Reviewerly/exHarmony huggingface dataset:
Role Of Pattern Characteristics In Cross Correlation Based Motion Estimation

Objectives: To establish a pattern tracking based motion estimation algorithm for the stereovision based system and to investigate the effect of threshold value (Thv), size and population of patterns on the pattern tracking value.Methods: Proposed motion estimation algorithm correlates the set of motion frames captured from two high speed cameras configured in stereovision system.The correlation scheme was based on grayscale pattern tracking in moving frames.Pattern development and correlation algorithms were developed.A spherical object was given small random displacements and the motion was captured using stereovision system.The effectiveness of algorithm is evaluated with the pattern tracking value which should be close to 1 for perfect pattern match.Findings: The correlation results indicate that pattern tracking value were found to be 0.920 and 0.899 for left and right cameras respectively when the threshold value (>10) and size 10 pixels are considered with high population (200 patterns).With the increase in pattern size, the pattern tracking value decreased.Another study revealed that pattern tracking value was comparatively higher when the pattern population was maximum (200 patterns).The pattern tracking value again decreased when the threshold value (>15) is considered.It was concluded that pattern size of 10 pixels with threshold value (>10) is more pronounced for motion estimation.The proposed algorithm is verified for the 3D displacements of a rectangular plate mounted on XYZ translation stage.The pattern tracking values were 0.97, 0.95, 0.96 and 0.96, 0.94, 0.95 for X, Y and Z displacements respectively.The correlation algorithm is also coupled with the compression technique using wavelet based data compression.Novelty/Applications: The proposed algorithm can be efficiently applied for both in plane and out of plane motion estimation.The algorithm can provide constructive outcomes for small motion prediction with proper selection of pattern size and threshold value.
Implementasi Realitas Berimbuh pada Antarmuka Manusia-Mesin di Industri Proses

Process industries such as oil refineries, petrochemical plants, and power plants require a human-machine interface system to monitor continuously. The operator usually carries out monitoring via a human-machine interface. However, it is difficult to know the condition of process equipment in real-time. The implementation of augmented reality allows engineers to visualize process equipment in real-time when conducting field inspections. The implementation of augmented reality at the human-machine interface to the fluid catalytic cracking process in an oil refinery is discussed in this paper. The design was started by developing a three-dimensional process equipment model using Autodesk Inventor. The result of the three-dimensional model then using Unity 3D software connected to the Vuforia Engine was implemented on a gadget into an augmented reality application. Data communication performance analysis was carried out using inferential statistics methods to test variations in service quality at levels 0, 1, and 2. The result of the Tukey test showed that the communication network latency value in level 2 was significantly higher than levels 0 and 1, which was 0.704±0.108 seconds. These results indicate that augmented reality can be implemented on human-machine interfaces by ensuring the quality of data communication services using Message Queue Telemetry Transport (MQTT) protocol at levels 0 or 1.
Self-Aware Contextual Behavior Analysis for Service Quality Assurance Over Social Networks

Social media allows people to share their ideologue through an efficient channel of communication. The social dialogues carry sentiment in expression regarding a particular social profile, trend, or topic. In our research, we have collected real-time user comments and feedbacks from Twitter portals of two food delivery services. This is followed by the extraction of the most prevalent contexts using natural language analytics. Further, our proposed algorithmic framework is used to generate a signed social network to analyze the product-centric behavioral sentiment. Analysis of sentiment with the fine-grained level about contexts gave a broader view to evaluate and perform contextual predictions. Customer behavior is analyzed, and the outcome is received in terms of positive and negative contexts. The results from our social behavioral model predicted the positive and negative contextual sentiments of customers, which can be further used to help in deciding future strategies and assuring service quality for better customer satisfaction.
Exploring Vocabulary Items in Malaysia Year 5 English Language Textbook (English Plus 1)

Many studies have shown the importance of vocabulary in learning a second language.However, only a few studies explored the presentation of vocabulary in Year 5 English textbook (English Plus 1).This study aims to explore teachers' evaluation of the new vocabulary in the textbook.This study employed a quantitative method.A total of 10 teachers from suburban and urban Chinese Type Primary School in Perak, Malaysia were selected using a convenience sampling technique.The questionnaires were adapted from Hussin, Nimehchisalem and Kalajahi (2015).The findings revealed that the textbook does an excellent job of presenting vocabulary.It was discovered that no particular approach was used to teach the new vocabulary.At the end of the textbook, there was no glossary of the new vocabulary.The findings have consequences for students and English language teachers.This paper is also meant to give teachers with ideas for to improve their approaches in teaching vocabulary and correcting inadequacies in the presentation of vocabulary in textbooks.
Relational reasoning for real-time object searching

