Reviewerly/exHarmony huggingface dataset:
Project of implementation strategies formation by alternation in education media técnico profesional

Technical-vocational education is one of the fundamental pillars that governs the educational system, and its strengthening is a task assumed by the Ministry of Education through the implementation of the National Policy on Technical-Vocational Training implemented through the National Strategy for Technical-Vocational Training. There are multiple challenges involved in carrying out actions to promote and generate instances of participation and decision-making that enable the involvement of all actors to articulate the educational system and economic development. In this sense, professional technical high schools with a specialty in preschool care assume a strategic and preponderant role in the education sector, since the young people who study the specialty put into practice what they have learned in classrooms and workshops allow them to effectively insert themselves into the world of work, access their professional practice and insert themselves into higher education. And, in most cases, to be an agent of change at the family level to improve the socioeconomic reality of their homes. For this reason, Chile needs to have technicians in the care of middle schools that will allow it to address the challenges of its own development and its growing participation in the preschool care system, with demands of greater added value, dynamic and challenging. In this scenario, alternation training will not only strengthen the specific technical competencies of the specialty, but also the capacity for innovation, entrepreneurship, teamwork, and a set of transversal competencies that will prepare students to work in multiple areas of education.
Fast and Secure Image Encryption System Using New Lightweight Encryption Algorithm

The rapid evolution of network technologies, where computer vision and the Internet seamlessly merge, poses significant challenges in safeguarding end-user data. To address the growing need for secure data in smart environments, lightweight and efficient security solutions are in high demand. Notably, the Trivium, a well-known encrypt stream cipher (eSTREAM) project, has been the target of several attacks. This paper proposes an innovative approach for fast and highly secured image encryption using byte scrambling and a modified version of the Trivium algorithm. The Henon map is utilized to generate random numbers for permutation, reducing time and increasing security. Performance is measured on 100 colour images with different several tests. The results present a fast and robust security system for the transmission process. It is more efficient than the traditional Trivium method. The encrypted-decryption time is reduced by 1:24 times, making it a quick security system for surgical telepresence and multimedia visuals.
An Efficient Machine Learning Prediction Method for Vehicle Detection: Data Analytics Framework

The availability of transportation is considered a significant hallmark of a developed society. Since the evolution of the human species, the imperative to relocate from one location to another has been a fundamental requirement. At present, there exists a plethora of transportation options in Indonesia. However, most individuals favor road transportation due to its ease and convenience. The rise in population has led to a corresponding increase in the number of vehicles on the roadways. Hence, it presents a challenge for security authorities and governmental bodies to oversee all automobiles' mobility across various locations effectively. The present study proposes a methodology for detecting and tracking vehicles using video-based techniques. The process's initial stages involve preprocessing, including frame conversion and background subtraction. Next, the process of detecting vehicles involves the utilization of change detection and a model of body shape. Subsequently, the next stage entails the feature extraction process, focusing on extracting energy features and directional cosine. Subsequently, a technique for optimizing data is employed on the vector comprising excessively extracted features. The methodology integrates a data mining technique based on association rules, which is subsequently complemented by a random forest classification algorithm. The approach generally integrates multiple methodologies to attain effective and precise identification of automobiles in video-derived datasets.
Exploring Multidimensional Embeddings for Decision Support Using Advanced Visualization Techniques

As artificial intelligence has evolved, deep learning models have become important in extracting and interpreting complex patterns from raw multidimensional data. These models produce multidimensional embeddings that, while containing a lot of information, are often not directly understandable. Dimensionality reduction techniques play an important role in transforming multidimensional data into interpretable formats for decision support systems. To address this problem, the paper presents an analysis of dimensionality reduction and visualization techniques that embrace complex data representations and are useful inferences for decision systems. A novel framework is proposed, utilizing a Siamese neural network with a triplet loss function to analyze multidimensional data encoded into images, thus transforming these data into multidimensional embeddings. This approach uses dimensionality reduction techniques to transform these embeddings into a lower-dimensional space. This transformation not only improves interpretability but also maintains the integrity of the complex data structures. The efficacy of this approach is demonstrated using a keystroke dynamics dataset. The results support the integration of these visualization techniques into decision support systems. The visualization process not only simplifies the complexity of the data, but also reveals deep patterns and relationships hidden in the embeddings. Thus, a comprehensive framework for visualizing and interpreting complex keystroke dynamics is described, making a significant contribution to the field of user authentication.
Research on the Resilience Evaluation of Urban Rail Transit Construction Organization Based on the Cloud Matter-Element Model: A Case Study of Nanchang West Station

