Reviewerly/exHarmony huggingface dataset:
Poetry education method using metacognition

Objectives In this study, away from the fragmentary knowledge and cognitive thinking-centered educational methods that have been attempted for poetry appreciation, the concept of metacognition is used to prepare students' self-directed and individual method elements, and to verify them through specific works. Through motivation, reflective practice, and readjustment, autonomy and activeness in poetry appreciation education were pursued.&#x0D; Methods For this purpose, the concept of metacognition was identified, and the fact that spontaneity, adjustment, and cyclical continuity can be applied to poetry appreciation education was proved through the foregoing. Furthermore, ‘reflective adjustment by internal spontaneity’ and ‘process as a cyclical process’ were proposed as the main points applicable to actual educational situations. In addition, the stages of preparation, application, expansion, advancement, and circulation were set, and the educational plan was verified through specific works.&#x0D; Results By breaking away from the passiveness of appreciation that can be borne by the poetry appreciation education concentrated on the cognitive dimension, and pursuing poetry appreciation education applied with a metacognitive concept, this study is expected to induce students’ spontaneous and self-monitoring activities so that the possibility of creative poetry appreciation and student-led appreciation education will be implemented.&#x0D; Conclusions This study is judged to be educationally valuable because it critically examined the education centered on the authority of texts and teacher-centered delivery of fragmentary knowledge that have been shown in educational activities thus far, and enabled students to motivate the interpretive tolerance for the diversity of emotions, images, and symbolic meanings of works of poetry by themselves and implement it by adjusting it through a cyclical checking process.
Exploring Learning Analytics Data for Supporting Self-Directed Learning in Smart Learning Environment

Objectives The purpose of this study was to explore and define the factors that constitute self-directed learning in smart learning from a learning analytics perspective and to validate the appropriateness of the type and characteristics of the learning data.&#x0D; Methods To ensure the validity of the learning analytics data, we used an expert validation method. For this purpose, a total of seven experts with over 8 years of experience in e-learning, educational research, and educational practice participated. We explained the target service of this study and the purpose and tools of the validation test to each expert and conducted the test twice via email.&#x0D; Results We defined the self-directed learning data that can be collected and analyzed among various data generated throughout the learning process in smart learning. Specifically, we classified them into three factors of self-directed learning: 1) meta-cognition (planning, monitoring, self-evaluation, and reflection), 2) learning strategies (performance, review, problem-solving strategies, elaboration, and exploratory learning strategies), and 3) learning behaviors (time management, learning performance, immersion, concentration, problem-solving habits, and social learning participation) and a total of 41 detailed learning analysis data were derived.&#x0D; Conclusions This study provides meaningful directions for smart learning system to support learners' self-directed learning under the learning analytics. Also, it is expected to help lay the foundations for future AI-based learning system.
Chapter 10: Review of Multivariate Differentiation

In this chapter we briefly review derivatives in multiple dimensions. A solid working understanding of multivariate differentiation is essential for the remainder of the text, most notably for the theory of optimization. We cover differentiation only briefly here, and mostly without proofs. For a rigorous treatment of the topic, see Volume 1, Chapter 6.
Chapter 50: Evaluating Confidence Intervals: Length and Coverage Probability

There are two ways to assess confidence intervals. The first is the coverage probability, or the probability that the confidence interval captures the parameter. For exact confidence intervals, the coverage probability is equal to the target coverage probability (if the model is correct). For example, the confidence interval for the mean of a normal population, created using a pivotal quantity with a t-density, is exact; therefore, if the population is truly normal, a 95% confidence interval will capture the population mean exactly 95% of the time.
Chapter 3: Data Structures

A data structure is a specialized format for organizing, storing, and processing data. Seemingly inconsequential differences between similar data structures can have a profound impact on the complexity of the algorithms that use them. In this chapter we discuss a few widely used data structures and prove complexity bounds for several important algorithms.
Chapter 5: Discrete Random Variables and Expected Value

Random variables are the building blocks of statistical models. They are functions of outcomes of a random experiment, and because we like numbers so much, we insist that they take on numerical values only. Hence, we have the following definition. A random variable is a mapping of outcomes in the sample space to the real numbers.
Chapter 8: Harmonic Analysis

