Reviewerly/exHarmony huggingface dataset:
Emergence of metrically structured rhythms and inter-partner coordination in joint drum improvization

Improvization is a common aspect of music across cultures and eras. Due to its spontaneous nature, it often proves difficult to examine empirically how collective improvization evolves, unless genre-specific constraints are available and followed by experts (e.g., jazz). Here, we investigate the rhythmic dimension of free improvization by non-experts. We focus on two widely used approaches in joint musical improvization: call-and-response (trading) and simultaneous playing (tandem). We hypothesized that non-experts could engage in meaningful joint music-making with a model partner, and that they produce rhythmic patterns with different emergent structures depending on the task. The first author served as a confederate playing with each participant for five blocks each of trading and tandem tasks. To preserve the open-ended nature of improvization, neither metronome nor background music were provided. Preliminary analysis of the rhythmic content indicates that within-player inter-onset-intervals (IOI) reflected a clear hierarchical metrical structure. Additional analysis suggests bi-directional influence between partners in terms of IOI distribution, note density, and timbre choice. During tandem improvization, each partner favored timbres the other did not, showing a complementary pattern. During trading, timbre choice appears more imitative. Subjects also reported an increase in their self-assessed competence and enjoyment of the task over successive blocks.
Performance of Lambda Expressions in High Level Programming Languages

Functional programming is a programming paradigm that is becoming increasingly popular among software developers. This is due in part to the rise of distributed systems and the need for more robust and scalable code. In the paper is presented an overview of the syntax and capabilities of Lambda expressions in three programming languages – C#, Java and Python. Performance of programming language constructions is an important research task. Some popular topics for investigation are comparison of programming languages efficiency in fields like bioinformatics or classification of Lambda expression usage and their productiveness. In the current study the performance of Lambda expressions is tested with three specific test cases and the results are compared to alternative technologies that could be used to solve similar problems. The results shows that speed performance of C# is the best from the compared languages and that List Comprehensions is the optimal method for collection filtering in Python.
Application of Decision Tree Classification Algorithm in Quality Assessment of Distance Learning in Colleges

INTRODUCTION: The quality assessment technology of distance education in colleges and universities, as the critical technology for identifying the quality of distance education in colleges and universities, is conducive to the improvement of the quality of distance teaching and the progress of the existing means and methods of distance education, which makes the means of distance teaching in colleges and universities rich in science.&#x0D; OBJECTIVES: Aiming at the evaluation methods of higher education institutions, there are problems such as insufficient objectivity and comprehensiveness of the evaluation system, single process, and inadequate quantitative analysis.&#x0D; METHODS:Proposes a decision tree and intelligent optimization algorithm for the college distance teaching quality assessment method. Firstly, the kernel principal component analysis method is used to carry out dimensionality reduction analysis on the index system of college distance teaching quality assessment; then, the decision tree parameters are optimized through the marine predator algorithm to construct a college distance teaching quality assessment model; finally, the robustness and efficiency of the proposed method are verified through simulation experimental analysis.&#x0D; RESULTS: The results show that the proposed method improves the accuracy of the assessment model.&#x0D; CONCLUSION: The problem of insufficient objective and scientific evaluation and low precision of distance teaching quality assessment methods in colleges and universities is solved.
The intersection of fuzzy subgroups and relation

In this paper we have defined fuzzy set, fuzzy relation, fuzzy subgroup, complex fuzzy subgroup, and fuzzy normal subgroup. By using our new perspective, we have introduced the idea of the intersection of fuzzy sets and provided several theorems on fuzzy subgroup. Additionally, we have used our novel notion for fuzzy relations and provided several instances.
Application Deep Extreme Learning Machine in Multi-dimensional Smart Teaching Quality Evaluation System

INTRODUCTION: The construction of the wisdom teaching evaluation system, as the essential part of the institution's teaching reform, is conducive to developing the institution's disciplines, making the existing teaching more standardized, and making the means of teaching diversified, intelligent, and convenient.&#x0D; OBJECTIVES: Aiming at the current intelligent teaching evaluation design method, there are evaluation indexes that need to be more comprehensive, a single method, and system standard limitations.&#x0D; METHODS: Proposes an intelligent optimization algorithm for a multi-dimensional innovative teaching quality evaluation method. First of all, the multi-dimensional wisdom teaching evaluation system is constructed by analyzing the influencing factors of teaching quality evaluation; then, the parameters of the depth limit learning machine are optimized by the bird foraging search algorithm, and the multi-dimensional wisdom teaching evaluation model is constructed; finally, the validity and stability of the proposed method are verified by the analysis of simulation experiments.&#x0D; RESULTS: The results show that the proposed method improves the accuracy of the evaluation model.&#x0D; CONCLUSION: Solves the problem of low evaluation accuracy and incomplete system of teaching quality evaluation methods.
Impact of COVID-19 research: a study on predicting influential scholarly documents using machine learning and a domain-independent knowledge graph

