Reviewerly/exHarmony huggingface dataset:
Fostering future music teachers' professional skills: developing a signature pedagogy using e-learning

Introduction The emergency crisis due to the COVID-19 pandemic caused noticeable changes that stimulated the transition to the new normal based on the digitalization of our systems. In this regard, the revision of learning tools in higher education exposes new challenges and requires the development and implementation of digital technologies. Fostering future music teachers' professional skills needs designing of online resources that provide close student–teacher interaction, as well as the engagement of students and regulation of their professional music practice. Based on the theory of Shulman, the signature pedagogy in music education was developed and implemented in an e-learning environment by a small private online course. The aim of the research was stated: How and which students' music teacher professional skills are affected in the process of e-learning based on the signature approach? The research was conducted at Kazan Federal University. During the educational process students studied the professional practicum. Methods Totally, 124 students from 1st to 4th academic level had taken part in the experimental work in the academic year 2021–2022. All of them studied bachelor programs for future music teacher as full- or part-time regime and studied the online course in MOODLE during the experiment. A combination of online tasks which cover all the types of the music teacher‘ professional activities was suggested. Considering the aim of the research, the multi-case study research methodology was chosen, using a mixed method approach, resourcing mainly to questionnaire, interview, documental analysis, and observation as data gathering methods, and descriptive statistical analysis, as data analysis techniques. Results The main results demonstrate the effectiveness of the developed online course that allowed to significantly foster students' music teacher professional skills. Discussion The investigation of the efficiency of using e-learning for training future music teachers' professional skills was carried out in this study. To improve future music teachers' skills according to the features of the professional field, a signature pedagogy approach was suggested. The contribution of the paper brings the detail characteristic of the signature pedagogy in music education, and the way of its implementation by the means of online tools.
A New Optimization Idea: Parallel Search-Based Golden Jackal Algorithm

As the variety and scope of optimization problems continue to expand, the applicability of a single algorithm may not be universal. To address this challenge, this paper introduces an enhanced iteration of the Golden Jackal Optimization (GJO) algorithm, termed the Parallel Search-based Golden Jackal Optimization (PGJO) algorithm. This algorithm integrates the concept of parallel search into the initialization, updating, and selection mechanisms. It includes a chaotic mapping preselection during the initialization phase, employs a cloning-like strategy for population updates, and incorporates an advanced simulated annealing approach during the selection phase. Through a comprehensive comparison of PGJO with various intelligent optimization algorithms across diverse benchmark functions and real-world scenarios, we affirm its effectiveness in terms of enhanced convergence accuracy and reduced required iterations.
Single-Pixel Chaotic Cavity Bandwidth Control using Rotman Lens-Based Multiplexer/Demultiplexer

This paper presents the integration of a microwave multiplexer/demultiplexer based on a Rotman Lens, with a cavity-backed metasurface antenna, for the first time, which represents a key step towards achieving efficient and cost-effective channel characterisation at microwave frequencies. Converting wideband signals into multiple sub-band signals and distributing their computations to parallel processing units can enable high-performance characterisation with low-order computational complexities.
Joint Low-Light Image Enhancement and Denoising via a New Retinex-Based Decomposition Model

It is well known that images taken in low-light conditions frequently suffer from unknown noise and low visibility, which can pose challenges for image enhancement. The majority of Retinex-based decomposition algorithms usually attempt to directly design prior regularization for illumination or reflectance. Nevertheless, noise can be involved in such schemes. To address these issues, a new Retinex-based decomposition model for simultaneous enhancement and denoising has been developed. In this paper, an extended decomposition scheme is introduced to extract the illumination and reflectance components, which helps to better describe the prior information on illumination and reflectance. Subsequently, spatially adaptive weights are designed for two regularization terms. The main motivation is to provide a small amount of smoothing in near edges or bright areas and stronger smoothing in dark areas, which could preserve useful information and remove noise effectively during image-enhancement processing. Finally, the proposed algorithm is validated on several common datasets: LIME, LOL, and NPE. Extensive experiments show that the presented method is superior to state-of-the-art methods both in objective index comparisons and visual quality.
Improvement and Repair Methods of Biomedical Images Used in the Diagnosis of Osteoporosis

