Reviewerly/exHarmony huggingface dataset:
Fuzzy Computing

Fuzzy computing is a computational intelligence technology based on fuzzy theory. Fuzzy computing can solve various problems such as identification and clustering. Automatic control problems remain the main application area of fuzzy computing. This chapter first introduces the basis of fuzzy computing including fuzzy set and fuzzy membership function. Fuzzy pattern recognition, fuzzy clustering and fuzzy inference are three kinds of problems that fuzzy computing can solve. Then this chapter introduces Mamdani fuzzy control system, which is one of the most important application of fuzzy computing. Finally, this chapter introduces fuzzy logic designer to build fuzzy controller for a control problem.
Evolutionary Computing

Evolutionary computing mimics the laws of biological evolution. It solves optimization problems through the reproduction of individuals and the competition between individuals. Evolutionary computing is a collection of evolutionary algorithms that follow the survival of the fittest law in species. Evolutionary algorithms are global probability search algorithms based on natural selection, genetic mutation and other biological evolution mechanisms. Evolutionary computing has been used in various fields such as pattern recognition, image processing, economic management, mechanical engineering, electrical engineering, wireless communication, etc. This chapter first introduces an overview of evolutionary computing and simple genetic algorithm. Genetic algorithm is then used to solve travelling salesman problem. Then, this chapter introduces ant colony optimization, particle swarm optimization and differential evolution algorithms. These algorithms have been used to solve both travelling salesman problem and continuous optimization problem.
Mapping and Visualization of Bitcoin Future Trends

The goal of this research is to identify the key present and future difficulties identified in the Bitcoin literature as searched by the Web of Science database, with a focus on papers released between 2014 and 2022. Furthermore, the purpose of this literature review is to emphasize the most important topics while also laying the groundwork for future studies on Bitcoin that employ bibliometric analysis. The analysis identifies some of the most common terms. In addition, we identified the most significant writers that referenced this topic in their study findings, the journals that are most cited in the articles evaluated, and the countries with the highest efficacy. The VOSviewer application was used to search for keywords and geographical locations in Bitcoin data. It obtained Web of Science publications in 1766. At this point, topics for summarization and acquisition will be useful in the future.
Unification Modulo Equational Theories in Languages with Binding Operators (Invited Talk)

Unification (i.e., solving equations between terms) is a key step in the implementation of logic programming languages and theorem provers, and is also used in type inference algorithms for functional languages and as a mechanism to analyse rewrite-based specifications (e.g., to find critical pairs). Matching is a version of unification in which only one of the terms in the equation can be instantiated. In this talk, we present matching and unification algorithms for languages that include binding operators as well as operators that satisfy equational axioms, such as associativity and commutativity.
Obstacle-Avoidance State Characterization Models Based on Hybrid Geometric Descriptions for Mobile Manipulators

With advantages of large working space and high flexibility, mobile manipulators has been rapidly developed and applied to manufacturing system and assembly shop etc. But how to ensure their motion safety is the key problem in practical applications. To accurately predict the obstacle-avoidance state of the mobile manipulator can provide important information for its motion trajectory/path planning. For the above problem, this work proposes three distance estimation models to characterize the obstacle-avoidance state based on hybrid geometric descriptions (polygon/polyhedron, polygonal/circular slices) for three motion stages, which are the motion of the mobile device, pre-operating pose adjustment motion and operating motion of the manipulator. Three models are respectively the rough estimation model, the general estimation model and the fine estimation model of the shortest distance between robot and surrounding obstacles for each motion stage. The calculation accuracy and efficiency are analyzed during simulation. This work can provide algorithm basis for the safe motion control of mobile manipulators under open three-dimensional (3D) environments.
The Bonny Method for Children: Adapting the Method, Selecting the Music

This article outlines examples from some of the many practitioners who utilize GIM with children and youth. The adapted forms for this population can include structural changes (e.g., shortened duration of music and induction) or a greater variety of processing methods (e.g., sand play or musical improvisation). After describing some of my own experiences with adapting GIM to the needs of children, I include a list of musical selections that are appropriate for sessions with young listeners.
Pedestrian re-identification domain generalization algorithm based on causal strong and weak alignment