The field of harmonic analysis is concerned with both the representation and the approximation of functions, including signals36 and images, as linear combinations of basic waves. In this chapter we consider two branches of harmonic analysis corresponding to two different classes of “basic waves.” The first comes from the class of trigonometric functions (sines and cosines) and is called Fourier analysis. The second deals with self-similar waves and is called wavelet analysis.36 In many quantitative disciplines a signal is a function that contains information about the behavior or attributes of some phenomenon. In this text, when we say signal we mean a function that depends on time.
A Survey Research on the Perception and Needs of Secondary Music Teachers about Graduate College of Education as In-Service Training Institution

Objectives The purpose of this study is to investigate the perception and demand of in-service secondary music teachers regarding graduate college of education as in-service training institution; and discuss about the curriculum improvement of graduate college of education for in-service training.&#x0D; Methods A survey of 139 secondary music teachers nationwide was conducted to find out the awareness and necessity of the current graduate college of education as in-service training institution and its curriculum. In addition, interviews with two music teachers were conducted to find more specific causes and perceptions of the survey results and discuss about the future direction of the institution from the perspective of in-service teachers.&#x0D; Results In-service secondary music teachers tended to characterize the functions of the current graduate college of education and music education major as a pre-service teacher training institution rather than for in-servicet teacher training. This indicates that the functions of the graduate college of education are far from the purpose of initial establishment, and suggests the need to further strengthen the functions to enhance the expertise of in-service music teachers. Furthermore, secondary music teachers expected curriculum with high field connectivity, which was mainly related to the development of teaching methods.&#x0D; Conclusions First, the music education major of graduate college of education should develop curriculum for in-service training, focusing on the contents that require long-term education. In addition, a completion system that teachers have more autonomous curriculum decision should be provided as well as completion process according to the needs of in-service teachers. Second, curriculum for in-service teacher training should be developed to help teachers understand the rapidly changing needs of society and the latest philosophy and theory. In this way, research on developing high-quality teaching-learning methods and materials should be conducted; so that teachers can be far more accountable for professional development and improving their classrooom.
AutoML Framework for Labor Potential Modeling

Labor potential estimation is a complex analysis task of a large data sets of digital profiles of workers. Digital profile is a structure that contains heterogeneous data and has high complexity. The attribute space of digital profiles datasets has a large dimension. Individual features are distinguished by a large variability of types and include qualitative and quantitative characteristics that can be both continuous or discrete. This fact significantly complicates the use of traditional statistical methods and hinder manual selection of machine learning algorithms for labor potential estimation. Automated machine learning (AutoML) can help to deal with this problem. The results of AutoML can significantly depend on the used AutoML library. AutoML libraries differ in the set of algorithms among which they select the best one, in the constrains that are used to stop the search of the optimal solution, in the techniques used for hyperparameters optimization and in metrics that are calculated for the obtained results. In this work we develop an AutoML framework that unites several commonly used AutoML libraries and enables use of common constrains and metrics. Processing dataset about employees’ performance using the developed framework we show the applicability of the AutoML for the task of labor potential estimation.
Automatic User Testing and Emotion Detection in Interactive Urban Devices

Automated testing and evaluation of interfaces is a well-established reality supported by many tools that shorten the time to deploy new software versions to the user. However, exploring users’ emotions while interacting with interfaces as a tool to further increase the quality of traditional usability evaluation methods is still far from being a reality. This work uses the automatic analysis of users’ emotions while interacting with touchable interactive urban devices to detect usability issues. To this end, a coupled approach is implemented: the data is acquired from the interaction, and user emotions are extracted and processed to determine the emotional status during the interaction. This data is integrated into a web application so that designers can further improve the quality of the interface in the presence of negative emotions. Results show that the experimental tests showed that different users manifest similar negative emotions in the same contexts, which is a clear sign of usability issues that are to be corrected by the design team.
Evaluation of Time Series Causal Detection Methods on the Influence of Pacific and Atlantic Ocean over Northeastern Brazil Precipitation

