Reviewerly/exHarmony huggingface dataset:
Factors influencing the development of pre-service science teachers’ technological pedagogical content knowledge in a pedagogical makerspace

In an increasingly digitalized world, pre-service and in-service teachers need subject-specific didactic competencies to be able to plan their lessons appropriately and use their knowledge to promote digital competencies among students. Building on competency models such as the Technological pedagogical content knowledge (TPACK) framework, this article explores the extent to which specific digital competencies relevant to pre-service teachers can be developed through project work in a pedagogical makerspace and examines the extent to which contextual factors such as technological self-efficacy, motivation and technology acceptance influence the development of pre-service teachers’ TPACK and their intention to use digital media. To this end, 495 pre-service science teachers from both intervention and control groups completed a pre-post digital questionnaire before and after the intervention. The data were used for structural equation modeling. The results show that the level of TPACK before the intervention is an important predictor of TPACK after project work. Furthermore, TPACK before the intervention positively influences pre-service teachers’ intention to use digital media in the future. Also, the perceived usefulness for professional use and the intention to use information and communication technologies (ICT) are strongly influenced by TPACK. Consequently, it appears significant to enable a low-threshold entry point at the beginning of the study to provide a solid foundation upon which more advanced TPACK can be built. Motivation and technology acceptance are strongly correlated. Therefore, teacher training should focus on motivation and acceptance of technology.
Eye tracking technology in medical practice: a perspective on its diverse applications

Eye tracking technology has emerged as a valuable tool in the field of medicine, offering a wide range of applications across various disciplines. This perspective article aims to provide a comprehensive overview of the diverse applications of eye tracking technology in medical practice. By summarizing the latest research findings, this article explores the potential of eye tracking technology in enhancing diagnostic accuracy, assessing and improving medical performance, as well as improving rehabilitation outcomes. Additionally, it highlights the role of eye tracking in neurology, cardiology, pathology, surgery, as well as rehabilitation, offering objective measures for various medical conditions. Furthermore, the article discusses the utility of eye tracking in autism spectrum disorders, attention-deficit/hyperactivity disorder (ADHD), and human-computer interaction in medical simulations and training. Ultimately, this perspective article underscores the transformative impact of eye tracking technology on medical practice and suggests future directions for its continued development and integration.
Developing the framework of blockchain-enabled agri-food supply chain

Purpose The food supply chain has faced many challenges due to its complex and complicated nature. Blockchain technology is one of the mechanisms used to improve agri-food supply chain processes by evolving organization capabilities. A study is being conducted to scrutinize the adoption of blockchain technology in the agri-food supply chain through the lens of the operational capability approach. It further makes an attempt to identify the capabilities of blockchain to improve supply chain processes. Design/methodology/approach The qualitative research method with semi-structured interviews was used to gather information from experts and professionals in the food supply chain and blockchain technology. The authors have adopted a systematic approach of coding using open, axial and selective methods to depict and identify the themes that represent the blockchain-enabled agri-food supply chain. The data were collected from 32 interviews of selected participants. Findings The result shows five critical areas where blockchain can come up to enhance the agri-food supply chain performance by providing traceability, transparency, information security, transactions, and trust and quality. Further, the study reveals that blockchain will provide safety, lower the cost of transactions and can create trust among users to communicate within the whole supply chain without the intervention of a third party. This study demonstrated that the capabilities need to be considered when introducing technology into the practice. Research limitations/implications The study implies thought-provoking implications for bridging the theory-practice gap by examining the empirical data to demonstrate how the operational capabilities of blockchain technology further strengthen the agri-food supply chain. Additionally, this study provides some suggestions for utilizing the results and proposes a framework to understand more about blockchain use cases in the agri-food supply chain as well as extend the application of blockchain using an operational capability approach for future academic researchers in this area. Practical implications This study presented some more important managerial implications which reveal that the majority of organisations were in the initial stages of adoption process of blockchain technology. Further, the positive influence of managers and IT experts can help the information technology companies (IT) and stakeholders for developing and promoting blockchain solutions in the agri-food supply chain. The important implication of blockchain enabled agri-food supply chain is to maintain information security and incresae supply chain performance. Originality/value The study shows the operational capabilities of agri-food supply chain using blockchain technology. Blockchain can contribute in enhancing the agri-food supply chain to increase traceability and transparency and helps to reduce the risk of disruptions.
A Process Mining-based unsupervised Anomaly Detection technique for the Industrial Internet of Things

