Reviewerly/exHarmony huggingface dataset:
An Empirical Examination of Formation of Trust in Blockchain

Blockchain’s subversive potential challenges the traditional paradigm by centering around a complex equation that embodies the fundamental principle of trust. In this study, a model known as the Chain of Trust is proposed to uncover the embedded trust within this equation. Through a web-based self-administered survey involving 258 users, the research examines users’ willingness to trust and the factors contributing to trust formation in blockchain, thereby establishing a theoretical foundation for understanding the role of blockchain features and characteristics in shaping trust. The Chain of Trust Model serves as a comprehensive framework dedicated to enhancing trust in the disruptive realm of blockchain technology. It sheds light on the intricate relationship between trust and blockchain, offering valuable insights for the development of more dependable and efficient blockchain systems. This study introduces the innovative Chain of Trust model, adeptly demonstrating the intricate connections between various dimensions of blockchain and the establishment of trust. It emphasizes the criticality of comprehending these dimensions and characteristics to elucidate the phenomenon of trust formation within the blockchain ecosystem. Additionally, this research provides organizations with practical implications that underscore the importance of considering identified limitations and caveats, ultimately urging further investigation in this domain.
Security Analysis of Hybrid Attack for NTRU-Class Encryption Schemes

One of the significant post-quantum cryptographic candidates is the NTRU public key cryptosystem. It operates on polynomial rings, where the parameter largely determines the security of the system. Although NTRU is being studied currently, it has a long and well-established history. There are several lattice-based attacks on NTRU-like systems that exploit the special structures of the rings used in these systems. The aim of this paper is to analyze the original NTRU, NTRU Encrypt, and NTRU Primes encryption schemes by structuring their common elements and showing the strongest hybrid attack using both lattice reduction and meet-in-the-middle (MITM) search on them. Furthermore, it is noted that, ignoring a polynomial factor of the not-well-studied cost of Block Korkin-Zolotarev (BKZ) algorithm, we estimate the security of the construction of encryption keys and show that by balancing lattice reduction costs and a MITM search cost, one can achieve better performance than using any of these methods on their own. Unlike previous studies, we found the way to ignore polynomial impact 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> from BKZ loops with multiple shortest vector problem (SVP) and the factor of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> was omitted from the cost of one step in guessing the SVP.
Efficient and Robust Time-Optimal Trajectory Planning and Control for Agile Quadrotor Flight

Agile quadrotor flight relies on rapidly planning and accurately tracking time-optimal trajectories, a technology critical to their application in the wild. However, the computational burden of computing time-optimal trajectories based on the full quadrotor dynamics (typically on the order of minutes or even hours) can hinder its ability to respond quickly to changing scenarios. Additionally, modeling errors and external disturbances can lead to deviations from the desired trajectory during tracking in real time. This letter proposes a novel approach to computing time-optimal trajectories, by fixing the nodes with waypoint constraints and adopting separate sampling intervals for trajectories between waypoints, which significantly accelerates trajectory planning. Furthermore, the planned paths are tracked via a time-adaptive model predictive control scheme whose allocated tracking time can be adaptively adjusted on-the-fly, therefore enhancing the tracking accuracy and robustness. We evaluate our approach through simulations and experimentally validate its performance in dynamic waypoint scenarios for time-optimal trajectory replanning and trajectory tracking.
YOLO-Class: Detection and Classification of Aircraft Targets in Satellite Remote Sensing Images Based on YOLO-Extract

With the continuous advancement of remote sensing technology, satellite remote sensing images have become one of the important means of obtaining information on the earth surface. But in the current research on aircraft target detection and classification in remote sensing images, the imbalanced data samples, large variations in target scales and backgrounds, and target occlusion have led to low average precision and slow detection speed in detection and classification tasks. Therefore, this paper proposes the YOLO-class model. Firstly, the YOLO-Extract model is transferred to optimize the detection of small targets, dense targets, and occluded targets. Secondly, Representative Batch Normalization and Mish activation function are used to optimize the Conv module, and VariFocal loss is used to optimize the classification loss function to improve the accuracy caused by imbalanced data samples. Finally, RepVGG modules are designed in the Backbone to further improve the detection accuracy of the model. Simulation results show that compared with the YOLO-Extract model, YOLO-class improves the detection accuracy from 0.608 to 0.704 and FPS from 36.16 to 39.598.
UNet Based Multi-Scale Recurrent Network for Lightweight Video Deblurring

