Reviewerly/exHarmony huggingface dataset:
Comparison of Workers Performance in Human-Human and Human-Cobot Interactions

Collaborative robots (cobots) are widely used in manufacturing industries however there are some concerns whether they distract workers and affect their performance. The aim of this study was to investigate whether there exist any differences between the workers' eye behaviors while interacting with a human or a Cobot to complete a task. A total of 5 participants were enrolled in this study and performed 10 similar tasks with the help of a Cobot and a human. In the present study the eye movements of participants were recorded and four performance measures including total fixation duration, number of fixations, pupil diameter and peak velocity of saccades were reported. The results indicate that the average fixation duration and the number of fixations in Human-Cobot interaction were slightly higher than that in Human-Human interaction. However, the difference was not statistically significant. Similarly, no noticeable between-group differences in terms of other measured variables were observed.
Bosons vs. Fermions – A computational complexity perspective

Recent years have seen a flurry of activity in the fields of quantum computing and quantum complexity theory, which aim to understand the computational capabilities of quantum systems by applying the toolbox of computational complexity theory. This paper explores the conceptually rich and technologically useful connection between the dynamics of free quantum particles and complexity theory. I review results on the computational power of two simple quantum systems, built out of noninteracting bosons (linear optics) or noninteracting fermions. These rudimentary quantum computers display radically different capabilities—while free fermions are easy to simulate on a classical computer, and therefore devoid of nontrivial computational power, a free-boson computer can perform tasks expected to be classically intractable. To build the argument for these results, I introduce concepts from computational complexity theory. I describe some complexity classes, starting with P and NP and building up to the less common #P and polynomial hierarchy, and the relations between them. I identify how probabilities in free-bosonic and free-fermionic systems fit within this classification, which then underpins their difference in computational power. This paper is aimed at graduate or advanced undergraduate students with a Physics background, hopefully serving as a soft introduction to this exciting and highly evolving field.
Technology and Innovation in COVID-19 Pandemic Response in the Philippines

This chapter systematically collated and critically analyzed the practices, technologies, and innovations in the Philippines’ response to the COVID-19 pandemic from January to May 2020. The first part provided an overview of the State’s response to the outbreak and the evolution of its case categorization. The second part discussed the practices and tech-based innovations in the country’s health response: (a) the turn from traditional to new media reporting, (b) the utilization of website and App-based contact tracing mechanisms, and (c) the application of telemedicine and online health consulting. Meanwhile, the last part analyzed the role of technology and innovation in managing the COVID-19 outbreak. The authors argue that while technology-based innovations promote resource efficiency, people under poverty and those who do not have access to these technologies should be capacitated and aided by government institutions to ensure that no one is left behind in its COVID-19 response, in particular, and in strengthening public health in general.
Construction of Knowledge Graph Corpus Under the Framework of Artificial Intelligence

In recent years, artificial intelligence technology has been widely used. With the development of artificial intelligence technology, the traditional corpus building mode has entered the era of knowledge atlas corpus. In this process, artificial intelligence technology plays an important role, but at the same time, the existing construction technology of artificial intelligence knowledge graph corpus is still insufficient. Therefore, this paper establishes the research on the construction of knowledge graph corpus under the framework of artificial intelligence. This paper makes an in-depth study on the characteristics of corpus data under big data. According to the results, the construction of corpus under big data is faced with various types of data and uneven data quality. How to build a corpus under big data needs to be carried out from many aspects. In view of these problems, this paper puts forward the optimization and improvement strategy. The strategy is mainly operated from corpus construction and text preprocessing, optimization of knowledge reasoning algorithm, and knowledge fusion. Through the optimization measures in this paper, we can make up for the shortcomings of the existing knowledge graph corpus construction method. Compared with HanLP algorithm, the accuracy of part of speech tagging is improved by 27.242%, recall rate is increased by 20.364%, and harmonic average value is increased by 23.866%. Moreover, the accuracy of calculation is greatly improved by weighting.
Influence of supplement book implementation with project-based learning model on student communication skills in ethnobotanical courses

A supplement book is a book used as an additional teaching ingredient in a study. The research aims to know the supplement book effectiveness in learning on the ethnobotany course by looking at students’ communication skills. The research using the one-group pretest-posttest design. Implementation of the supplement book conducted in the Ethnobotany class of 2019-2020, Department of Biology, Universitas Islam Negeri Maulana Malik Ibrahim. Data analysis using paired sample test. The results showed a significant value of 0.000 smaller than alpha 0.05, so the supplement book with the implementation of project-based learning models capable of improving student communication skills in ethnobotanical courses.
Pengayaan Materi Praktikum Akuntansi Instansi Pemerintahan bagi Tim MGMP Akuntansi SMK Provinsi Sumatera Barat

