Reviewerly/exHarmony huggingface dataset:
PARTICIPATORY MODELLING OF THE CAUSAL RELATIONSHIP BETWEEN SELF-BELIEFS AND PERSONALITY USING AGGREGATED FUZZYRELATIONAL MAPMODEL AND KOSKO-HAMMING DISTANCE

Fuzzy Relational Map is an efficient tool in establishing the causal relationship between two disjoint sets of concepts. In situations, wherein the data available is unsupervised involving emotions and reasons described in a language that is vague or difficult to interpret, Fuzzy Relational Map is the pertinent approach of choice. Personality is a psychological construct that has different traits and these traits have some unique behavioral beliefs underneath. In this paper, an extension of a fuzzy relational map called aggregatedFuzzyRelational Map is used to study the association between the traits of personality and the behavioral beliefs that influence a certain type of personality. Further, the fixed points are analyzed with the aid of Kosko-Hamming Distance.
How do Public Figures Get Involved in the Fight Against a Pandemic? Analysis of Selected Proﬁles in Social Media

Due to the subsequent isolation caused by the coronavirus pandemic, social media has become a key element in promoting social activity and civic engagement. The purpose of the article is to present a review and analysis of proﬁles in social media of public ﬁgures who were involved in the ﬁght against the virus during a pandemic in Poland. The choice of proﬁles was made on the basis of the popularity of fanpages in social media. The presentation contains fanpages in social media. The presentation contains an analysis of the results of quantitative and qualitative research on Facebook: Robert Lewandowski, Ewa Chodakowska and Agnieszka Radwańska. In the article, the author attempts to answer the question: Do recipients who observe a given fanpage receive social activity of their idol positively? Will pandemic posts be more commented on, liked by recipients than posts from everyday life, if so why? What social campaigns are published most often?
DMI Apprenticeship: Sharing and Replicating Musical Artefacts

The nature of digital musical instruments (DMIs), often bespoke artefacts devised by single or small groups of technologists, requires thought about how they are shared and archived so that others can replicate or adapt designs.The ability for replication contributes to an instrument's longevity and creates opportunities for both DMI designers and researchers.Research papers often omit necessary knowledge for replicating research artefacts, but we argue that mitigating this situation is not just about including design materials and documentation.Our way of approaching this issue is by drawing on an age-old method as a way of disseminating knowledge, the apprenticeship.We propose the DMI apprenticeship as a way of exploring the procedural obstacles of replicating DMIs, while highlighting for both apprentice and designer the elements of knowledge that are a challenge to communicate in conventional documentation.Our own engagement with the DMI apprenticeship led to successfully replicating an instrument, Strummi.Framing this process as an apprenticeship highlighted the non-obvious areas of the documentation and manufacturing process that are crucial in the successful replication of a DMI.
Analysis of Topic Modeling on Phrase-Based SMT system for English-Hindi Translation

After availability of cheaper large memory and high performance processors, Statistical Machine Translation (SMT) methods have drawn attention of researchers NLP. Phrase-based SMT has shown better results than word-based SMT. To improve performance of machine translation system further, different systems have been developed which use phrase-based SMT as a baseline system. Domain adaptation is one of most popular example of such systems. In this paper also phrase-based SMT system is used as baseline to apply topic model for English-Hindi translation. This baseline system is also used for result comparison with topic model system. Both systems are trained using MERT. The analysis shows improvement in results obtained by using topic modeling system.
On the possibilities of interpreting Neolithic pottery

The task of processing the multi-traditional ceramic material of the Late Neolithic settlement at Polgár-Csőszhalom-dűlő prenecessitated the development of a comprehensive approach by which all kinds of available information that may be detected in a ceramic object or in its find context are utilized and integrated into a closed, unbroken logical system interlinking objects and culture. This paper surveys the main topics and aspects of this theoretical structure, summarizing the author’s current understanding on the possible roles and functions of Neolithic pottery.
Vision-Based Navigation and Control of Unmanned Aerial Vehicles

The use of visual recognition to control unmanned drones is a relatively new field of research.The pathing especially can come in many forms, usually in the form of giving destination points which get turned into directions and altitudes for the flight.In an attempt to create a more intuitive control experience we are implementing a visual shape pathing algorithm.This means that instead of feeding some destination, we can draw some shape that the drone's pathing will track.To accomplish this we are using the popular mathematics programming language matlab, which allows us to make a control scheme for various functions of the drone.Next we developed a system to track the movements of the drone using a visual recognition algorithm that follows certain color on the video feed.Finally we want to implement some machine learning aspects by tracking the drone's distance from the original provided shape, creating a self-correcting pathing system to closely follow the shape.By creating a shape based pathing system, we are enabling a more intuitive way to draw pathing for unmanned aircraft on any scale from the micromanagement in a factory or warehouse setting, to the zoomed out pathing of a crosscontinental map.
Kesulitan Belajar Siswa Dalam Pembelajaran Daring Pada Pokok Bahasan Hidrokarbon