With the recent widespread use of smartphones and digital video cameras, the opportunities to handle digital video have increased significantly. However, despite improvements in the performance of the hardware, the captured video often contains information that is not necessary for the purpose of the video. In particular, factors such as camera shake and object movement can cause blurring in video. So, we propose a method to deblur a video by software processing of the video after shooting. In conventional methods, video deblurring is often performed using a network whose main task is video super-resolution. However, in super-resolution, the size of the input and output images are different, while the input and output images are the same size in deblurring. In the case of deblurring, the input images are input to the network after a simple downsampling process, which is not optimized for the same size as the input and output images. Therefore, the proposed method constructs a multi-scale network based on UNet. The UNet-based network is a successful method for single image deblurring. Because a video is a sequence of multiple images, we use a method that has been successful in single image deblurring. Furthermore, we add improvements to the network based on the structure of MPRNet. The feature extraction modules of the bottom and the second stage of the network are replaced with a single-stage UNet. These improvements resulted in a 34.80dB in PSNR and 0.973 in SSIM on the GoPro dataset despite about 75% of the FLOPs of BasicVSR++ and 3% of the FLOPs of VRT. On the DVD dataset, the proposed model achieved 34.36dB in PSNR and 0.966 in SSIM. Further ablation studies show the effectiveness of various components in our proposed model.
A Deep Learning-Based National Digital Literacy Assessment Framework Utilizing Mobile Big Data and Survey Data

With the rapid advancement of digital technology, artificial intelligence has ushered in a digital society. In this era, digital literacy has become a prerequisite for individuals, as its absence can lead to new vulnerabilities and inequalities, hindering the pursuit of sustainable development goals. Previous researches predominantly relied on questionnaires to assess digital literacy, often focusing on specific groups due to survey costs, making their methodology unsuitable for comprehensive countrywide measurement. To address these limitations, we propose FLAKE, a national digital literacy assessment framework. Within this framework, we devise a multi-task deep learning model called DLMaN, which employs mobile big data, such as users’ digital behaviors, to predict citizens’ digital literacy. FLAKE enables cost-effective assessment of digital literacy for massive citizens by surveying only a fraction of them and it also has valuable implications for other social research tasks. We test the framework’s performance using authentic survey data and mobile big data, achieving RMSE and MAPE of 5.233 and 8.65% respectively, and the improvement is significant compared to the baseline model. We further employ this model to assess the digital literacy of numerous citizens in China and explore the implications for the society and individuals based on the obtained results.
Diagnostic Methodology for Surface Defects in Curved Automotive Radomes Using a Phase Correlation Matching Process

In this paper, a new class of diagnostic methodology based on reconstruction phase deviation is proposed. Using Fourier Optics and the Tikhonov method, the original wavefront distribution fields are reconstructed on the surface of real-life automotive radomes. During the phase deviation reconstruction, wrapped phase errors significantly degrade the quality of the reconstructed field. To mitigate the effect of wrapped phase errors, the Phase Correlation Matching (PCM) process is proposed. The simulation results of the diagnostic methodology demonstrate that the PCM process effectively identifies dielectric defect regions on curved surfaces. In addition, both simulation and experimental diagnostics scenarios are conducted to validate the proposed methodology. The scenarios are structured based on various parameters, such as dielectric size, source directivity and the radius of curvature for both homogeneous and inhomogeneous radome covers. As a result, the proposed diagnostic methodology achieves an accuracy of over 90% in localizing defects such as dielectric patches and protrusions on the radome surface. Finally, the proposed method exhibits higher computational efficiency compared with previously reported diagnostic methods. Furthermore, the proposed algorithm presents a potential solution for quality control procedures on automotive assembly lines and real-life radome applications.
Blind Motion Deblurring With Pixel-Wise Kernel Estimation via Kernel Prediction Networks