The implementation of the 2013 revised 2017 curriculum in Vocational High Schools has introduced the field of government in the SMK curriculum with the subject accounting for government institutions / agencies. The 2013 curriculum introduces the terms remedial and enrichment in the learning process. The challenge in the future of professionalism faced by teachers is how teachers can develop the existing potential for students who can quickly understand the material that has been given considering that the field of government is a new field of for SMK teachers. This PKM aims to provide material enrichment for the subject of practicum for government institutions / agencies consisting of PPKD financial report preparation and government financial report consolidation. The implementation method applied in this PKM is capacity building and workshops. At the end of the activity the participants were able to understand 73.65% of the important material for PPKD and consolidation of regional government financial reports and were able to solve cases of PPKD financial report preparation and consolidation of regional government financial statements independently and correctly
Online education in the digital transformation system of Russian universities: Statistics 2020

Subject. In the context of digital transformation of the society, online courses, as a form of basic and additional education in universities, play a crucial role. Objectives. I consider the types and content of online courses used by universities for distance education, analyze the perception of the new forms of educational process by teachers and students, and positive and negative aspects of distance learning in 2020. Methods. The study employs statistical methods of data collection, generalization of basic statistics, analysis of obtained results and materials that are published in scientific publications and mass media, best practices for remote learning. Results. The paper provides consolidated assessment of positive and negative aspects of remote regime of the traditional form of education, the participation of universities in programs for online mass education, the demand for and directions of supplementary education in the Nizhny Novgorod Oblast, State support to digital transformation of universities in 2020. Conclusions. At the current stage of digital transformation of education, universities use different options to implement distance learning. Online courses, being the main form of modern university education, are developing and improving rapidly. They play an important role in the system of training specialists for the national economy and individual development.
A Critical Assessment of Poverty Reducing Employment Strategies for Africa

This paper critically assessed some employment poverty reducing strategies put in place in Africa.From the assessment, it was realized that the strategies did not adequately contribute to generate decent and well remunerating jobs that could substantially curb poverty.As a result of this limited success of the strategies was partly attributed to limited public investments, inadequate planning and execution of development projects, dreadful mismanagement and poor allocation of resources, etc.Consequently, Africa habours about half of the world's poor, vulnerable and underprivileged population.Thus, in an effort to reverse the situation, it is recommended that African countries should: provide professional and scientific training and education which is relevant to the needs of their economies; put in place a range of incentives which would attract foreign investments that will transform their primary and agricultural products locally; promote the intensive use of labour in implementing sustainable projects; and increase their investment in Science, Technology and Innovation.
Identification of Corrupted Data via $k$-Means Clustering for Function Approximation

In addition to measurement noises, real world data are often corrupted by unexpected internal or external errors.Corruption errors can be much larger than the standard noises and negatively affect data processing results.In this paper, we propose a method of identifying corrupted data in the context of function approximation.The method is a two-step procedure consisting of approximation stage and identification stage.In the approximation stage, we conduct straightforward function approximation to the entire data set for preliminary processing.In the identification stage, a clustering algorithm is applied to the processed data to identify the potentially corrupted data entries.In particular, we found k-means clustering algorithm to be highly effective.Our theoretical analysis reveal that under sufficient conditions the proposed method can exactly identify all corrupted data entries.Numerous examples are provided to verify our theoretical findings and demonstrate the effectiveness of the method.
Blind Non-Data-Aided Signal-to-Noise Ratio Estimation with Convolutional Neural Networks

Signal-to-noise ratio (SNR) estimation has always been an important part of the communication systems and multiple methods have been developed within that scope. In this paper, an SNR estimation method, which can work under difficult conditions, blindness to the modulation type, that many other previously developed methods would fail under, is presented. In the proposed method, deep learning and convolutional neural networks (CNN), which have shown high performances in recent years, were exploited. In conclusion, deep learning methods, that have applications in numerous areas, and SNR estimation, which is an essential problem in the communication systems, were brought together, and experimental results were presented.
The Prediction of Solar Radiation for Five Meteorological Stations in Libya Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

The prediction of solar radiation is very important tool in climatology, hydrology and energy applications, as it permits estimating solar data for locations where measurements are not available. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is presented to predict the monthly global solar radiation on a horizontal surface in Libya. The real meteorological solar radiation data from 5 stations for the period of 1982 - 2009 with diffrent latitudes and longitudes were used in the current study. The data set is divided into two subsets; the fist is used for training and the latter is used for testing the model. (ANFIS) combines fuzzy logic and neural network techniques that are used in order to gain more effiency. The statistical performance parameters such as root mean square error (RMSE), mean absolute percentage error (MAPE) and the coeffient of effiency (E) were calculated to check the adequacy of the model. On the basis of coeffient of effiency, as well as the scatter diagrams and the error modes, the predicted results indicate that the neuro-fuzzy model gives reasonable results: accuracy of about 92% - 96% and the RMSE ranges between 0.22 - 0.35 kW.hr/m2/day
Κατασκευή και αξιοποίηση συστήματος αυτόματης εκμάθησης πλοήγησης και αποφυγής εμποδίων σε ρομποτικά οχήματα

