Reviewerly/exHarmony huggingface dataset:
An interval type-3 fuzzy PID control system design and its application in solid oxide fuel cells power plant

Compared with type-2 fuzzy sets, the secondary membership degree of interval type-3 fuzzy sets is an interval rather than crisp value, which makes interval type-3 fuzzy sets can obtain more degree of freedoms. This article studies an interval type-3 fuzzy PID controller based on interval type-3 fuzz sets. The framework of interval type-3 fuzzy PID controller is identical with type-2 fuzzy PID controller, but it contains more adjustment controller parameters and its type reduction procedure is more complex. In this paper, type reduction of interval type-3 fuzzy sets is derived from general type-2 fuzzy sets represented by α-plane and a direct NT type reduction algorithm is applied. The control effects of interval type-3 fuzzy PID controller are firstly tested by 2 nonlinear plants, the simulation results show that interval type-3 fuzzy PID controller has better control performance indexes than PID controller, type-1 fuzzy PID controller, interval type-2 fuzzy PID controller and general type-2 fuzzy PID controller. Furthermore, the interval type-3 fuzzy PID controller will be applied in rated voltage control of solid oxide fuel cells (SOFC) power plant. The output voltage control of SOFC is quite challenging because of the strong nonlinearity, limited fuel flow, and rapid variation of the load disturbance. The simulation results demonstrate the advantages and robustness of proposed interval type-3 fuzzy PID controller.
Localization of quantum walks with classical randomness: Comparison between manual methods and supervised machine learning

A transition of quantum walk induced by classical randomness changes the probability distribution of the walker from a two-peak structure to a single-peak one when the random parameter exceeds a critical value. We first establish the generality of the localization by showing its emergence in the presence of random rotation or translation. The transition point can be located manually by examining the probability distribution, momentum of inertia, and inverse participation ratio. As a comparison, we implement three supervised machine learning methods, the support vector machine (SVM), multilayer perceptron neural network, and convolutional neural network with the same data and show they are able to identify the transition. While the SVM sometimes underestimates the exponents compared to the manual methods, the two neural-network methods show more deviations for the case with random translation due to the fluctuating probability distributions. Our work illustrates potentials and challenges facing machine learning of physical systems with mixed quantum and classical probabilities.
Improving Knowledge Learning Through Modelling Students’ Practice-Based Cognitive Processes

Abstract Practice is an essential means by which humans and animals engage in cognitive activities. Intelligent tutoring systems, with a crucial component of modelling learners’ cognitive processes during learning and optimizing their learning strategies, offer an excellent platform to investigate students’ practice-based cognitive processes. In related studies, modelling methods for cognitive processes have demonstrated commendable performance. Furthermore, researchers have extended their investigations using decision-theoretic approaches, such as a partially observable Markov decision process (POMDP), to induce learning strategies by modelling the students’ cognitive processes. However, the existing research has primarily centered around the modelling of macro-level instructional behaviors rather than the specific practice selection made by the students within the intricate realms of cognitive domains. In this paper, we adapt the POMDP model to represent relations between the student’s performance on cognitive tasks and his/her cognitive states. By doing so, we can predict his/her performance while inducing learning strategies. More specifically, we focus on question selection during the student’s real-time learning activities in an intelligent tutoring system. To address the challenges on modelling complex cognitive domains, we exploit the question types to automate parameter learning and subsequently employ information entropy techniques to refine learning strategies in the POMDP. We conduct the experiments in two real-world knowledge concept learning domains. The experimental results show that the performance of the learning strategies induced by our new model is superior to that of other learning strategies. Moreover, the new model has good reliability in predicting the student’s performance. Utilizing an intelligent tutoring system as the research platform, this article addresses the modelling and strategy induction challenges of practice-based cognitive processes with intricate structures, aiming to tutor students effectively. Our work provides a new approach of predicting the students’ performance as well as personalizing their learning strategies.
Using Mobile Phone to Assist DHH Individuals

Past research on sign language recognition has mostly been based on physical information obtained via wearable devices or depth cameras. However, both types of devices are costly and inconvenient to carry, making it difficult to gain widespread acceptance by potential users. This research aims to use sophisticated and recently developed deep learning technology to build a recognition model for a Taiwanese version of sign language, with a limited focus on RGB images for training and recognition. It is hoped that this research, which makes use of lightweight devices such as mobile phones and webcams, will make a significant contribution to the communication needs of deaf and hard-of-hearing (DHH) individuals.
Modified Attribute-Based Authentication for Multi-Agent Systems

