Reviewerly/exHarmony huggingface dataset:
Parameter Justification of a Signal Recognition Algorithm Based on Detection at Two Intermediate Frequencies

Introduction. The signal recognition task for the purposes of RF spectrum management can be solved using a signal recognition algorithm with detection at two intermediate frequencies. This algorithm is based on time–frequency analysis using fast Fourier transform (FFT) and signal envelope processing. Due to the relative simplicity of transformations, this algorithm is implemented on commercially available field programmable gate arrays and allows processing received signals in near real-time. However, the justification of the algorithm parameters providing effective signal recognition by the criterion of minimizing the signal-to-noise ratio (SNR) has not performed so far. Aim. Justification of parameters of the developed signal recognition algorithm, providing the minimum required SNR at the algorithm input. Materials and methods. The efficiency of the developed algorithm was estimated by computer simulation in the MATLAB environment. Results. The influence of the parameters of functional blocks and received signals on the efficiency of the developed algorithm was investigated. For chirp, simple pulse, binary, and quadrature phase shift keying signals, the following parameters are recommended: a pulse duration of 5…20 μs; a chirp rate of 0.8…24 MHz/μs; a code duration of 0.5…1 μs. For these signal parameters, the parameters of the algorithm ensuring its efficiency according to the given criterion are as follows: the number of FFT points equals 1024; the Hamming weight window; bandwidths of band-pass filters are 4 MHz; signal envelope amplitude averaging coefficient equals 0.15…0.25. Conclusion. The algorithm with the scientifically valid parameters can be used for recognition of signals at the input minimum SNR for the given types and parameters of signals.
Large-field Astronomical Image Restoration and Superresolution Reconstruction using Deep Learning

Abstract The existing astronomical image restoration and superresolution reconstruction methods have problems such as low efficiency and poor results when dealing with images possessing large fields of view. Furthermore, these methods typically only handle fixed-size images and require step-by-step processing, which is inconvenient. In this paper, a neural network called Res&amp;RecNet is proposed for the restoration and superresolution reconstruction of astronomical images with large fields of view for direct imaging instruments. This network performs feature extraction, feature correction, and progressive generation to achieve image restoration and superresolution reconstruction. The network is constructed using fully convolutional layers, allowing it to handle images of any size. The network can be trained using small samples and can perform image restoration and superresolution reconstruction in an end-to-end manner, resulting in high efficiency. Experimental results show that the network is highly effective in terms of processing astronomical images with complex scenes, generating image restoration results that improve the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) by 4.69 (dB)/0.073 and superresolution reconstruction results that improve the PSNR and SSIM by 1.97 (dB)/0.077 over those of the best existing algorithms, respectively.
Flexible Global Aggregation and Dynamic Client Selection for Federated Learning in Internet of Vehicles

Federated Learning (FL) enables collaborative and privacy-preserving training of machine learning models within the Internet of Vehicles (IoV) realm. While FL effectively tackles privacy concerns, it also imposes significant resource requirements. In traditional FL, trained models are transmitted to a central server for global aggregation, typically in the cloud. This approach often leads to network congestion and bandwidth limitations when numerous devices communicate with the same server. The need for Flexible Global Aggregation and Dynamic Client Selection in FL for the IoV arises from the inherent characteristics of IoV environments. These include diverse and distributed data sources, varying data quality, and limited communication resources. By employing dynamic client selection, we can prioritize relevant and high-quality data sources, enhancing model accuracy. To address this issue, we propose an FL framework that selects global aggregation nodes dynamically rather than a single fixed aggregator. Flexible global aggregation ensures efficient utilization of limited network resources while accommodating the dynamic nature of IoV data sources. This approach optimizes both model performance and resource allocation, making FL in IoV more effective and adaptable. The selection of the global aggregation node is based on workload and communication speed considerations. Additionally, our framework overcomes the constraints associated with network, computational, and energy resources in the IoV environment by implementing a client selection algorithm that dynamically adjusts participants according to predefined parameters. Our approach surpasses Federated Averaging (FedAvg) and Hierarchical FL (HFL) regarding energy consumption, delay, and accuracy, yielding superior results.
Interactive Transformer for Small Object Detection

