Reviewerly/exHarmony huggingface dataset:
Knowledge Graphs for Explaination of Black-Box Recommender System

Machine learning models, particularly black-box, make powerful decisions and recommendations. However, these models lack transparency and hence cannot be explained directly. The respective decisions need explanation with the help of techniques to gain users' trust and ensure the correct interpretation of a particular recommendation. Nowadays, Knowledge graphs (K-graph) has been recognized as a powerful tool to generate explanations for the predictions or decisions of black-box models. The explainability of the machine learning models enhances transparency between the user and the model. Further, this could result in better decision support systems, improvised recommender systems, and optimal predictive models. Unfortunately, while these black box devices have no detail on the reasons behind their forecasts, they lack clarity. White box structures, on the other hand, will quickly produce interpretations due to their existence. The chapter presents an exhaustive review and step-by-step description for using knowledge graphs in generating explanations for black-box recommender systems, which further helps in generating more persuasive and personalized explanations for the recommended items. We also implement a case study on the MovieLens dataset and WikiData using K-graph to generate accurate explanations.
Comprehensive Study on Shape Representation Methods for Shape-Based Object Recognition

Abstract This study presents a thorough analysis of shape representation techniques. Furthermore, a taxonomy of strategies for form representation and description is provided. This study aims to review the current achievements comprehensively, highlight the weaknesses and advantages of various existing methods in shape representation methods, addressing current research issues and challenging tasks in this research scope. The contributions of this study lie in providing a comprehensive analysis of shape representation techniques, along with a taxonomy of strategies for form representation and description. The research explores the application of shape border information and inner content in various domains, such as form retrieval, shape classification, and medical image analysis. The novelties include the systematic categorization of methods, addressing current research issues, and offering insights into the benefits and drawbacks of existing shape representation approaches, thus paving the way for potential advancements in the field. In the end, the performance of existing methods is discussed to address the drawbacks and effectiveness of current methods.
Short‐term prediction of behind‐the‐meter PV power based on attention‐LSTM and transfer learning

Abstract Distributed photovoltaic (PV) systems often lack adequate measurements due to cost considerations, which makes it very difficult to predict them accurately. Here, an approach is proposed for behind‐the‐meter (BTM) PV power prediction using attention‐LSTM neural network and transfer learning. First, the weather is classified into four types based on the deviation ratio β. Second, the correlation analysis algorithm identifies the weather factors that contribute the most to PV power generation as GHI, DNI, humidity, and temperature. Then, attention‐LSTM pre‐trained model is constructed, in which the LSTM network fully extracts the temporal characteristics of PV power generation, while the attention mechanism enhances the attention to the important information in the input. Finally, a novel BTM PV short‐term power prediction method using transfer learning that the upper layer parameters of the pre‐trained model were frozen, and a small amount of DP‐NB data was used to fine‐tune the model. The proposed methodology has higher prediction accuracy compared with other benchmark methods under four weather types, and the time cost is saved by freezing the parameters of the first layer of the network by 37.2%.
ASPECT-ORIENTED PROGRAMMING AND EXCEPTION WRAPPING: A CASE STUDY OF A WRAPPER LIBRARY

Today, aspect-oriented programming is often used as a supplement to object-oriented code – AOP practices can be found in many libraries and frameworks [1]. The main AOP practices used in conjunction with OOP are:
How to set safety boundary in virtual reality: A dynamic approach based on user motion prediction

Abstract Virtual reality (VR) interaction safety is a prerequisite for all user activities in the virtual environment. While seeking a deep sense of immersion with little concern about surrounding obstacles, users may have limited ability to perceive the real‐world space, resulting in possible collisions with real‐world objects. Nowadays, recent works and rendering techniques such as the Chaperone can provide safety boundaries to users but confines them in a small static space and lack of immediacy. To solve this problem, we propose a dynamic approach based on user motion prediction named SCARF, which uses Spearman's correlation analysis, rule learning, and few‐shot learning to achieve prediction of user movements in specific VR tasks. Specifically, we study the relationship between user characteristics, human motion, and categories of VR tasks and provides an approach that uses biomechanical analysis to define the interaction space in VR dynamically.We report on a user study with 58 volunteers and establish a three dimensional kinematic dataset from a VR game. The experiments validate that our few‐shot learning model is effective and can improve the performance of motion prediction. Finally, we implement SCARF in VR environment for dynamic safety boundary adjustment.
보편적 학습설계에 기반한 수업혁신 실행연구