Attribute-Based Encryption (ABE) is a type of authentication mechanism that validates both the users and their attributes. It is practical for the systems that need authorization according to credentials. In a multi-agent system, specifying an access policy within the user groups is crucial to enable authentic and confidential communication. This paper proposes an attribute-based authentication framework based on elliptic curves to provide privacy in multi-agent systems. In this system, we aim to alleviate the required burden of verification by ensuring that each unit verifies only a small amount of messages. Inspired by Zhang et al. [1], we use ABE for the multi-agent system to authenticate more than one user at a time; our scheme uses elliptic curve groups, unlike Zhang et al. We have thoroughly evaluated the various security attributes and discussed computational overheads for our proposed scheme.
High Dimensional Quantum Digital Signature Depending on Entanglement Swapping

While a single qubit information can be carried with a single photon in 2−dimensional quantum technology, it is possible to carry more than one qubit information with a single photon in high-dimensional quantum technologies. The amount of qubit to be transported depends on the size of the system obtained in the high dimension. In other words, the more high-dimensional quantum structure it creates, the more qubit-carrying system is obtained. In this study, a high dimensional quantum digital signature(QDS) scheme is proposed for multi-partied by using entanglement swapping and super-dense coding. QDS, which is proposed as highdimensional, allows more data and high-rate keys to be transferred. Security analysis of propesed QDS in high-dimensional show that the propablity of anyone obtaining information is much lower than in qubit states. Since all data(quantum and classic) in this protocol is instantly sent by using entanglement channels it is more resilient eavesdropping attacks. Today, developments in highdimensional experimental studies show that the high-dimensional QDS proposed in this study can be implemented practically.
Artistic pedagogies in an emergency: Perspectives from nine visual arts teachers in the north, centre and south of Chile

Due to the COVID-19 pandemic of 2020–21, schools in Chile suspended face-to-face activities. This led to an ‘educational emergency’, generating proposals for what some authors have called Emergency Remote Teaching (ERT). This article describes the teaching experiences of nine visual arts teachers from northern, central and southern Chile, who implemented their pedagogical proposals by dispensing with the workshops and materials commonly used in visual arts classes. The study examined the processes of uncertainty experienced by the teachers by collecting and analysing ‘in situ’ accounts and arguments using a qualitative approach. The main results indicate that the teachers showed agency and a great capacity to be flexible in response to changing scenarios. It can also be observed that ERT emphasizes the ambivalent position of visual arts as a subject in schools and demonstrates the difficulty that some students experience accessing and using certain technologies, questioning the belief that they are ‘digital natives’.
Efficient Lossless Compression of Integer Astronomical Data

Abstract Each new generation of telescope produces increasingly larger astronomical data volumes, which are expected to reach the order of exabytes in the next decade. Effective and fast data compression methods are paramount to help the scientific community contain storage costs and improve transmission times. Astronomical data differs significantly from natural and Earth-observation images, asking for specifically tailored compression approaches. This paper presents a novel lossless compression technique that employs the discrete Haar wavelet transform within the JPEG 2000 standard. Its performance is compared to that of a comprehensive selection of compressors, including fpack, the most common technique in astronomical observatories, as well as other algorithms highly competitive for other types of data. Experiments are performed on a large data set of 16 bit integer images, produced by telescopes around the world and representative of a wide variety of astronomical scenarios. The proposed technique has two modes. The first mode outperforms all the other tested techniques in terms of compression performance. It surpasses the most competitive configuration of fpack by, respectively, 5.3% (about 0.3 bits per sample), having also 4.5% lower compression and decompression times. The second mode is the fastest among all tested techniques. Its compression and decompression times are 2.5 and 3.5 times faster than the fastest configuration of fpack, while also yielding a 2.4% better compression performance (0.15 bits per sample).
A hybrid meteorological data simulation framework based on time-series generative adversarial network for global daily solar radiation estimation

