Reviewerly/exHarmony huggingface dataset:
Aesthetic Language Guidance Generation of Images Using Attribute Comparison

With the vigorous development of mobile photography technology, major mobile phone manufacturers are scrambling to improve the shooting ability of equipments and the photo beautification algorithm of software. However, the improvement of intelligent equipments and algorithms cannot replace human subjective photography technology. In this paper, we propose the aesthetic language guidance of image (ALG). We divide ALG into ALG-T and ALG-I according to whether the guiding rules are based on photography templates or guidance images. Whether it is ALG-T or ALG-I, we guide photography from three attributes of color, lighting and composition of the images. The differences of the three attributes between the input images and the photography templates or the guidance images are described in natural language, which is aesthetic natural language guidance (ALG). Also, because of the differences in lighting and composition between landscape images and portrait images, we divide the input images into landscape images and portrait images. Both ALG-T and ALG-I conduct aesthetic language guidance respectively for the two types of input images (landscape images and portrait images).
Efficient Novelty Detection Methods for Early Warning of Potential Fatal Diseases

Fatal diseases, as Critical Health Episodes (CHEs), represent real dangers for patients hospitalized in Intensive Care Units. These episodes can lead to irreversible organ damage and death. Nevertheless, diagnosing them in time would greatly reduce their inconvenience. This study therefore focused on building a highly effective early warning system for CHEs such as Acute Hypotensive Episodes and Tachycardia Episodes. To facilitate the precocity of the prediction, a gap of one hour was considered between the observation periods (Observation Windows) and the periods during which a critical event can occur (Target Windows). The MIMIC II dataset was used to evaluate the performance of the proposed system. This system first includes extracting additional features using three different modes. Then, the feature selection process allowing the selection of the most relevant features was performed using the Mutual Information Gain feature importance. Finally, the high-performance predictive model LightGBM was used to perform episode classification. This approach called MIG-LightGBM was evaluated using five different metrics: Event Recall (ER), Reduced Precision (RP), average Anticipation Time (aveAT), average False Alarms (aveFA), and Event F1-score (EF1-score). A method is therefore considered highly efficient for the early prediction of CHEs if it exhibits not only a large aveAT but also a large EF1-score and a low aveFA. Compared to systems using Extreme Gradient Boosting, Support Vector Classification or Naive Bayes as a predictive model, the proposed system was found to be highly dominant. It also confirmed its superiority over the Layered Learning approach.
Ultra Lite Convolutional Neural Network for Fast Automatic Modulation Classification in Low-Resource Scenarios

Automatic modulation classification (AMC) is a key technique for designing non-cooperative communication systems, and deep learning (DL) is applied effectively to AMC for improving classification accuracy. However, most of the DL-based AMC methods have a large number of parameters and high computational complexity, and they cannot be directly applied to low-resource scenarios with limited computing power and storage space. In this letter, we propose a fast AMC method with lightweight and low-complexity using ultra lite convolutional neural network (ULCNN) consisting of data augmentation, complex-valued convolution, separable convolution, channel attention, and channel shuffle. Simulation results demonstrate that our proposed ULCNN-based AMC method achieves an average accuracy of 62.47% on RML2016.10a and only 9,751 parameters. Moreover, ULCNN is verified on a typical edge device (Raspberry Pi), where the interference time per sample is about 0.775 ms. The reproducible code can be downloaded from GitHub\footnote{https://github.com/BeechburgPieStar/Ultra-Lite-Convolutional-Neural-Network-for-Automatic-Modulation-Classification}.
Application of federated learning in manufacturing

A vast amount of data is created every minute, both in the private sector and industry. Whereas it is often easy to get hold of data in the private entertainment sector, in the industrial production environment it is much more difficult due to laws, preservation of intellectual property, and other factors. However, most machine learning methods require a data source that is sufficient in terms of quantity and quality. A suitable way to bring both requirements together is federated learning where learning progress is aggregated, but everyone remains the owner of their data. Federate learning was first proposed by Google researchers in 2016 and is used for example in the improvement of Google's keyboard Gboard. In contrast to billions of android users, comparable machinery is only used by few companies. This paper examines which other constraints prevail in production and which federated learning approaches can be considered as a result.
Bottom-up Trust Registry in Self Sovereign Identity

Self sovereign identity is a form of decentralised credential management. During credential verification, data exchange only happens between the data owner and the verifier without passing through any third parties. While this approach offers a privacy-centric solution, it poses a challenge. How do verifiers trust that the credential is vouched by a trusted source? More specifically, how do verifiers know that the issuer has the reputation or is authorised to issue the credential? In this paper, we propose a trust registry design that handles the aspect of human trust in self sovereign identity. We also introduce an incentivisation mechanism for the trust registry in order to motivate each stakeholder to participate actively and honestly.
U(1)-symmetric Gaussian fermionic projected entangled paired states and their Gutzwiller projection

