Reviewerly/exHarmony huggingface dataset:
Introduction

This chapter considers what VR means in a way that captures the most crucial aspects in spite of rapidly changing technology. Relevant terminology is introduced. The subsequent discussion covers what VR is considered to be today and what we envision for its future. The chapter starts with two thought-provoking examples: (1) A human having an experience of flying over virtual San Francisco by flapping his own wings and (2) a gerbil running on a freely rotating ball while exploring a virtual maze that appears on a projection screen around the mouse.
HOW IS THE FEASIBILITY AND READABILITY OF 10TH GRADE ARABIC LANGUAGE AND LITERATURE TEXTBOOK? / BAGAIMANA KELAYAKAN DAN KETERBACAAN BUKU TEKS BAHASA DAN SASTRA ARAB KELAS X?

This research aims to determine the suitability of Arabic Language and Literature textbooks for 10th grade High School/MA Students with the BSKAP standards, the readability level, and readability level by student grade. This research used the evaluative method. The researchers used content analysis technique and collected data through literature study and document study to analyze the feasibility of textbooks. The method used to determine the readability level was the Raygor’s graph and the fog index. The results of the study indicate that this book is suitable for use with an overall score of 92.42% which includes aspects of the feasibility of standard content, presentation, design and graphics. However, based on the results of Raygor's graphic analysis, this textbook is not appropriate for use at the 10th grade student because none of the eight texts studied correspond to the 10th grade level. Meanwhile, based on the results of the fog index, this textbook is in easy to read category with a score of 3.72. Overall this book is suitable for use in the 10th grade student, but it needs some adjustments to design standards such as selecting interesting illustrations. Even though it has a high level of feasibility, the readability of this book is not up to the 10th grade student and the readability level is not ideal, so it needs improvement.
The Influence of the Discovery Learning Model on the Ability to Find Intrinsic Elements in Short Stories

Text for abstract. Type your abstract Font Calibri here (10pt). An abstract (minimum 150 word, maximum 200 words) is typed single space. The abstract should be written briefly and factually in English, and be written separately from the article. The abstract contains a clear elaboration of (1) background or research purpose, (2) methods, (3) results, and (4) conclusion.&#x0D; Short stories are one of the literary works that are interesting to read. Most of the stories in short stories are purely fictional; however, the stories in short stories have relevance to real life because the sources of the stories that occur in short stories partly come from human life. The aim of this research is to determine the effect of the discovery learning model on students' ability to determine the intrinsic elements of short stories. The method used in this research is quantitative in the form of an experiment with a one-group pretest-posttest design. Data analysis in hypothesis testing uses a paired sampling t-test. The results of the research show that the value of the paired sample t-test shows that the sign value is 0.000 with a result of less than 0.05, which means that there is an influence of discovery learning on students' ability to determine the intrinsic elements of short stories in class XI of SMK Negeri 1 Jember
Effect of Flash Card Media Effectiveness on Beginning Reading Ability and Symbolic Thinking in Early Childhood 5-6 Years: Case Study in Kindergarten. Imam Syafi'I Jember Odd Semester 2022-2023 Academic Yea

Learning media is an important tool in supporting the effectiveness of learning in schools, one of the visual learning media uses flash card media that displays a series of cards containing pictures, letters, and words that can attract interest in learning for early childhood. This study aims to see the effect of the effectiveness of flashcard media on the ability to read the beginning and think symbolically in early childhood 5-6 at Imam Syafi'i Kindergarten. This study uses a simple regression analysis method to see the effect of effectiveness of flashcard media on the ability to read beginning and think symbolically in early childhood 5-6 in Imam Syafi'i Kindergarten. It was concluded that partially the flash card media had a significant effect on beginning reading ability, partially the flash card media had a significant effect on children's symbolic thinking while simultaneously the flash card media had a significant effect on children's beginning reading ability and symbolic thinking
FORMATION OF DIGITAL COMPETENCIES IN THE EDUCATIONAL TRAJECTORY OF STUDENTS OF ECONOMIC SPECIALTIES

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
MANAGER’S COMPETENCE: METHODS OF FORMATION AND DIAGNOSTIC TOOLS AT THE STAGE OF HIGHER EDUCATION

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
SOME ASPECTS OF THE IMPLEMENTATION OF SCIENTIFIC AND EDUCATIONAL PROGRAMS OF INTERNATIONAL COOPERATION

Innovative educational technologies: European experience and its application in training in economics and management : Proceedings of scientific and pedagogical internship (February 13 – March 26, 2023, Riga, Latvia). Riga, Latvia : “Baltija Publishing”, 2023.
Efficient Match Pair Retrieval for Large-Scale UAV Images via Graph Indexed Global Descriptor