Solar energy has received considerable attention worldwide because of its clean, safe and easily accessible characteristics. Accurate global solar radiation estimation is crucial for local energy potential assessments and deployment of photovoltaic power generation systems. However, the distribution of metrological data varies considerably between regions, and its quality considerably affects the accuracy of the estimation model. Motivated by these concerns, a metrological data estimation framework based on the time-series generative adversarial network (TimeGAN) and deep belief network (DBN) was proposed. First, the metrological data collected from 30 urban meteorological stations in China were analyzed through time-series aggregation. Next, a novel metrological data simulation model based on TimeGAN was proposed to enrich the quality of training samples. Furthermore, a generalized estimation model of daily solar radiation based on DBN was constructed. In the case study in China, TimeGAN outperforms other conventional generative adversarial networks in terms of data generation performance. The simulation results reveal that the TimeGAN-based simulation model can capture the inherent temporal characteristics of the original data, generate high-quality simulation data to reasonably improve the distribution of training data in various cities. In addition, the estimation results indicate that the proposed hybrid framework improve the accuracy of daily solar radiation estimation.
Enhanced Robust Fuzzy K-Means Clustering joint <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg" display="inline" id="d1e1383"><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math>-norm constraint

Clustering is an unsupervised classical data processing technique, in which Fuzzy K-Means is extensively researched in practical application owing to its efficiency. However, common outliers in real-world always lead to clustering degradation. In this paper, we design an Enhanced Robust Fuzzy K-Means clustering method (ERFKM) joint ℓ0-norm sparse nearest neighbor constraint as a countermeasure to above challenge. On one hand, we adaptively weigh different samples by binary vector to measure each contribution for robustness, among which the outliers can be effectively recognized to eliminate their impact on clustering. That is, the weighted term is able to steadily correct the cumulative drift deviation of cluster centroid via ignoring extremely large sample-centroid residuals from outliers. On the other hand, the fuzzy regularizer with ℓ0-norm constraint aims to further enhance the segmentation ability of indicated normal samples through automatic cluster neighbor assignment. Finally, an optimization algorithm for our model is devised to simultaneously update the binary weight of each sample and sparse membership matrix for achieving robust clustering. Comprehensive experiments demonstrate the advantages and effectiveness of the proposed method.
Block-RACS: Towards Reputation-Aware Client Selection and Monetization Mechanism for Federated Learning

Federated Learning (FL) is a promising solution for training using data collected from heterogeneous sources (e.g., mobile devices) while avoiding the transmission of large amounts of raw data and preserving privacy. Current FL approaches operate in an iterative manner by selecting a subset of participants each round, asking them to training using their latest local data over the most recent version of the global model, before collecting these local model updates and aggregating them to form the next iteration of the global model, and so forth until convergence is reached. Unfortunately, existing FL approaches typically select randomly the set of clients to use each round, which can negatively impact the quality of the model trained, as well the training round time due to the straggler problem. Moreover, clients, especially mobile devices with limited resources, should be incentivized to participate as federated learning is essentially a form of crowdsourcing for AI which requires monetization. We argue that integrating blockchain and smart contract technologies into FL can solve the two aforementioned issues. In this paper, we present Block-RACS (Blockchain-based Reputation Aware Client Selection), a mechanism for FL operating in a smart contract which rewards clients for their participation using cryptocurrencies. Block-RACS employs a multidimensional auction mechanism for selecting users based on the compute and network resources offered by each client, as well as the quality of their local data. This auction is realized in a reliable and auditable manner through a smart contract. This allows Block-RACS to measure the relative contribution of each client by calculating a Shapley value and allocating rewards accordingly. Moreover, a blockchain-based reputation mechanism enables audibility and non-repudiation. The security analysis of the system is also presented to check the security vulnerabilities. We have implemented Block-RACS using Solidity and tested on the Ethereum blockchain with various popular datasets. Our results show that Block-RACS outperforms existing baseline schemes by improving accuracy and reducing the number of FL rounds.
Application of quantum key distribution in intelligent security operation and maintenance of power communication networks