We develop a formalism for constructing particle-number-conserving Gaussian fermionic projected entangled pair states [U(1)-GfPEPS] and show that these states can describe ground states of band insulators and gapless fermions with band touching points. When using them as variational Ansätze for two Dirac fermion systems ($π$-flux model on the square lattice and $[0,π]$-flux model on the kagome lattice), we find that the U(1)-GfPEPS, even with a relatively small bond dimension, can accurately approximate the Dirac Fermi sea ground states. By applying Gutzwiller projectors on top of these U(1)-GfPEPS, we obtain PEPS representation of U(1)-Dirac spin liquid states for spin-1/2 systems. With state-of-the-art tensor network numerics, the critical exponent in the spin-spin correlation function of the Gutzwiller-projected $π$-flux state is estimated to be $η\approx 1.7$.
On the enumeration of orbits of unipotent groups over finite fields

We show that the enumeration of linear orbits and conjugacy classes of $\mathbf{Z}$-defined unipotent groups over finite fields is "wild" in the following sense: given an arbitrary scheme $Y$ of finite type over $\mathbf{Z}$ and integer $n\geqslant 1$, the numbers $\# Y(\mathbf{F}_q) \bmod q^n$ can be expressed, uniformly in $q$, in terms of the numbers of linear orbits (or numbers of conjugacy classes) of finitely many $\mathbf{Z}$-defined unipotent groups over $\mathbf{F}_q$ and finitely many Laurent polynomials in $q$.
Clustering Based on Nonparanormal Graphical Mixture Models

Graphical mixture models provide a powerful tool to visually depict the conditional independence relationships between high-dimensional heterogeneous data. In the study of these models, the distribution of the mixture components is mostly considered multivariate normal with different covariance matrices. The resulting model is known as the Gaussian graphical mixture model. The nonparanormal graphical mixture model has been introduced by replacing the limiting normal assumption with a semiparametric Gaussian copula, which extends the nonparanormal graphical model and mixture models. This study proposes clustering based on the nonparanormal graphical mixture model with two forms of $ell_1$ penalty function (conventional and unconventional), and its performance is compared with the clustering method based on the Gaussian graphical mixture model. The results of the simulation study on normal and nonparanormal datasets in ideal and noisy settings, as well as the application to breast cancer data set, showed that the combination of the nonparanormal graphical mixture model and the penalty term depending on the mixing proportions, both in terms of cluster reconstruction and parameters estimation, is more accurate than the other model-based clustering methods.
Comparison of Clustering High Dimensional Data by Random Projections Method and Some Common Methods of Dimensional Reduction

Comparison of Clustering High Dimensional Data by Random Projections Method and Some Common Methods of Dimensional Reduction
Advanced Panels

So far, we’ve looked at working with data directly in panels and arranging those panels on dashboards. Now we can take things a step further and see how to customize visualizations in more detail. In this chapter, we’ll look at overriding default behavior for visualizations for specific portions of data while leaving the rest unchanged. And we’ll look more deeply at transformations, learning how to reformat and alter the data itself.
Lossless Compression Methods for Real-Time Images

This paper proposes and implements two lossless methods, to compress real-time greyscale medical images, which are Huffman coding and a new lossless method called Reduced Lossless Compression Method (RLCM), both of which were tested when applying a random sample of greyscale medical images with a size of 256x256 pixels.Different factors were measured to check the compression method performances such as the compression time, the compressed image size, and the compression ratio (CR).The system is fully implemented on a field programmable gate array (FPGA) using a fully hardware based (no software driven processor) system architecture.A Terasic DE4 board was used as the main platform for implementing and testing the system using Quartus-II software and tools for design and debugging.The impact of compressing the image and carrying the compressed data through parallel lines is like the impact of compressed the same image inside a single core with a higher compression ratio, in this system between 7.5 and 126.8.
Spam Comments Detection on Instagram Using Machine Learning and Deep Learning Methods

The more popular a public figure on Instagram (IG), the number of followers also increase. When a public figure posts something, there are many comments from other users. In fact, from all the comments, not all of them are relevant to the post, such as advertising, links, or clickbait comments. The type of comments that are irrelevant to the post is usually called spam comments. Spam comments will interfere with information flow and may lead to misleading information. This research compares machine learning (ML) and deep learning (DL) classification methods based on our collected Indonesian IG spam comment dataset. This research was conducted in the following steps: dataset preparation, pre-processing, simple normalization, features generation using TF-IDF and word embedding, application of ML and DL classification methods, performance evaluation, and comparison. The authors compare accuracy, F-1, precision, and recall from ML and DL results. This research shows that ML and DL methods do not significantly differ. The Linear SVM, Extreme Tree (ET), Regression, and Stochastics Gradient Descent algorithms can reach the accuracy of 0.93. At the same time, the DL method has the highest accuracy of 0.94 using the SimpleTransformer BERT architecture. The difference between ML and DL methods is not significantly different.
Entrepreneurship Education at Faculties of Education