In the construction of urban rail transit projects, the disturbance of equipment, sudden failure, rainstorms, and other emergencies may bring serious safety risks. Resilience theory emphasizes the ability of the system to resist, adapt, absorb, and learn from risks in the whole process before, during, and after the occurrence of risks. It is introduced into the safety management of construction organization of urban rail transit projects to describe the ability of urban rail transit projects to cope with risks in the whole process of dealing with construction risks. This study defines the connotation of the resilience of the project construction organization and uses the literature frequency statistics method to determine the resilience evaluation indexes. The game theory combination weighting method is used to determine the index weights, and the cloud matter element model is used to establish the evaluation model of construction organization resilience of urban rail transit projects. Taking Nanchang West Station of Phase 1 Project of Nanchang Line 2 as an example, the validity and accuracy of the model are verified. The results show that the resilience grade of the construction organization of the project is “higher resilience,” which is consistent with the actual survey situation, and the evaluation model is reasonable. In addition, in the evaluation results, the key indexes and risk indexes of the project are determined, and the safety management measures of the construction organization of the project are put forward according to the key indexes and risk indexes.
Advances in Architectures for Deep Learning: A Thorough Examination of Present Trends

This article explores the transformative potential of integrating artificial intelligence (AI) with environmental science to address pressing challenges and foster sustainable solutions. The interdisciplinary synergy between AI technologies and environmental science is examined across key domains, including environmental monitoring, predictive modeling for climate change, conservation and biodiversity, and sustainable resource management. The article highlights the role of AI in real-time data analysis, predictive modeling, and optimization, offering innovative approaches to tackle issues such as climate change, biodiversity loss, and resource depletion. Emphasizing the significance of collaborative efforts, the abstract underscores the need for interdisciplinary insights to harness the full potential of AI in promoting environmental sustainability.
CYBERSECURITY DYNAMICS IN NIGERIAN BANKING: TRENDS AND STRATEGIES REVIEW

This paper provides an in-depth review of the cybersecurity dynamics within the Nigerian banking sector, emphasizing recent trends and strategic approaches to address emerging challenges. As a review paper, it synthesizes existing literature, reports, and case studies to offer a comprehensive understanding of the current cybersecurity landscape in Nigerian banks. The focus is on identifying the predominant cyber threats, analyzing the sector's response strategies, and evaluating the effectiveness of these measures in the context of Nigeria's unique socio-economic and regulatory environment. Our analysis reveals a notable escalation in cyber threats, particularly phishing, ransomware, and insider attacks, which have been intensified by the rapid digital transformation in banking services. The review identifies key factors contributing to these challenges, such as the increasing sophistication of cybercriminals, the digital literacy gap among customers, and the evolving nature of cyber threats. It also examines the strategic responses of Nigerian banks, including the adoption of advanced security technologies, enhanced staff training, and collaboration with government and international cybersecurity bodies.The paper concludes that Nigerian banks have made significant strides in fortifying their cybersecurity defenses. However, it also highlights the need for more robust regulatory frameworks, increased customer awareness initiatives, and a shift towards more integrated and proactive cybersecurity strategies. The findings of this review underscore the critical need for continuous evolution and investment in cybersecurity measures to effectively counter the dynamic and complex nature of cyber threats in the Nigerian banking sector.&#x0D; Keywords: Cybersecurity, Cybersecurity Dynamics, Nigerian Banking Sector, Digital Transformation, Cyber Threats, Phishing Attacks, Ransomware, Insider Threats, Regulatory Framework, Central Bank of Nigeria (CBN), Compliance Challenges, Security Technologies, Cybersecurity Awareness, Artificial Intelligence (AI), Machine Learning (ML), Blockchain Technology.
A Text Analysis Method for Student Learning Feedback on Network Teaching Platform Based on Natural Language Processing

&lt;p&gt;With the emergence and end of the COVID-19, online learning has become an irreplaceable way of learning. In order to promote the improvement and enhancement of online curriculum resources and increase the learning effect of students, the content of curriculum evaluation is an important reference for the direction of curriculum improvement. Therefore, this article focuses on the student learning feedback of course resources. Firstly, through data collection algorithms, effective evaluation information is crawled, and then based on the collected information, the course evaluation text is annotated and classified, forming a reasonable corpus. Finally, through feature collection and sentiment analysis algorithms, sentiment analysis is performed on the evaluation content, effectively distinguishing between positive and negative evaluations, and guiding teachers to improve the course content.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Robust Zero-Watermarking by Circular Features and 1-D NRDPWT Transformation