SfM (Structure from Motion) has been extensively used for UAV (Unmanned Aerial Vehicle) image orientation. Its efficiency is directly influenced by feature matching. Although image retrieval has been extensively used for match pair selection, high computational costs are consumed due to a large number of local features and the large size of the used codebook. Thus, this paper proposes an efficient match pair retrieval method and implements an integrated workflow for parallel SfM reconstruction. First, an individual codebook is trained online by considering the redundancy of UAV images and local features, which avoids the ambiguity of training codebooks from other datasets. Second, local features of each image are aggregated into a single high-dimension global descriptor through the VLAD (Vector of Locally Aggregated Descriptors) aggregation by using the trained codebook, which remarkably reduces the number of features and the burden of nearest neighbor searching in image indexing. Third, the global descriptors are indexed via the HNSW (Hierarchical Navigable Small World) based graph structure for the nearest neighbor searching. Match pairs are then retrieved by using an adaptive threshold selection strategy and utilized to create a view graph for divide-and-conquer based parallel SfM reconstruction. Finally, the performance of the proposed solution has been verified using three large-scale UAV datasets. The test results demonstrate that the proposed solution accelerates match pair retrieval with a speedup ratio ranging from 36 to 108 and improves the efficiency of SfM reconstruction with competitive accuracy in both relative and absolute orientation.
Risk Management for Artificial General Intelligence by Limited Liability Company

The recent emergence of generative Artificial Intelligence (“AI”) and chatbots has significantly advanced the field of AI, making the prospect of achieving Artificial General Intelligence (“AGI”) more feasible and tangible than ever before. The recent revision of the EU AI Act also mentioned general-purpose AI systems (GPAIS) as foundation models underlying any type of AI. Although various technical and ethical issues in realizing AGI systems have been discussed extensively, it remains organizational issues in the risk management of AGI systems. This paper proposes the limited liability company (LLC) for an AGI system as a business entity. The research question investigated in this paper is: How do LLCs mitigate potential risks of investors and enjoy benefits to promote AGI businesses? Two kinds of liability shields, vertical and horizontal, are proposed with LLCs and series LLCs as business entities.
Specifying Preferences over Policies Using Branching Time Temporal Logic

Automated Planning is the subarea of AI devoted to developing algorithms that can solve sequential decision making problems. By taking a formal description of the environment, a planning algorithm generates a plan of actions (also called policy) that can guide an agent to accomplish a certain task. Classical planning assumes the environment is fully-observed and evolves in a deterministic way considering only simple reachability goals (e.g. a set of states to be reached by a plan or policy). In this work, we approach fully-observed non-deterministic planning (FOND) tasks which allow the specification of complex goals such as the preference over policy quality (weak, strong or strong-cyclic) and preferences over states in the paths generated by a policy. To solve this problem we propose formulae in $$\alpha $$ -CTL (branching time) temporal logic and use planning as model checking algorithms based on $$\alpha $$ -CTL to generate a solution that captures both, agent's preferences and the desired policy quality. To evaluate the effectiveness of the proposed formulae and algorithms, we run experiments in the Rovers benchmark domain. Up to our knowledge, this is the first work to solve non-deterministic planning problems with preferences using a CTL temporal logic.
Establishing Trustworthiness: Rethinking Tasks and Model Evaluation

Language understanding is a multi-faceted cognitive capability, which the Natural Language Processing (NLP) community has striven to model computationally for decades. Traditionally, facets of linguistic intelligence have been compartmentalized into tasks with specialized model architectures and corresponding evaluation protocols. With the advent of large language models (LLMs) the community has witnessed a dramatic shift towards general purpose, task-agnostic approaches powered by generative models. As a consequence, the traditional compartmentalized notion of language tasks is breaking down, followed by an increasing challenge for evaluation and analysis. At the same time, LLMs are being deployed in more real-world scenarios, including previously unforeseen zero-shot setups, increasing the need for trustworthy and reliable systems. Therefore, we argue that it is time to rethink what constitutes tasks and model evaluation in NLP, and pursue a more holistic view on language, placing trustworthiness at the center. Towards this goal, we review existing compartmentalized approaches for understanding the origins of a model's functional capacity, and provide recommendations for more multi-faceted evaluation protocols.
Multiscale hierarchical decomposition methods for images corrupted by multiplicative noise