Abstract Multiple studies have investigated bibliometric features and uncategorized scholarly documents for the influential scholarly document prediction task. In this paper, we describe our work that attempts to go beyond bibliometric metadata to predict influential scholarly documents. Furthermore, this work also examines the influential scholarly document prediction task over categorized scholarly documents. We also introduce a new approach to enhance the document representation method with a domain-independent knowledge graph to find the influential scholarly document using categorized scholarly content. As the input collection, we use the WHO corpus with scholarly documents on the theme of COVID-19. This study examines different document representation methods for machine learning, including TF-IDF, BOW, and embedding-based language models (BERT). The TF-IDF document representation method works better than others. From various machine learning methods tested, logistic regression outperformed the other for scholarly document category classification, and the random forest algorithm obtained the best results for influential scholarly document prediction, with the help of a domain-independent knowledge graph, specifically DBpedia, to enhance the document representation method for predicting influential scholarly documents with categorical scholarly content. In this case, our study combines state-of-the-art machine learning methods with the BOW document representation method. We also enhance the BOW document representation with the direct type (RDF type) and unqualified relation from DBpedia. From this experiment, we did not find any impact of the enhanced document representation for the scholarly document category classification. We found an effect in the influential scholarly document prediction with categorical data.
UK AI cybersecurity guidelines state aspirations

Headline UK: AI cybersecurity guidelines state aspirations
An Image Detection Model for Aggressive Behavior of Group Sheep

Sheep aggression detection is crucial for maintaining the welfare of a large-scale sheep breeding environment. Currently, animal aggression is predominantly detected using image and video detection methods. However, there is a lack of lightweight network models available for detecting aggressive behavior among groups of sheep. Therefore, this paper proposes a model for image detection of aggression behavior in group sheep. The proposed model utilizes the GhostNet network as its feature extraction network, incorporating the PWConv and Channel Shuffle operations into the GhostConv module. These additional modules improve the exchange of information between different feature maps. An ablation experiment was conducted to compare the detection effectiveness of the two modules in different positions. For increasing the amount of information in feature maps of the GhostBottleneck module, we applied the Inverted-GhostBottleneck module, which introduces inverted residual structure based on GhostBottleneck. The improved GhostNet lightweight feature extraction network achieves 94.7% Precision and 90.7% Recall, and its model size is only 62.7% of YOLOv5. Our improved model surpasses the original model in performance. Furthermore, it addresses the limitation of the video detection model, which was unable to accurately locate aggressive sheep. In real-time, our improved model successfully detects aggressive behavior among group sheep.
Synthesizing medical terms—Quality and naturalness of the deep Text-to-Speech algorithm

The main purpose of this study is to develop a deep text-to-speech (TTS) algorithm designated for an embedded system device. First, a critical literature review of state-of-the-art speech synthesis deep models is provided. The algorithm implementation covers both hardware and algorithmic solutions. The algorithm is designed for use with the Raspberry Pi 4 board. 80 synthesized sentences were prepared based on medical and everyday language employing the TTS algorithm developed. For tests, an application is built, containing a questionnaire allowing for evaluating the quality and naturalness of the synthesized speech, for both types of language. It is followed by the algorithm efficiency tests. A presentation of the performed tests, along with the results obtained from 30 respondents, is shown. The discussion consists of a statistical analysis of the obtained results and a comparison with other speech recognition solutions used as a reference. Finally, in the summary section, there is an overall conclusion of this approach and promising directions for future development. [This work is supported by the Polish National Center for Research and Development (NCBR) project: “ADMEDVOICE-Adaptive intelligent speech processing system of medical personnel with the structuring of test results and support of therapeutic process,” no. INFOSTRATEG4/0003/2022.]
An experiment to measure changes in violin frequency response due to playing-in

