Reviewerly/exHarmony huggingface dataset:
Regularization

The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
Introduction

The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
FORBID: Fast Overlap Removal by Stochastic GradIent Descent for Graph Drawing

While many graph drawing algorithms consider nodes as points, graph visualization tools often represent them as shapes. These shapes support the display of information such as labels or encode various data with size or color. However, they can create overlaps between nodes which hinder the exploration process by hiding parts of the information. It is therefore of utmost importance to remove these overlaps to improve graph visualization readability. If not handled by the layout process, Overlap Removal (OR) algorithms have been proposed as layout post-processing. As graph layouts usually convey information about their topology, it is important that OR algorithms preserve them as much as possible. We propose a novel algorithm that models OR as a joint stress and scaling optimization problem, and leverages efficient stochastic gradient descent. This approach is compared with state-of-the-art algorithms, and several quality metrics demonstrate its efficiency to quickly remove overlaps while retaining the initial layout structures.
CL <sup>2</sup> R: Compatible Lifelong Learning Representations

In this paper, we propose a method to partially mimic natural intelligence for the problem of lifelong learning representations that are compatible. We take the perspective of a learning agent that is interested in recognizing object instances in an open dynamic universe in a way in which any update to its internal feature representation does not render the features in the gallery unusable for visual search. We refer to this learning problem as Compatible Lifelong Learning Representations (CL2R) as it considers compatible representation learning within the lifelong learning paradigm. We identify stationarity as the property that the feature representation is required to hold to achieve compatibility and propose a novel training procedure that encourages local and global stationarity on the learned representation. Due to stationarity, the statistical properties of the learned features do not change over time, making them interoperable with previously learned features. Extensive experiments on standard benchmark datasets show that our CL2R training procedure outperforms alternative baselines and state-of-the-art methods. We also provide novel metrics to specifically evaluate compatible representation learning under catastrophic forgetting in various sequential learning tasks. Code at https://github.com/NiccoBiondi/CompatibleLifelongRepresentation.
Improving The Speaking Ability by Using Story Games Technique

At the university Ibrahimy Sukorejo, the researcher found that many students still get difficulty learning speaking skills in class, especially in the first semester. The lecturer had already done so much direct research but still didn’t find a perfect way in solving the problem for the student. This research was taken at Ibrahimy University Sukorejo and used the Class action research (CAR) Method. To collect the data, the researcher used the value of the test. the researcher directly got in touch with students. The researcher gave the pre-test and post-test to know the student’s speaking ability. The test is taken from an oral test because it measures speaking ability. As shown in the research of the fourth meeting the student’s assessments showed that the student’s speaking proficiency progressed. Many students, who were at level 2 in the previous meeting, could reach level 3 in the last cycle. The amounts were 21 students. The rest were 9 students at level 3+.
Digital Images Representation

Computers allow us to store an enormous amount of data. Data come in several types, such as numbers, sound, and images. But how are these types of data represented in the computer’s memory?
Augmented Reality in Education

Abstract: Virtual things are superimposed over the real-world surroundings in Augmented Reality. Educators understand that learning is enhanced not only by reading and hearing but also by creating and participating. Our suggested idea attempts to use Augmented Reality to improve the current educational system. Present working on an app that will display educational information in the form of animations, movies, and 3D models. This initiative has the potential to improve student learning, creativity, and retention. Our application will also encourage pupils' intellectual curiosity, making them smarter. We want to disrupt the traditional educational model and create a new path to a smart classroom.
Evaluating Diversification in Group Recommender Systems

The formation of groups is an ordinary event in our routines.For example, people used to lunch, travel, or hang out in groups.Conversely, getting a consensus over an item may be difficult for some groups as the number of digital information increases.Group Recommender Systems (GRS) rise to assist in this task, as they filter which items may be more relevant to the group.Although there are consensus techniques to help in this matter, recommendations to groups can become monotonous, and this opens space for applying diversification techniques to improve recommendations.In this paper, we expose a model for recommendation to groups using diversification techniques and present the results of the online experiment where the proposal obtained an increase in precision at all levels compared with baseline.
Pembangunan Aplikasi Mobile Kontributor untuk Pengisian Informasi Publikasi dengan Model Agile

The Pancasila Idealism Development Agency is a body that is under and responsible to the President, determines the policy direction for the development of Pancasila idealism, and assists, harmonizes, and advances Pancasila idealism. Carry out standardization, prepare education and training, organize education and training in a comprehensive and sustainable manner, and develop research results on policies or regulations that are not in accordance with Pancasila, ministries/agencies, and local governments. Socio-political organizations and other parts of society. The problem faced by the Pancasila Ideology Development Agency, especially at the Data and Information Center, is the need for an application that can provide convenience for contributors in filling out activity information on the Pancasila Idealism Development Agency website along with integration between mobile applications and websites. Based on the problems obtained, the researcher formulates the problem of how to design a mobile application that is able to accommodate existing business processes quickly and accurately, as well as make system documentation and report generation as a leader with a mobile application. The system development used in this study uses the Agile model.
Basic Line and Scatter Plots

Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.
Las nuevas realidades de la educación a distancia y virtual en Colombia

In this paper, the authors will talk about the development of technological platforms and tools that will support the creation and implementation of techno-pedagogical paradigms to tech the XXI Century generations. By proving how the innovative model of the open and distance learning universities (UNAD, for its Spanish acronym) is not run out and can be renewed, taking as an example the COVID-19 pandemic and analyzing the carriers that provide change to a new education, where the need of innovation in higher education in open and distance modalities is an urgent matter. Undoubtedly, learning is one of the human capacities that has most influenced their development and evolution. Thus, the e-education concept is presented as an effort to develop talents and human capacities using the Internet and new information and communication technologies (ICT). This will allow introducing a generalized remote mode, which will create a different culture adjusting the school curriculum to an online strategy, and not a rigid one, that involves technological improvements, so general and specialized knowledge are combined to the needs of scientific, technological, and social development. Teachers work with students differently and provide help through more knowledge and experience. Recent studies are showing that there are more open and online options for teacher professional development and teachers access the Internet to expand their development opportunities through social media platforms. In this sense, the non-attendance process of education has improved its relevance globally. Although, the COVID-19 pandemic caused an accelerator effect on the digital transformation of the educational institutions.
The Function: Programs as a Series of Statements

The function is the simplest construct able to describe a behavior. Functions can behave similarly as in math: they compute a number from a given number. Functions in the programming languages are a somewhat more general construction, though: they generate a new object by executing statements from given objects. In this chapter, you will learn how functions work in programming languages and about related concepts such as blocks.
EDUCATION MOBILE APPLICATION CHEMISTRY FORM 4

Nowadays, people around the world are learning chemistry through various means and every student has a mobile phone to help them to connect to the Internet which is a treasure trove of educational material. An app available for mobile phones known as Chemistry Form 4 focuses on developing a student’s knowledge of chemistry using smartphones as a platform has been developed for students in Malaysia. This mobile app was developed using Android Studio and Unity 2019.1 programs. The agile method was used to develop this project because of its flexibility with regards to its working process. Starting from planning of the problem statement, to the provision of solutions and objectives of this project, and the analysis of the requirements from client phase. To analyze this project, a few diagrams such as use case diagram, use-case description, activity diagram, sequence diagram, and class diagram were used. Thereafter, the designing phase kicked in that included the normalization of data, the development of entity relationship diagram, data dictionary and CRUD statement for the project. This project also focused on the students’ needs from a chemistry app, which included preparation, presentation, practice and performance. Those functions include preparing and reading notes that can be updated by teachers. Students can also answer questions provided by teachers and view their results at the end of the sessions. Last but not least the students can view the atomic structures and chemical nature using an augmented reality (AR) model after the scanning cards provided by developers. Students can use the app to enhance their creativeness in getting to know atomic structures in this manner. Providing updated notes and questions with the aid of instructors will allow the students to benefit from the advanced know-how and chemistry expertise of the instructors. The mobile apps help students to improve their basic understanding of chemical processes and recognise the value of learning chemistry.
THE MAIN ASPECTS OF THE TRAINING PROGRAM “INCREASING METACOGNITIVE MONITORING ACCURACY OF UNIVERSITY STUDENTS’ LEARNING ACTIVITIES BY MEANS OF EMOTIONAL INTELLIGENCE”

The article substantiates main characteristics and content of the training program for increasing metacognitive monitoring accuracy of university students’ learning activities by means of emotional intelligence. The tasks of the training program are aimed at forming the skills of perceiving emotions accurately, using emotions to facilitate thoughts, understanding emotions, and managing emotions; imparting students’ acknowledgement about the specifics of metacognitive monitoring accuracy of university learning activities; development of reflexivity, internal learning motivation, metacognitive awareness; students’ awareness of the characteristic features of their own processes of understanding, evaluating and reproducing information; formation of the skills of comprehension and analysis of tasks, understanding of the specifics of the tasks performed. The tasks of the stages of the developed training program, which consisted of seven blocks, are outlined: introduction, formation of the ability to perceive emotions, formation of the ability to use emotions to facilitate thoughts, formation of the ability to understand and analyze emotions, formation of the ability to manage emotions, formation of reflexivity and metacognitive awareness, as well as formation of skills for metacognitive monitoring accuracy of university students’ learning activities. The proposed training program will help students to perceive, evaluate and express emotions and related needs of physical and mental state and appearance; use emotions to increase efficiency of thinking and activities; understand and analyze emotional information; consciously manage emotions for personal growth and improvement of interpersonal relationships; flexibly approach the setting of the learning goals, monitor and control one’s own cognitive activity, use independent planning during learning. Moreover, the article describes the results of the testing of the training program. A strong evidence of its efficiency and effectiveness is the absence of statistically significant differences in the control group, as well as positive shifts in students’ implementation of more accurate metacognitive judgments while performing the tasks of the experiment. A promising direction of research of emotional intelligence is the study of the peculiarities of its influence on metacognitive monitoring accuracy of the learning activities in students of higher education.
Fast and Blind Detection of Rate-Distortion-Preserving Video Watermarks

