difference between the pre-test and the post-test, especially in the writing section of the test, while the results of the control group did not show any statistically significant difference.
FORMATION OF A CULTURE OF FOREIGN LANGUAGE PROFESSIONAL COMMUNICATION IN THE MODERN TECHNICAL EDUCATION SYSTEM OF UKRAINE

Єльцова С. С
PINGU Principles of Interactive Navigation for Geospatial Understanding

Monitoring conditions in the periglacial areas of Antarctica helps geographers and geologists to understand physical processes associated with mesoscale land systems. Analyzing these unique temporal datasets poses a significant challenge for domain experts, due to the complex and often incomplete data, for which corresponding exploratory tools are not available. In this paper, we present a novel visual analysis tool for extraction and interactive exploration of temporal measurements captured at the polar station at the James Ross Island in Antarctica. The tool allows domain experts to quickly extract information about the snow level, originating from a series of photos acquired by trail cameras. Using linked views, the domain experts can interactively explore and combine this information with other spatial and non-spatial measures, such as temperature or wind speed, to reveal the interplay of periglacial and aeolian processes. An abstracted interactive map of the area indicates the position of measurement spots to facilitate navigation. The design of the tool was made in tight collaboration with geographers, which resulted in an early prototype, tested in the pilot study. The following version of the tool and its usability has been evaluated in the user study with five domain experts and their feedback was incorporated into the final version, presented in this paper. This version was again discussed with two experts in an informal interview. Within these evaluations, they confirmed the significant benefit of the tool for their research tasks.
Towards an Integrated Meta-Model for Requirements Engineering

Traditional, plan-driven, requirements engineering identifies separate phases in the process with well-documented outputs associated with of each of them. The plan-driven system development is suit ...
Using the Cuckoo Search for Generating New Particles in Particle Swarm Optimization Algorithm

This study is focused on as Cuckoo Search (CS), one of the current meta-heuristic optimization algorithm.The CS algorithm is useful in generating and searching for the most optimum particles of important meta-heuristic optimization algorithm, known as the Particle Swarm Optimization (PSO), to enhance its performance.This optimization is confirmed through a benchmark online optimization and actual problems.The PSO algorithm performance is also compared with differing algorithms representative of the area.The CS optimal solutions outperform alternative current solutions as CS has distinct search features.The study findings have implications for future studies and practice.
IMAGE RESTORATION AND ENHANCEMENT USING DEEP LEARNING

During the process of image acquisition, sometimes images are degraded because of various reasons like low resolution of camera, motion blur, noise etc.This paper presents the work associated with the Image Restoration & Enhancement techniques.The process of recovering degraded image is known as Image Restoration.Image restoration includes denoising image, image inpainting, etc.Here we proposed Convolution Neural Network (CNN) with Median Filter for removing noise, Region filling Exemplar Based Inpainting Algorithm for image inpainting.Image enhancement is one amongst the problem in image processing.Haze, low lighting etc. are the various problems in images.The aim of Image enhancement is to process an image such that result is more suitable than original image for specific application.Here for haze removal we implement dark channel prior algorithm and for lightning low-light image we proposed functions.Image enhancement improves the appearance of the image.
A Study on Optimization Algorithm (OA) in Machine Learning and Hierarchical Information

Genetic Algorithm is a division of machine learning, where the computers are programmed to teach themselves to complete the given task over time. In our project, we simulate many rockets to fly towards the target specified. Genetic algorithm revolves around three main concepts. First generate a population of random rockets that fly in random directions. Each rocket is implemented as an array of Vectors, where each vector points to a specific direction at a given time. We then apply a fitness function that calculates the best performing rockets in each generation. With the fitness function, we now select the best rockets with which we form the next population. This involves two steps: First step is the crossover. Choose two parents i.e., two rockets and use their vector values to create a child rocket. This is done by retrieving the first half vectors from the first parent and second half vectors from the second parent and fuses them to build the child rocket, Second step is the mutation. This step is very crucial. If mutation is not applied, we will receive a new population that is only built around best performing ones from the previous population.We will then land in local maxima and may never reach the target. Mutation helps create individual rockets that go beyond the local maxima to reach the target. But over mutation will lead to too much diversity that is not beneficial to the system. Thus, define a mutation rate that is optimally balanced. In mutation, we choose a rocket with random probability, and alter its vector values randomly. This new population of rockets forms the next generation.
Multi-source relational data fusion

Pandey 等 [3] 基于语义 Web 技术, 提出了 安全警示系统 Safety Check