There is a widespread (but not universal) conviction among luthiers and performers of stringed instruments that a new instrument will sound “better” after it has been played for a certain amount of time. It is not uncommon for instruments to be mechanically stimulated to accelerate the process. The goal of the present experiment is to determine whether sustained mechanical stimulation of previously unplayed violins produces measurable changes in bridge admittance or radiativity. Our test instruments comprised three sibling violins (Andre Tellis, model 200), two of which were excited continuously for 12 weeks using different methods, while the third served as a control. Once per week during the excitation period, we measured bridge admittance with a laser vibrometer and radiativity in an anechoic chamber for each violin using the hammer tap method. The mass of each violin was also recorded prior to each measurement to account for fluctuations in moisture content. The resulting frequency response functions were analyzed within specific bands for changes that followed a trend over time or exceeded the standard deviation derived from a set of baseline measurements. Preliminary results do not show any significant changes in either the bridge admittance or radiativity for any of the violins.
(Re-)exploring the normal modes of axisymmetric structures: An English church bell case study

The normal modes of axisymmetric structures are of interest to structural engineers, physicists and musical acousticians. Previously, some of the present authors have made studies of church bells, hand bells, elephant bells, various gongs and rings. Group theoretical arguments have been used, with considerable success, in classifying the normal modes of these structures and in understanding how “beats” arise from split degenerate doublets. It is now pointed out that further information can be obtained from group theory using a variety of additional arguments. In particular, it infers a basic distinction between “in-extensional” and “extensional” types of modes. In the present work, we concentrate on the case of an English church bell, as an example axisymmetric structure, whose normal modes were previously measured in a frequency range of up to about 10 kHz. In that earlier work, the results were analyzed with what was then considered a state-of-the-art finite-element package. We now repeat this exercise with a modern finite-element package to explore the differences between the types of modes and validate the Group theory observations. The agreement with experiment is much improved and some new level of understanding of the spectrum of the bell is achieved.
Raising the Trust in Research and Education Digital Services using Levels of Assurance Profiles

In contemporary society, the digitization of societal functions necessitates the implementation of robust mechanisms to authenticate user identities. The imperative nature of establishing digital trust is particularly pronounced in the realm of electronic services, notably those administered by governmental and institutional entities. Trustworthiness of an electronic identity triggers the exigency for an accompanying process known as electronic authentication (e-authentication), which substantiates the legitimacy of the digital identity. The intricate task of managing digital identities and authentication credentials manifests as a challenge for a majority of end-users. Notwithstanding this complexity, service providers recurrently mandate digital identities for the provision of identity-centric services or for alerting users to irregularities. In light of these considerations, the conceptual framework of Identity Federation emerges as a paradigm, orchestrating the dissemination of identity information within predefined parameters. This research undertakes the examination of federated identity systems in the ecosystem of research and educational institutions, with a specific focus on the different authentication mechanisms and levels of assurance. We examine both the technologies (multi-factor authentication) and protocols (SAML) involved in these processes and currently defined attribute exchange policies used.
The Practicality of Economic Teaching Tools for Class XI Semester II Learning Based on 5.0 Using Wordpress Website in Economic Lessons for SMA 1 Solok Sumatera Barat Indonesia

This study is driven by the necessity to cater to students' requirements for more engaging online learning resources. Its primary objective is to create an online learning resource in the form of a WordPress website-based textbook. This resource is intended to enhance the learning experience and fac
In Memoriam: Rumen Maleev (1943-2019)

Professor Rumen Maleev passed away on December 16, 2019. With more than forty years service in Sofia University, he will be remembered by his colleagues, friends and students as one of the most distinguished Professors in the Faculty of Mathematics and Informatics. We present here a short CV of Rumen Maleev, followed by a paper (in Bulgarian) of Academician Stanimir Troyanski, who shares some memories about his long-term friendship and collaboration with Rumen Maleev.
A Survey of Learning Criteria Going Beyond the Usual Risk

Virtually all machine learning tasks are characterized using some form of loss function, and “good performance” is typically stated in terms of a sufficiently small average loss, taken over the random draw of test data. While optimizing for performance on average is intuitive, convenient to analyze in theory, and easy to implement in practice, such a choice brings about trade-offs. In this work, we survey and introduce a wide variety of non-traditional criteria used to design and evaluate machine learning algorithms, place the classical paradigm within the proper historical context, and propose a view of learning problems which emphasizes the question of “what makes for a desirable loss distribution?” in place of tacit use of the expected loss.
Graphmax for Text Generation

