Reviewerly/exHarmony huggingface dataset:
ACMEOLOGICAL PRINCIPLES OF PROFESSIONAL TRAINING OF FUTURE TEACHERS

The article deals with the problem of training future teachers based on the acmeological approach. According to the analysis of theoretical sources of recent decades, the relevance of the outlined issue is argued and the need to introduce its basic postulates into the educational process of institutions of higher pedagogical education is proven. The role of acmeological knowledge as a theory and practice of self-improvement of a person through activity, which grants them knowledge about life barriers and ways to overcome them based on reason, higher ethical feelings, and spirituality to achieve success in life, is defined. Modern theories of success (material existence, existential type of life success, etc.) are supplemented with an acmeological concept. In this context, the acme theory of "life success" considers a person as a dynamic system (a person in their development) striving for perfection.&#x0D; The teacher's orientation towards success is defined as a prerequisite for upbringing a successful individual – a person of the future. It is proposed to create the acme potential of educational and professional training programs for future teachers by introducing the educational component "Fundamentals of acmeology" into the curricula. The scientific apparatus of the relevant educational component (goal, task, program results) is outlined. The promising lines of teaching the course are revealed based on the general characteristics of the following concepts: "acme" – top, "acmeology" – pedagogy of success, new quality of professional activity. The content of the basic theoretical provisions of the educational component is characterized, the topics of the lecture course tested during the educational process, the practically oriented context of the educational component, educational and organizational measures of its implementation in the conditions of training of future teachers – applicants of the second (educational and professional) level of higher education are presented.&#x0D; The peculiarities of conducting practical/seminar classes are revealed using examples. An important aspect of the students' understanding of the concepts of "perfection", "integrated personality development", "self-improvement", "success" is determined by the use of a set of exercises, project tasks, discussion questions, etc. Examples of work on the implementation of practical tasks are considered. Based on the quantitative and qualitative analysis of the results of students' work in practical classes, it is predicted that they will achieve the planned program results, and the prospects for researching the acmeological principles of training future teachers by expanding the arsenal of practically oriented author systems are determined.
The Problems in Indonesian Language Teaching During the Covid-19 Pandemic

The rationale behind this study was the occurring problems in online teaching and learning of Indonesian during the Covid-19 pandemic. The purpose of this study was to investigate the problems in Indonesian teaching and learning during the Covid-19 pandemic at SD Negeri 02, North Samarinda in the 2020/2021 academic year. This study applied a qualitative approach. The instruments used were interviews and documentation. The results of the study showed that there were existing problems in Indonesian language teaching and learning during the Covid-19 pandemic at SD Negeri 023, North Samarinda in the 2020/2021 school year. The problems identified were mostly related to the students. First, they were reluctant to read Indonesian textbooks. Additionally, they spent more time playing at home. It was unfortunate since to understand Indonesian, students should read Indonesian language textbooks more frequently. Besides, parents share the responsibility to supervise and control their children to learn how to read textbooks on Indonesian. Therefore, well-organized cooperation among the classroom teachers, parents, and students is needed to minimalize the problems. Hence, Indonesian language teaching and learning will create better outcomes
Exploring Lexical Density in the New York Times

The goal of the study was to evaluate the lexical density and readability of five news articles from The New York Times. A qualitative content analysis methodology was employed in conducting the research. Methods from Ure (1971) and Halliday (1985) were used to analyze the lexical density, and Flesch’s (1948) Reading Ease Score and Grade Level were used to evaluate the readability of the news articles. The findings showed that The New York Times news articles had lexical densities that were higher than 50%, significantly lower readability scores than 50, and grade levels greater than 12.The percentages of lexical density were higher than Ure’s (1971) minimum score for written texts. The readability score also revealed that the news articles required a high level of reading comprehension and were best understood by readers who were at least 18 years old or at least in college. The study came to the conclusion that The New York Times is one of the most trustworthy and reputable news sources.
MENELAAH KESALAHAN EJAAN PADA KARYA ILMIAH (MAKALAH) MAHASISWA SEMESTER I KOMUNIKASI PENYIARAN ISLAM IAIN TAKENGON