The purpose of this study is to derive the principle of instruction innovation through action research that applies universal design for learning to middle school classes. Teachers of A Middle School has been trying to implement a learning-oriented instruction in which all students participate. In this process, there were many difficulties in solving the learning-gap between students. To solve this problem, an action research was conducted to apply the principle of universal design for learning in A middle school. Under the consultation of middle school teachers and researchers, principles such as
Comparison and analysis of CEFR reception, production skills and achievement standards of the 2022 revised elementary English curriculum

The CEFR, which affected the content system, subject, and achievement standard description of the 2022 revised English curriculum, is used as a guideline for teaching, learning, and evaluation by those involved in the curriculum. This study compared and analyzed CEFR and achievement standards which organized into reception and production to reflect real-life communication aspects. The purpose of this study is to reveal the similarities and differences between the two descriptions and to point out the supplementary components to achievement standards through the analysis based on CEFR reception and production skills. The 2022 revised English curriculum
Visual perception-oriented quality assessment for high dynamic range stereoscopic omnidirectional video system

High dynamic range stereoscopic omnidirectional video (HSOV) system provides users with an immersive visual experience, but the distortions generated in the process of visual content generation, coding, and visualization also brings challenges to visual quality assessment. This paper proposes a visual perception-oriented quality metric for HSOV system. Considering the computational efficiency, for frames within each second, several key frames are selected so that features reflecting distortions can be extracted from the viewports and equi-angular cubemap (EAC) projection surfaces of these frames. In viewport feature extraction module, the sum and difference maps of the left and right views of the key frames are used to simulate the binocular perception. Considering that distortions will affect the structure, color and statistical properties of the image, the gradient, local binary pattern and statistical features are extracted, respectively. To reduce projection distortion, EAC projection is used to instead of other projections normally used in quality assessment, and the Sigma domain feature extraction and color feature extraction are performed on EAC projection map. In temporal feature extraction module, wavelet decomposition is used to perform temporal domain bandpass filtering on partial continuous frames to calculate the naturalness features. Finally, the above features of the frames at different scales are pooled through random forest model to predicate the quality score of HSOV. Experimental results on the NBU-HSOVD dataset show that the proposed metric is superior to some blind image/video quality assessment metrics and has higher consistency with the human visual perception.
Monitoring of teaching and learning and evaluation in the phased introduction of the High School Credit System

The purpose of this study is to monitor how to prepare and implement teaching, learning, and evaluation-related contents at schools in accordance with the roadmap for the phased introduction of the high school credit system, and to propose policy improvements related to the establishment of the high school credit system. To this end, surveys and interviews were conducted with teachers and students at the schools, and through this, it was possible to understand the guidance situation and difficulties of students with the minimum achievement level, as well as efforts to improve teaching and learning methods and evaluation capabilities due to the introduction of the high school credit system. Based on this, policy proposals related to on-site support for schools, such as providing training opportunities to enhance expertise in teaching and learning and evaluation to stabilize the high school credit system in the future, and expanding administrative financial support for guidance to ensure minimum achievement level, were derived. This study is expected to provide implications for stably promoting the operation of the high school credit system in 2024 and the full implementation of the high school credit system in 2025
Analysing domestic research trends of self-assessment for elementary and secondary school students using LDA based topic modeling

This study aimed to identify the trends and primary topics in domestic research on self-assessment for elementary and secondary school students using the topic modeling. A total of 113 papers with title or key words related to self-assessment were collected from the Research Information Sharing Service. The Korean abstracts of these papers were analyzed using Latent Dirichlet Allocation(LDA) to discern latent topics. Consequently, the 113 studies were classified into three main topics. namely: ‘Analyzing the effectiveness of self-assessment’, ‘Validation of consistency in self-assessment results’, and ‘Qualitative understanding and exploring applicability of self-assessment’. The distribution of topics revealed that ‘Analyzing the effectiveness of self-assessment’ constituted the largest proportion, while the proportions of the other two topics were similar. When comparing the changes in topic distribution over time, in the early stages of self-assessment research, the proportion of ‘Validation of consistency in self-assessment results’ was higher than those of others. However, as time progressed, the proportion of ‘Analyzing the effectiveness of self-assessment’ exhibited a gradual increase, and in more recent times, the proportion of ‘Qualitative understanding and exploring applicability of self-assessment’ was relatively high. Based on these research findings, this study concludes with discussing future directions and implications for self-assessment research.
Motivational Exploration of Explanations in Industrial Analytics<sup>*</sup>