This paper discusses the importance of entrepreneurship education and development of entrepreneurial competencies.Authors started from the meaning of entrepreneurial competencies as the person's ability to realise an idea, to possess a tendency of introducing changes and to accepting, supporting and adapting to innovations arising from external factors.Through a comparative overview of entrepreneurship education in some European countries and the Republic of Serbia, we wanted to underline the framework of this system with special emphasis on entrepreneurship education in higher education.Given the psychological attributes important for the development of entrepreneurial competencies (need for achievement, locus of control, tolerance of ambiguity, self-confidence and innovativeness), we presented ways to encourage and develop these attributes at faculties of education.The paper also proposes specific ways and forms of entrepreneurship education within compulsory and elective modules, as well as extracurricular activities through project teaching, practical activities within institutions and/or companies, and adequate theoretical framework.
Teachers and Challenges of Digital Technology

Historical changes in communication technology substantially affect the education system.Today's youth is fully dependent on digital technology and social networks.Until recently, students learned linearly, rationally mastering curriculum by reading.Digital environments offer great opportunities for developing education.Apart from entertainment, students use the media for educational purposes as they can find a lot of teaching materials on the Internet.Teachers must be aware of these generational shifts in order to adjust the dynamics of teaching as new generations of students require a faster change in teaching methods and forms of work.This paper quotes the results of the research on attitudes teachers in Croatia have towards the use of digital aids in classroom teaching.High percentage of respondents believe that digital technology helps in motivating students to work and adopt content and improves the quality of teaching educational content.Teachers are aware of the importance of digitization in teaching and express a positive attitude towards the utility of technological aids in teaching.
THE CORRELATION BETWEEN STUDENTS’ MOTIVATION AND SPEAKING ABILITY OF THE ELEVENTH GRADE STUDENTS IN SMK N 03 KOTABUMI ACADEMIC YEAR 2020/2021

There are some problems found in students’ speaking ability. Students’ seems not confident to speak English in the class, students have a lack of vocabulary, and the students have low motivation. In addition, motivation is very important for students to improve their speaking in the class. If the students have high motivation in learning, they will be interested in speaking English.The purpose of this research to find out whether or not there is the correlation between students’ motivation and speaking ability of the eleventh grade in SMK Negeri 3 Kotabumi academic year 2020/2021. This research was descriptive correlational study. This research had two variables, they were independent variable (X) which was students’ motivation and dependent variable (Y) was speaking ability. The researcher applied Proportional random sampling technique to determine the sample of the research. The total sample of this research was 37 students. The population in this research was thirteen classes of the eleventh grade in SMK Negeri 3 Kotabumi, which consist of 405 students. The instrument used to collect the data were an oral test and questionnaire of students motivation. The result of this research showed that Ha is accepted with the result of hypothesis test tobserved is greater that ttable which is 15.652 ≥ 1.687, it means that there is significant correlation between students motivation and speaking ability of the eleventh grade in SMKN 03 Kotabumi academic year 2020/2021.
Citizen science as a data-based practice: A consideration of data justice

[This corrects the article DOI: 10.1016/j.patter.2021.100224.].
An Analysis of Students’ Difficulties in Understanding Simple Present Tense at Tenth Grade Student’s Marketing SMKN 2 Kotabumi

Simple present tense is part of the basic tense that must be mastered by students in learning English if students do not fully understand the simple present tense material, it can result in the achievement of learning outcomes. present tense and the most dominant difficulties experienced by students in understanding the simple present tense. This study uses a qualitative descriptive method which aims to determine the difficulties of students in understanding the simple present tense and to find out the most dominant difficulties experienced by students of class X marketing at SMKN 2 Kotabumi. The data collection in this study was a questionnaire as the main data and follow-up interviews as supporting data. There are 8 students who are the object of research in data collection for marketing students in class X. The results showed that there were 4 aspects of students' difficulties in understanding the simple present tense, including concepts, functions, patterns, and time signals of the simple present tense. The most dominant difficulty experienced by students in understanding the simple present tense is the pattern, many students sometimes make mistakes in making sentences, compiling simple present tense sentences and sometimes greeting students using tobe, verbs in sentence patterns.
THE USE OF LEARNING SPEAKING STRATEGIES BY MALE AND FEMALE STUDENTS OF THE FIFTH SEMESTER ENGLISH EDUCATION STUDY PROGRAM IN MUHAMMADIYAH UNIVERSITY OF KOTABUMI