The detection of large-scale objects has achieved high accuracy, but due to the low peak signal to noise ratio (PSNR), fewer distinguishing features, and ease of being occluded by the surroundings, the detection of small objects, however, does not enjoy similar success. Endeavor to solve the problem, this paper proposes an attention mechanism based on cross-Key values. Based on the traditional transformer, this paper first improves the feature processing with the convolution module, effectively maintaining the local semantic context in the middle layer, and significantly reducing the number of parameters of the model. Then, to enhance the effectiveness of the attention mask, two Key values are calculated simultaneously along Query and Value by using the method of dual-branch parallel processing, which is used to strengthen the attention acquisition mode and improve the coupling of key information. Finally, focusing on the feature maps of different channels, the multi-head attention mechanism is applied to the channel attention mask to improve the feature utilization effect of the middle layer. By comparing three small object datasets, the plug-and-play interactive transformer (IT-transformer) module designed by us effectively improves the detection results of the baseline.
Notes on Convergence and Modeling for the Extended Kalman Filter

The goal of this work is to provide an understanding of estimation technology for both linear and nonlinear dynamical systems. A critical analysis of both the Kalman filter (KF) and the extended Kalman filter (EKF) will be provided, along with examples to illustrate some important issues related to filtering convergence due to system modeling. A conceptual explanation of the topic with illustrative examples provided in the paper can help the readers capture the essential principles and avoid making mistakes while implementing the algorithms. Adding fictitious process noise to the system model assumed by the filter designers for convergence assurance is being investigated. A comparison of estimation accuracy with linear and nonlinear measurements is made. Parameter identification by the state estimation method through the augmentation of the state vector is also discussed. The intended readers of this article may include researchers, working engineers, or engineering students. This article can serve as a better understanding of the topic as well as a further connection to probability, stochastic process, and system theory. The lesson learned enables the readers to interpret the theory and algorithms appropriately and precisely implement the computer codes that nicely match the estimation algorithms related to the mathematical equations. This is especially helpful for those readers with less experience or background in optimal estimation theory, as it provides a solid foundation for further study on the theory and applications of the topic.
Leveraging Blockchain with Optimal Deep Learning-Based Drug Supply Chain Management for Pharmaceutical Industries

Due to its complexity and involvement of numerous stakeholders, the pharmaceutical supply chain presents many challenges that companies must overcome to deliver necessary medications to patients efficiently. The pharmaceutical supply chain poses different challenging issues, encompasses supply chain visibility, cold-chain shipping, drug counterfeiting, and rising prescription drug prices, which can considerably surge out-of-pocket patient costs. Blockchain (BC) offers the technical base for such a scheme, as it could track legitimate drugs and avoid fake circulation. The designers presented the procedure of BC with fabric for creating a secured drug supply-chain management (DSCM) method. With this motivation, the study presents a new blockchain with optimal deep learning-enabled DSCM and recommendation scheme (BCODL-DSCMRS) for Pharmaceutical Industries. Firstly, Hyperledger fabric is used for DSC management, enabling effective tracking processes in the smart pharmaceutical industry. In addition, a hybrid deep belief network (HDBN) model is used to suggest the best or top-rated medicines to healthcare providers and consumers. The spotted hyena optimizer (SHO) algorithm is used to optimize the performance of the HDBN model. The design of the HSO algorithm for tuning the HDBN model demonstrates the novelty of the work. The presented model is tested on the UCI repository’s open-access drug reviews database.
Automated Video Generation of Moving Digits from Text Using Deep Deconvolutional Generative Adversarial Network

Generating realistic and synthetic video from text is a highly challenging task due to the multitude of issues involved, including digit deformation, noise interference between frames, blurred output, and the need for temporal coherence across frames. In this paper, we propose a novel approach for generating coherent videos of moving digits from textual input using a Deep Deconvolutional Generative Adversarial Network (DD-GAN). The DD-GAN comprises a Deep Deconvolutional Neural Network (DDNN) as a Generator (G) and a modified Deep Convolutional Neural Network (DCNN) as a Discriminator (D) to ensure temporal coherence between adjacent frames. The proposed research involves several steps. First, the input text is fed into a Long Short Term Memory (LSTM) based text encoder and then smoothed using Conditioning Augmentation (CA) techniques to enhance the effectiveness of the Generator (G). Next, using a DDNN to generate video frames by incorporating enhanced text and random noise and modifying a DCNN to act as a Discriminator (D), effectively distinguishing between generated and real videos. This research evaluates the quality of the generated videos using standard metrics like Inception Score (IS), Fréchet Inception Distance (FID), Fréchet Inception Distance for video (FID2vid), and Generative Adversarial Metric (GAM), along with a human study based on realism, coherence, and relevance. By conducting experiments on Single-Digit Bouncing MNIST GIFs (SBMG), Two-Digit Bouncing MNIST GIFs (TBMG), and a custom dataset of essential mathematics videos with related text, this research demonstrates significant improvements in both metrics and human study results, confirming the effectiveness of DD-GAN. This research also took the exciting challenge of generating preschool math videos from text, handling complex structures, digits, and symbols, and achieving successful results. The proposed research demonstrates promising results for generating coherent videos from textual input.
Understanding the black-box: towards interpretable and reliable deep learning models