Recovering images corrupted by multiplicative noise is a well known challenging task. Motivated by the success of multiscale hierarchical decomposition methods (MHDM) in image processing, we adapt a variety of both classical and new multiplicative noise removing models to the MHDM form. On the basis of previous work, we further present a tight and a refined version of the corresponding multiplicative MHDM. We discuss existence and uniqueness of solutions for the proposed models, and additionally, provide convergence properties. Moreover, we present a discrepancy principle stopping criterion which prevents recovering excess noise in the multiscale reconstruction. Through comprehensive numerical experiments and comparisons, we qualitatively and quantitatively evaluate the validity of all proposed models for denoising and deblurring images degraded by multiplicative noise. By construction, these multiplicative multiscale hierarchical decomposition methods have the added benefit of recovering many scales of an image, which can provide features of interest beyond image denoising.
New Solution to 3D Projection in Human-like Binocular Vision

A human eye has about 120 million rod cells and 6 million cone cells. This huge number of light sensing cells inside a human eye will continuously produce a huge quantity of visual signals which flow into a human brain for daily processing. However, the real-time processing of these visual signals does not cause any fatigue to a human brain. This fact tells us the truth which is to say that human-like vision processes do not rely on complicated formulas to compute depth, displacement, and colors, etc. On the other hand, a human eye is like a PTZ camera. Here, PTZ stands for pan, tilt and zoom. We all know that in computer vision, each set of PTZ parameters (i.e., coefficients of pan, tilt and zoom) requires a dedicated calibration to determine a camera’s projection matrix. Since there is an infinite number of PTZ parameters which could be produced by a human eye, it is unlikely that a human brain stores an infinite number of calibration matrices for each human eye. Therefore, it is an interesting question for us to answer, which is to say whether simpler formulas of computing depth and displacement exist or not. Moreover, these formulas must be calibration friendly (i.e., easy process on the fly or on the go). In this paper, we disclose an important discovery of a new solution to 3D projection in a human-like binocular vision system. The purpose of doing 3D projection in binocular vision is to undertake forward and inverse transformations (or mappings) between coordinates in 2D digital images and coordinates in a 3D analogue scene. The formulas underlying the new solution are accurate, easily computable, easily tunable (i.e., to be calibrated on the fly or on the go) and could be easily implemented by a neural system (i.e., a network of neurons). Experimental results have validated the discovered formulas.
Schrödinger’s Cat May Be Dead All Along

By framing the problem of determining the state of Schrodinger&amp;rsquo;s cat as a problem of searching for Hidden Markov Model (HMM), this work shows that Schrodinger&amp;rsquo;s cat is dead with stationary probabilities P(Cat = Dead) = 1 and P(Cat = Alive) = 0 consistently in various observation-based modelling of the HMM.
A Modular Approach to Design an Experimental Framework for Resource Management Research

Abstract Research on resource management focuses on optimizing system performance and energy efficiency by distributing shared resources like processor cores, caches, and main memory among competing applications. This research spans a wide range of applications, including those from high-performance computing, machine learning, and mobile computing. Existing research frameworks often simplify research by concentrating on specific characteristics, such as the architecture of the computing nodes, resource monitoring, and representative workloads. For instance, this is typically the case with cloud systems, which introduce additional complexity regarding hardware and software requirements. To avoid this complexity during research, experimental frameworks are being developed. Nevertheless, proposed frameworks often fail regarding the types of nodes included, virtualization support, and management of critical shared resources. This paper presents Stratus, an experimental framework that overcomes these limitations. Stratus includes different types of nodes, a comprehensive virtualization stack, and the ability to partition the major shared resources of the system. Even though Stratus was originally conceived to perform cloud research, its modular design allows Stratus to be extended, broadening its research use on different computing domains and platforms, matching the complexity of nowadays cloud environments.
Effects on Sensory Transfer in Immersive Virtual Reality Environments

Recently, virtual reality widely used in education, industry, and entertainment, and so on. In this paper, we present effects on sensory transfer, which is a fake feeling such as illusion to make the specific sense in five senses with other senses for participants in immersive virtual environments (IVE). In particular, we perform an analysis of the effect through interactive virtual reality contents based on hearing and touch. As our result of the experiment, it was found that the sense of touch using hearing could be sufficiently provided, and on the contrary, transfer feeling of the sense of touch had a few limitations. Through our method presented in this paper, it is expected that the limitations of the device to make immersive virtual reality contents can be overcome.
Bayesian Answers

This chapter provides a primer on the logic of Bayesian updating and shows how it is used for answering causal queries. We illustrate with applications to correlational and process-tracing inferences.
Clue Selection as a Decision Problem

This chapter is the first of three in which we investigate how causal models can inform research design. In this chapter, we draw out the implications of the causal-model approach for clue-selection strategies, for figuring out which pieces of evidence are likely to be most informative about a question of interest. We demonstrate procedures for assessing which clues minimize expected posterior variance and how to construct an optimal decision tree for determining a dynamic clue-gathering strategy.
Lithological Mapping Research Based on Feature Selection Model of ReliefF-RF

