Reviewerly/exHarmony huggingface dataset:
Hybrid Authentication and Confidentiality framework for Secure Cloud Computing

Cloud Computing has emerged as a revolutionary technology which has proven its impact on various fields which require large data storage and high end processing services. However, the loosely defined interfaces between the layers pose a serious challenge to the security and privacy in the communication framework. This paper presents an encryption based security and privacy model using hybrid encryption scheme. It proposes a combination of Blowfish algorithm and RC6 to target the confidentiality and authentication aspects of security framework. The speed of Blowfish algorithm and the complexity of RC6 have been used in this work to present a highly secured cloud computing scenario. The effectiveness of the proposed technique is verified through the simulation analysis in terms of processing time and file size.
Semantic Tree Driven Thyroid Ultrasound Report Generation by Voice Input

The automatic speech recognition has achieved quite good performance in the medical domain in the past several years. However, it is still lacking of enough practical solutions with considering the characteristics of real applications. In this work, we develop an approach to automatically generate semantic-coherent ultrasound reports with voice input. The solution includes key algorithms based on a proposed semantic tree structure. The radiologists do not need to follow the fixed templates. They just need to speak their specific observations for individual patients. We have carried out a set of experiments against a real world thyroid ultrasound dataset with more than 40,000 reports from a reputable hospital in Shanghai, China. The experimental results show that our proposed solution can generate concise and accurate reports.
Passenger Flow Statistics Algorithm of Scenic Spots Based on Multi-Target Tracking

According to the real-time and accuracy requirements of obtaining passenger flow by surveillance videos in scenic spot, a model based on deep learning is proposed. Aided by Yolov4 and Deep Sort, passenger flow is counted by detection and tracking tourists. Aiming at the real-time requirement, the model compression method is used to replace the backbone network of Yolov4 with lightweight network mobileNetv3 to improve the detection speed. For the accuracy of the model, a detection scale is used to Yolov4 for extracting shallow features and the features are concatenated with deep features. Furthermore, Soft-NMS is used to process the detection results. The purpose of these improvements is to solve the dense tourists and small target problems in the surveillance videos. Then Deep Sort tracks the tourists target and obtains passenger flow information in the scenic spot. Through the experiment of the model in the surveillance videos, it is verified that this model meets the real-time requirements and has high accuracy in passenger flow statistics.
Proximity-Based Unification and Matching for Fully Fuzzy Signatures

We consider the problem of solving approximate equations between logic terms. The approximation is expressed by proximity relations. They are reflexive and symmetric (but not necessarily transitive) fuzzy binary relations. The equations are solved by variable substitutions that bring the sides of equations “close” to each other with respect to a predefined degree. We consider unification and matching equations in which mismatches in function symbol names, arity, and in the argument order are tolerated (i.e., the approximate equations are formulated over so called fully fuzzy signatures). This work generalizes on the one hand, class-based proximity unification to fully fuzzy signatures, and on the other hand, unification with similarity relations over a fully fuzzy signature by extending similarity to proximity.
Automatic Generation of Chemical Auxiliary Graphs Based on GDI+

It is unrealistic to draw different auxiliary graphs manually for different virtual chemistry experiments, which is time-consuming, laborious and inefficient. We provide an automatic graph generation module based on GDI + for the virtual experiment platform, and use object-oriented method to analyze and classify the features of virtual experiment schematics. Chemical graphic library is designed and constructed, including basic primitives such as lines, triangles, curves and circles, as well as complex graph primitives combined by these basic primitives. Singleton pattern is used to design the auxiliary class, and simple factory pattern is used to manage all the primitives classes, and assembly sequence and assembly relation information combined with connection nodes are used to automatically generate the best matching combination graphics and experimental schematic diagram.
Handwritten number recognition based on PCA and neural network

In recent years, Artificial Neural Network (ANN) has been widely used in digital handwriting recognition by virtue of its strong fault-tolerant ability and classification ability. However, in traditional recognition methods, taking the number of image pixels as the input number of neurons will cause problems such as long learning and training time and low efficiency. This paper combines principal component analysis (PCA) algorithm with BP algorithm for handwritten digit recognition. After image preprocessing, PCA algorithm is used to reduce dimension of original data. The first 10 principal components, the first 30 principal components, the first 45 principal components, and the first 60 principal components are sent to the neural network as input neurons for training. Then, the test data set was used for testing. Finally, the simulation was analyzed from three dimensions: training efficiency, learning time and identification accuracy. The results show that when the number of input neurons is 60 and the number of hidden layer neurons is 30, the highest recognition rate is only 0.13% lower than that of the number of input neurons is 784, but the training time of the neural network is reduced by 94 seconds, and the efficiency is improved by 32%. When the number of hidden layer neurons is 50, the highest recognition rate is only 0.25% lower than that of input neurons is 784, but the time is reduced by 237 seconds and the efficiency is improved by 63%. This design solves the problem of low learning efficiency in digital recognition well.
Review of Bowker &amp; Ciro (2019): Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community