In recent years, the removal of motion blur in photographs has seen impressive progress in the hands of deep learning-based methods, trained to map directly from blurry to sharp images. For this reason, approaches that explicitly use a forward degradation model received significantly less attention. However, a well-defined specification of the blur genesis, as an intermediate step, promotes the generalization and explainability of the method. Towards this goal, we propose a learning-based motion deblurring method based on dense non-uniform motion blur estimation followed by a non-blind deconvolution approach. Specifically, given a blurry image, a first network estimates the dense per-pixel motion blur kernels using a lightweight representation composed of a set of image-adaptive basis motion kernels and the corresponding mixing coefficients. Then, a second network trained jointly with the first one, unrolls a non-blind deconvolution method using the motion kernel field estimated by the first network. The model-driven aspect is further promoted by training the networks on sharp/blurry pairs synthesized according to a convolution-based, non-uniform motion blur degradation model. Qualitative and quantitative evaluation shows that the kernel prediction network produces accurate motion blur estimates, and that the deblurring pipeline leads to restorations of real blurred images that are competitive or superior to those obtained with existing end-to-end deep learning-based methods. Code and trained models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/GuillermoCarbajal/J-MKPD/</uri> .
On the Optimality, Stability, and Feasibility of Control Barrier Functions: An Adaptive Learning-Based Approach

Safety has been a critical issue for the deployment of learning-based approaches in real-world applications. To address this issue, control barrier function (CBF) and its variants have attracted extensive attention for safety-critical control. However, due to the myopic one-step nature of CBF and the lack of principled methods to design the class- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {K}$</tex-math></inline-formula> functions, there are still fundamental limitations of current CBFs: optimality, stability, and feasibility. In this letter, we proposed a novel and unified approach to address these limitations with Adaptive Multi-step Control Barrier Function (AM-CBF), where we parameterize the class- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {K}$</tex-math></inline-formula> function by a neural network and train it together with the reinforcement learning policy. Moreover, to mitigate the myopic nature, we propose a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-step training and single-step execution</i> paradigm to make CBF farsighted while the execution remains solving a single-step convex quadratic program. Our method is evaluated on the first and second-order systems in various scenarios, where our approach outperforms the conventional CBF both qualitatively and quantitatively.
Design and Implementation of Deep Learning 2D Convolutions on Modern CPUs

In this paper, a new method is provided for accelerating the execution of convolution layers in Deep Neural Networks. This research work provides the theoretical background to efficiently design and implement the convolution layers on x86/x64 CPUs, based on the target layer parameters, quantization level and hardware architecture. The proposed work is general and can be applied to other processor families too, e.g., Arm. The proposed work achieves high speedup values over the state of the art, which is Intel oneDNN library, by applying compiler optimizations, such as vectorization, register blocking and loop tiling, in a more efficient way. This is achieved by developing an analytical modelling approach for finding the optimization parameters. A thorough experimental evaluation has been applied on two Intel CPU platforms, for DenseNet-121, ResNet-50 and SqueezeNet (including 112 different convolution layers), and for both FP32 and int8 input/output tensors (quantization). The experimental results show that the convolution layers of the aforementioned models are executed from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\times 1.1$</tex-math></inline-formula> up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\times 7.2$</tex-math></inline-formula> times faster.
FloreView: An Image and Video Dataset for Forensic Analysis

Linking a digital image or video to its originating device, or checking the content integrity still represent challenging forensic tasks.Even though several technologies based on metadata, file format, and sensor noise have been developed to address these problems, current methods are frequently made obsolete by new customized acquisition pipelines implemented by manufacturers.Therefore, to assess the performance of the available tools and push the research activity, researchers continuously need new datasets containing contents captured with recent technologies.In this paper, we present a new image and video dataset for forensic analysis.Data, acquired by the most recent acquisition devices, were collected under strictly controlled procedures designed to limit the bias induced by differences in the acquisition process between different devices.The dataset includes over 9000 media contents captured by 46 smartphones of 11 major brands.For each device, we collected at least 100 unique natural images, 30 unique natural videos, 30 flat images, and 4 flat videos.Great care has been taken in collecting data that can be used for multiple forensic tasks; moreover, images and videos have been carefully organized so that FloreView could be used by the community immediately and effortlessly.Finally, two case studies related to image source identification and video brand identification have been performed, using state-of-the-art methods, to show how the proposed dataset can be effectively used for forensic tasks.
Blockchain and IoE for Smart Power Grid 2.0 - A Systematic Review