With the continuous development of power communication network and the improvement of intelligent degree, network security and operation and maintenance become more and more important. Therefore, this paper proposes a solution for intelligent security operation and maintenance of power communication network based on Quantum key distribution (QKD). QKD is an encryption technology based on the principle of quantum mechanics, which can realize secure Key exchange and resist attacks of traditional encryption algorithms. We use QKD technology to generate and distribute security keys for protecting communication and data transmission in power communication networks. The results show that the efficiency and accuracy of communication data acquisition, transmission, analysis and processing of the Artificial Intelligence for IT Operations system of the power communication network using the power Internet of Things technology are far higher than that of the traditional operation and maintenance technology, which improves the operation and maintenance accuracy of the power communication network by about 25%. The intelligent operation and maintenance system of the power communication network designed by the author, it effectively improves the communication data processing capability and the reliability of operation and maintenance results, and can meet the current practical engineering application requirements. This study provides an innovative solution for the secure operation and maintenance of power communication networks, which is of great significance for protecting the security and stable operation of power communication networks.
Interactive Visualization to Enhance Learning Experienced on Machining Process of EDM Wire Cut Through Augmented Reality (AR)

Globally, the manufacturing industry continues to move towards industrial revolution 4.0 (IR 4.0) especially in advanced factory systems. The challenges for industrial players are to equip and produce their users with the necessary knowledge and skill-based requirements. The rapid growth of Augmented Reality (AR) technology has gained a great deal of interest from people in recent years. In general, the increasingly complex AR industry has been placed in the Industrial Revolution 4.0. The objectives of this project are to design and develop the AR Aid as an alternative learning tool training guidance for operational setup of electrical discharge machine (EDM) wire cut as well as to evaluate the effectiveness of the AR Aid for EDM wire cut application. Therefore, this project is focusing on the operational setup of the EDM wire cut at the EDM laboratory of Universiti Teknologi Mara, Pulau Pinang. Firstly, 3D modelling of related components for operational setup in EDM wire cut were created using CATIA V5. Meanwhile Blender software was used for animations. In order to develop the AR tools, the UNITY software and Vuforia Engine were used. In this experiment, 26 students from the Centre for Mechanical Engineering Studies were assigned to the project for the evaluation of the tool. From the result, it shows that female group who had experienced learning using AR app vote higher in the experience and performance section compared to the male group. Furthermore, 77% respondents from female group preferred AR app as a learning tool to boost their understanding in the machining processes. Therefore, the proposed delivery methods were aligned for educational sectors in adopting new technology as a platform in education via digital learning and training practice. Moreover, this study envisions the industrial sectors requirement in integrating technologies to expedite the march towards industrial revolution 4.0, as we know that Augmented Reality is one of the nine pillars of technological enhancement in achieving industry 4.0.
Development and Practice of Ball Sports Robotics System Based on Competition-Integrated Teaching

Currently, there are 301 robotics engineering programs in universities across the country. With the development of robotics majors in higher education institutions, universities have begun to combine engineering maker ideas with robotics technology to carry out corresponding higher engineering education. This paper extends and improves the robotics technology practice course in Harbin Institute of Technology's Engineering Innovation Practice Center. Based on the competition-integrated teaching model, it demonstrates the development of a ball sports robotics teaching system and competition assessment methods. The practice goal is set as the competition tasks, and the self-developed robotics teaching system and processing equipment are used to achieve practical results as well as to cultivate students' ability to learn independently through practice. This paper aims to build a coordinated and unified system with distinctive features, incorporating education, competition, intelligence, and innovation, and to cultivate a large number of engineering makers among the new generation of university students.
Pedagogical conditions for the formation of professional competence of students in the direction of training “the art of folk singing”

The purpose of the article is to analyze the existing approaches to the professional training of folk singers, to identify the necessary approaches for concretizing professional competencies, taking into account the specifics of professional activity and the national and regional specifics of the folklore material of the Volga region, in particular the Chuvash cultural tradition. The material for the study includes normative documents, theoretical works of leading scientists, methodological works of practicing teachers and personal experience of performing and teaching. Based on the method of theoretical analysis, the definition of professional competencies is given as the ability and willingness of students to apply knowledge, skills, experience and personal qualities in accordance with the professional and creative activity in the folk performing arts. It was revealed that the main goal of the implemented approaches in the professional training process is aimed at developing competencies for working with folk song material, vocal and stage work, pedagogical and scientific work. The use of the ethno-cultural approach in the training of folk singers is one of the main ones, the implementation of which allows supplementing the educational standard by determining the content of professional training, taking into account the regional specifics of folklore material, the local folk song tradition, and the characteristic features of the national manner of performance.