Reviewerly/exHarmony huggingface dataset:
Pengaruh Motivasi Belajar Terhadap Tingkat Keberhasilan Belajar Siswa pada Pembelajaran PJOK di SMP N 4 Kajen

The purpose of this study was to determine whether learning motivation has an effect on student learning success and to find out how much influence learning motivation has on student learning success at SMP N 4 Kajen Pekalongan Regency. This type of research uses quantitative descriptive. The sample is 52 students from the total population of class VIII students of SMP N 4 Kajen. The sampling technique used is Simple Random Sampling or random selection of respondents. The data collection instruments used in this study were Questionnaires and Documentation. The results of the study based on the data obtained showed that learning motivation had a significant influence from the results of calculations using the F test (simultaneous test) showing that the f value of 7.165 was greater than the f table. , and the sig value of 0.010 is smaller than 0.050. And to find out how big the influence of learning motivation on student learning outcomes can be shown from the results of calculating the coefficient of determination using the Summary Model showing that R Square is 0.125, which means that the magnitude of the influence of learning motivation is 12.5%, and 87.5% is influenced by another factor.
3D Illustration from a Single Input Image using Depth Estimation Method

People can completely absorb a scene through the emerging technique of 3D imaging. In this study, we introduce a method to generate a 3D view from a single input image, enabling captivating 3D scenes using Korean historic images. We present a layered illustration approach for viewpoint generation that incorporates color and depth features in areas where the original image is obscured. Our technique involves a multilayered Depth Illustration that achieves precise spatial interaction for a 3D perspective. Additionally, we utilize a deep learning-based coloring approach that enhances regional shades and depth information in obscured areas while considering spatial information. By employing this method, we create 3D images from a single image input with fewer parameters compared to other methods. The generated 3D images can be effectively displayed with motion using conventional display engines. We evaluate the efficiency of our approach on a range of challenging images, demonstrating its effectiveness in conserving RAM and minimizing delays at high rates. This approach simplifies the process of 3D imaging by enabling the generation of 3D views from a single image, which can be used to create virtual reality (VR) and augmented reality (AR) experiences as well as can be used to reconstruct ancient artifacts and structures by estimating the depth of features in an image.
Regularizing Neural Network for Phase Unwrapping

Quantitative phase imaging (QPI) refers to label-free techniques that produce images containing morphological information. In this chapter, we focus on 2D phase imaging with a holographic setup. In such a setting, the complex-valued measurements contain both intensity and phase information. The phase is related to the distribution of the refractive index of the underlying specimen. In practice, the collected phase happens to be wrapped (i.e., modulo 2π of the original phase) and one gains quantitative information on the sample only once the measurements are unwrapped. The process of phase unwrapping relies on the solution of an inverse problem, for which numerous methods exist. However, it is challenging to unwrap the phases of particularly complex or thick specimens such as organoids. Under such extreme conditions, classical methods often exhibit unwrapping errors. In this chapter we first formulate the problem of phase unwrapping and review the existing methods to solve it. Then, we present an application of a regularizing neural network to phase unwrapping, which allows us to outline the advantages of a training-free approach, i.e., a deep image prior, over classical methods or supervised learning.
Link Prediction Based on the Relational Path Inference of Triangular Structures

Link prediction is used to complete the knowledge graph. Convolutional neural network models are commonly used for link prediction tasks, but they only consider the direct relations between entity pairs, ignoring the semantic information contained in the relation paths. In addition, the embedding dimension of the relation is generally larger than that of the entity in the ConvR model, which blocks the progress of downstream tasks. If we reduce the embedding dimension of the relation, the performance will be greatly degraded. This paper proposes a convolutional model PITri-R-ConvR based on triangular structure relational inference. The model uses relational path inference to capture semantic information, while using a triangular structure to ensure the reliability and computational efficiency of relational inference. In addition, the decoder R-ConvR improves the initial embedding of the ConvR model, which solves the problems of the ConvR model and significantly improves the prediction performance. Finally, this paper conducts sufficient experiments in multiple datasets to verify the superiority of the model and the rationality of each module.
QUARE: 2nd Workshop on Measuring the Quality of Explanations in Recommender Systems

QUARE1—measuring the QUality of explAnations in REcommender systems—is the second workshop which focuses on evaluation methodologies for explanations in recommender systems. We bring together researchers and practitioners from academia and industry to facilitate discussions about the main issues and best practices in the respective areas, identify possible synergies, and outline priorities regarding future research directions. Additionally, we want to stimulate reflections around methods to systematically and holistically assess explanation approaches, impact, and goals, at the interplay between organisational and human values. To that end, this workshop aims to co-create a research agenda for evaluating the quality of explanations for recommender systems.
CIDER: Collaborative Interior Design in Extended Reality