Multispectral satellite data allow experts to discriminate rock units based on their spectral signature characteristics. Here, Sentinel-2, ASTER and the Landsat-8 Operational Land Imager (OLI) were assessed for lithological mapping by using a random forest (RF) classifier for a study area located in Xitieshan, Northwest China. The classification accuracy of Sentinel-2 was 60.71%, which was 5.24% and 4.77% higher than the accuracies for ASTER and the Landsat-8 OLI, respectively. Three image enhancement techniques, namely, principal component analysis (PCA), independent component analysis (ICA) and minimum noise fraction (MNF), were used with grey-level cooccurrence matrices (GLCMs) to increase the quality of the input datasets. The ICA could discriminate between rock unit datasets better than the other approaches. In contrast, GLCM performed poorly when used independently. The overall classification accuracies were 60.71%, 62.63%, 64.34%, 65.21% and 58.87% for the 10 bands of Sentinel-2, PCA, MNF, ICA and GLCM, respectively. Then, five datasets were combined as a single group and applied in RF classification. Sentinel-2 obtained an overall accuracy of 73.96% and performed better than the other single-dataset approaches used in this study. Furthermore, the classification result of RF was achieved better performance than that of the support vector machine algorithm (SVM). During feature selection processing, ReliefF, the most successful pre-processing algorithm, was employed to preliminarily perform feature screening. Then, the optimal dataset was selected on the basis of the importance ranking of RF. A total of 20 more important predictors were selected from 114 original features using the ReliefF-RF model. These predictors were used in the lithological mapping, and an overall accuracy of 77.63% was reached.
Design and Optimization of Improved Recognition Algorithm for Piano Music Based on BP Neural Network

Based on the theory of speech recognition and the characteristics of music recognition, this study studies, recognizes and processes music sounds. Artificial Neural Network (ANN) is a distributed parallel information processing system, which can train and identify comprehensive input patterns without selecting specific speech parameters. An improved back propagation neural network (BPNN) model is proposed and applied to piano music recognition to find the maximum autocorrelation function on a small scale to adapt to fast-paced music. The simulation results show that the improved method solves the problems encountered in the application of neural network in music recognition, and at the same time makes use of various advantages of neural network pattern recognition, reducing the stage of manual extraction and processing knowledge in traditional recognition methods. To some extent, this algorithm avoids the situation of missing detection, false detection or recognition error of fast-paced music by traditional algorithms, and can significantly improve the recognition accuracy.
Optimization of Moving Object Tracking Algorithm Based on Computer Vision and Vision Sensor

Machine vision uses the collected image information to understand and analyze the real world, and has been applied in more and more scenes. Many factors will have a substantial impact on the performance and efficiency of the algorithm in the process of target movement. In this paper, an improved convolutional neural network (CNN) is proposed to optimize the design of moving target tracking algorithm. The targets with the same characteristics in each frame of image are correlated to obtain the information of the target's position, speed, acceleration and so on. Then the complete driving trajectory of the target in the field of view is obtained. The simulation results show that compared with the traditional support vector machine (SVM) algorithm, the improved CNN has obvious advantages in the later stage of operation, and the error is reduced by 33.57%. The moving target tracking model based on improved CNN algorithm has more advantages than SVM algorithm in both monitoring accuracy and error. Using the tracking algorithm, the moving target can be tracked stably in real time, thus providing a reliable data source for the deep analysis of target behavior and higher-level visual tasks in the later period according to the actual application requirements.
Exploring Group Work in ESL Classroom Using Tuckman’s Model

Interaction among students especially in ESL classrooms is highly valued, and group work is one of the ways to encourage interaction among students. In addition, group work offers many advantages and according to the Tuckman Model, during group work, participants will go through four stages; forming, storming, norming, and performing. This study aims to investigate the influence that all these four stages have on group work, as well as the relationship that exist between the stages. The quantitative approach was adopted. Purposive sampling method was selected. A questionnaire consisting of 29 items was distributed online to 200 undergraduate students of Universiti Teknologi Mara, Shah Alam, Malaysia. The questionnaire contains four sections, and these sections aim to elicit respondents’ experience while going through the four stages in Tuckman Model. This study found that all stages do influence students group work experience positively. Additionally, it is also found that there is moderate positive relationship between the stages forming and norming as well as between forming and performing. Hence, educators should observe these stages as students go through them when performing group work and offer guidance to students to enhance the effectiveness of group work in ESL classrooms.