Industrial Internet of Things (IIoT) applications in Industry 4.0 collect and process Time Series (TS) originating from heterogeneous sources. Many data-driven techniques have been proposed over the years for unsupervised collective Anomaly Detection (AD) to detect anomalous TS and improve quality of service. These techniques build statistical and/or behavioral models through data-driven algorithms often based on machine and deep learning. However, these algorithms may have black-box behavior, may require too much computational effort, and may not be trustworthy. In order to address these challenges, this paper proposes and evaluates an unsupervised AD technique that pre-processes noisy and complex TS and detects anomalous patterns. Pre-processing handles TS complexity through Feature Extraction and Dimensionality Reduction based on Autoencoders (AEs), whereas anomalous patterns are classified through Process Mining (PM), which unsupervisedly captures TS patterns and compares unknown behavior to such patterns for collective AD. We apply the technique to a scale replica of an assembly plant adopted in a smart automotive factory to evaluate our technique with respect to several configurations to analyze their impact on both detection and timing performance. Our contribution improves and extends the existing state-of-the-art work in the literature regarding the application of PM to IIoT for collective AD in TS. Moreover, it compares the obtained results with a baseline approach based on AEs.
Design and implementation of an edge computing-based GPS tracking system

This paper presents a comprehensive study on the technical aspects of edge-enabled GPS tracking systems, their evolution, and an analysis of existing problem-solving approaches. It introduces mathematical models that simulate the operation of the hardware and software components of these systems. An adaptive user interface has been developed, enabling usage across various platforms such as smartphones and personal computers. The paper also explores innovative methods for visualizing the trajectory of a moving object on an electronic map. Specialized software for the Atmega162-16PU microcontroller has been developed to control the GSM module and GPS receiver. A novel method for data transmission from a GPS tracker to a web server is proposed. The paper concludes with the successful testing of two experimental GPS-tracker prototypes under uncertain conditions. The developed edge-enabled GPS tracking software and hardware show potential for monitoring moving objects within the coverage of GSM cellular networks.
Design and implementation of an IoT-based educational model for smart homes: a STEM approach

This work presents a comprehensive study on the design and implementation of an educational model for a smart home, leveraging the principles of the Internet of Things (IoT). The model encompasses three key levels: command, communication, and management, and integrates several subsystems, including communication, signalling, lighting control, temperature regulation, garbage container filling, nd sensor data monitoring. The hardware components for implementing the Mini Smart House, a practical application of the model, are detailed in the study. The Mini Smart House utilizes a range of technologies for efficient management and incorporates renewable energy sources for power. The model was independently produced by students participating in a STEM project, with research activities encompassing sketching, construction part creation, sensor assembly, Arduino board programming in the Arduino IDE environment, and system functionality testing. The Mini Smart House was validated within the STEM-center of the Physics and Mathematics Faculty of Ternopil Volodymyr Hnatiuk National Pedagogical University during the educational process and numerous trainings and seminars for pupils and teachers of computer science.
The Impact of Online Social Behavior on College Student’s Life Satisfaction: Chain-Mediating Effects of Perceived Social Support and Core Self-Evaluation

To examine the influence of social behavior on college students' life satisfaction and the mediating effects of perceived social support and core self-evaluation.779 college students were investigated with the online social behavior scale, life satisfaction scale, core self-evaluation scale and perceived social support scale.Online social behavior significantly positively predicted the perceived social support and life satisfaction; Perceived social support significantly positively predicted the core self-evaluation and life satisfaction; Core self-evaluation significantly positively predicted life satisfaction; Perceived social support and core self-evaluation have a significant chain mediating effect between online social behavior and life satisfaction.This study confirmed the positive effects of online social behavior on college students, through improving college students' level of perceived social support and core self-evaluation to increase life satisfaction.
An Improved Hashing Approach for Biological Sequence to Solve Exact Pattern Matching Problems

Pattern matching algorithms have gained a lot of importance in computer science, primarily because they are used in various domains such as computational biology, video retrieval, intrusion detection systems, and fraud detection. Finding one or more patterns in a given text is known as pattern matching. Two important things that are used to judge how well exact pattern matching algorithms work are the total number of attempts and the character comparisons that are made during the matching process. The primary focus of our proposed method is reducing the size of both components wherever possible. Despite sprinting, hash-based pattern matching algorithms may have hash collisions. The Efficient Hashing Method (EHM) algorithm is improved in this research. Despite the EHM algorithm’s effectiveness, it takes a lot of time in the preprocessing phase, and some hash collisions are generated. A novel hashing method has been proposed, which has reduced the preprocessing time and hash collision of the EHM algorithm. We devised the Hashing Approach for Pattern Matching (HAPM) algorithm by taking the best parts of the EHM and Quick Search (QS) algorithms and adding a way to avoid hash collisions. The preprocessing step of this algorithm combines the bad character table from the QS algorithm, the hashing strategy from the EHM algorithm, and the collision-reducing mechanism. To analyze the performance of our HAPM algorithm, we have used three types of datasets: E. coli, DNA sequences, and protein sequences. We looked at six algorithms discussed in the literature and compared our proposed method. The Hash-q with Unique FNG (HqUF) algorithm was only compared with E. coli and DNA datasets because it creates unique bits for DNA sequences. Our proposed HAPM algorithm also overcomes the problems of the HqUF algorithm. The new method beats older ones regarding average runtime, number of attempts, and character comparisons for long and short text patterns, though it did worse on some short patterns.
A comprehensive survey of robust deep learning in computer vision