The domain generalisation pedestrian re-identification problem aims to generalise features learned in a known pedestrian data domain to an unknown pedestrian target domain. Most traditional domain generalization methods assume that the statistical properties between features and categories remain consistent across data domains. However, actual pedestrian data is often susceptible to domain factors such as lighting, colour shifts, and camera differences. This results in the data distribution of pedestrians under different domains not being identical and hinders the generalisation of the model. To address the above issues, this paper proposes a representation learning algorithm based on causal strong and weak alignment by constructing a structural causal model for the pedestrian domain generalization problem from the perspective of causal inference. The algorithm first performs causal intervention on the input pedestrian data to obtain causally enhanced images, then the image features are fed into the strong alignment module to achieve feature alignment in each dimension and obtain a preliminary invariant representation, finally, the features are then subjected to the constraints of the weak alignment module contrastive loss to further optimise the causal features under different cameras and improve the stability of the model’s cross-domain causal prediction. The method was compared and ablation experiments were carried out on the Market-1501 and DukeMTMC-reID datasets, demonstrating the effectiveness of the proposed method.
Qualitative characterization of residual stress of injection molded polycarbonate goggles based on photoelasticity and digital image processing technique

Transparent polymeric materials have gained significant popularity as replacements for glass in various industries due to their low cost, lightweight nature, and high processing efficiency. Injection molding is the primary method for producing transparent polymer parts. However, residual stress often poses a challenge, leading to various defects. Traditional approaches utilize photoelasticity and polarizers to determine stress in transparent parts, which costs time and cannot be easily used for online monitoring and real time quality inspection. The digital image processing (DIP), combined with photoelasticity, offers a promising solution for detecting residual stress and assessing product quality in real-time during manufacturing. In this study, we propose a photoelastic digital image processing (PDIP) approach that combines photoelasticity and DIP techniques to identify residual stress and evaluate part quality using a single digital polarized image without the need for a rotation process. By collecting and analyzing the gray values and variations from the photoelastic images through PDIP, we compared and correlated the gray values of the entire image, a specific area on one side lens, a warp line, and a weft line. Additionally, numerical simulations were performed to validate the proposed method. The results demonstrated the feasibility of this instant identification method. The PDIP technique should be applied to a specific area or line within the parts. By obtaining the average gray value, the instantaneous identification of residual stress can be achieved. The determination of the specific area or line can be tailored according to the quality requirements of the parts.
Resource-Aware Soundness for Big-Step Semantics

We extend the semantics and type system of a lambda calculus equipped with common constructs to be resource-aware . That is, reduction is instrumented to keep track of the usage of resources, and the type system guarantees, besides standard soundness, that for well-typed programs there is a computation where no needed resource gets exhausted. The resource-aware extension is parametric on an arbitrary grade algebra , and does not require ad-hoc changes to the underlying language. To this end, the semantics needs to be formalized in big-step style; as a consequence, expressing and proving (resource-aware) soundness is challenging, and is achieved by applying recent techniques based on coinductive reasoning.
Lightweight Three-Dimensional Pose and Joint Center Estimation Model for Rehabilitation Therapy

In this study, we proposed a novel transformer-based model with independent tokens for estimating three-dimensional (3D) human pose and shape from monocular videos, specifically focusing on its application in rehabilitation therapy. The main objective is to recover pixel-aligned rehabilitation-customized 3D human poses and body shapes directly from monocular images or videos, which is a challenging task owing to inherent ambiguity. Existing human pose estimation methods heavily rely on the initialized mean pose and shape as prior estimates and employ parameter regression with iterative error feedback. However, video-based approaches face difficulties capturing joint-level rotational motion and ensuring local temporal consistency despite enhancing single-frame features by modeling the overall changes in the image-level features. To address these limitations, we introduce two types of characterization tokens specifically designed for rehabilitation therapy: joint rotation and camera tokens. These tokens progressively interact with the image features through the transformer layers and encode prior knowledge of human 3D joint rotations (i.e., position information derived from large-scale data). By updating these tokens, we can estimate the SMPL parameters for a given image. Furthermore, we incorporate a temporal model that effectively captures the rotational temporal information of each joint, thereby reducing jitters in local parts. The performance of our method is comparable with those of the current best-performing models. In addition, we present the structural differences among the models to create a pose classification model for rehabilitation. We leveraged ResNet-50 and transformer architectures to achieve a remarkable PA-MPJPE of 49.0 mm for the 3DPW dataset.
Unleashing the Potentials of Flexible Education with Pedagogical and Technological Innovations