Speaking is a natural ability possessed by every human being. Speaking is seen as a necessary positive personal characteristic. In speaking, there are several types including imitative, intensive, responsive, interactive and extensive, this type is one of the things that can support the ability to learn and speak in English. This is what makes researchers interested in raising the title of the study to find out what strategies are used by male and female when speaking. This study uses a qualitative research method by taking six students of the 5th semester English education study program class 2019 Muhammadiyah University of Kotabumi, three males and three females. Researchers used triangulation techniques, namely, observation, interview and questionnaire. Data analysis was carried out in several stages, namely, translating data, reducing data, displaying data, and drawing conclusions. The results showed that the researchers found differences in the strategies used by male and female students in the process of learning English. Male students use more metacognitive strategies to improve learning abilities and scores than female students, male students also do not use affective strategies in the learning process due to the ineffectiveness of these strategies. While female students use affective strategies in their learning process. On the other hand there are similarities in the use of strategies in learning but the lack of application and training so that there are differences in abilities and results obtained by each student.
An ANALYSIS OF STUDENTS DIFFICULTIES IN SPEAKING SKILL AT THE FIFTH SEMESTER OF UNIVERSITY OF MUHAMMADIYAH KOTABUMI ACADEMIC YEAR 2021/2022

Speaking English is very difficult for non-native speakers. Speaking skill is ability to interactive process which involves face to face conversation partner and it is an esential part of humans life because without speaking it will be difficult for them to interact each other smoothly and efectively in environment. Speaking difficulties is a condition when students experience an obstacle when speaking in public. The purpose of this study was to describe the students difficulties in speaking skills at the fifth semester of the University of Muhammadiyah Kotabumi. The data from this study were taken from the results of interviews with 5 students in the fifth semester of English education. The instrument used in this study was an interview. In this study, the data collection technique used by the researcher is the in-depth interview technique and documentation. The results of this study illustrate that in fact there are 2 aspects of the problem that causes speaking difficulties faced by students. the two aspects of the problem are linguistic problems and non-linguistic/psychological problems. In linguistic problems there is a of pronunciation, lack of vocabulary and lack of grammar mastery. while non-linguistic/psychological problems consist of lack of confidence, anxiety, shyness, lack of motivation and fear of mistake.
The Correlation Between Students’ Pronunciation and Their Speaking Ability of the Eighth Grade in SMPN 07 Kotabumi Academic Year 2021/2022

Pronunciation is the sound of production words used by someone to communicate creates, and conveys meaning with others. It is useful for the listeners to understand a suitable flow in communication. However, when the researcher had done observation at the eighth grade of SMP Negeri 7 Kotabumi, the researcher found that some of the students have low ability in speaking ability. The purpose of this research is to investigate whether there is significant correlation between students' pronunciation mastery and their speaking ability of the eighth grade in SMP N 7 Kotabumi academic year 2021/2022. The population in this research was eight classes of the eighth grade in SMPN 07 Kotabumi which consist of 256 students. The instrument used to collect the data of pronunciation mastery and speaking ability were an oral test. After collected data, it was analyzed by pearson product moment, and the results of statistical data analysis of this research was robserved &gt; rtable (32) (0.05) or 0,923 &gt; 0,349, and tobserved is greater than ttable (32-2) (0.05) or 13.108&gt; 1.694, this indicate that there is significant correlation between students' pronunciation mastery and their speaking ability of the eighth grade in SMP N 7 Kotabumi academic year 2021/2022.
THE CORRELATION BETWEEN LEARNING STYLE AND STUDENTS’ VOCABULARY SIZE OF THE TENTH GRADE OF SMA KEMALA BHAYANGKARI KOTABUMI ACADEMIC YEAR 2020/2021

The lack of vocabulary size can affect their responsiveness to the material presented by the teacher in the learning process. In this case the right learning style is very important to increase the size of students' vocabulary in the learning process. If students have appropriate learning styles in learning, they will be interested in understanding each vocabulary in the learning process. The objective of this research to determine whether there is significant correlation between learning style and students' vocabulary size of the tenth grade of SMA Kemala Bhayangkari Kotabumi academic year 2020/2021. This research is a descriptive correlational research. This research uses a proportional random sampling technique to determine the sample in this research. The total sample of this research was 30 students. The population in this research consist of 86 students. The instruments used to collect the data were a multiple choice test and questionnaire of learning style. After collected data, it it was analyzed by Product Moment formula, and the results of statistical data analysis of this research was ≥ which is 0,913 ≥ 0,361 (30) (0,05). The researcher used T-test to know the significant of the correlation. The result of this research showed is grather than which is 11,819 ≥ 1,697, it means that there is significant significant correlation between learning style and students' vocabulary size of the tenth grade of SMA Kemala Bhayangkari Kotabumi academic year 2020/2021.