We propose a method that can reason and find hidden objects from a set of images. It discovers relationships between all objects detected by a backbone neural network. We focus on object searching and location determination in the real world. Given the name of an object, the system detects the object and outputs a bounding box containing it if it is in sight. Otherwise, the system outputs bounding boxes containing relevant objects around which the target object is most likely to appear. The primary process of the system includes training and reasoning. The former establishes system experience of object relations, and the latter implements object searching based on this experience. The system consists of a relational discovery module and a searching module. We have tested the proposed method on multiple datasets (COCO, PASCAL VOC, and ImageNet) and multiple backbone networks, and the results show that the proposed method has strong robustness and generalizability. The system can continuously highlight the most relevant objects in the line of sight, thus providing hints for approaching and locating the target objects until they are found, and the proposed method can meet the requirements of real time and accuracy if a suitable backbone network is selected.
Evaluation of melt quality during polymer extrusion

Polymer materials are widely spread and used in all industry sectors. The operating properties of polymer products make them practically indispensable in various industries. They have acquired widespread popularity and their production volumes are predicted to grow every year. The extrusion of polymers is one of the main methods for their processing. The melt uniformity is one of the main factors that influence the quality of the products and is determined by the level of mixing, i.e., by the redistribution of additives in a dispersion medium when these additives are introduced into the main polymer. The influence of working conditions, geometry of working bodies and other parameters on the melt temperature uniformity was determined. Nowadays, there are a lot of methods for determining the melt uniformity. They can be used indirectly in or after the extrusion process and can be short or long lasting, which in fact determines the acceptability of each of the methods. To determine the melt uniformity, statistical evaluation methods, which have been verified by many experiments and are well known, are often used. The analysis showed that the mixing index is most sensitive among the criteria described. &#x0D; Although statistical criteria of evaluating the melt uniformity are widespread, it is better to use parameters that allow the state of the mixture to be analyzed directly in the work process for the extrusion of polymers, to identify those that depend on the mixing process. Methods and criteria for verifying the melt quality were analyzed and potential use of their temperature uniformity for measuring and evaluating the melt quality directly in the extrusion process, to eliminate the costly sampling process, was determined.
Improvement of Accuracy and Handling of Missing Value Data in the Naive Bayes Kernel Algorithm

The lost impact on the research process, can be serious in classifying results leading to biased parameter estimates, statistical information, decreased quality, increased standard error, and weak generalization of the findings. In this paper, we discuss the problems that exist in one of the algorithms, namely the Naive Bayes Kernel algorithm. The Naive Bayes kernel algorithm has the disadvantage of not being able to process data with the mission value. Therefore, in order to process missing value data, there is one method that we propose to overcome, namely using the mean imputation method. The data we use is public data from UCI, namely the HCV (Hepatisis C Virus) dataset. The input method used to correct the missing data so that it can be filled with the average value of the existing data. Before the imputation process means, the dataset uses yahoo bootstrap first. The data that has been corrected using the mean imputation method has just been processed using the Naive Bayes Kernel Algorithm. From the results of the research tests that have been carried out, it can be obtained an accuracy value of 96.05% and the speed of the data computing process with 1 second.
Dependence of Mathematical Errors on Mathematical Thinking Style

The results of research of creative mathematical thinking have been analyzed and the expediency of studying its individual differences through the analysis of mental styles of solving mathematical problems has been stated. The aim of the article is to identify the reasons of students’ mathematical errors and to analyze the influence of mathematical thinking style on the reasons and sense of mathematical errors in the process of solving creative mathematical tasks. To identify and determine the essence of mathematical thinking styles, the method of analysis of search actions of subjects during the solution of mathematical problems of different classes was used. The results of the research. It is stated that mathematical thinking style is a holistic system of interconnected actions, by means of which the mental mathematical result is achieved. It has been found that mental errors occurred at all stages of the search process of students with different mathematical thinking styles. Differences in mathematical thinking styles become the basis of the content of errors. There are three groups of causes of mental errors: ignorance, low-quality analogy, irrelevance of links. Different influence of ignorance on the search actions of students with different styles of mathematical thinking is proved. It is stated that the introduction of low-quality analogy in different styles of mathematical thinking is different in content. It was found that the irrelevance of references and conclusions has a different nature in the thinking of students with different thinking mathematical styles. Conclusions. Thinking style is manifested throughout the process of solving a mathematical problem, provides a different content of the search process, through which a positive mathematical result is achieved or leads to mental errors.
Analyzing Linguistic Features for Answer Re-Ranking of Why-Questions