Papers are one of the daily tasks for students. There are still many student papers that are not in accordance with the EYD or vocabulary in the KBBI. This research focuses on how the form of errors in writing capital letters, writing hyphens, punctuation marks, and standard language in scientific papers (papers) of students, especially the IAIN Takengon KPI Study Program. This study aims for KPI (Islamic Broadcasting Communication) undergraduate students to be trained in good and correct writing skills in accordance with spelling rules. The approach used in this research is qualitative with descriptive method. The results of the research conducted, the researcher found spelling errors in the contents of the paper, namely: (1) there were 15 capital letter writing errors, (2) there were 20 mistakes in the location of conjunctions, (3) there were 10 non-standard language errors, (4) writing errors there are 20 punctuation marks (colons, commas, quotation marks, punctuation marks).
AEIMPS: Deep Autoencoder for Image Retargeting Quality Assessment

Evaluating retargeting image operators is a subjective task and, therefore, challenging to execute without human interference. Image Retargeting Quality Algorithms execute this task, giving some score to the retargeted image and, usually, trying to get a result similar to a human opinion since humans generally agree with each other on the quality of a resized image. Therefore, we propose an Autoencoder-based IRQA named AutoEncoder Information MaP Similarity (AEIMPS) to address this task using the NVAE architecture. In our experiments, besides the retargeting ratio, we use the latent space and the reconstructed image in the IRQA. AIEMPS achieved an average performance compared to other IRQAs in the literature.
Transferring Human Motion and Appearance in Monocular Videos

This thesis investigates the problem of transferring human motion and appearance from video to video preserving motion features, body shape, and visual quality. In other words, given two input videos, we investigate how to synthesize a new video, where a target person from the first video is placed into a new context performing different motions from the second video. Possible application domains are on graphics animations and entertainment media that rely on synthetic characters and virtual environments to create visual content. We introduce two novel methods for transferring appearance and retargeting human motion from monocular videos, and by consequence, increase the creative possibilities of visual content. Differently from recent appearance transferring methods, our approaches take into account 3D shape, appearance, and motion constraints. Specifically, our first method is based on a hybrid image-based rendering technique that exhibits competitive visual retargeting quality compared to state-of-the-art neural rendering approaches, even without computationally intensive training. Then, inspired by the advantages of the first method, we designed an end-to-end learning-based transferring strategy. Taking advantages of both differentiable rendering and the 3D parametric model, our second data-driven method produces a fully 3D controllable human model, i.e., the user can control the human pose and rendering parameters. Experiments on different videos show that our methods preserve specific features of the motion that must be maintained (e.g., feet touching the floor, hands touching a particular object) while holding the best values for appearance in terms of Structural Similarity (SSIM), Learned Perceptual Image Patch Similarity (LPIPS), Mean Squared Error (MSE), and Fréchet Video Distance (FVD). We also provide to the community a new dataset composed of several annotated videos with motion constraints for retargeting applications and paired motion sequences from different characters to evaluate transferring approaches.
On Model Complexity Reduction in Instance-Based Learners

Instance-based learners habitually adopt instance selection techniques to reduce complexity and avoid overfitting. Such learners’ most recent and well-known formulations seek to impose some sparsity in their training and prediction structure alongside regularization to meet such a result. Due to the variety of such instance-based learners, we will draw attention to the Least-Squares Support Vector Machines and Minimal Learning Machines because they embody additional information beyond the stored instances to perform predictions. Later, this thesis proposes variants constraining candidate solutions within a specific functional space where we avoid overfitting and reduce model complexity. The central core of such variants is related to penalizing samples with a specific condition during learning. For regressors, we adopted strategies based on random and observed linearity conditions related to the data. At the same time, we borrowed definitions from the computer vision field for classification tasks to derive a concept we call the classcorner relationship (in which we designed an instance selection algorithm). In the Least-Squares Support Vector Machines context, this thesis follows the pruning fashion by adopting the samples that share such a class-corner relationship. As for the Minimal Learning Machine model, this thesis introduces a new proposal called the Lightweight Minimal Learning Machine, a faster model for out-of-sample prediction due to the reduced number of computations inherent in the original proposal’s multilateration process. Another remarkable feature is that it derives a unique solution when other formulations rely on overdetermined systems.
Interactive Image Segmentation: From Graph-based Algorithms to Feature-Space Annotation

In recent years, machine learning algorithms that solve problems from a collection of examples (i.e. labeled data), have grown to be the predominant approach for solving computer vision and image processing tasks. These algorithms’ performance is highly correlated with the abundance of examples and their quality, especially methods based on neural networks, which are significantly data-hungry. Notably, image segmentation annotation requires extensive effort to produce high-quality labeling due to the fine-scale of the units (pixels) and resorts to interactive methodologies to provide user assistance. Therefore, improving interactive image segmentation methodologies with the goal of improving data labeling problems is of paramount importance to advance applications of computer vision methods. With this in mind, we investigated the existing literature on interactive image segmentation, contributing to it by introducing novel algorithms that perform the segmentation from markers, contours, and finally proposing a new paradigm for image annotation at scale.
Visual crime pattern analysis