Osteoporosis is a health problem with multiple causes, characterized by decreased bone mineral density and deterioration of the microstructure of bone tissue. Osteoporosis, the incidence of which has increased with the prolongation of human life, is at the top of the list in important public health rankings today. In addition, osteoporosis can be said to be a costly disease. In terms of days of stay in the hospital, osteoporosis causes more cost burden and longer treatment processes on health systems than many major diseases such as lung disease due to chronic vascular occlusion, stroke, and breast cancer. Image enhancement and enhancement techniques help physicians diagnose diseases and accelerate disease diagnosis studies. This indirectly reduces patient costs and saves time. In this chapter, the techniques used for the improvement and repair of biomedical images in the diagnosis of osteoporosis from the past to the present are examined. Modern biomedical imaging technologies are reviewed. Imaging tools used in the diagnosis of osteoporosis have also been investigated.
Tamed Warping Network for High-Resolution Semantic Video Segmentation

Recent approaches for fast semantic video segmentation have reduced redundancy by warping feature maps across adjacent frames, greatly speeding up the inference phase. However, the accuracy drops seriously owing to the errors incurred by warping. In this paper, we propose a novel framework and design a simple and effective correction stage after warping. Specifically, we build a non-key-frame CNN, fusing warped context features with current spatial details. Based on the feature fusion, our context feature rectification (CFR) module learns the model’s difference from a per-frame model to correct the warped features. Furthermore, our residual-guided attention (RGA) module utilizes the residual maps in the compressed domain to help CRF focus on error-prone regions. Results on Cityscapes show that the accuracy significantly increases from 67.3% to 71.6%, and the speed edges down from 65.5 FPS to 61.8 FPS at a resolution of 1024×2048. For non-rigid categories, e.g., “human” and “object”, the improvements are even higher than 18 percentage points.
A Privacy-Preserving Technique for Data Differencing Using the K-means Clustering Algorithm

The K-means algorithm is an unsupervised learning method. The existing unsupervised learning methods have poor data classification accuracy, serious data loss, and large information disclosures. A differential privacy protection method for massive data based on the K-means clustering algorithm is proposed. First, the isolated points of the original data is removed. Then, the initial clustering center is determined according to the average density of the dataset. Then, the dataset is clustered and processed according to the clustering center. Using the roulette selection algorithm, the average allocation during data segmentation is converted to adaptive allocation to improve the segmentation factor. In addition, noise data is added to the processed dataset to complete the data differential privacy protection. The experimental results show that the proposed method can effectively reduce the execution time, data loss and information disclosure, and have high security.
Stable and Efficient Reinforcement Learning Method for Avoidance Driving of Unmanned Vehicles

Reinforcement learning (RL) has demonstrated considerable potential in solving challenges across various domains, notably in autonomous driving. Nevertheless, implementing RL in autonomous driving comes with its own set of difficulties, such as the overestimation phenomenon, extensive learning time, and sparse reward problems. Although solutions like hindsight experience replay (HER) have been proposed to alleviate these issues, the direct utilization of RL in autonomous vehicles remains constrained due to the intricate fusion of information and the possibility of system failures during the learning process. In this paper, we present a novel RL-based autonomous driving system technology that combines obstacle-dependent Gaussian (ODG) RL, soft actor-critic (SAC), and meta-learning algorithms. Our approach addresses key issues in RL, including the overestimation phenomenon and sparse reward problems, by incorporating prior knowledge derived from the ODG algorithm. With these solutions in place, the ultimate aim of this work is to improve the performance of reinforcement learning and develop a swift, stable, and robust learning method for implementing autonomous driving systems that can effectively adapt to various environments and overcome the constraints of direct RL utilization in autonomous vehicles. We evaluated our proposed algorithm on official F1 circuits, using high-fidelity racing simulations with complex dynamics. The results demonstrate exceptional performance, with our method achieving up to 89% faster learning speed compared to existing algorithms in these environments.
Blockwise vs. and General MNA for MOR

This paper proposes two distinct approaches for construction of the Modified Nodal Analysis (MNA) matrices in Model Order Reduction (MOR) techniques, namely the Blockwise MNA and the General MNA. In this work, we apply the proposed approaches to construct MNA matrices of a multi-port network, then reduce their order using the MNA matrices through each approach. As a numerical example, the Y-parameters of full- order and reduced-order networks for a 4-port optical network, operating in the frequency range 175–215 THz, are obtained and are compared in terms of execution time, memory usage, Root Mean Square Error (RMSE), reduction controllability, efficiency, and passivity point of view.
Detailing a Case of Cyber Fraud Through Telephone in Brazil