Abstract This article reviews Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community £60978-1-78756-722-1
Aspect Detection Enhancement for Aspect Based Sentiment Analysis

This paper proposes a new approach to aspect-based sentiment analysis to provide a more accurate insight about sentiment towards certain aspects relevant for the military environment. The goal of this paper is to obtain an assessment of different approaches to aspect detection applicable to NATO specific corpora.
The Nonlinear Time Sequence Analysis in the Alpine-Himalayan Earthquake Zone

The characteristics of the earthquake activity in the Eurasian earthquake zone, which is the second largest earthquake zone in the world, was investigated by researchers. The earthquake activity of the Eurasian earthquake zone was analysed in various disciplines, such as earth dynamics, rock mechanics, geology and tectonics. The emergence of fractal theory provided a new direction in exploring the characteristics of the earthquake activity in the Eurasian earthquake zone. This study processed the data on the earthquake activity in the Eurasian earthquake zone by self-similarity method and scaled invariant feature test and used the rescaled range analysis method to analyse the nonlinear time series fractal characteristics of the earthquake activity in the Eurasian earthquake zone. Results show that the time series of earthquake activity in the study area is not an independent Poisson process, which exhibits the characteristics of scale invariance and long-range correlation. Approximately 80% of the H values of the earthquake activity iteratively increase and decrease for moderate earthquakes, which is mainly concentrated during the increasing stage. The time difference of the H value between the two-neighbouring earthquake shows that the H value fluctuates in the active earthquake region and is stationary in the relatively stable region. Strong earthquakes will likely occur in the next few years because the H value fluctuates.
Malaysian public university students’ challenging skills to pass Malaysian University English Test (MUET)

The Malaysian University English Test (MUET) was introduced in 1999 to bridge the gap between secondary and tertiary English language needs, primarily in English language competency. MUET examination consists of four language skills and some of the skills become a great challenge for students to pass. Apparently, it is compulsory for students to pass MUET examination with at least Band 3 before entering tertiary education system. However, many students were found to struggle with this requirement. Thus, this study aimed to investigate the most challenging skill that affects the students to obtain the minimum requirement. The study employed probability sampling method involving undergraduates from Faculty of Technical and Vocational Education (FPTV), Universiti Tun Hussein Onn Malaysia (UTHM), Johor. Data was collected from 500 students whose did not achieve Band 3 score from their previous MUET examination through an online questionnaire where 217 of them responded. Based on the frequency and percentage of the data analysis, 180 students (83%) of the population were very weak in speaking skills followed by 150 students who were weak in writing skills. However, 109 students were good in reading skills but there were 100 out of 217 students who were weak in listening skills. Thus, speaking skill was found to be the main challenge of MUET examination followed by writing skills. Meanwhile, listening skill is the third most difficult skill and followed by reading skill at the last place for MUET examination among UTHM students. Based on the findings, sufficient effort must be taken by the educational system to improve the speaking skills competency of the students. In fact, it is relevant to investigate in detail the way English language proficiency could help the Malaysian students during their tertiary education.
SERVICE CREATION FOR RE-NORMALISING ESSENTIAL BUSINESS ENVIRONMENT: A PREVENTION BASED DIGITAL TECH APPROACH

Abstract This research investigates service creation in/after effect of coronavirus pandemic targeting the essential business environment. It follows prevention through design approach to facilitate business owners to maintain their business environments at low COVID contraction risks, for both customers and staff. The effectiveness of recommended prevention practices (like social distancing and hand-sanitising) is uncertain at public workplaces, simply due to inevitable workers and customers interactions. Such uncertainty, especially in cases of retail stores and hospitals, raises a need for the design of services and support systems for common/necessary public business activities to reduce the burden on people involved. This research investigates the risk-related metrics to realise such digital services, focussing on three types: congestion at the work environment, disinfection of store area/objects, and sanitisation of people and staffs involved. Based on this, a digital technology-based service COVSAFE was created and tested through a proof-of-concept implementation for a supermarket business environment. This implementation and its evaluations highlight the bottlenecks/challenges for realising this system in everyday scenarios.
Machine Learning Constructives and Local Searches for the Travelling Salesman Problem