Explainable AI (XAI) provides approaches and techniques for building trust in AI models. This paper presents and explores XAI approaches focusing on user interface concepts in predictive maintenance. The underlying AI model is based on an open dataset for wind turbines. An enhanced multi-class self-conceived labeling strategy improves the model and, thus, supports the XAI approaches. Previous research in user-centered XAI shows that users do not exploit the possibilities of XAI methods and instead rely on their intuition. To counter this tendency, we present user interfaces incorporating gamification elements to enhance understanding of AI outputs. We highlight our approach via two examples, demonstrating a local and a global XAI technique respectively. A preliminary user study was conducted to assess the value added by these gamification aspects. While the findings were inconclusive, they provided an initial insight into the potential of these design elements to foster user engagement in the realm of XAI.
Self-supervised Learning with Temporary Exact Solutions: Linear Projection

Self-supervised learning has emerged as a promising method for training neural networks without needing annotated data. In this paper, we present a self-supervised learning method for training, not limited to but especially visual transformers that are able to learn meaningful representations of images and videos without requiring large amounts of labeled data. Our method is based on using exact solutions of the representations that the model generates. It is shown that the model is able to learn useful features that can be later fine-tuned on industrial downstream tasks. We demonstrate the effectiveness of our method on a subset of the Universal Image Embeddings 130k dataset [1], a private industrial Pill Identification dataset, and standard Cifar-10 dataset [20]. We show that our method outperforms solid baselines which are BYOL [2] and Barlow Twins [3] while using fewer parameters and resources. We show the capability of the trained model on a Deep Metric Learning task by comparing the Swin Transformer [4] backbones that are trained with our method, BYOL [2], and Barlow Twins [3]. The results also show that the proposed method achieves higher accuracy than others in pre-training and fine-tuning processes with fewer parameters. GitHub: https://github.com/rootvisionai/solo-learn.
AGC-DP: Differential Privacy with Adaptive Gaussian Clipping for Federated Learning

Federated learning provides techniques for training algorithms using mobile or decentralized devices, in contrast to traditional machine learning in which algorithm training is performed on centralized devices. In addition, federated learning provides privacy and security features, as the client and server do not share raw data, which may contain confidential information. A number of studies have shown, however, that using federated learning alone is not enough to protect data privacy in certain situations. To overcome this problem, differential privacy is proposed, which is a technique in which artificial noise is added to the raw data. By implementing this method, a high level of privacy protection can be obtained, however this added noise also reduces model accuracy. To address this issue, this paper proposes a new approach to implement differential privacy in federated learning using adaptive Gaussian clipping. We implemented the method by tightening the privacy budget, and introducing dynamic sampling probability, adaptive clipping based on hyperparameters, and a new privacy loss calculation. Our method’s main objective is to adaptively change the amount of noise given to the model, thereby maximizing the model’s accuracy performance, while maintaining privacy protection levels. Evaluation results show that our proposed method presents slightly better accuracy when compared to other existing differential privacy variants such as RDP, DP-SGD, and ZcDP, for both balanced (i.i.d.) and unbalanced datasets (non-i.i.d.), for a lower total communication cost than some variants.
Pose recognition of dancing images using fuzzy deep learning technique in an <scp>IoT</scp> environment

Abstract With the development of smart technologies (e.g., Internet of Things (IoT), Artificial Intelligence (AI), and Big Data), people have start using them for various purposes. The real‐time IoT devices generate an enormous amount of video and imaging data, leading to the concept of complex data structure. And people face major challenges in mining and extraction of useful features and information from such data. How to efficiently analyse and process video data to obtain valuable information has become a key research topic. The traditional manual annotation methods are unable to meet the current demand for the growing number of videos. Therefore, a more convenient method for processing video data needs to be developed. The research objective of this paper is dance videos, and the goal is to realize automatic recognition of dance movements. In this paper, a Dual Convolutional Neural Network Algorithm (DCNNA) is proposed for the automatic recognition of different dance movements in live and remote videos. DCNNA can extract video information more comprehensively and efficiently. It can simultaneously extract the light flow features corresponding to the action changes and the information contained in each frame of the video. Therefore, the dance movements can be more accurately identified. In the experiments, the performance of DCNNA is evaluated based on dance videos and compared with Inception V3 and 3D‐CNN. All the experiments illustrate the superior performance of the proposed DCNN algorithm. From the experimental results, it is quite obvious that the F1 score of the proposed DCNNA is 11% and 6% higher than that of the Inception V3 and 3D‐CNN, respectively.
Predominant Research Themes in Using Machine Learning in Structural Health Monitoring