The goal is to initiate a discussion over the measures to protect the victims, whom unfortunately, in Brazil, composes 30% of elderly people (older than 60 years old). The case report portrays the scam through spoofing, programs that change the caller's identification in a communication, together with Social Engineering, to trick the victim into carrying out operations in the bank's application, thus encouraging transfers to the scammer's account. The victim ended up following these guidelines and made appointments to send money to the scammer, however, he ended up suspecting the situation, decided to interrupt that communication and seek the bank branch for clarification, thus managing to cancel the appointments and reverse the transfer amounts. Actions need to be taken, urgently, because the predictions show a gigantic increase of this type of fraud.
Synergistically segmenting and reducing fracture bones via whole-to-whole deep dense matching

In bone fracture preoperative surgical planning, 3D fracture reduction is of great significance. However, segmenting 3D fracture bones into fragments and assembling the bone fragments to restore their original morphology are labor intensive and time consuming. Current works, however, omit their synergistic effects between reduction and segmentation, which, we envision, can significantly benefit the fracture reduction. To this end, a method is proposed to alternatively segment and assemble the fragments by incorporating the contralateral bone as template and leveraging the synergistic effect on segmentation and reduction. The 3D fracture model is initially segmented into separated outer surface of fragments. The reduction is then conducted by template matching, specifically, a whole-to-whole matching strategy through deep learning of dense features is employed. Thereafter, the segmentation is further refined with the assistance of the template. The process of reduction and segmentation is iterated until the algorithm converges. Experiments were conducted on simulated data and clinical data, the mean segmentation accuracy is 95.26%, the assembling translation error is 3.28 ± 3.01 mm, and the assembling rotation error is 1.62 ± 1.71°. Results demonstrated the superiority of our method over state-of-the-art methods, and it can be used as a feasible scheme for fracture reduction planning. Moreover, it is also proved that segmentation and reduction can promote each other.
Transport Object Detection in Street View Imagery Using Decomposed Convolutional Neural Networks

Deep learning has achieved great successes in performing many visual recognition tasks, including object detection. Nevertheless, existing deep networks are computationally expensive and memory intensive, hindering their deployment in resource-constrained environments, such as mobile or embedded devices that are widely used by city travellers. Recently, estimating city-level travel patterns using street imagery has been shown to be a potentially valid way according to a case study with Google Street View (GSV), addressing a critical challenge in transport object detection. This paper presents a compressed deep network using tensor decomposition to detect transport objects in GSV images, which is sustainable and eco-friendly. In particular, a new dataset named Transport Mode Share-Tokyo (TMS-Tokyo) is created to serve the public for transport object detection. This is based on the selection and filtering of 32,555 acquired images that involve 50,827 visible transport objects (including cars, pedestrians, buses, trucks, motors, vans, cyclists and parked bicycles) from the GSV imagery of Tokyo. Then a compressed convolutional neural network (termed SVDet) is proposed for street view object detection via tensor train decomposition on a given baseline detector. The method proposed herein yields a mean average precision (mAP) of 77.6% on the newly introduced dataset, TMS-Tokyo, necessitating just 17.29 M parameters and a computational capacity of 16.52 G FLOPs. As such, it markedly surpasses the performance of existing state-of-the-art methods documented in the literature.
High Gain 1-D Complementary Paired Combline PLWA with Backward to Forward Scanning

A design of a 1-D PLWA is discussed where a pair of complementary radiating stubs are connected to microstrip fed line. The edges of the stubs are tapered to get good impedance matching and OSB suppression. A slot is further introduced to this design to improve the gain and scanning range. The proposed antenna can achieve 17. 75dbi peak gain and have scanning range from −61° to +19°, bandwidth 6.1GHz – 10.5GHz, its dimensions are <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8.25\lambda_{0}\times 1. 5 \lambda_{0}$</tex> with a small 1.5dbi gain variation at the broadside.
A Brief Overview of Cyber Security Advances and Techniques Along With a Glimpse on Quantum Cryptography

The vulnerability of wireless communication systems and technologies to various cyberattacks has been made public in recent years via different researchers; these cyberattacks not only impact private businesses but also government institutions. Sensitive and private data safety is a major challenge because of hackers since they are always trying to find loopholes in security models with different strong tactics and tools for cracking any sized keys. For minimizing such invasions, various developments have been made. This chapter carries out an in-depth study of several standard cyber security strategies currently in practice and the difficulties to be faced in cyber security practices. The modern efficient key systems are also shown, and the latest generation vulnerabilities are well described and reported. The advantages of quantum cryptography are highlighted along with its potential in the future. It presents a summarized glimpse in different aspects for novice researchers to educate themselves with recent developments in the cyber security field.
Bibliometrics Study of Organizational Cybersecurity