The goal of this project is the development of a system whose objective is the ability to navigate in agiven space as well as the circumvention of obstacles in said space, which will then be applied to arobotic vehicle. A monocular vision camera was used for the detection of the 3-d space. The branchof monocular vision has evolved rapidly in recent years and is utilized several fields. Specifically thecombination of mechanical vision and machine learning is considered to be of great interest to aplethora of researchers and investors seeking to produce great results in different research fields. Inthe general space of machine learning, the concept of neural networks has been rather useful which,due to their versatility, have boosted the methods and algorithms that have been used up to nowand has contributed to the solution of complex problems. Within the confines of this project 2methods have been utilized: In the first method we achieved the detection of obstacles using ArUcomarkers while in the second method we utilized neural networks for depth perception. To evaluatethe 2 methods we made use of a simulation environment as well as a compact developer kit.The goal of this project is the development of a system whose objective is the ability to navigate in agiven space as well as the circumvention of obstacles in said space, which will then be applied to arobotic vehicle. A monocular vision camera was used for the detection of the 3-d space. The branchof monocular vision has evolved rapidly in recent years and is utilized several fields. Specifically thecombination of mechanical vision and machine learning is considered to be of great interest to aplethora of researchers and investors seeking to produce great results in different research fields. Inthe general space of machine learning, the concept of neural networks has been rather useful which,due to their versatility, have boosted the methods and algorithms that have been used up to nowand has contributed to the solution of complex problems. Within the confines of this project 2methods have been utilized: In the first method we achieved the detection of obstacles using ArUcomarkers while in the second method we utilized neural networks for depth perception. To evaluatethe 2 methods we made use of a simulation environment as well as a compact developer kit.
Trace Transform Feature Learning for Offline Jawi Handwritten Recognition

Offline Jawi handwritten recognition is very important to allow efficient archiving and retrieving the original documents and increase the availability of the content. It is challenging task and still considered an open problem because the state-of-the-art recognizer performance is considered sub-par. The tradition trace Transform features extractor has potential, however the complexity of parameters tuning in feature engineered approach combine with independent non-learnable sub-word classifier produce sub-par Jawi sub-word recognition accuracy. The proposed trace Transform feature learning address the features extraction complexity by automatically discovers the features according to data. The features extractor and classifier trained end-to-end from raw input data to target class to find the optimum parameters. The trace transform process defined as layer similar with convolution process in Convolution Neural Network. This approach improves data representation and produce better Jawi handwritten recognition performance. trace Transform feature learning are more robust to Affine Transformations compared to the state-of-the-arts Convolution Neural Networks feature learning because its data representation invariant to rotation, slanting and skewing. This proposed feature learning performance evaluated with its performance in sub-word recognition performance using Jawi dataset. In this paper only single layer of trace transform feature learning compare with traditional trace transform feature and Convolution Neural network as the state-of-the-art feature learning. The performances are significantly better compared to traditional trace transform feature and able to compete with convolution neural network in single layer, three layers and comparable with eight layers.
Discrimination of Neoproterozoic granitic-phases in Southern-Sinai, Egypt. Remote sensing, petrography and geochemistry

At Sharm El-Sheikh area a unique association of Neoproterozoic Younger granitic phases is found. These granitic rocks are petrographically identified as monzogranite, syenogranite, alkali feldspar granite, and riebeckite granite. The magma sources of monzogranite and syenogranite are generally of magnesian affinity while those of Alkali-feldspar granite and riebeckite granite are ferroan affinity. Tectonically, the monzogranite and syenogranite were generated during syn- collision tectonic regime and those of Alkali-feldspar granite and riebeckite granite are (within-plate granite). Geochemically they are discriminated into fractionated A-type granites and I-type granite. Alkaline granite samples have the highest ∑REE upto 497.4 ppm while monzogranite samples have the lowest ∑REE reach 153.7. The most conspicuous negative Eu anomaly (Eu/Eu* = 0.1) is recorded in the in alkali feldspar and riebeckite granitoids while monzogranite and syenogranite samples display negligible negative to slight positive Eu anomalies (Eu/Eu* = 1.8). Landsat-8 bands (7, 4, 2) and (7, 3, 5) in RGB used in discrimination and lithologic mapping of the different granitoid rocks. Band rationing technique was successful in discrimination of the two main granitic phases of the study area.
MVCC Libraries: Evaluate Sources: Home