Structural health monitoring (SHM) using non-destructive machine learning (ML) based technologies has gained considerable interest in research and industrial communities.Integrating the conventional methods of SHM with novel ML techniques gives robust, sustainable, and promising solutions to SHM.This study presents text mining-based methodology to identify predominant research themes in using ML in SHM.Two analyses are performed on literature data of 375 research studies; (1) co-occurrence analysis of keywords applied on author specified keywords and (2) topic modeling using latent dirichlet allocation (LDA) approach applied on abstracts.The finding shows that the research studies predominantly focus on detecting and classifying structural damages, investigating sensing systems or sensors, and feature extraction and analysis.Moreover, convolutional neural networks and support vector machines are the two mainly used ML algorithms, and bridges, dams, and wind turbines are found as the top three investigated engineering structures.This work can be further extended to include a systematic review of past studies to have an in-depth understanding of using ML in SHM and to find potential contributions and research gaps in the studied area.
Proposal for a Deep Neural Network with Module Architecture for Model Reduction

This paper proposes a deep neural network with module architecture for model reduction, and a cost function suitable for training the model. In the proposed model architecture, each layer is modularized for reducing the model by adjusting the number of layer. This feature allows the computational load of the model to be quickly switched. In order to maintain the accuracy of the reduced model even if it is not retrained, the cost function is defined as a weighted average of the errors of the model output over the number of layers. The effectiveness of the proposed method is validated through numerical examples for small tasks. Our implementation is available at https://github.com/sy-takano/modularized_dnn_for_model_reduction.
A generic framework for federated CDEs applied to Issue Management

This paper analyses the requirements for managing interoperable building data in a federated Common Data Environment (CDE). We discuss the need for generic (meta)data storage patterns, semantic query interfaces, decentral authentication, data aggregation, and adaptation and prove that their combination is feasible with current-day technologies. We illustrate the mechanisms of such federated CDE by considering the topic of digital Issue Management, one of the primary functions of a CDE. In an exemplary data flow process, we show how generic (federated, Semantic Web-based) data patterns for Issue Management can be aggregated and restructured to match existing industry standards like buildingSMART’s BIM Collaboration Format (BCF) API. Finally, we show the methodology is compatible with current-day practice by implementing this process in a proof of concept. The main contribution of this research is a generic, federated framework for project-related, interdisciplinary collaboration for CDEs.
Cutoff phenomenon and entropic uncertainty for random quantum circuits

Abstract How fast a state of a system converges to a stationary state is one of the fundamental questions in science. Some Markov chains and random walks on finite groups are known to exhibit the non-asymptotic convergence to a stationary distribution, called the cutoff phenomenon. Here, we examine how quickly a random quantum circuit could transform a quantum state to a Haar-measure random quantum state. We find that random quantum states, as stationary states of random walks on a unitary group, are invariant under the quantum Fourier transform (QFT). Thus the entropic uncertainty of random quantum states has balanced Shannon entropies for the computational basis and the QFT basis. By calculating the Shannon entropy for random quantum states and the Wasserstein distances for the eigenvalues of random quantum circuits, we show that the cutoff phenomenon occurs for the random quantum circuit. It is also demonstrated that the Dyson-Brownian motion for the eigenvalues of a random unitary matrix as a continuous random walk exhibits the cutoff phenomenon. The results here imply that random quantum states could be generated with shallow random circuits.
Immersive environment for water pumping station and fire system operation training

This paper aimed to create a 3D virtual reality simulator for automation training, becoming a training solution for monitoring, controlling, and operating a water pumping station and fire system to survey the industrial infrastructure of the virtual environment and design software. The Unity3D graphic engine was used, which develops the virtualization of the elements involved in the process with greater detail and realism. The interface of peripheral devices such as virtual reality glasses allows interaction and immersion between the user and the virtual environment to complement the learning experience. Within the immersive environment, the operator can see the status of the process and the actions generated through the virtual objects and instruments created in the 3D environment.