Studying and analyzing crime patterns in big cities is a challenging Spatio-temporal problem. The problem’s difficulty is linked to different factors such as data modeling, unsophisticated hotspot detection techniques, Spatio-temporal patterns, and study delimitation. Previous works have mostly focused on the analysis of crimes with the intent of uncovering patterns associated to social factors, seasonality, and urban activities in whole districts, regions, and neighborhoods. Those tools can hardly allow micro-scale crime analysis closely related to crime opportunity, whose understanding is fundamental for planning preventive actions. Visualizing different patterns hidden in crime time series data is another issue in this context, mainly due to the number of patterns that can show up in the time series analysis. In this dissertation, we propose a set of approaches for interactive visual crime analysis. Relying on machine learning methods, statistical and mathematical mechanisms, and visualization, each proposed methodology focus on solving specific crime-related problems. These proposed tools to explore specific city locations turned out to be essential for domain experts to accomplish their analysis in a bottom-up fashion, revealing how urban features related to mobility, passerby behavior, and the presence of public infrastructures can influence the quantity and type of crimes. The effectiveness and usefulness of the proposed methodologies have been demonstrated with a comprehensive set of quantitative and qualitative analyses, as well as case studies performed by domain experts involving real data from different-sized cities. The experiments show the capability of our approaches in identifying different crime-related phenomena.
Food Data Analysis using Multidimensional Visualizations based on Point Placement

Food data comprise records regarding nutrients, ingredients, amounts of different vitamins and minerals that can be found in foods. The wide variety of food products that can be stored in large datasets makes the traditional analysis tasks unfeasible and time-consuming when conducted manually by the dietitians and related professionals. This paper describes a method for visualizing food data using point placement strategies to support specialists in tasks related to determining similar food products that can be replaced in specific diets. The proposed method generates a structured representation for food data to be used as input to some state-of-the-art and recent visualizations, such as PCA, t-SNE, UMAP and TriMap. Experiments were conducted to assess the quality of visualizations and the results reported that the nonlinear visualizations presented satisfactory discriminability regarding some food categories and better preservation of the data patterns. A case study based on a visual exploration process was also conducted and demonstrates the specialist successfully finding substitute food products for planning a vegan diet plan.
A Machine Learning Approach for DeepFake Detection

With the spread of DeepFake techniques, this technology has become quite accessible and good enough that there is concern about its malicious use. Faced with this problem, detecting forged faces is of utmost importance to ensure security and avoid socio-political problems, both on a global and private scale. This paper presents a solution for the detection of DeepFakes using convolution neural networks and a dataset developed for this purpose - Celeb-DF. The results show that, with an overall accuracy of 95% in the classification of these images, the proposed model is close to what exists in the state of the art with the possibility of adjustment for better results in the manipulation techniques that arise in the future.
Research on multi-task cooperative control and management method of industrial robot multi-manipulator

In order to overcome the problems of large cooperative control errors and trajectory tracking errors due to the lack of kinematics uncertainties in the traditional manipulator task control method, a new collaborative multi-task control method for industrial robots was designed. The Lagrange method was used to construct the single manipulator dynamics model, the Newton-Euler recursion method was used to construct the target object dynamics model, and then the multi-manipulator model was constructed. Based on the kinematics uncertainty, the multi-task cooperative control model of multi-manipulator was constructed, and the multi-task cooperative robust and adaptive controller was designed with the state feedback control, so as to realise the multi-task cooperative control. Experimental results show that the maximum error of multi-manipulator cooperative control is less than 20 mm, and the minimum error of object trajectory tracking is 10 mm, which proves that the proposed method can effectively achieve the design expectation.
DP and the Art of Perfect Positioning

How does a vessel keep its position at sea without mooring?The answer is through dynamic positioning, or DP.Advanced technology, combining position reference sensors, computers, propellers and thrusters, is used to counteract environmental forces such as wind, currents and waves to dynamically position the vessel without using anchors or mooring lines.Today, DP systems are found in every ocean region in the world.The Kongsberg DP adventure began in 1975 when Kongsberg Våpenfabrikk signed its first contract.Eighteen months later, an Albatross DP system was installed on board a Norwegian diving vessel in the North Sea.Today, dynamic positioning equipment from the inland town of Kongsberg is found onboard a myriad of ship types including supply vessels, drilling rigs, pipe and cable layers, stone-dumping and bulk vessels, shuttle tankers, crane and lifting vessels, mine hunting vessels, cruise ships, floating hotels and yachts.Dynamic positioning is not limited to keeping a vessel on a fixed location but also to controlling the motions of vessels along predetermined tracks.After over 40 years in business, Kongsberg Maritime still maintains a world-leading position within dynamic positioning.This paper is based on the book "The Jewel in the Crown:
Toward an Utra-Low Latency Based Q-learning Assisted 6TiSCH Network in Smart Industries