Forensic watermarking enables the tracing of digital pirates that leak copyright-protected multimedia. To prevent a negative impact on the video quality or bit rate, rate-distortion-preserving watermarking exists, which represents a watermark as compression artifacts. However, this method has two main disadvantages; the detection has a high complexity and it is non-blind. Although a method based on perceptual hashing exists that speeds up the detection of a fallback watermarking system, it decreases its robustness. Therefore, this paper proposes a novel fast detection method that has less impact on the robustness than related work. Our method optimized NS-DCT-DST hashes for rate-distortion-preserving watermarking, which are more robust to content-preserving attacks. Moreover, a blind version is proposed which does not require the original video for hash extraction. As such, the detection is experimentally measured to be up to 5700 times faster, at the cost of a modest decrease in robustness. In fact, the proposed method shows good robustness to content-preserving recompression attacks when using hashes that are as small as 432 bytes. This is much smaller than related work at comparable performance. In conclusion, this paper enables fast adversary tracing using watermarks that do not impact the video’s compression efficiency.
Comparison on Routing Processing Performance between Shortest Paths Tree (SPT) and Minimum Spanning Tree (MST)

This study’s objective is to assess the performance of routing processing between Shortest Paths Tree (SPT) and Minimum Spanning Tree (MST). In this paper we propose and create software to calculate the efficiency path of routing processing in the network. Extensive simulations are being carried out with two different algorithms specific for Dijkistra’s algorithm and Kruskal’s algorithm. The network loads in the performance plots are characterized by two important parameters (a number of vertices) - 7 nodes and 10 nodes, more vertices could show good performance in the simulation results and showed that the performance of Kruskal’s algorithm is better than Dijkistra’s algorithm when the network loads are increased from 7 nodes to 10 nodes. In this simulation, Because of the total cost of distance matrix for Kruskal is less than Dijkistra when the number of routers or nodes is increased from 7 nodes to 10 nodes. This software namely Network Aids Design (NAD), is useful for calculation about the performance of routing processing in the network.
AUTOMATIC CONTROL OF THE TECHNOLOGICAL PROCESS USING NEURAL NETWORKS TO DETERMINE THE PARAMETERS OF THE PRODUCTION PROCESS

In multifactorial systems using textual and graphical information in matrix factorization to facilitate the problem of separate data processing. Recently, in some studies, the study of neural networks to understand the content of text and graphic elements more deeply and to achieve efficacy by creating more accurate patterns of recognition of elements. However, the open question remains about how to effectively use graphic data from the thermal imager in matrix factorization. In this paper, we proposed a double-regularized matrix factorization with deep neural networks (DRMF) to solve this problem. DRMF applies a multilayered neural network model by stacking a convolutional neural network and a secured repetitive neural network to create independent distributed views of user content and objects. Then representations serve to regularize the generation of hidden models for both users and for elements of matrix factorization. So the proposed new model of the neural network works better than a model with a single convergent neural network. In this paper, we propose double - regularized matrix factorization with deep neural networks (DRMF) to solve this problem. DRMF uses a multi-layered neural network model by enclosing a convoluted neural network and a secure repeating neural network to create independent distributed representations of user content and objects. Then the representations are used to regularize the generation of hidden models for both users and elements of matrix factorization. Thus, the proposed new neural network model works better than the model with a single converging neural network. In traditional SF methods, only a feedback matrix is ​​used, which contains explicit (eg, estimates) or implicit feedback to train and predict the life of the motor. As a rule, the feedback matrix is ​​liquid, which means that most users encounter several elements. Based on this was presented in Proc. BigData Congress. However, this view has been significantly expanded using a new deep neural network model and adding new experimental attachments compared to the conference publication.
Tool support to aligning requirements and testing through behaviour-driven requirements patterns

The software industry still struggles with adverse effects of a weak alignment between requirements and testing. The Software Pattern Metamodel (SoPaMM) aligns requirements and test patterns under the influence of agile practices. However, these patterns will be more beneficial for professionals if development activities are supported by a software tool. This paper presents the behaviour-DRivEn Application Model generator (DREAM) tool, automatically generating requirements and test specifications from SoPaMM-based patterns. We show how DREAM supports requirements elicitation and specification, test case elaboration, and software documentation using a patterns catalogue for electronic health record systems.
Going from a Program Working to Working Well

Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.
Bayesian Command Selection

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