The COVID-19 global pandemic had a profound impact on the education sector, forcing many educational institutions worldwide to adapt to new modes of teaching and learning rapidly [...]
Analysis of Language-Model-Powered Chatbots for Query Resolution in PDF-Based Automotive Manuals

In the current scenario of fast technological advancement, increasingly characterized by widespread adoption of Artificial Intelligence (AI)-driven tools, the significance of autonomous systems like chatbots has been highlighted. Such systems, which are proficient in addressing queries based on PDF files, hold the potential to revolutionize customer support and post-sales services in the automotive sector, resulting in time and resource optimization. Within this scenario, this work explores the adoption of Large Language Models (LLMs) to create AI-assisted tools for the automotive sector, assuming three distinct methods for comparative analysis. For them, broad assessment criteria are considered in order to encompass response accuracy, cost, and user experience. The achieved results demonstrate that the choice of the most adequate method in this context hinges on the selected criteria, with different practical implications. Therefore, this work provides insights into the effectiveness and applicability of chatbots in the automotive industry, particularly when interfacing with automotive manuals, facilitating the implementation of productive generative AI strategies that meet the demands of the sector.
Instructors' Perception Survey on Artificial Intelligence Education Platforms for Elementary School Students

이 연구는 인공지능 교육 플랫폼에 대한 전문강사의 인식을 분석하고, 이를 토대로 인공지능 교육의 기초자료를 제공하기 위해 수행하였다. 이를 위해 사전에 전문강사 196명을 대상으로 많이 사용하는 인공지능 교육 플랫폼을 조사하여 12개를 선정한 후, 전국 360명의 전문강사를 대상으로 플랫폼의 접근성, 교육용 콘텐츠, 플랫폼 성능, 교수⋅학습 관리, 공공성, 초등 수준 적합도, 피지컬 컴퓨팅과의 연계 가능성, 커뮤니티에 대한 기준을 바탕으로 플랫폼 선정기준과 각각의 플랫폼에 대한 인식조사를 시행하였다. 연구 결과는 아래와 같다. 첫째, 인공지능 교육 플랫폼을 선정하는 기준으로 전문강사들이 가장 중요하게 여기는 항목은 접근성으로 조사되었고 그 뒤를 이어 교육용 콘텐츠, 교수ㆍ학습관리, 플랫폼 성능, 공공성, 초등 수준 적합도, 피지컬 컴퓨팅과의 연계 가능성, 커뮤니티 순으로 나타났다. 둘째, 전문강사들이 인공지능 교육 플랫폼에 대해 선정기준 항목별 우수한 정도를 매긴 점수를 살펴보면, 엔트리, 스크래치, 메이크코드, AI for oceans, 티처블머신, 엠블록, KT AI Codiny, 머신러닝 포키즈 등의 순으로 조사되었다. 이 연구 결과는 인공지능 교육 플랫폼 개발 시 고려할 사항과 인공지능 교육을 담당하는 교수자 대상의 연수 시 각 플랫폼에대한 특징을 제시해 주는 데 기초자료로 활용할 수 있을 것이다. 또한 인공지능 교육 플랫폼은 관련 기술의 급격한 발달로 인해 지속으로 업데이트가 되고 있으므로, 교육 플랫폼에 대해서도 주기적인 인식조사를 하고 이를 통해 인공지능 교육이 효과적으로 이루어질 필요가 있다.This study was conducted to analyze professional instructors' views of artificial intelligence education platforms and to derive foundational data for AI education. To reach this goal, 12 artificial intelligence education platforms were selected based on a survey, and 196 professional instructors were chosen for the study. The instructors, coming from different areas across the nation, were surveyed on platform accessibility and performance, its content and suitability for elementary education, teaching and learning management, popularity, connection with physical computing, and community awareness. The results of the study are as follows: Accessibility was the most important factor revealed for professional instructors while selecting artificial intelligence education platforms. This factor was followed by the quality of the content and its suitability for elementary-level students, teaching and learning management, platform performance, popularity, and community. The survey result was measured according to the scores of professional instructors based on their selection criteria and entry on the following AI education platforms: Scratch, MakeCode, AI for Oceans, Teachable Machine, mBlock, KT, AI Codiny, and Machine Learning for Kids. The results of this study can be used as a reference to represent the characteristics of these AI learning platforms to train AI education instructors. Since these platforms are continuously updated along with the rapid development of new technologies, artificial intelligence education needs to keep up with the pace with frequent awareness surveys of these education platforms.
Analysis of the 2022 Revised Elementary Social Studies Curriculum Achievement Standards