The detection of causation in natural systems or phenomena has been a fundamental task of science for a long time. In recent decades, data-driven approaches have emerged to perform this task automatically. Some of them are specialized in time series. However, there is no clarity in literature what methods perform better in what scenarios. Thus this paper presents an evaluation of causality detection methods for time series using a well-known and extensively studied case study: the influence of El Niño-Southern Oscillation and Intertropical Convergence Zone on precipitation in Northeastern Brazil. We employed multiple approaches and two datasets to evaluate the methods, and found that the SELVAR and SLARAC methods delivered the best performance.
Chapter 5: Method Ranking

So, what makes one optimization method better than another? The answer sometimes depends on the situation, as some methods are better than others in some cases but worse in other cases. One generic measuring stick of a method’s value is the time and effort it takes to produce an acceptable result. Since it’s not possible beforehand to know what degree of accuracy every practitioner will deem acceptable, it is typically required that the updated guesses become arbitrarily close to an optimizer x* (since an optimizer is the star of this show, it gets a superscript *).
INSIGHTS ON MACHINE LEARNING

As opposed to exclusively depending on explicit programming, an area of artificial intelligence called machine learning allows computers to get better at what they do by gaining experience. Due to its numerous practical uses across numerous sectors, it has experienced a major increase in popularity in recent years. This chapter seeks to introduce the principles of machine learning, delve into more complex ideas, and look at how it might solve problems in the real world. We strive to provide insightful information for everyone, whether you are a beginner trying to grasp the fundamentals or an expert data scientist trying to keep up with the most recent developments. What does machine learning mean? Computers can learn and form opinions without explicit programming thanks to machine learning, a statistically-based application of artificial intelligence. It is a branch of AI that focuses on teaching computers to behave and make decisions as people do by enabling them to learn and develop their own programs with little to no human input. Machine learning entails giving input data and related outputs to the machine during the learning phase, as opposed to traditional programming, which requires feeding a machine well-written and verified code together with input data to create output. This procedure allows the machine to learn from the data, see trends, and create its own program. The type of data being utilized and the particular task that has to be automated determine which algorithms are employed in machine learning. Enabling computers to make judgements and complete tasks without explicit programming, machine learning automates the learning process by training them on high-quality data using a variety of techniques.
Announcing new resources from the FORCE11-COPE Research Data Publishing Ethics Working Group: flowcharts!

Recognizing the need for dialogue on best practices around ethical challenges in data publication, the FORCE11 Research Data Publication Ethics Working Group started in early 2021 as a multi-stakeholder community effort involving representatives of data repositories, journals, researchers, institutional research integrity officers and libraries. The group was set up in collaboration with the Committee on Publication Ethics (COPE), to leverage COPE’s expertise in the development of guidelines...
A Case for Intranet-Based Online Portal in Undergraduate Computer Science Teaching

Our proposal for selective subjects especially those involving intensive problem-solving assignments and/or tutorials, such as Introduction to Algorithms and Data structures, Discrete Mathematics, Coding Theory, Number theory, Combinatorics and Graph Theory (CGT), Automata theory, is to supplement lectures with a moderated online forum against an intranet portal. By way of illustration we take the example of a restricted view of OEIS (http://oeis.org). The restriction can be w.r.t. sequences in OEIS that are directly relevant to say CGT. N.J.A.Sloane's OEIS is a collection of over 2,39,147 integer sequences and their properties. In particular OEIS contains definitions of many combinatorial structures, dense range of interpretations, generating functions and conjectured ones, cross references within OEIS and to outside resources, references to texts and technical articles, codes in Maple, Mathematica etc. For organizing courses such as the above mentioned, a first task is to partially create an OEIS-like instructor-moderated portal in a university intranet. During the course of lectures and tutorials students are invited to contribute to the portal and these may be augmented/approved by instructors suitably, to find a place in the portal. By this many concepts can be conveyed to the students in an interesting way with the desired results. In the arguments presented, examples related to Combinatorics and Graph Theory are given.
A formation cooperative reconnaissance strategy for multi-UGVs in partially unknown environment