In text generation, a large language model (LM) makes a choice of each new word based only on the former selection of its context using the softmax function. Nevertheless, the link statistics information of concurrent words based on a scene-specific corpus is valuable in choosing the next word, which can help to ensure the topic of the generated text to be aligned with the current task. To fully explore the co-occurrence information, we propose a graphmax function for task-specific text generation. Using the graph-based regularization, graphmax enables the final word choice to be determined by both the global knowledge from the LM and the local knowledge from the scene-specific corpus. The traditional softmax function is regularized with a graph total variation (GTV) term, which incorporates the local knowledge into the LM and encourages the model to consider the statistical relationships between words in a scene-specific corpus. The proposed graphmax is versatile and can be readily plugged into any large pre-trained LM for text generation and machine translation. Through extensive experiments, we demonstrate that the new GTV-based regularization can improve performances in various natural language processing (NLP) tasks in comparison with existing methods. Moreover, through human experiments, we observe that participants can easily distinguish the text generated by graphmax or softmax.
Flat EEG Image Segmentation by Fuzzy Entropy-Based Multi-Level Thresholding

Thresholding is a type of image segmentation that deals with the conversion of an image with many gray levels into another image with fewer gray levels. It classifies grayscale pixels into two categories which creates a binary image. However, the output image is not alwayssatisfying due to several factors such as inherent image vagueness as uncertainty arises within the gray values of an image. In this paper, a multi-level image thresholding based on fuzzy entropy is applied on Flat Electroencephalography (Flat EEG) image. The outcomes are compared visually with global thresholding.
THE ENGLISH LEARNING QUALITY DURING PANDEMIC COVID-19 OF STUDENTS AT SMP NEGERI 1 TELUK DALAM

This research aimed to know how The English learningQuality during pandemic of students at SMP Negeri 1 Telukdalam. The total participants was nine participants; three participants from seventh grade, three participants from eigth grade and three participants from ninth grade. The research is used qualitativeresearch approach which used descriptive type of research to describe the English learning quality by using seven indicators of learning quality by Departemen Pendidikan Nasional. The technique of data collection in the research were interview the students and English teachers and observation using field note inEnglish teaching learning process in the class. The result shows that the students in SMP Negeri 1 Teluk Dalam was unfulfilled the learning quality indicators. There are three indicators that was unfulfilled, the indicators were Student’s Activity indicator, Material indicator, and Students’ outcome indicator. Based onthe result of the research, it can be concluded that the English learning quality during pandemic covid-19 of students at SMP Negeri 1 TelukDalam is unfulfilled in some indicators. The researcher suggest that, the english teachers should be able to explorate the class in online or offline class to improve the learning toreach the indicators learning quality, and also for the students should be able to adapt with any situation in learning even in online or offline class.
The Harm in Hate Speech and in Holocaust Denial

This chapter begins by providing a general contextualisation of hate speech and the controversies around it. It then asks two questions: is Holocaust denial a form of hate speech? If the answer is positive, should Holocaust denial be banned?Section II explains what is hate speech. Section III is concerned with the relationship between hate speech and hate crime. Section IV discusses Holocaust denial, explains why it is a form of hate speech and why it cannot and should not be protected by the Free Speech Principle. Appropriate legislation should be in place to bar it. Countries that suffered from the Nazi terror legislated against Holocaust denial.
AVALIAÇÃO DAS PREVISÕES NUMÉRICAS DE CURTO PRAZO DA IRRADIÂNCIA SOLAR PARA REGIÃO DE ILHA SOLTEIRA - SP

Solar energy has presented itself as a valid and sustainable alternative for generating electricity. However, its viability depends on the accuracy of the solar irradiance prediction and on meteorological parameters that influence the amount of solar radiation that reaches the surface. Numerical forecasting has become one of the main tools for estimating solar irradiance in advance. The objective of this work is to evaluate the prediction of solar Global Horizontal Irradiance (GHI) and temperature near the surface (T2M) through numerical modeling of the atmosphere, for the region of Ilha Solteira. Two numerical experiments with different atmospheric conditions (clear and cloudy sky) were designed, using Weather Research and Forecasting (WRF), to evaluate the potential of this model to simulate the variables GHI and T2M. The WRF was configured with a horizontal resolution of 5 km for 72 hours of forecast for the months of September/2021 and October/2021. The average diurnal cycle of GHI and T2M was relatively well represented by the model in the 3 forecast days. For the GHI the model tries to overestimate the measured values in both experiments. The largest errors were observed for the experiment with cloud cover and rain condition. For T2M the model tends to underestimate the observed values. Additional studies with different sets of parameterizations are needed to adjust the model to local conditions, as well as to evaluate a larger set of experiments.