이 연구는 2022 개정 초등학교 사회과 교육과정의 성취기준을 대상으로 역량기반 교육과정의 양상을 분석하고자 하였다. 이를 위해 성취기준을 추출하여, ‘지식⋅이해’, ‘기능⋅과정’, ‘가치⋅태도’, ‘사회과 교과 역량’이라는 네 가지 측면에서 그 양상을 분석하였다. 분석한 결과는 다음과 같다. 첫째, ‘지식⋅이해’ 측면에서는 개념적 지식의 비중이 가장 높았다. 또한 2015 개정 사회과 교육과정에서 거의 제시되지 않는 것으로 나타난 절차적 지식의 비중은 늘어났지만, ‘메타인지적 지식’은 전혀 반영되지 않았다. 둘째, ‘과정⋅기능’ 측면에서는 ‘이해하다’와 ‘분석하다’에 해당하는 진술이 집중되어 있어서, 다양한 인지 과정이 다뤄지지 못하고 있다는 한계점이 여전히 존재했다. 셋째, 기존 연구와 달리 이 연구에서는 ‘가치⋅태도’ 측면의 분석을 새롭게 시도하였는데, ‘공동체 의식’이 가장 높은 비중을 차지했으나 ‘준법정신’이나 ‘공공선’과 같은 가치가 거의 제시되지 않았다. 넷째, 사회과 교과 역량 측면에서는 ‘정보 활용 능력’이 가장 높은 비중을 차지하여, 사회과 교과의 정체성과 특성을 제대로 드러내는 교과 역량을 강조하는 성취기준 진술을 위한 노력이 필요해 보인다. 이러한 분석 결과는 향후 초등 사회과 교육과정 개발에 반영되어야 할 것이며, 2022 개정 교육과정을 적용하는 초등학교 현장에서 이러한 문제를 개선하기 위한 방향으로의 수업 재구성이 필요하다.This study attempted to analyze different aspects of the competency-based curriculum to reach the achievement standards of the 2022 revised elementary school social studies curriculum. The findings present the following as major areas for improvement: 1. The examination of the “knowledge and understanding” component of the curriculum showed the percentage of conceptual knowledge as the highest; the procedural knowledge that was minimal in the 2015 revised social studies curriculum has increased significantly, but “metacognitive knowledge” was not reflected at all. 2. The “process and function,” bearing statements having the words “understand” and “analyze” were found to be concentrated in a few places. This is a limitation of the course content, as various cognitive processes are not present in all necessary areas. 3. Unlike previous studies, this research has employed a new analysis of the “value and attitude” aspect; although “community consciousness” accounted for the highest proportion, values such as “compliance spirit” or “public good” were rarely presented. This study has brought out the absence of attitudinal and goal-oriented aspects of the endeavor. 4. From the aspect of “competency,” this social studies curriculum has a high degree of “information utilization ability.” The study has proposed improvement efforts to define achievement standards that correspond with the curriculum competency by clearly explaining the identity and characteristics of social studies. These research results will need to be reviewed and employed in developing the elementary social studies curriculum. Also, class reorganization would be a significant measure towards rectifying the problems noted at the elementary school according to the 2022 revised curriculum standards.
Six Convolutional Layered Deep Convolutional Neural Network Based Real Time Single Image Super Resolution