The ML-Constructive heuristic is a recently presented method and the first hybrid method capable of scaling up to real scale traveling salesman problems. It combines machine learning techniques and classic optimization techniques. In this paper we present improvements to the computational weight of the original deep learning model. In addition, as simpler models reduce the execution time, the possibility of adding a local-search phase is explored to further improve performance. Experimental results corroborate the quality of the proposed improvements.
НАУЧНО-ИССЛЕДОВАТЕЛЬСКАЯ ДЕЯТЕЛЬНОСТЬ БУДУЩЕГО УЧИТЕЛЯ МУЗЫКИ

The article shows the role of the teacher in the development of students' research activities by combining theory with practice, since a future music teacher, along with a personal and creative position and pedagogical skill, should have competencies in assessing the ideas and approaches put forward by modern theory and practice of education. In professional training, this task can be successfully implemented when the student performs elements of research activities. In this context, it seems relevant to identify the features of research activities and its role in the formation of the personality of a future music teacher.
Spectral Characteristics of Listvenites and Serpentineites Along Ophiolite-Decorated Megashears (Suture Zones) in the Arabian Shield Using ASD Fieldspec and Satellite Data

The present study is conducted to better understand the spectral signature of the listvenites and serpentinites in relation to their mineralogy and to apply the acquired knowledge to understand the mineralogical controls over the satellite reflectance data. Five areas located along the Yanbu, Tharwah-Bir Umq, Nabitah, and Al Amar-Idsas sutures, Arabian Sheild, Saudi Arabia are selected for the present study. Field spectral measurements have been performed using an ASD FieldSpec instrument to obtain the spectral properties of listvenites, serpentinites exposed at Jabal Ess, Bir Umq, Jizah, Jebel Tays and Al Amar areas. Results of the study revealed that: (1) The listevenites at the study areas have variable compositions and can easily differentiate between gray (non-oxidized) and brown (oxidized) listvenites exposed at the study areas. Gray listvenites are characterized by low reflectance values (below 20%) whereas brown listvenites are characterized by moderate to high reflectance values. Generally, all listvenites have absorption feature around 2.35 µm wavelength region which is attributed to the presence of carbonate minerals mainly magnesite and calcite. The listvenites appear as white (e.g., Bir Umq, Jabal Tays and Al Amar areas) to cyan (Jabal Ess area) on the false color composite band ratio image (5/7, 5/1 and 5/4 * 3/4 in RGB). (2) The serpentinites in the five examined locations have similar compositions and spectral signatures. They are largely composed of hydrous silicate minerals, carbonate minerals (~90 vol%), and opaque phases (10 vol%). The hydroxyl-carbonate absorptions are caused by vibrational processes that absorb in SWIR wavelength region. These absorptions could be readily observed on the handheld spectra for the serpentines from the Tays and Al Amar areas. Generally, serpentinites from Jabal Ess, Bir Umq, and Jizah area have low reflectance values due to the abundance of opaque phases (10 vol% Cr-spinel and magnetite) that lowers the overall spectra and spectral contrast making them dark on the 5/1 image. The serpentinites appear in shades of red on the false color composite band ratio image (5/7, 5/1 and 5/4 * 3/4 in RGB).
FORMATION AND DEVELOPMENT OF INTELLECTUAL POTENTIAL OF UNIVERSITY STUDENTS

This article focuses on the main task of strengthening national values - the modernization, informatization, further development of education, the development of intelligence, the formation of continuing education. The issue formation of the intellectual potential nation has been widely studied, considered, the main indicators are indicated. The formation of a competitive personality in line with modern requirements is accompanied by economic, social and political prosperity of the country. Information technology is developing rapidly, attitudes to education are changing, the world has changed. Today's approach to education has been updated and is still in the spotlight. We consider the education system not only as an education, but as a social phenomenon that contributes to the formation and development of the individual. The main task today is to improve the quality of education for school and university graduates. The main task is to develop the individual, to adapt to the environment - to cope with the current economic crisis, change and become a competitive person in this environment. Therefore, we know that the modernization of the structure of education in the country is the main mechanism for ensuring the civilized development of our modern society. Modernization of the entire structure of education, ensuring the competitiveness of Kazakhstan, is the main mechanism for ensuring the civilized development of modern society. The education system is not only a pedagogical category, but also a social phenomenon that has a huge impact on the development of human capital.
Dual-Channel Recalibration and Feature Fusion Method for Liver Image Classification