Smart industry is a significant implementation of Internet of Things (IoT).IPv6 over Time Slotted Channel Hopping (6TiSCH) is a new standard that emerges as promising technology for industrial communications.The demanding issues of smart industries include scalability, and latency.Literature works presented a scheduling scheme for 6TiSCH-based industries.However, the prime limitation in those works is that the works are not suitable for practical scenario where emergency situations frequently occur.This paper aims to improve scalability, and latency in 6TiSCHbased smart industries for practical scenario.New Fog-assisted 6TiSCH Q_learning algorithm is designed, and smart communication layer.Scalability and latency is handled by fog computing in smart communication layer.In fog layer, rank based Q-learning algorithm performs offloading to manage energy consumption among fog nodes.
Archiving a TEI Project FAIRly

The Inscriptions of Israel/Palestine project is an online corpus of over four thousand inscriptions from Israel and Palestine, written in Hebrew, Greek, Latin, and Aramaic, dating roughly from the Persian Period to the Arab Conquest. The source files with inscription text and metadata are encoded using EpiDoc, a TEI customization widely used by epigraphers. As the project prepared to deposit its XML files in an institutional repository, it transformed them into a locally developed robust archival format. This paper evaluates these decisions against the FAIR metrics, using IIP as a test case. This allows us to suggest improvements for our own archival encoding as well as to see where EpiDoc and TEI enhance FAIRness and where they could provide more support. Finally, we suggest some ways to use FAIR metrics that are more amenable to TEI documents and corpora.
Domesticity and Persona

Because personas are performances of identity, strategically enacted for an immediate or imagined audience, the study of personas to date has concentrated on the public realm of life. Professional personas enacted in workplaces have taken up much attention, whether for artists, comedians, scientists, actors, musicians, or politicians. Similarly (and often overlapping the professional persona), the performance of self online that is constituted in and through social media has proven a generative space for research. Mediatised personas generally open up a space for understanding persona performances, while the non-human, the institutional, the collectively constituted persona come as a logical extension of the theorisation of persona as strategic identity display.
Authoring System of Mobile Tutorial Modules Based on Auditory Multimedia

In this study, we consider technologies of auditory multimedia as a mode of communication between a mobile device and blind students regarding instructional material.We propose an authoring system designed to create mobile tutorial modules (MTMs) for use by blind students.The system includes two major phases.In the first phase, the instructor submits the contents of a tutorial module in natural language into a server.The server saves the contents of tutorial modules in a database.The server then creates a website based on PHP with MYSQL.Thus, the system creates a website with all the material entered by the instructor.The second phase considers an Android environment with XML for the graphical user interface (GUI) and Java source coding for activities).An API library in the server manages the tutorial content into typical tutorial module.Because the application is designed for totally blind students, all the contents of the GUI are converted into corresponding text-to-speech data (Audio files) using Android.Finally, the blind student receives the MTMs as sound output from the device.We invited several instructors to experimentally evaluate some sample MTMs.The results show that the proposed approach was considered successful in this application.Additionally, an algorithm was applied to reduce the time required for multiple users to interact with the application.We also implemented text-to-speech on Android as well.The proposed approach was received positively by the participants and users.
Impact of Mind Maps on Student's Reading Comprehension at Secondary and Higher Secondary Levels in District Buner

This study aims to determine how mind-mapping strategies develop the reading comprehension of English language learners in grades ten and twelve. The study focuses on teaching English grammar and text, keeping in view their textbooks. The researcher has used a quasi-experimental design. The study sample comprised sixty students, thirty from the tenth and thirty from the twelfth grade. Thirty students were placed in the experimental group and thirty in the control group. The experimental group received the mind mapping treatment for one month, whereas the control group was given instructions via traditional methods. The researcher analyzed both qualitative and quantitative data from pre-and post-tests and an interview to test the impact of mind-mapping strategies on the participants' reading comprehension. The data were analyzed through a t-test. The statistical analysis showed a significant difference in the mean score of both groups and showed that mind-mapping strategies positively impact the students reading comprehension.