Blockchain is a distributed decentralized peer-to-peer network that is used to securely and trustworthily share data across a large number of entities. For data verification and validation, blockchain employs several processes like as hash functions, consensus algorithms, and so on. Blockchain technology is utilized in modern power systems to balance supply and demand, contribute to demand-side management programs, and, most importantly, transition consumers to prosumers to trade electricity and cut operational costs. The energy sector is the most typical use for power systems. When blockchain technology is employed in the energy sector, the phrase energy blockchain is used. This article aims to provide a broad perspective on the use of blockchain technology in power systems, clarifying some technical aspects of this promising technology, as well as the features and applications that have been developed thus far, while focusing on the future of innovative applications in the electrical energy sector.
Reversible Data Hiding of Grayscale Images Using General Distortion Metric

In reversible data hiding (RDH), the use of general distortion metric for improvement of image visual quality is a new issue. In this letter, a new RDH method of grayscale images using general distortion metric is proposed. The proposed method can adaptively compute different distortions for different pixels by considering the influence of surrounding pixels. Due to the fact that the pixel distortion in smooth areas is more obvious than that in texture areas under the same modification, the modifications on pixels are adaptively adjusted by combining a minimum distortion coding to achieve better visual quality. Experimental results show that the proposed method can reduce modifications on smooth areas and achieve better embedding performance for grayscale images in comparison with several classical and advanced RDH methods
A Real-World Dataset Generator for Specific Emitter Identification

Generating high-quality, real-world, well-labeled datasets for radio frequency machine learning (RFML) applications often proves prohibitively cumbersome and expensive, leading to the lowavailability of high-fidelity, low-cost datasets. Specific emitter identification (SEI) in particular requires a hardware setup capable of supporting transmitting using many different radios, while automated modulation classification (AMC) performance is primarily driven by SNR, channel effects, and the similarity of modulation types. These factors give rise to the need for scalable methods of inexpensive dataset generation. This paper describes the design considerations and a proof-of-concept implementation of a blind user reconfigurable platform capable of creating SEI and AMC datasets throughout a variety of real-world conditions. This paper additionally describes the reliability and performance of the platform relative to existing real-world data generation methods and compares generated datasets to those already present in the literature. This work also describes the software post-processing steps taken to isolate, label, and cull captured data and transform these into a high-quality dataset.
Advancements and Perspectives in Machine Translation: A Comprehensive Review

Machine translation is discussed in this review article, emphasizing its importance in facilitating multilingual communication. The article examines the evolution of machine translation from rule-based approaches to neural machine translation. In this paper, recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer models are explored, and their impact on translation performance is explored. It explains how attention mechanisms improve translation quality in machine translation. Furthermore, the article discusses how transfer learning techniques and multilingual training can improve translation accuracy and efficiency. In addition, it introduces the concept of multimodal machine translation, which combines textual and visual information to improve translation accuracy. This review highlights the need for better metrics for measuring machine translation quality and assessing evaluation metrics. Various advances in this field, as well as emerging research directions, are discussed, along with implications and future prospects. Finally, the article summarizes the key findings and suggests areas for further research.
Подготовка Специалистов в Высших Учебных Заведениях России Сегодня: Анализ и Перспективы. (Specialist's Degree in Higher Education Institutions in Russia as of Today: Analyses and Prospects.)

Abstract: This article analyses the system of higher education in Russia in view of its withdrawal from the Bologna process. Both positive and negative aspects of participation in international educational standards are assessed. Withdrawal from the Bologna Process has provided Russia with an opportunity to develop its own educational programme, taking into account national needs and global challenges. The prospects open to Russia in this context are discussed, including more flexible and individualised education and the preservation of cultural characteristics in education. The importance of universities adapting to new challenges and providing high quality education to prepare qualified specialists for the modern world is also emphasised.
Validitas Lembar Kerja Peserta Didik (Lkpd) Berbasis Discovery Learning Pada Materi Sistem Peredaran Darah Untuk Peserta Didik Kelas Viii SMP/MTs

This study aims to develop discovery learning-based worksheets on the circulatory system material that is suitable for use in learning activities for class VIII students. Discovery learning is a learning model in which students can play an active role in the learning process and are able to find concepts from learning materials. This research includes the type of research and development (Research and Development). The research procedure used in the development of discovery learning-based worksheets uses a 4-D model from Thiagarajan, Semmel and Semmel, namely 4 stages of development, namely define (defining stage), design (planning stage), develope (development stage), and disseminate (dissemination stage). ). Given the limitations of time and cost, this research is limited to only being carried out until the develop stage. So it can be concluded that the development of discovery learning-based worksheets on the circulatory system material for use in class VIII SMP/MTs students that was developed is very valid to be used later in learning