Online learning requires students and teachers to communicate interactively by utilizing information and communication technology, such as computer media with the internet, cellphones, videos. Hydrocarbons are an abstract subject matter of chemistry, involving concepts, using molecular models of one kind or another to describe molecules in three dimensions. This study aims to determine the difficulties of students in online learning the subject of hydrocarbons. This research is a descriptive study with the instrument used 15 item questions and a questionnaire of 20 items. Students have difficulty answering the problem of determining the primary, secondary, tertiary C atom; IUPAC nomenclature for alkanes, alkenes, alkenes; and reactions to hydrocarbons. Online learning on the subject of hydrocarbons is not enough to use an android cellphone, but requires a laptop, computer or notebook. Student interest and motivation are very much needed in independent learning to be the key to the success of online learning.
Load Allocation of Virtual Machines based on Enhanced Throttled Load Balancing Algorithm

Today’s world is fully dependent on the cloud services and it has become more than mandatory to prevent the faults that occur during the process. Cloud computing has grown tremendously in the past few years and the demand for its services are also drastically increasing all over the world. Virtualization technology is the base for cloud computing.  Virtual machines are provisioned during virtualization and care should be taken to handle the faults that arise during this process. To facilitate the proper functioning of the Virtual Machines (VMs), the proposed work uses the technique of an Enhanced Throttled Load Balancing Algorithm to efficiently allocate load to the VMs to the particular task and proactively handling faults. Cloud analyst, a GUI simulator is used to compare and analyse the performance and load balancing factors during virtualization and allocate load to the VM based on the outcome.
Study Of Personal Career Orientations At The Final Stage Of Higher Education

The article examines the problem of the development of career orientations among graduates as a necessary condition for their professional self-fulfillment.The process of career orientation development is influenced by both external (family, peers, society, culture) and internal (interests, values, motivation) factors.The university's objective is not only to give necessary theoretical and practical knowledge to students, but also to influencing their personal and professional development, helping to fulfill themselves in the profession, indicating the paths of development after completing their studies.The career orientations of students formed in the learning process determine career goals and prospects, which is reflected in the success of professional development in general.The existence of a variety of career orientations in professional interactions makes it easier to get through negative events and act more diligently in a changing environment.The article also presents an analysis of an empirical study of the characteristics of career orientations among graduates of various specialties (economists, psychologists, primary school teachers, and designers).On the basis of the data obtained, a project was developed to form career orientations and increase the level of professional self-development of graduates -Me and my career.The project is aimed at enhancing the career orientations of graduates as well as at the development of a mature professional personality and strengthening motivation for personal and professional growth.
Measuring the satisfaction of employees based on the service quality of (ATM) Automated Teller Machine of the directorate of education in Thi Qar province: قياس رضا الموظفين بناء على جودة الخدمة لماكينة الصرف الآلي (ATM) لمديرية التربية والتعليم في محافظة ذي قار-العراق

The internet and networks have broadened the electronic financial business specifically the transaction of money. The novel banking industry in Iraq countering many obstacles especially security issues. The aggressive competition among the banks to attracting more customers resulting that the quality of the services became the cornerstone. The private banks try to improve the services for the customer by using Automated Teller Machine (ATM). The purpose of this study is describing the explanatory variables for service quality in the research model and examine the effect of service quality variables on employee satisfaction. Two hypotheses were formed to verify if their effect on service quality or not. using Quantitative approach with survey method to quantifies the responses collected via questionnaire. The questionnaires distributed to employees working in the directorate of education are350 just 302was returned and after the examination only 210 questionnaire was valid. Descriptive statistics, Pearson-correlation and multiple linear regression techniques in SPSS were used to analyze quantitative data collected from the questionnaire. the findings are medium agree of ease of use ATM, moderate disagree for accessibility and moderate disagree of security. Regression model finds the service quality factors affect employee satisfaction and as result H1 hypothesis accepted. finally, Pearson-correlation showed a positive relationship to all independent variable on employee's satisfaction and mentioned the highest positive correlation was the ease of use variable.
CPU and eGPU Support System Based on Naive Bayes Classification