Why-type non-factoid questions are ambiguous and involve variations in their answers. A challenge in returning one appropriate answer to user requires the process of appropriate answer extraction, re-ranking and validation. There are cases where the need is to understand the meaning and context of a document rather than finding exact words involved in question. The paper addresses this problem by exploring lexico-syntactic, semantic and contextual query-dependent features, some of which are based on deep learning frameworks to depict the probability of answer candidate being relevant for the question. The features are weighted by the score returned by ensemble ExtraTreesClassifier according to features importance. An answer re-ranker model is implemented that finds the highest ranked answer comprising largest value of feature similarity between question and answer candidate and thus achieving 0.64 Mean Reciprocal Rank (MRR). Further, answer is validated by matching the answer type of answer candidate and returns the highest ranked answer candidate with matched answer type to a user.
Surface Reconstruction of Microscale Objects Based on Grid-Patterned Structured-Light Measurements

A structured-light projection system was designed for microscale objects with surface heights that ranged from tens to hundreds of microns. The system was composed of a universal projector and microscope system that supported editing the attributes of structured-light patterns in real-time and was capable of projecting microscale patterns. On this basis, reconstructing the metal surfaces of microscale objects based on grid patterns of structured light was investigated, the internal and external parameters of microscope vision and projection systems were calibrated, and an image algorithm for grid-node detection was designed. The results indicated that the proposed method successfully reconstructed the three-dimensional (3D) surface of microscale objects, and the reconstruction results were consistent with the original surfaces. With 95% confidence, the reconstruction precision in the X- and Y-directions was approximately ±4.0 μm and in the Z-direction was approximately ±7.5 μm. The designed system and the proposed method were suitable for 3D-shape measurement of microstructures in microscopic fields and can be adapted to meet a broader range of applications, as compared to current methods.
WBC-CNN: Efficient CNN-Based Models to Classify White Blood Cells Subtypes

Blood is essential to life. The number of blood cells plays a significant role in observing an individual’s health status. Having a lower or higher number of blood cells than normal may be a sign of various diseases. Thus it is important to precisely classify blood cells and count them to diagnose different health conditions. In this paper, we focused on classifying white blood cells subtypes (WBC) which are the basic parts of the immune system. Classification of WBC subtypes is very useful for diagnosing diseases, infections, and disorders. Deep learning technologies have the potential to enhance the process and results of WBC classification. This study presented two fine-tuned CNN models and four hybrid CNN-based models to classify WBC. The VGG-16 and MobileNet are the CNN architectures used for both feature extraction and classification in fine-tuned models. The same CNN architectures are used for feature extraction in hybrid models; however, the Support Vector Machines (SVM) and the Quadratic Discriminant Analysis (QDA) are the classifiers used for classification. Among all models, the fine-tuned VGG-16 performs best, its classification accuracy is 99.81%. Our hybrid models are efficient in detecting WBC as well. 98.44% is the classification accuracy of the VGG-16+SVM model, and 98.19% is the accuracy of the MobileNet+SVM.
Correction to: Foreground segmentation with PTZ camera: a survey

A Correction to this paper has been published: https://doi.org/10.1007/s11042-021-10995-w
A Survey on Mining Cryptocurrencies

Advanced monetary standards have acquired huge ubiquity nowadays. Bitcoin is the decentralized, disseminated, distributed virtual cash known cryptographic money. Bitcoin mining chips away at standard of the blockchain, which is believed to be one of this present century’s sharp advancement. The blockchain is the arrangement of blocks that are associated so that in the current block there is the hash of the past block. Any adjustment of information in any block in a blockchain brings about a blunder in the entire blockchain. A strategy called mining, where excavators settle a complex numerical riddle, produces Bitcoins. The excavators contend as quickly as time permits to mine the Bitcoin and guarantee the award. Mining should be possible by a solitary individual or by a pool, where a lot of excavators join to mine a solitary block in an organization.
Fuzzy Theory-based Air Valve Control for Auto-Score-Recognition Soprano Recorder Machines

The Arduino
Performance-efficient Recommendation and Prediction Service for Big Data frameworks focusing on Data Compression and In-memory Data Storage Indicators