Deep learning has presented remarkable progress in various tasks. Despite the excellent performance, deep learning models remain not robust, especially to well-designed adversarial examples, limiting deep learning models employed in security-critical applications. Therefore, how to improve the robustness of deep learning has attracted increasing attention from researchers. This paper investigates the progress on the threat of deep learning and the techniques that can enhance the model robustness in computer vision. Unlike previous relevant survey papers summarizing adversarial attacks and defense technologies, this paper also provides an overview of the general robustness of deep learning. Besides, this survey elaborates on the current robustness evaluation approaches, which require further exploration. This paper also reviews the recent literature on making deep learning models resistant to adversarial examples from an architectural perspective, which was rarely mentioned in previous surveys. Finally, interesting directions for future research are listed based on the reviewed literature. This survey is hoped to serve as the basis for future research in this topical field.
A randomized benchmarking suite for mid-circuit measurements

Abstract Mid-circuit measurements are a key component in many quantum information computing protocols, including quantum error correction, fault-tolerant logical operations, and measurement based quantum computing. As such, techniques to quickly and efficiently characterize or benchmark their performance are of great interest. Beyond the measured qubit, it is also relevant to determine what, if any, impact mid-circuit measurement has on adjacent, unmeasured, spectator qubits. Here, we present a mid-circuit measurement benchmarking suite developed from the ubiquitous paradigm of randomized benchmarking. We show how our benchmarking suite can be used to both detect as well as quantify errors on both measured and spectator qubits, including measurement-induced errors on spectator qubits and entangling errors between measured and spectator qubits. We demonstrate the scalability of our suite by simultaneously characterizing mid-circuit measurement on multiple qubits from an IBM Quantum Falcon device, and support our experimental results with numerical simulations. Further, using a mid-circuit measurement tomography protocol we establish the nature of the errors identified by our benchmarking suite.
Deep Representation Learning: Fundamentals, Technologies, Applications, and Open Challenges

Machine learning algorithms have had a profound impact on the field of computer science over the past few decades. The performance of these algorithms heavily depends on the representations derived from the data during the learning process. Successful learning processes aim to produce concise, discrete, meaningful representations that can be effectively applied to various tasks. Recent advancements in deep learning models have proven to be highly effective in capturing high-dimensional, non-linear, and multi-modal characteristics. In this work, we provide a comprehensive overview of the current state-of-the-art in deep representation learning and the principles and developments made in the process of representation learning. Our study encompasses both supervised and unsupervised methods, including popular techniques such as autoencoders, self-supervised methods, and deep neural networks. Furthermore, we explore a wide range of applications, including image recognition and natural language processing. In addition, we discuss recent trends, key issues, and open challenges in the field. This survey endeavors to make a significant contribution to the field of deep representation learning, fostering its understanding and facilitating further advancements.
Differential Evolution With Exponential Crossover: An Experimental Analysis on Numerical Optimization

Since the inception in 1995, Differential Evolution (DE) has gained significant attention from researchers worldwide, and many DE variants proposed in the last decades obtained excellent performance in many scientific and engineering applications. However, the vast majority of these well-performed DE variants employ binomial crossover rather than exponential crossover in generating trial vector candidates though both of the two schemes were proposed simultaneously. That may be also the reason why there still doesn’t exist such a thorough analysis of DE with exponential crossover. Different from the majority of DE researchers believing that DE variants with binomial crossover usually exhibit superior performance than the ones employing exponential crossover and DE variants with exponential crossover are good at tackling optimization problems with linkages among neighboring variables, we found that DE variants with exponential crossover can achieve competitive performance with the ones employing binomial crossover regardless of whether there are linkages among the variables or not after discovering the proper crossover rate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CR</i> and its corresponding parameter control. In order to enrich research of DE on exponential crossover, this paper presents an thorough experimental analysis of DE algorithm with exponential crossover in numerical optimization, presenting the basic concepts of exponential crossover, giving the mathematical analysis on can actually achieve similar optimization between exponential crossover and binomial crossover, experimental validation under the 100 benchmark functions from CEC2013, CEC2014, CEC2017, CEC2022 test suites as well as the tension/compression spring design problem, and summarizing and classifying various engineering applications that exponential crossover DE are used to solve. Furthermore, we also look into the future challenges and potential directions for further development of DE with exponential crossover.
Enhancing Secret Data Detection Using Convolutional Neural Networks With Fuzzy Edge Detection