Despite significant efforts dedicated to exploring the potential applications of collaborative mixed reality, the focus of the existing works is mostly related to the creation of shared virtual/mixed environments resolving concurrent manipulation issues rather than supporting an effective collaboration strategy for the design procedure. For this reason, we present CIDER, a system for the collaborative editing of 3D augmented scenes allowing two or more users to manipulate the virtual scene elements independently and without unexpected changes. CIDER is based on the use of "layers" encapsulating the state of the environment with private layers that can be edited independently and a global one collaboratively updated with "commit" operations. Using this system, implemented for the HoloLens 2 headsets and supporting multiple users, we performed a user test on a realistic interior design task, evaluating the general usability and comparing two different approaches for the management of the atomic commit: forced (single-phase) and voting (requiring consensus), analyzing the effects of this choice on the collaborative behavior.
An elephant in the room—EU policy gaps in the regulation of moderating illegal sexual content on video-sharing platforms

Abstract With the availability of broadband internet and a significant increase in storage capacity, people are sharing a dynamically growing amount of multimedia content on video-sharing platforms, including sexually explicit content. While sexual content on the internet is not illegal per se and remains in the domain of economic services, there are certain forms of sexually explicit content that are criminally unlawful, such as child pornography or non-consensual pornography. This article explores why the current business-oriented regulatory environment fails to provide effective tools to combat illegal sexual content: first, from the perspective of content moderation on video-sharing platforms, and then from the perspective of substantive criminal law. After mapping the gaps in policy at all levels of content regulation, the article offers recommendations to address these issues, open up a policy debate and influence policy makers.
A simpler security proof for 6-state quantum key distribution

Six-state Quantum Key Distribution (QKD) achieves the highest key rate in the class of qubit-based QKD schemes. The standard security proof, which has been developed since 2005, invokes complicated theorems involving smooth R\'{e}nyi entropies. In this paper we present a simpler security proof for 6-state QKD that entirely avoids R\'{e}nyi entropies. This is achieved by applying state smoothing directly in the Bell basis. We obtain the well known asymptotic rate, but with slightly more favorable finite-size terms. We furthermore show that the same proof technique can be used for 6-state quantum key recycling.
Meningkatkan Motivasi dan Kemampuan Menulis Recount Text dengan Metode Picture Series bagi Siswa Kelas VIII-8

The fact of teaching recount text is currently quite far from the goal. This is the background of the research. Therefore, the picture series method was applied. This study aims to describe and analyze the process of teaching English. The population in this study were all students of class VIII-8 MTs. Negeri 1 Mataram, Mataram City. While the sample in this study were 34 students of class VIII-8 MTs. Negeri 1 Mataram. This type of research is classroom action research (CAR) using a qualitative approach. The data collection techniques for this research are observation, questionnaires, and documentation. The results showed that the teaching and learning process was quite boring and the application of picture series as a teaching method changed the teaching environment. Students become more active and the good impact of this condition is that the evaluation results increase. This is known from the evaluation results starting from the pre-cycle to cycle I and cycle II. The average value of students is 78.00 (did not pass individual and classical), 81.29 (passed individual not classical), and 91.00 (passed individual and classical).
Career guidance work in the branch of the Emperor Alexander I St. Petersburg State Transport University in Ryazan

Modern socio-economic conditions, the labor market, fi erce competition between educational institutions of secondary vocational education for applicants require eff ective career guidance in the region and beyond. Vocational guidance among young people is an important direction in the process of education and upbringing. The Ryazan branch of PGUPS has developed a comprehensive career guidance program “Create the Future of Russian Railways”. The program sets the following tasks: to form a positive image of the Ryazan branch of PGUPS; increase the level of public awareness of the educational institution in the region and the competitiveness of the branch in the educational services market; create conditions for conscious professional self-determination and disclosure of personality abilities; to strengthen relations with employers, social partners in matters of career guidance. Career guidance activities in an educational institution of secondary vocational education are organized in three areas: work among students of schools in the city and region; among students in order to retain the contingent and graduates to ensure successful employment. For successful career guidance, it is necessary to use both traditional methods and forms, and information technology. The data of the admission campaign from 2018 to 2021 were analyzed. The analysis showed the effectiveness of complex career guidance work among applicants.
Targeted training in the system of secondary vocational education of Roszheldor. Organization of work, prospects, problems