Learn how to decide whether a source is credible, and worthy of being used in your assignment.
An Assessment of the Role of National Culture as a Determinant of Entrepreneurial Orientation

Entrepreneurship is an important factor of production.It is considered as a source of innovative change.Thus it catalyzes enhancement in sustainable economic development of a nation.Entrepreneurship is inseparably interlinked with flexibility and knowledge.These two factors have gained importance as a source of competitive edge in the present globalized & interconnected economy.Entrepreneurship prevents concentration of economic activities, income and wealth and promotes decentralized development of commerce, trade and industry.This in turn, leads to removal of regional and industrial imbalance.Development of entrepreneurial activities and sustainable development in entrepreneurship have gained priority in national agenda across the world.Entrepreneurship is even more crucial for developing countries as it has high employment elasticity and potential for earning foreign exchange.However, entrepreneurship is essentially a behavioural aspect.Hence culture has a causal relationship with entrepreneurship.This paper aims at assessing the role of Hofstede's dimensions of culture in developing entrepreneurship in nations by using the technique of linear multi-variate regression.
The dualistic perspective of optimal learning experience

Under the initial inspiration of Seligman and Csikszentmihalyi (2000), the 4th wave of contemporary scientific psychology is particularly interested in the empirical study of optimal human development. It also brings to light the theory of optimal or autothetic experience developed in the 1970s by Csikszentmihalyi. Over the past 35 years, empirical studies have mainly focused on the “bright sides of flow” (Csikszentmihalyi, 2014), because someone who is in a flow state is enjoying a positive subjective experience. However, flow has also an amoralcharacter (Delle Fave, Massimini & Bassi, 2011; Nakamura & Csikszentmihalyi, 2002). Some researchers are involved in the study of this phenomenon that has been called the “dark side of flow” (Schuler, 2012). In this presentation, I will first highlight the links between optimal experience in online learning (Heutte, Fenouillet, Martin-Krumm, Boniwell, & Csikszentmihalyi, 2016) and student motivation and persistence based on empirical research in the context of a Massive Open Online Course (MOOC) including 3132 students. The study shows the relationship between optimal motivation (Deci & Ryan, 2008; Fenouillet, Heutte, & Vallerand, 2015), orientation goals (Ames, 1992; Elliot, 2005) and self-regulation (Pintrich, 2003; Zimmerman, 2001). Next, the research results provide an opportunity to discuss the dualistic perspective of Flow (Heutte, 2020a, 2020b), including the effects of the “dark side of flow’. These effects include the peak of stupidity (Kruger & Dunning, 1999) linked to the illusion of having understood (ignoring one's own ignorance) and these are particularly shown by the “free learners” who learn in communities without interaction with the academic world.
EXPLORING THE NEXUS BETWEEN INTERNET USE AND UNDERGRADUATE STUDENTS PERFORMANCE IN READING COMPREHENSION TEST: A CASE STUDY OF TWO UNIVERSITIES IN ANAMBRA STATE

The study explores the nexus between internet use and undergraduate students’ performance in reading comprehension tests. The population consisted of undergraduate students of two universities in Anambra State: Nnamdi Azikiwe University, Awka and Chukwuemeka Odumegwu Ojukwu University, Uli. The sample size was 200 male and female students randomly selected from the two universities. A well-structured questionnaire and a reading test made up of a reading passage and comprehension questions were administered to respondents. The data collected from the questionnaire were analyzed using arithmetic mean while hypotheses were tested with Analysis of Variance (ANOVA).The result of the findings showed that the reading comprehension of undergraduate students that use the Internet to a high extent is 79.49 while that of those that use it to a low extent is 71.81, thus, a mean difference of 4.68 in favour of high internet users. This suggested that high internet users scored higher and performed better in the reading comprehension test irrespective of location. The study further revealed that there was no statistically significant interaction between gender and internet use on students’ reading test. This is shown by f= .84, P= .36. The study therefore recommended that undergraduate students should be encouraged to always use the Internet as a tool to tap valuable reading resources which can assist in the promotion of autonomous learning and make them more independent and resourceful.
Algoritma Machine Learning

daftar isi :Bab 1 pendahuluan Bab 2 linear regression Bab 3 logistic regressionBab 4 hierarchical clustreringBab 5 cartBab 6 reinforecement learningBab 7 suport vector machineBab 8 neural networkBab 9 deep learningBab 10 q-earningBab 11 k-nnBab 12 k-meansBab 13 naive bayes classifierBab 14 random forestBab 15 materi pengayaan Bab 16 pemrograman pythonBab 17 soal latihan