Aiming at the problem of poor medical image recognition accuracy caused by the large individual differences in liver pathological images and the large single feature size, a liver image classification method based on dual-channel recalibration and feature fusion is proposed. Firstly, we construct a dual-channel recalibration model based on attention mechanism, suppress useless features and calculate different feature channel weights, and embed them into the Inception_ResNet_V2 network structure. Secondly, we design fully connected layers on the basis of two-dimensional and three-dimensional convolutional neural networks, then add feature fusion layers to obtain deep semantic information in different dimensions. Finally, we use pre-training models to initialize the network structure and input the fused features into the XGBoost classifier for classification prediction. Experiments have been carried out with liver cancer race imaging datasets and a hospital patient dataset. Experimental results show that this method is superior to previous ones.
Salp Swarm Algorithm for Multimodal Image Registration

Salp Swarm Algorithm (SSA) is a relatively new metaheuristic optimization technique inspired by the swarming behavior of salps in oceans. Though used successfully in various engineering optimization problems, the use of SSA in image registration is yet to be explored. This work tests the effectiveness of SSA algorithm for multimodal image registration applications. The proposed algorithm is tested with different sets of infrared and visible images and its convergence rate is compared with the Particle Swarm Optimization (PSO) algorithm, frequently used in image registration. Results show that the Salp Swarm algorithm can effectively optimize the objective function and can provide accurate image registration results.
Image classification with multi-scale convolutional sparse representation

Convolutional sparse representations express a signal by a sum of convolutional filters and corresponding sparse coefficients. This paper proposes to employ the multi-scale, namely variable size, filters to extract features for image classification. The experimental results on the Yale B Face Database show that the proposed method increases the accuracy by up to 4% compared to conventional method using single-scale convolutional sparse representations which is the filter size is fixed in the image classification with convolutional sparse coding classification (CSCC).
Eyeball Movement Detection Using Sector Division Approach and Extreme Learning Machine

Eyeball movement is being widely used for many purposes. A lot of research is trying to find the best approaches and methods to detect, track and recognize the movements. In this research, we propose an approach to detect the direction of eyeball movements using Sector Division Approach and Extreme Learning Machine (ELM). The extraction process of Sector Division is detecting facial image, detecting the eye location using subset points in the Facial Landmark. Selected eye location is segmented and through several processes such as image cropping, conversion into grayscale image, blurring process, and finally binary process. The final image in the binary process is divided into 9 (nine) sectors and extracted resulting in 9 feature vectors. ELM is used to classify the eyeball movement. The optimal number of hidden neurons identified first before the model is used in the testing step. A total of 50 data is used to train the ELM to classify the eyeball movement. The ELM model is executed 5 (five) times to reduce the variability of the random weight in the ELM model. Testing is done by evaluating each eyeball movement using 12 still images in each direction. Based on the experiment, a number of 20 hidden neurons results in the highest predictive accuracy and is used in the testing step. The result shows that the proposed model is able to achieve a satisfactory result by showing an accuracy of 81.67%. The result of this study could be beneficial to be used in similar studies as using a small number of training data, basic feature extraction, and a small number of feature vectors could achieve satisfactory accuracy.
Multilevel structured convolution neural network for speech keyword location and recognition: MSS‐Net

Language keyword extraction has broad application prospects. Most current keyword search networks are based on speech recognition and text search. This method is helpless for some languages that are difficult to text and cannot locate the location where the keywords appear. A multi-level structure of speech keyword location and recognition system (MSS-Net), which can locate the location interval of keywords while recognizing keywords is proposed here. MSS-Net contains three levels. The first level is the voice start and end detection module, which is responsible for detecting the start and end positions of valid audio. When valid audio is detected, the second level is activated: the keyword detection module. The keyword detection module detects keyword information in the audio signal and locates the keyword position interval. The third level is the keyword recognition module. The system recognizes the detected position interval of the location to obtain specific keyword information. Comparative experiments show that MSS-Net is more accurate for keyword positioning, with a network recall rate of 94.91% and an accuracy rate of 96.32%, which is better than other network models participating in the experiment. In addition, the network has strong anti-interference ability, which can achieve good suppression effect for normal life noise. The ablation experiment verified the effect of the hierarchical network structure on the performance of speech recognition tasks.
Development of Teaching Materials in COVID-19 Pandemic

As an urgent issue, current education and training institutions are required to deal with the new coronavirus disease 2019. Against this background, it is necessary for any instructors to easily create teaching materials that can be quickly switched to distance learning at the minimum cost. This paper describes teaching materials development method that uses existing software and tools. Distance learning utilizes cloud computing. Based on these policies, we have created injection molding mold design teaching materials that students studying molds can learn while self-checking their understanding. With these texts, explanatory materials, and recorded teaching materials, the design process can be systematically grasped. Therefore, students studying mold design can easily understand. In addition, since these teaching materials are digital data, they can also be used as implementation reports to appeal in job hunting. The instructors who use these teaching materials repeat editing according to the vocational training. Thus, it will be highly effective training materials.