Cybersecurity is one of the main concerns of most organizations, derived from the violation of their cybersecurity. The objective of this chapter is the development of a bibliometric study on cybersecurity from the organizational level. For the method, the Web of Science platform was chosen, since it indexes and organizes scientific production and has the high impact factor of the Journal Citation Report (JCR). The results show evidence of the evolution in which cybersecurity has been addressed within the organizational field, so the originality is related to the business importance in this globalized world. The various theories and approaches used in empirical studies provide valuable perspectives to understand the behavior of the actors involved, the motivations behind criminal actions, and cyber risk prevention and mitigation strategies, contributing to the good performance of organizations. These findings show the interest of the scientific community, observing a growing trend around publications within the subject of study.
The state of scientific <scp>PDF</scp> accessibility in repositories: A survey in Switzerland

Abstract This survey analyzes the quality of the portable document format (PDF) documents in online repositories in Switzerland, examining their accessibility for people with visual impairments. Two minimal accessibility features were analysed: the PDFs had to have tags and a hierarchical heading structure. The survey also includes interviews with the managers or heads of multiple Swiss universities' repositories to assess the general opinion and knowledge of PDF accessibility. An analysis of interviewee responses indicates an overall lack of awareness of PDF accessibility, and shows that online repositories currently have no concrete plans to address the issue. This paper concludes by presenting a set of recommendations for online repositories to improve the accessibility of their PDF documents.
Sentiment induced phrase-based machine translation: Robustness analysis of PBSMT with senti-module

Every type of machine translation system (i.e. neural, statistical, rule-based machine translation system) is equal important to build a sophistical hybrid machine translation system. Keeping this fact in my mind, I concentrate to improve statistical machine translation system with more natural way. In this paper, I try to preserve sentiment after translation to improve the overall accuracy of the machine translation system. So, I introduced senti-model here. A senti-model (sentiment model), translation model, language model, and distortion model are incorporated on the top of the beam search algorithm for decoding. At first, sentiment information is learned and modeled with translation probability by using this algorithm. Thereafter, I decode the source sentences-based on the contextual information. Overall procedure of translation modeling with a sentiment, parameter estimation for it, and senti-translation decoding (decoding with the sentiment model) are presented with empirical evidence. Experiments on a benchmark English–Hindi dataset shows that the proposed model is capable to improve the accuracy (in terms of 4.66 BLEU points, 4.09 LeBleu points, 4.67 NIST points, 5.71 RIBES points) significantly and preserves sentiment 7.79% more than the state-of-the-art technique.
Advancements in Quantum Machine Learning for Intrusion Detection

This chapter provides a comprehensive overview of the recent developments in quantum machine learning for intrusion detection systems. The authors review the state of the art based on the published work “Quantum Machine Learning for Intrusion Detection of Distributed Denial of Service Attacks: A Comparative View” and its relevant citations. The chapter discusses three quantum models, including quantum support vector machines, hybrid quantum-classical neural networks, and a two-circuit ensemble model, which run parallel on two quantum processing units. The authors compare the performance of these models in terms of accuracy and computational resource consumption. Their work demonstrates the effectiveness of quantum models in supporting current and future cybersecurity systems, achieving close to 100% accuracy, with 96% being the worst-case scenario. The chapter concludes with future research directions for this promising field.
Enhancing Web Application Security: Advanced Biometric Voice Verification for Two-Factor Authentication

This paper presents a voice biometrics system implemented in a web application as part of a two-factor authentication (2FA) user login. The web-based application, via a client interface, runs registration, preprocessing, feature extraction and normalization, classification, and speaker verification procedures based on a modified Gaussian mixture model (GMM) algorithm adapted to the application requirements. The article describes in detail the internal modules of this ASR (Automatic Speaker Recognition) system. A comparison of the performance of competing ASR systems using the commercial NIST 2002 SRE voice dataset tested under the same conditions is also presented. In addition, it presents the results of the influence of the application of cepstral mean and variance normalization over a sliding window (WCMVN) and its relevance, especially for voice recordings recorded in varying acoustic tracks. The article also presents the results of the selection of a reference model representing an alternative hypothesis in the decision-making system, which significantly translates into an increase in the effectiveness of speaker verification. The final experiment presented is a test of the performance achieved in a varying acoustic environment during remote voice login to a web portal by the test group, as well as a final adjustment of the decision-making threshold.