The MapReduce framework manages Big Data sets by splitting the large datasets into a set of distributed blocks and processes them in parallel. Data compression and in-memory file systems are widely used methods in Big Data processing to reduce resource-intensive I/O operations and improve I/O rate correspondingly. The article presents a performance-efficient modular and configurable decision-making robust service relying on data compression and in-memory data storage indicators. The service consists of Recommendation and Prediction modules, predicts the execution time of a given job based on metrics, and recommends the best configuration parameters to improve Hadoop and Spark frameworks' performance. Several CPU and data-intensive applications and micro-benchmarks have been evaluated to improve the performance, including Log Analyzer, WordCount, and K-Means.
Online Shopping Featuring My Customized Avatar - Generating Customized Avatars for a Sustainable Shopping Experience in E-Commerce

Nowadays E-commerce and E-shopping become more and more popular, supported by the actual pandemic situation on one side but especially driven by technology and society transformation on the other.The product development process in clothing industry is in a state of constant change, implementing more and more digital, and especially virtual tools, within the past years.[1] Beside the product itself and physical as well as optical virtual fabrics and trimmings, especially the avatar plays an important role in this process -from the beginning in design and ending up in Ecommerce when presenting the products on matching avatars gains an attractive importance for the consumer.Companies developing their collections and products for target groups are aiming for fit and brand consistency.Anyway, within the product development process, there is a totally different understanding when it comes to design of avatars, visual expression on one side and technical and measurement reliable avatars on the other.[1, 2] Beside the question of individualization of customized avatars, the importance of avatar design at the beginning of the process becomes "cruelly" important again at the end when getting in "touch" with the customer and its individual and subjective feelings.[3,4] Though the complete product development process is lifted up to a cloud-based system -at the end of the day a valuable customer has to buy the products to make them "alive", and keep the company running.
A Novel Parallel Algorithm with Map Segmentation for Multiple Geographical Feature Label Placement Problem

Multiple geographical feature label placement (MGFLP) is an NP-hard problem that can negatively influence label position accuracy and the computational time of the algorithm. The complexity of such a problem is compounded as the number of features for labeling increases, causing the execution time of the algorithms to grow exponentially. Additionally, in large-scale solutions, the algorithm possibly gets trapped in local minima, which imposes significant challenges in automatic label placement. To address the mentioned challenges, this paper proposes a novel parallel algorithm with the concept of map segmentation which decomposes the problem of multiple geographical feature label placement (MGFLP) to achieve a more intuitive solution. Parallel computing is then utilized to handle each decomposed problem simultaneously on a separate central processing unit (CPU) to speed up the process of label placement. The optimization component of the proposed algorithm is designed based on the hybrid of discrete differential evolution and genetic algorithms. Our results based on real-world datasets confirm the usability and scalability of the algorithm and illustrate its excellent performance. Moreover, the algorithm gained superlinear speedup compared to the previous studies that applied this hybrid algorithm.
Faculty Perfectionism And University Human Resource Management

The paper considers perfectionism as a social phenomenon which characterizes professional activity of university research and teaching staff aimed at leadership of employee and university as an educational institution.It is determined by high professional standards which are specified by the Ministries of Education in the United States of America and United Kingdom of England and Northern Ireland.Universities implement policies which follow rigorous recruitment and selection process, performance evaluation according to high standards and advanced expectations.Perfectionism in the academic environment is viewed as a product of social mindset traditionally established in the system of higher education as an aspiration to advanced development, economic and social stability, and foreseeable future.Therefore, perfectionism can be considered as both personal and public phenomenon.Perfectionism among research and teaching staff is referred to professional activity, in the first instance; however, it is also cultivated by university social wellbeing policy conducted by human resource management department aimed at providing social conditions for a long lifespan, physical, emotional and psychological health, financial solvency, social activity and continuous professional development.All of these measures create opportunities for personal perfectionism expressed through successful professional growth and achievement of high goals.Public perfectionism in higher education constitutes a basis for human capital development and, ultimately, social welfare.
APLIKASI ALGORITMA GENETIKA DALAM PENJADWALAN MATA KULIAH

Making a class schedule becomes problem and takes a long time because of several obstacles such as the lack of lecture rooms, the lack of teaching staff, and the high of courses available in one semester. This study aims to apply genetic algorithms in making class schedules to facilitate the process of making class schedules. The method used is the waterfall method with the stages of the Software Development Life Cycle. The results of the application of genetics application show that the process of making course schedules can overcome the constraints of 1) space and time clashes, 2) lecturer conflicts, 3) Friday prayer times clashing, 4) there is a time when the lecturer wants for certain reasons, and 5) practicum in the laboratory room. By passing these constraints, the application of genetic algorithms in course scheduling is categorized as effective. Based on the results of running on 51 lecturers (51 chromosomes), the average running time 30 times in a row is 25.86 minutes so that the use of genetic algorithm applications in scheduling courses is efficient.