Central Processing Unit (CPU) and External Graphics Processing Unit (eGPU) technology are known as overclocks which aim to make the device exceed the benchmarks set by the device maker. Until now there is no determination to rank the two hardware within certain limits such as hardware price range and year-by-year. Therefore, it is necessary to process the ranking of the hardware using Simple Additive Weighting (SAW) to obtain a ranking range and determine the weight per type of hardware analyzed. It can be classified using Naïve Bayes to determine results of criteria combination between two hardware to determine possible criteria into "not good" and "good". This classification used to determine probability criteria of choosing a combination of CPU and eGPU hardware. The results of this study are getting the best CPU and eGPU every year using SAW and then classifying it for pricing. In testing conducted on application of Naïve Bayes using 80% of training data has 2776 data and 20% of testing data has 695 data that will be tested for accuracy, precision, recall, and F1-score. For results of tests that have been carried out get 0.78 accuracy results, precision 1, Recall 0.764, and F1-Score 0.866.
Application of Deep Learning for Ergonomic Data Augmentation and Human State Recognition

In ergonomic experiments, a small number of participants is often a problem because a sufficient amount of data is not obtained. In recent years, human state recognition is wide-spread, and estimating the human state from biological information acquired from a wearable device, is useful for improving living behavior. While it is necessary to collect a sufficient amount of data in order to perform state estimation with a certain degree of accuracy, collecting the amount of data requires a considerable cost. This study attempted to expand physiological and psychological data using deep learning. Specifically, information on physiological indicators was added to ACGAN. From the verification using the actual experimental results, it was found that the accuracy of recognizing the human state was improved by using the augmented data compared to the case of learning with a small number of original data.
An Improved Detection Method for Dressing Behavior of Substation Workers Based on SSD

In the process of safe production, it is necessary to carry out rapid and accurate detection and timely warning of personnel's safe dress and operation to reduce casualties and property losses. However, the existing detection methods for personnel dressing and construction operations have problems such as slow detection speed and low detection accuracy. To solve such problems, an improved rapid detection algorithm for substation on-site operation based on single shot detector (SSD) is proposed. The convolution neural network vgg-16 of SSD detection algorithm was replaced by the lightweight convolution neural network mobile netv3-small to reduce the model parameters and improve the detection rate. At the same time, the feature pyramid network structure is used to fuse the more abstract features in the deep layer with the more detailed features in the shallow layer to improve the detection accuracy. Training and testing experiments are carried out by making the helmet data set HWear independently, and data enhancement technology is used to improve the detection performance of the model during training. The experimental results show that the improved SSD method improves the risk detection rate of substation field operation to 108 fps. And at the same time, compared with the average accuracy of traditional SSD algorithm, it is increased by 0.5%, which has certain practical significance.
Quantum Computing - An Introduction and Cloud Quantum Computing

Quantum computing uses the phenomena of quantum mechanics to perform operations on data and it’s a huge leap in the field of computation. A quantum computer is a computational device which uses quantum mechanical phenomena like superposition and entanglement of atoms, photons, electrons. The difference between Quantum computers and digital computers is that digital computers are based on transistors and require data to be encoded into binary digits (bits). Quantum computers use quantum bits (qubits) and use quantum properties to represent data. Quantum computers can solve certain computational problems like integer factorization faster than classical computers. Quantum computers can solve certain problems that no classical computer could solve in any feasible amount of time. The Quantum Computing Market was valued at USD 89M in the year 2016 and is expected to reach USD 949M by 2025. Industry leaders are trying to develop and launch an actual quantum computer and make it available for everyone. Many companies are working in this area and are providing platforms to build quantum algorithms. There could be around 2,000 to 5,000 quantum computers in the world by 2030. This paper provides an introduction to quantum computing, quantum mechanical phenomena used in quantum computing and quantum computing service providers
DEVELOPING STUDENTS’ WRITING SKILL IN ANALYTICAL EXPOSITION TEXT THROUGH CONTEXTUAL TEACHING AND LEARNING (CTL)

This research is aimed to find out whether there is difference of students’ writing skill in analytical exposition text after the implementation of Contextual Teaching and Learning (CTL). The participants for this research were one class consisting of thirty students from the eleventh grade of a vocational school in Garut. It involved accounting class b which was selected by using purposive sampling. This research implemented pre-experimental design as research method. The data were obtained from English essay writing skill test involving pre-test and post-test in order to know the students’ writing skill before and after using contextual teaching and learning (CTL) approach. The result of the instruments was analyzed quantitatively using statistical analysis. It can be seen in the result of the hypothesis testing through Wilcoxon Test shows that Zcritical= 0.0000082 &lt; significance level (α) =0.05. Therefore the alternative hypothesis of this research is accepted and null hypothesis of this research is rejected. And the normalized gain of this research is categorized as middle with average value 0.46. It means that contextual teaching and learning has significant effect to develop students’ writing skill in analytical exposition text.
Lightweight LSTM for CAN Signal Decoding