Progress in Deep Learning (DL), particularly Convolutional Neural Networks (CNNs), has significantly improved the accuracy of steganographic image detection. However, the applications of CNNs have several challenges, mainly due to insufficient dataset quality and quantity, the heightened imperceptibility of low payload capacities, and suboptimal feature learning procedures. This paper proposes an enhanced secret data detection approach with a CNN architecture that includes convolutional, depth-wise, separable, pooling, and spatial dropout layers. An improved fuzzy Prewitt approach is employed for pre-processing the images prior to being fed into the CNN. Experimental results show a significant outperformance over the state-of-the-art methods.
Tourist Mobility Patterns: Faster R-CNN Versus YOLOv7 for Places of Interest Detection

The mobility of tourists plays a significant role in shaping their travel experiences and the overall dynamics of a destination. In recent years, the proliferation of social media platforms has provided a rich source of visual data, allowing us to leverage the abundance of pictures shared by tourists to extract meaningful information. Using computer vision techniques and deep learning algorithms, such as object detection, it becomes possible to extract useful information from tourist pictures. In this study, we look for the best way to detect objects from pictures shared by tourists during their journey in order to determine their locations. To achieve our goal we propose a new methodology composed by; database creation, database annotation, preprocessing, deep learning implementation and evaluation. We implemented two deep learning object detection methods: YOLOv7 and Faster R-CNN. A dataset has been created to provide examples of training and testing for neuronal networks. The training was performed on various basic models, in order to increase the efficiency of the training time and to compare the results. We evaluated the results using three parameters: precision, recall and mAP. The results indicate that YOLOv7 has the precision and performance, with over 90% mAP, 92.1% precision and 92.7% recall.
Decoupled and Reparameterized Compound Attention-Based Light Field Depth Estimation Network

A light field (LF) camera captures both spatial and angular information of the real world, and the intertwined nature of these dimensions presents a pressing challenge in effectively disentangling meaningful LF information for depth estimation. This paper introduces a feature extraction network based on LF decoupling, which ingeniously separates the LF. Furthermore, given the extensive volume of input data inherent in LF images, a novel reparameterizable Residual-Densely Branched Leaky-ReLU Block(Res-DBLB) architecture was developed to replace conventional residual structures and multibranch architectures and enhance inference efficiency. Incorporating an attention mechanism further refines the network, effectively addressing the computational intensity and time-consuming nature of LF depth estimation, thus furthering the advancement of this technology. Our model was applied to widely used datasets as well as the latest LF datasets, namely HCI and UrbanLF, showing superior performance over six other popular models across four evaluation metrics.
TrustFedHealth: Federated Learning with Homomorphic Encryption and Blockchain for Heart Disease Prediction in the Smart Healthcare

In recent years, the advancements in the Internet of Medical Things (IoMT) or smart devices have enabled the automatic monitoring of human health. Using smart healthcare devices can not only reduce the burden on hospitals but also save costs, travel time, and provide a way to diagnose diseases at home, such as stroke rates. The IoMT generates a large amount of data, which can be used to train machine learning (ML) models for accurate disease diagnosis. However, the data used to train ML models is usually private, making it challenging to share and requiring high security. To overcome this challenge, this paper proposes a collaborative framework called TrustFedHealth for training ML-based heart disease prediction models. TrustFedHealth uses Federated Learning (FL) to allow training with decentralized data stored separately on multiple machines. Moreover, Mobile Edge Computing (MEC) is incorporated into the model as a solution to optimize communication time between the different elements of the system and reduce congestion. Homomorphic Encryption (HE) is also combined with FL to protect the confidentiality of model updates between clients and aggregation servers. Additionally, Blockchain (BC) is leveraged to tackle the traceability of contributions and guarantee transparency of model updates. Through evaluation results on the Physionet’s MIT-BIH Arrhythmia Dataset, TrustFedHealth provides a promising approach for training ML-based heart disease prediction models while maintaining privacy and security.