&lt;p&gt;This paper introduces a secure and robust zero-watermarking framework that leverages the advantages of zero-watermarking, ensuring non-destructive modification of original images and unlimited capacity. The proposed method enables robust watermark embedding while preserving the original image. It employs a novel feature extraction approach using circular areas based on image radius, enhancing feature resilience. Additionally, applying one-dimensional non-recursive discrete periodized wavelet transform (1-D NRDPWT) converts feature values into phi, contributing to enhanced stability and robustness. Enhanced security is achieved through the use of Shuffle and Pseudo-Random Number Generator (PRNG). Experimental results, evaluated using metrics such as Bit Error Rate (BER) and Normalized Correlation (NC), validate the exceptional performance of this watermarking technique. These findings underscore the framework&amp;rsquo;s robustness, security, reliability, and integrity against both general and geometric noise attacks, making it a secure and robust solution for modern digital image copyright protection. In summary, our method offers an effective defense against various noise attacks while ensuring the highest watermark quality without compromising the original image. It is a significant advancement in copyright protection applications.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Effects of Assistance Media on Active Learning and Creative Thinking in Online Learning

Online learning is the best learning strategy during the Covid-19 pandemic. However, it has obstacles for students who are still learning to adapt to internet technology. This research aims to determine the effect of using online learning strategies on students' active learning and creative thinking. The subjects of this study were 52 students of the Indonesian Language Education Study Program at the Indonesian Catholic University of Saint Paul. They are taught using the Moodle application. The experimental class was taught using media assistance in the form of WhatsApp, while the control class was not taught. Data collection uses tests and observation sheets. Data analysis used ANOVA with the help of SPSS version 27. The results showed that 1) there were significant differences in student learning activities in online learning with media assistance and students without media assistance; 2) there is no significant difference in creative thinking between students who are taught using media and students who are not given media assistance. Based on the results, findings and conclusions, suggestions will be explained for implementing future learning and further research. Especially the creation of an online learning environment for students who are still adapting to advances in internet technology.
APLIKASI SIFAT GOLONGAN ULU AL-ALBAB DAN FASIK DI ERA SOCIETY 5.0

This study discusses the application of the ul? al-alb?b and wicked groups in the era of society 5.0 based on QS. al-Ra'd/13:19-25. This research is a library research using primary and secondary data with interpretation and sociology approaches. The results of this study are first, that the ul? al-alb?b group in QS. al-Ra'd/13:19-25 has several characteristics, namely taking lessons from everything, fulfilling promises, connecting something that is ordered to be connected, fearing painful reckoning, being patient, establishing prayer, giving alms, and repaying evil with good. Second, the wicked group in the verse are breaking promises, breaking something that is ordered to be continued, and doing damage. The three forms of application of the ul? al-alb?b group in QS. al-Ra'd/13:19-25 in the era of society 5.0, among others, using the internet wisely, as a means of connecting, as a means of giving alms, and as a means of spreading goodness. Meanwhile, the application of the wicked groups in this verse in the era of society 5.0 includes using the internet for immorality, breaking social relations or hospitality, and using it to do damage. The urgency of this research is to provide understanding to Muslims about the characteristics of ul? al-alb?b and wicked, so that they can be used to face the era of society 5.0.
A hybrid neural network based on the small-world network for inner speech recognition

This study is devoted to the identification of human inner speech using an electroencephalogram (EEG), where inner speech refers to an individual's subjective experience of language, disconnected from discernible audible articulation. The core aim of this system is the rapid and precise classification of signals via human inner speech, thus facilitating enhanced control and interaction functionalities. The research entails a comprehensive analysis of 10 volunteers' brain activity across 128 channels from OpenNeuro's Inner speech dataset. A hybrid neural network, which incorporates the small-world network structure, is employed to model neural activity within the brain. This approach outperforms random chance and aligns with current research expectations.
Bearing fault diagnosis based on online symbolic aggregation approximation and streaming deep discriminant analysis

Abstract Rolling bearings are one of the important components of many industrial equipment, and the timely detection of faults in these bearings significantly contributes to ensuring equipment safety. To achieve real-time diagnosis of bearing faults, this paper proposes an online fault diagnosis framework that utilizes online symbolic aggregation approximation (SAX) and streaming deep discriminant analysis. This method uses the moving window approach to segment the vibration data collected online, and then uses SAX for symbolic representation.The obtained icons are input into stacked denoising convolutional autoencoder (SDCAE) for&amp;#xD;classification. The model consists of several denoising convolutional autoencoders (DCAEs) and a linear discriminant analysis (LDA) module. To accommodate online data changes, while fixing the main structure of the network, the streaming method is used to update the parameters of LDA. Compared with other traditional approaches for diagnosing bearing faults, the proposed method exhibits distinct advantages. Specifically, it utilizes a stable neural network model, updates classifier parameters in real-time, and demonstrates superior computational efficiency. The application results demonstrate the effectiveness of the proposed method.
Underwater image enhancement by using amalgamation of colour correction, contrast-enhancing and dehazing