This paper describes an approach to identify undecoded Controller Area Network (CAN) data from one vehicle, based on the data similarity to previously decoded CAN data from another vehicle. Modern vehicles communicate data and signals from on-board sensors and controllers through the CAN bus. Networked sensors contain information such as wheel speeds, fuel gauges, turn signals, and radar signals. In the effort to use this information and make cars safer through human-in-the-loop CPS, signals on the CAN bus such as wheel speed and radar can be used to support the driver. However, data from the CAN bus are encoded and in some cases compressed, and different car manufacturers use different encoding schemes to represent data on the CAN bus. With hundreds of messages and thousands of possible encoding schemes to consider, it is laborious to identify the unique bits and encoding schemes that represent signals on each vehicle. In this study, we propose a method for training a Long Short-Term Memory (LSTM) neural network on known radar signals from one vehicle manufacturer, a Toyota, and successfully apply the network to identify the encoding for radar signals on a different vehicle, a Honda. By augmenting the training dataset with varied encoding bit boundaries, a small and lightweight LSTM network can learn to recognize radar data across different encoding schemes. The results are an improvement on exhaustive-search algorithms and other methods previously used in the search for such signals.
Subsets of Scales in Compositions Constructed by Similarity

We present techniques in musical composition based on subsets of scales, built on theoretical notions, together with a number of examples.The techniques we describe are for constructing compositions with reference to memory, via similarity.We begin with some technical elements: after introducing the technique of intersecting accompaniments, we describe similarity concatenation compositions, which are special compositions constructed via similarity.We then outline a method to solve the problem of approximating scales with frequency ratios generated by rational numbers with small numerators and denominators, via equal temperament.As well as the standard solution via 12 tone equal temperament, we present a solution via 31 tone equal temperament.We then introduce the notion of a connected triheptad, generalising the tonic, subdominant and dominant of the major scale.We next present some examples of the notions previously introduced.Example 1 features a connected triheptad, and Example 2 features a connected triheptad, a similarity concatenation composition, and an intersecting accompaniment.There follows a section on cubist sets, featuring a returning similarity concatenation composition.We then move a conceptual level higher: we consider the concatenation of similarity concatenation compositions via similarity.This is reminiscent of higher dimensional algebra, and there follows a formal approach to higher dimensional relations, together with an example in 31 tone equal temperament using the formalism described earlier.We use the formalism of braids for our higher dimensional relations.We end with a section on musical applications of paths in graphs, generalising the chromatic scale.
THE EFFECTS OF SIOP MODEL ON SELF-EFFICACY AND TEACHING SKILLS OF FOREIGN LANGUAGE TEACHERS

The article reveals the issue of the effects of using SIOP model on self-efficacy and teaching skills of foreign language teachers. It is stressed that SIOP is an approach that can help English language learners to achieve academic success while gaining English language proficiency. It is worth noting that sheltered instruction offers the model for making academic material understandable and increasing the academic English language skills of learners through teaching. Attention has been paid to language skills that can be developed through collaboration and interaction. It has been concluded that the using SIOP model in modern educational process opens up unlimited possibilities for the foreign language teachers and students.Key words: teacher efficiency, professional training, professional competence, language proficiency, academic success.
Investigating Electronic Defamation Using The Internet

The study was aimed at identifying the most important methods that help in developing criminal investigation skills to confront cyber-crimes. The study was based on more than one approach. It was based on the inductive method, which is based on the deductive approach. The most important findings were: Electronic evidence has a specific scientific value in referring to the facts it contains, but it is indispensable that it be legitimate, both in terms of existence, to be among the legally accepted evidence as a means of proof.&nbsp; 1. The reasons for the difficulty of detecting cybercrime can be explained by the fact that this crime does not leave any visible external impact that the offender can commit this crime in other countries and continents because cybercrime is a transnational crime. 2. The lack of technical and technical expertise in the police, prosecutors, and the judiciary. This is a major obstacle to proving cybercrime. This type of crime requires training and qualification in information technology, evidence collection, research, and trial in the computer and Internet environment. 3. Cybercrime may be part of traditional crime, in the sense that there is a traditional crime involving an electronic aspect.