The training of graduates of secondary vocational education for JSC “Russian Railways” in targeted training at the branch of the RSTU in Voronezh was analyzed. The main results of the study are presented, the features of the organization of targeted training, based on many years of experience, are noted. An assessment is made of the need to select applicants for targeted training by the structural divisions of Russian Railways. The eff ectiveness of targeted training based on the results of graduation from the department of secondary vocational education of the RSTU branch in Voronezh over the past three years is shown. Of the total number of graduates with a target area, 100 % are employed in divisions of Russian Railways. Graduates who studied under targeted contracts are 100 % fi xed in their places of work. The branch attaches great importance to vocational guidance work with schoolchildren. The branch of RSTU in Voronezh became a participant in an experiment on the development, testing and implementation of a new educational technology for designing educational programs for secondary vocational education within the framework of the federal project “Professionalitet”. Currently, active work is underway to prepare for the implementation of this line of activity by the branch with the participation of industry enterprises. The factors of interaction between an educational institution of secondary vocational education, subordinated to Roszheldor, and an employer to improve the targeted training of graduates of secondary vocational education are considered.
AI Case-Based Reasoning for Artificial Neural Networks

Faced with the triad of time-cost-quality, the realization of production tasks under economic conditions is not trivial. Since the number of Artificial-Intelligence-(AI)-based applications in business processes is increasing more and more nowadays, the efficient design of AI cases for production processes as well as their target-oriented improvement is essential, so that production outcomes satisfy high quality criteria and economic requirements. Both challenge production management and data scientists, aiming to assign ideal manifestations of artificial neural networks (ANNs) to a certain task. Faced with new attempts of ANN-based production process improvements [8], this paper continues research about the optimal creation, provision and utilization of ANNs. Moreover, it presents a mechanism for AI case-based reasoning for ANNs. Experiments clarify continuously improving ANN knowledge bases by this mechanism empirically. Its proof-of-concept is demonstrated by the example of four production simulation scenarios, which cover the most relevant use cases and will be the basis for examining AI cases on a quantitative level.
Iran prepares to erect a digital wall

Researchers feel increasingly isolated as government moves to restrict internet access
Visualization and Decision Making Design Under Uncertainty

Uncertainty is an important aspect to data understanding. Without awareness of the variability, error, or reliability of a dataset, the ability to make decisions on that data is limited. However, practices around uncertainty visualization remain domain-specific, rooted in convention, and in many instances, absent entirely. Part of the reason for this may be a lack of established guidelines for navigating difficult choices of when uncertainty should be added, how to visualize uncertainty, and how to evaluate its effectiveness. Unsurprisingly, the inclusion of uncertainty into visualizations is a major challenge to visualization. As work concerned with uncertainty visualization grows, it has become clear that simple visual additions of uncertainty information to traditional visualization methods do not appropriately convey the meaning of the uncertainty, pose many perceptual challenges, and, in the worst case, can lead a viewer to a completely wrong understanding of the data. These challenges are the driving motivator for this special issue.
Causal Quartets to the Rescue

Jessica Hullman's award-winning work explores how to combine human reasoning with data-driven representation and even build software to help others do the same. Currently an Associate Professor of Computer Science at Northwestern, she publishes prolifically and often speaks on topics related to visualizing uncertainty.
An Analysis Of Fourth Grade English Textbooks In Elementary School

Abstract&#x0D; The study is titled "fourth-grade English textbook material analysis ". Titled "my next words grade 4 - student's book for elementary school ". The purpose of the study is to decide whether the content in an English textbook entitled "my next words grade 4 - student's book for elementary school" has the criteria for a good English textbook recommendation from some experts. This textbook analyzes the use of standards based entirely on dictation. Is made up of 4 standards; Materials, language, material presentations and image retrieval of the text book. Qualitative descriptive USES asa study technique to answer the problem of the study. Regarding the statistics collected, the writer concluded that the English textbook "my next words grade 4-student's book for elementary school" has met the remarkable English textbook that is based entirely on what some scholars mean. This suggests that the target of learning has met the criteria of this English textbook. In the book's criteria, there may be one element that does not meet his criteria. And in the practice unit, there are several chapters that don't fit the criteria of a good English textbook. But at maximum they had met the standards of an excellent English textbook.&#x0D; &#x0D; Keywords: Textbook analysis; English textbook
Preface