Abstract Underwater images can be captured either with the help of light waves or sound waves. Images that are taken underwater typically are not of optimum quality as they suffer from issues such as low contrast, blurring of detail, colour distortion, and greenish tones. Several physical processes that take place in the aquatic environment, such as light absorption, refraction, and scattering, are responsible for the existence of such degradation in underwater images. To address these challenges, numerous researchers have put forth a range of cutting-edge techniques for enhancing and restoring such degraded underwater images, with the aim of addressing these issues. These techniques primarily focus on improving visibility and enhancing the level of detail. To achieve this, we propose a method that performs White Balancing in the LAB colour space to remove the bluish-greenish tones present in the image. Next, we enhance the contrast by first converting the RGB image into HSV (Hue, Saturation, Value) and HLS (Hue, Lightness, and Saturation) colour spaces and then by using the S &amp; V channels in HSV and L &amp; S colour channels in HLS, we apply Contrast Limited Adaptive Histogram Equalization (CLAHE). To control the brightness of the enhanced image, we apply Gamma Correction. Lastly, by using the method Dark Channel Prior (DCP), we separate the image’s red channel from the RGB colour space and perform the dehazing operation to get the final enhanced image. We have conducted a comprehensive qualitative analysis of our proposed approach as well as existing techniques, evaluating them objectively and subjectively through metrics such as Peak Signal-to-Noise Ratio (PSNR), Root-Mean-Square Error (RMSE), Structural Similarity (SSIM), and the Underwater Colour Image Quality Evaluation metric (UCIQE).&amp;#xD;
Riemannian quantum circuit optimization for Hamiltonian simulation

Abstract Hamiltonian simulation, i.e., simulating the real time evolution of a target quantum system, is a natural application of quantum computing. Trotter-Suzuki splitting methods can generate corresponding quantum circuits; however, a faithful approximation can lead to relatively deep circuits. Here we start from the insight that for translation invariant systems, the gates in such circuit topologies can be further optimized on classical computers to decrease the circuit depth and/or increase the accuracy. We employ tensor network techniques and devise a method based on the Riemannian trust-region algorithm on the unitary matrix manifold for this purpose. For the Ising and Heisenberg models on a one-dimensional lattice, we achieve orders of magnitude accuracy improvements compared to fourth-order splitting methods. The optimized circuits could also be of practical use for the time-evolving block decimation (TEBD) algorithm.
Depth_ ASPP_ UNet++: A novel 2D ESPI phase unwrapping based on convolutional neural network

Abstract Phase unwrapping is a key step in optical metrology and physical optics to obtain accurate &amp;#xD;phase distributions. In practice, phase images obtained from electronic speckle pattern &amp;#xD;interferometry (ESPI) exhibit diverse and complex morphology, with significant shape &amp;#xD;variations and non-uniform densities among different individuals. This takes challenges for &amp;#xD;accurately extracting phase information and unwrapping the phase. With the progress of deep &amp;#xD;learning technology in optical image processing, real-time performance and accuracy have &amp;#xD;become concerned issues. In this paper, an ESPI phase unwrapping method based on &amp;#xD;convolutional neural network (CNN) UNet++ is proposed. The proposed network combines &amp;#xD;the depthwise separable convolution (DSC), atrous spatial pyramid pooling (ASPP), defined &amp;#xD;as Depth_ ASPP_ UNet++. In this model, the use of DSC improves network computational &amp;#xD;efficiency and provides better feature representation capability. In addition, ASPP is &amp;#xD;introduced to pay more attention to the phase information of the phase image, and then obtain &amp;#xD;better phase unwrapping results. The experimental results show that our proposed method can &amp;#xD;obtain excellent results, especially with various of variable density, different noise levels, and &amp;#xD;different speckle sizes.
A simplified Mølmer-Sørensen gate for the trapped ion quantum computer

Abstract We discuss how to simplify the Mølmer-Sørensen (MS) gate which is used for the trapped ion quantum computer. The original MS gate is implemented by illuminating two ions with bichromatic coherent light fields separately at the same time. In this paper, we propose a method for transforming a separable state of two ions into one of the Bell states by illuminating the two ions with monochromatic coherent light fields individually and this point is the advantage of our scheme over the original MS gate. The length of the execution time of our proposed gate is comparable to that of the original MS gate, however, numerical calculations show that our proposed gate is weakly sensitive to thermal fluctuations of the phonons. By giving another example of a simple two-ion gate that can generate entanglement but is strongly vulnerable to thermal fluctuations, we show that our simplified MS gate is more marked than usual.