Deep learning (DL) has revolutionized the field of artificial intelligence by providing sophisticated models across a diverse range of applications, from image and speech recognition to natural language processing and autonomous driving. However, deep learning models are typically black-box models where the reason for predictions is unknown. Consequently, the reliability of the model becomes questionable in many circumstances. Explainable AI (XAI) plays an important role in improving the transparency and interpretability of the model thereby making it more reliable for real-time deployment. To investigate the reliability and truthfulness of DL models, this research develops image classification models using transfer learning mechanism and validates the results using XAI technique. Thus, the contribution of this research is twofold, we employ three pre-trained models VGG16, MobileNetV2 and ResNet50 using multiple transfer learning techniques for a fruit classification task consisting of 131 classes. Next, we inspect the reliability of models, based on these pre-trained networks, by utilizing Local Interpretable Model-Agnostic Explanations, the LIME, a popular XAI technique that generates explanations for the predictions. Experimental results reveal that transfer learning provides optimized results of around 98% accuracy. The classification of the models is validated on different instances using LIME and it was observed that each model predictions are interpretable and understandable as they are based on pertinent image features that are relevant to particular classes. We believe that this research gives an insight for determining how an interpretation can be drawn from a complex AI model such that its accountability and trustworthiness can be increased.
Lightweight Network for Single Image Super-Resolution with Arbitrary Scale Factor

The existing single image super-resolution (SISR) methods that consider integer scale factors (X2, X3, X4, and X8), have been developed well, but SISR methods with arbitrary scale factors (X1.3, X2.5, and X3.7) have gradually gained attention recently. Therefore, we proposed an efficient, lightweight model. In this study, there are two contributions as follows. (1) An efficient and lightweight network for SISR is combined with the up-scaled module, which determines its weights based on the size of the high-resolution (HR) image. (2) All scale factors are applied simultaneously using one model, which saves more storage and computational resources. Finally, we design various experiments to evaluate the proposed method based on multiple general datasets. The experimental results show that the proposed model is lightweight while the performance is relatively competitive.
Innovative Recall Farm Situation with Sharing Experiential Learning by VR Technology

In the early days of southern Taiwan, agriculture was important for living. Farmers used various agricultural implements for the cultivation and planting of rice, vegetables, and fruits for food and clothing. With the progress of the social environment, they gradually forgot the traditional agricultural implements that they depended on for a living. Recently, the government has promoted agricultural experience activities, and citizens’ health concept has been improved. Growing vegetables and fruits on farms leads to the concept of shared experience. In particular, technological equipment has become popular. For leisure activities, the public has gradually accepted novel technologies (such as virtual reality games) and integrated them into their usual leisure and entertainment. For this reason, we investigate the degree of virtual interaction with the recall farm as a development basis and use the experiential learning theory to encourage the Silvers to learn the innovative recall farm situation to bring about the effect of leisure and entertainment. The results of this study show that the Silvers experience early agricultural situations and farming implements for entertainment purposes and listen to the agricultural stories to reminisce.
The effects of extensive reading on child learners’ English book reading ability: Reading motivation as predictor variables

The current study aimed to investigate the impact of extensive reading (ER) on reading comprehension in child second language learners. Specifically, the study examined changes in learners' comprehension of English reading materials after participating in a 40-week ER program. Additionally, the study explored whether the learners' reading motivation in their first language (L1) and second language (L2) contributed to these changes in comprehension. A total of 61 child English L2 learners took part in the 40-week research project. During this period, participants visited the English library daily (for 200 days) and read graded English fiction/non-fiction books of their choice. Their comprehension of the reading materials was assessed through daily comprehension tests, while their L1/L2 reading motivation was measured using a questionnaire. Latent growth modeling was utilized to assess learners’ development of English book reading ability across three distinct time intervals: Week 1, Week 20, and Week 40. Additionally, the study explored whether learners’ L1/L2 reading motivation could predict these changes. The results found a significant development of learners’ English book reading ability throughout the 40-week period. Furthermore, it was observed that learners’ L1 motivation significantly predicted their progress in reading ability, indicating that child learners with higher L1 reading motivation develop their English book reading ability more efficiently than those with lower counterparts. However, L2 reading motivation did not emerge as a significant predictor affecting the child learners’ reading ability. The findings are discussed in light of previous research and offer evidence-based pedagogical implications relevant to the elementary level.
Performance Analysis of Probabilistic Roadmaps in Simulation for Mobile Robot Path Planning