The International Conference on Signal Processing and Machine Learning (CONF-SPML) is an annual conference focusing on research areas including applied signal processing, biomedical signal processing, and machine learning. It aims to establish a broad and interdisciplinary platform for experts, researchers, and students worldwide to present, exchange, and discuss the latest advance and development in signal processing and machine learning. This volume contains the papers of the 3rd International Conference on Signal Processing and Machine Learning (CONF-SPML 2023). Each of these papers has gained a comprehensive review by the editorial team and professional reviewers. Each paper has been examined and evaluated for its theme, structure, method, content, language, and format. Cooperating with prestigious universities, CONF-SPML 2023 organized two workshops in China and the US. Dr. Marwan Omar chaired the workshop “Building a Digital Forensics Lab”, which was held at the Illinois Institute of Technology. Dr. Ziyi Yang chaired the workshop “Space-Air-Ground Integrated Wireless Communication” at the Beijing Institute of Technology. Besides these workshops, CONF-SPML 2023 also held an online session. Eminent professors from top universities worldwide were invited to deliver keynote speeches in this online session, including Prof. Roman Bauer from the University of Surrey, Prof. Brian A. Barsky from the University of California, Berkeley, Prof. Festus Adedoyin from Bournemouth University, Prof. Arshad Hassan Khan from the Department of Electrical Engineering NUCES-FAST, Islamabad, and Prof. Mazhar Javed Awan from University of Management &amp; Technology. They have given keynote speeches on related topics of computational modeling, machine learning, electronic biosensor, etc. On behalf of the committee, we would like to give sincere gratitude to all authors and speakers who have made their contributions to CONF-SPML 2023, editors and reviewers who have guaranteed the quality of papers with their expertise, and the committee members who have devoted themselves to the success of CONF-SPML 2023. List of Committee Members are available in this pdf.
Sequential Prediction

In this chapter, we address the issue of sequential prediction. The goal is to make the cumulative prediction loss as small as possible. This issue is reduced to the problem of minimizing the cumulative code-length when the code-length is calculated sequentially. We consider three types of prediction algorithms; maximum likelihood prediction algorithm, Bayesian prediction algorithm, and sequentially normalized maximum likelihood algorithm. We give their asymptotic analysis in terms of the so-called redundancy. Here, the asymptotic analysis means that the data length is sufficiently large for a fixed number of parameters. We also provide high-dimensional asymptotic analysis for the Bayesian prediction algorithm when the number of parameters increases together with the data length. In it, the spike and tail density function is introduced as the prior density, and its effectiveness is discussed.
Researches advanced in fine-grained image classification based on deep learning

Abstract In the recent past, the task of fine-grained image recognition has become a hot research topic for scholars. Due to its substantial intra-class and little inter-class variance, fine-grained image classification task is more difficult than traditional image classification task. From a deep learning standpoint, this paper first introduces representative algorithms for strongly supervised fine-grained image classification, consisting of the Part-based R-CNN algorithm, the Pose Normalized CNN algorithm, and the Mask-CNN model. Second, weakly supervised process in image classification is expounded from the representative methods of attention mechanism, end-to-end visual coding, zero-shot learning with adaptive domain decomposition. Third, in the experimental part, the most popular databases for precise picture categorization, the evaluation metrics and conclusion of the algorithms are listed. Finally, it summarizes and looks forward, points out the problems and deficiencies of current research, and gives guidance for possible solutions in the future.
Algorithms for Checking Isomorphism of Two Elementary Conjunctiоns

When solving AI problems related to the study of complex structured objects, a convenient tool for describing such objects is the predicate calculus language. The paper presents two algorithms for checking two elementary conjunctions of predicate formulas for isomorphism (matches up to the names of variables and the order of conjunctive terms). The first of the algorithms checks for isomorphism elementary conjunctions containing a single predicate symbol. In addition, if the formulas are isomorphic, then it finds a one-to-one correspondence between the arguments of these formulas. If all predicates are binary, the proposed algorithm is an algorithm for checking two directed graphs for isomorphism. The second algorithm checks for isomorphism elementary conjunctions containing several predicate symbols. Estimates of their time complexity are given for both algorithms.
PERENCANAAN PUSAT SINEMATOGRAFI DI TENGGARONG DENGAN PENDEKATAN ARSITEKTUR METAFORA

Along with the development of the world of cinematography among young people in Tenggarong, where their activities include making films, short films, film screenings, and film competitions. However, there are no facilities or gathering places for them, so it is necessary to create a place or place that can provide facilities for the cinematographic community. The purpose of this research is to plan a Cinematography Center with a metaphorical architectural concept in the form of a building taken from the formation of a film camera, and to provide a forum for the activities of the cinematography community in Tenggarong including through production, training of various film works, exhibitions, various competitions and events. It is hoped that this plan will accommodate communities in Tenggarong who are interested in cinematography, both theoretically and practically, so that they can produce the best works that can compete nationally. By emphasizing the architectural style of concrete metaphors (tangible) in the planning of the building by taking the formation of a retro film camera that is transformed, it is hoped that it will be able to give the characteristics of a building that will be planned.