The super-resolving images investigation has advanced in recent years through the use of cutting-edge Deep Learning-based architectures. Numerous previously documented super-resolution-based solutions need the most advanced and top-tier Graphics Processing Units (GPUs) to execute picture super-resolution. With the growing advancement of technology, this research focuses on suggesting the needed quantity of convolutional layers for creating the real time super resolution of single image using Convolutional Neural Network. The proposed Six Convolutional Layered Deep Convolutional Neural Network (6CL-DCNN) to predict the super resolution of images with high accuracy and ideal Peak Signal-to-Noise Ratio. The dataset extracted from eecs.berkeley.edu(http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/BSR_bsds500.tgz). The dataset contains 800 images with the combination of low resolution and high-resolution images having the image resolution of 300 * 300 pixels. The proposed 6CL-DCNN built with single input layer followed by six convolutional layers followed by single lambda optimized output layer that predicts the super resolution of both high resolution images and low resolution images. Python was used through 500 training iterations and a 64-bit block size on a NVidia Geforce Tesla V100 GPU workstation. The processed low resolution images and high-resolution images are applied with proposed 6CL-DCNN model and also the performance is compared using Peak Signal to Noise Ratio with other optimized output layers. Experimental results shows that the proposed model 6CL-DCNN shows the maximum Peak Signal to Noise Ratio of 48 dB when compared to other optimized output layers.
A Fast Distributed Supply Chain Infrastructure based on Blockchain

Traditional supply chain systems often employ centralized servers, leading to transparency issues, high maintenance costs, security vulnerabilities, and poor risk resistance. We introduce a blockchain-based supply chain transaction infrastructure that emphasizes decentralization, distributed storage, tamper-resistance, traceability, and cost-effectiveness. Built on a consortium blockchain, our architecture uses a dual-layer storage structure combining blockchain and a business database, optimizing transaction data security and accessibility. Simulated tests confirm its efficiency, transparency, security, and ability to handle industrial-scale applications.
An Imbalanced Financial Fraud Data Model Based on Improved XGBoost and RUS Boost Fusion Algorithm with Pairwise

As the socio-economic landscape evolves, the investigation into anti-fraud behaviors in shopping gains increasing significance. Although prior studies have utilized machine learning to tackle this issue, they often grapple with two key obstacles. First, an imbalance between positive and negative data samples exists. Second, the presence of redundant features leads to suboptimal model performance. In order to surmount these challenges, we've developed a new machine learning framework. This innovative solution automatically selects features and balances the data set's positive and negative samples. Our framework's outstanding performance on the IEEE-CIS Fraud Detection dataset thoroughly validates the efficacy of our approach.
Sound and video examples – issue 28 (2)

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Immersive technologies: a symbiosis of digital technologies and art

Immersive technologies are one of the strategic vectors of modern education. Immersive in English means “immersion”. In the context of pedagogical science, it is a method of teaching with elements of relaxation, play, and immersion in an artificially modeled reality. Several concepts of immersive technologies are defined: virtual reality (VR); augmented reality (AR); mixed reality (MR); holography, TelePresence; digital twin; FPV drone flight, etc. The author has revealed the meaning of the immersive approach in education, which we interpret as a set of methods and means of organizing productive interaction of participants in the educational process in a virtual (immersive) educational environment that provides interactive learning, comprehensive and targeted professional development. An immersive learning environment is understood as a dynamic process of influencing the user by various elements of the simulated external/internal environment. The author substantiates immersive in literature, painting, and theater. Thus, immersive is primarily associated with the object-spatial modeled environment, which is designed to draw the viewer inside. The article pays special attention to the creation of an immersive learning environment, which has certain characteristic features, namely: redundancy, saturation, constructiveness; observation, autonomy; integrity; motivational; interactivity. The advantages of the immersive environment are identified: the ability to visualize complex processes and phenomena; minimization of extraneous factors; high degree of personalization of the learning process; stimulation of the accumulation of experience of independent cognitive activity, development of activity in educational activities; prompt and visual display of the results obtained; facilitation of communication between participants in the educational process, etc.