Many challenges, including path-planning for a mobile robot, revolve around finding the lowest-cost path through a graph. A quick path-planning algorithm for a mobile robot in a dynamic environment is proposed by integrating Dijkstra's algorithm. Dijkstra's method and Probabilistic Roadmaps are used to determine an initial path from the starting point to the goal. This method searches for an optimal path from the robot's current location to this local target, and as the number of nodes increases, a new path that brings the robot from the current place to the goal is obtained. Meanwhile, the re-planning time is substantially reduced. The simulation results validate the suggested algorithm's feasibility and effectiveness.
The Internet of Things, Its Risks, and Ways to Address It

The internet is a vast, interconnected network of computers and other network-enabled devices, which is: Globally available ,Easy to use, Compatible with other types of media, Affordable and Flexible .The term IoT, or Internet of Things, refers to the collective network of connected devices and the technology that facilitates communication between devices and the cloud, as well as between the devices themselves. Thanks to the advent of inexpensive computer chips and high bandwidth telecommunication, we now have billions of devices connected to the internet
Peningkatan Keterampilan Teknologi Guru TK Melalui Pelatihan Microsoft Word

This community service initiative is designed with the primary aim of providing training in the use of Microsoft Word to Kindergarten (TK) teachers in an effort to enhance their technological skills. The workshop aims to offer support to TK teachers in developing their competence in operating this word-processing software, which plays a crucial role in the modern education landscape. The background of this community service initiative reflects the educational transformation that has taken place in recent years. In this digital era, information technology and communication play an increasingly significant role in the learning process. Therefore, TK teachers need to have a strong understanding of relevant digital tools, with Microsoft Word being one of them. This workshop serves as a platform for TK teachers to comprehend and master various features and functions of Microsoft Word, ranging from basic to advanced levels. The importance of this workshop is also reflected in its potential positive impact. With TK teachers becoming more proficient in using Microsoft Word, they will be able to create more engaging, interactive, and relevant learning materials for children in today's digital age. This will enhance the learning experiences of TK students and provide a stronger foundation for their digital literacy development in the future. Furthermore, the workshop creates opportunities for TK teachers to share knowledge and experiences, strengthening collaborative networks among them. By sharing best practices in the use of Microsoft Word, they can support each other in their efforts to improve the quality of early childhood education. The workshop is expected to result in a significant improvement in the technological skills of TK teachers and, in turn, enrich the learning experiences of their students.
Development of Soft Skills for a Successful Career and Advance Education

In today's environment of rapid change, soft skills are becoming increasingly important. This essay examines how students develop soft skills, with a particular emphasis on teamwork, communication, problem-solving, adaptability, and emotional intelligence. These abilities are essential for pupils to succeed in both the classroom and in real-world situations. We begin by underlining the significance of soft skills in the context of contemporary education. These abilities help pupils overcome obstacles because they complement academic knowledge. We go over the numerous aspects that affect the development of soft skills, such as the role of schools, instructional strategies, family engagement, and socioeconomic backgrounds. To develop these abilities, cooperation between communities, families, and schools is crucial. The advantages of including soft skill development in the educational system are also emphasized in the article. Better interpersonal relationships, improved self-esteem, and a stronger desire for lifelong learning are all characteristics of students who excel in soft skills. Additionally, these abilities improve employability in a demanding employment market. We suggest a thorough framework for assessing and enhancing students' soft skills, placing special emphasis on formative assessment procedures that deliver rapid feedback. To aid in the development of skills, we also suggest adding role-playing, experiential learning, and group projects. In order to provide educators with the resources necessary for the development of soft skills, teacher training programmers are crucial. The digital divide must be closed, and equal access to resources for pupils from different backgrounds must be made a priority. In conclusion, it is critical to have a comprehensive approach to education that prioritizes both academic success and soft skills. To encourage the development of soft skills in school pupils and better prepare them for the complex problems of the 21st century, we support continuing research, policy creation, and collaboration among educational stakeholders.
English Language Skills and their Significance in Efficiency of Performance Workplace

The significance of effective English communication skills in the professional realm cannot be overstated, as they have been recognized as essential instruments for achieving triumph in the business domain. The proficiency in the English language is widely regarded as an essential prerequisite qualification for gainful employment. English has long been regarded as a gateway to securing a lucrative and stable position within the public sector, ensuring a comfortable retirement. The acquisition of proficiency in the English language is widely regarded as a fundamental component of one's employability repertoire.