Recently, multiple unmanned ground vehicles (multi-UGVs) have attracted a great deal of attention as viable solutions to a wide variety of military and civilian applications. Among many topics in the field of multi-UGVs, the formation cooperative reconnaissance is of great importance, which helps to concentrate firepower to suppress and eliminate the suspicious enemies. This paper presents a formation cooperative reconnaissance strategy for multi-UGVs to explore all the unknown areas in the environment, which is similar to the human soldiers paraded on patrol. First, the frontier-based exploration algorithm is proposed to get the reconnaissance targets in the unknown areas which is modeled as a global grid map. Then, A*-based multi-target path planning is applied to find the right exploring order of reconnaissance targets. Lastly, the distance-angle leader-follower formation control laws are designed to make the multi-UGVs rapidly form a desired formation after assigning a reconnaissance goal. The experiment results show that all the reconnaissance targets are searched and explored. The follower UGVs are gradually adjusting their position and orientation to follow the leader UGV while moving to each reconnaissance target sequentially. The motion of the multi-UGVs is reliable. Especially, the average position and heading errors of formation are less than 0.2%.
The Copyrightability of Art Generated by Artificial Intelligence in Canada

Historically, Canadian copyright law has sought to strike a balance between the rights of a work’s creator and the rights of individuals building upon such work. As artificial intelligence (“AI”) technologies continue to rise in prominence, balancing copyright protections with users’ rights has become increasingly difficult. AI’s exponential rise requires Canadian law to account for these new complexities in copyright.The Canadian government has also acknowledged that the Copyright Act may be due for an update to ensure that it adequately addresses technological developments which have occurred since it was last amended. One area where Canadian copyright law must evolve concerns AI-generated works, which can be found in various forms, including text, music, search engines, and of particular interest to this paper, art. This paper proposes that the Copyright Act be amended to include a new section addressing AI technologies, which explicitly outlines the new possibilities for infringement presented by AI in relation to artwork.
Transmission of Cultural Experience and Social Practices in the Age of Transitivity: The Outcome of an International Scientific and Practical Conference

The results of the discussion of transdisciplinary problems of cultural experience transfer to the growing generation, the use of innovative technologies in psychological practice, the choice of educational strategies in the current socio-cultural situation at the International Research and Practice Conference “Transmission of Cultural Experience and Social Practices in the Age of Transitivity” organized by the Udmurt State University (Izhevsk) on November 15-18, 2022 are presented. As a result of the conference, the need to develop new scientific approaches to address the issues raised in order to preserve the young generation in an era of global geopolitical and socio-cultural transformation, to study the new social situation of human development in a network society organization with a heterogeneous generation and multidirectional intergenerational transmission is noted. One of the most debated issues in contemporary science and psychological practice is the digital gap between different generations, the complication of intergenerational relations. In the context of digitalisation, the education system is faced with the important task of developing new technologies for socialization of the growing generation, aimed at harmonizing the modern technogenic values of world civilization with the timeless traditional values of the peoples of Russia, serving to consolidate modern multicultural Russian society. The conference, marking the thirtieth anniversary of psycho-pedagogical education at Udmurt State University, took place in 2022, declared the Year of Education in Udmurtia by the Head of the Udmurt Republic.
Exploring the predictive power of a sense of presence on learning outcomes in fully online flipped learning at college English writing classes

Conventional flipped learning instructional models are operated in a blended learning environment online and offline. In contrast, this study moved onto fully online systems and explored how a sense of presence worked for students’ learning outcomes at university English writing courses. The two research questions for this study are: 1) What is the relationship between a sense of presence (teaching, cognitive, social presence) and learning outcomes (group cohesion, class satisfaction)? and 2) What are the variables among a sense of presence that affect group cohesion and class satisfaction? For the purposes of this study, 46 university students from English composition courses answered student questionnaires in the spring of 2021. Correlation and multiple-regression analyses were conducted to look into the relationships among the variables. Additionally, focus-group interviews were conducted and teaching journals were analyzed. The major findings were revealed as follows: Firstly, a sense of presence was significantly related to group cohesion and satisfaction. Secondly, social presence and cognitive presence only had a predictive power of group cohesion. Thirdly, cognitive presence and teaching presence were significant predictors of class satisfaction. Pedagogical implications are discussed for those interested in applying flipped learning in a fully online setting.
A framework of genealogy knowledge reasoning and visualization based on a knowledge graph

Purpose With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy. Design/methodology/approach Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source. Findings The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities. Originality/value This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.