Reviewerly/exHarmony huggingface dataset:
A Compact TRNG Design for FPGA Based on the Metastability of RO-driven Shift Registers

True random number generators (TRNGs), as an important component of security systems, have received a lot of attention for their related research. The previous researches have provided a large number of TRNG solutions, however, they still failed to reach an excellent tradeoff in various performance metrics. This article presents a shift-registers metastability-based TRNG, which is implemented by compact reference units and comparison units. By forcing the D flip-flops in the shift-registers into the metastable state, it optimizes the problem that the conventional metastability entropy sources consume excessive hardware resources. And a new method of metastable randomness extraction is used to reduce the bias of metastable output. The proposed TRNG is implemented in Xilinx Spartan-6 and Virtex-6 FPGAs, which generate random sequences that pass the NIST SP800-22, NIST SP800-90B tests and show excellent robustness to voltage and temperature variations. This TRNG can consume only 3 slices of the FPGA, but it has a high throughput rate of 25 Mbit/s. In comparison with state-of-the-art FPGA-compatible TRNGs, the proposed TRNG achieves the highest figure of merit FOM, which means that the proposed TRNG significantly outperforms previous researches in terms of hardware resources, throughput rate, and operating frequency tradeoffs.
Mechanisms for managing the processes of digital transformation of the school: development of digital competencies of teachers

Problem statement. The results of a pilot project on the introduction of a structural-functional model for managing the process of developing digital competencies of teachers as an intra-school system of advanced training with a focus on professional deficits in the field of digital competencies of teachers and target methodological support are discussed. The goal of the project is to increase the level of digital competencies of teachers to modernize the educational process of a modern school, change pedagogical practices and learning formats. The project is intended for secondary schools implementing digital transformation scenarios. Methodology. The study of the deficits of digital competencies of teachers was carried out on the basis of SAMR tools and monitoring of digital transformation, the analysis of educational resources of digital platforms, learning formats for building programs for the development of digital competencies. Results. The obtained empirical data on the approbation of management technologies based on data is discussed. The classification of the resources necessary for the project implementation is given, the key performance indicators are determined, the mechanisms for the implementation of the project are shown. Conclusion. Practical experience in modeling the processes of managing the development of digital competencies of teachers can be used by the heads of educational organizations, education authorities to design digital transformation projects and programs, expand the practice of using digital tools by teachers, and accelerate the dynamics of digitalization processes in educational organizations.
Remote sensing image recognition based on YOLOv5

Currently, remote sensing technology is developing rapidly, and many satellites and airborne sensors can provide high spatial resolution optical images, especially visible light images, which can provide the most intuitive and clear information of the earth's surface. Therefore, how to effectively process large-scale data information and obtain better detection results has become an important challenge in remote sensing image target detection research. This paper proposes an improvement to the target detection algorithm based on YOLOv5. In the input stage, dynamic anchor boxes are introduced to improve the instability of generating anchor boxes based on K-Means clustering, thus reducing model loss and convergence difficulties. In the backbone network stage, the recognition accuracy of small targets and cohesive targets with mutual occlusion is improved by introducing attention mechanisms.
Quantum Adversarial Transfer Learning

Adversarial transfer learning is a machine learning method that employs an adversarial training process to learn the datasets of different domains. Recently, this method has attracted attention because it can efficiently decouple the requirements of tasks from insufficient target data. In this study, we introduce the notion of quantum adversarial transfer learning, where data are completely encoded by quantum states. A measurement-based judgment of the data label and a quantum subroutine to compute the gradients are discussed in detail. We also prove that our proposal has an exponential advantage over its classical counterparts in terms of computing resources such as the gate number of the circuits and the size of the storage required for the generated data. Finally, numerical experiments demonstrate that our model can be successfully trained, achieving high accuracy on certain datasets.
One-step implementation of multiqubit controlled–controlled-Z gates with Rydberg atoms

Abstract In this paper, we propose a one-step scheme for generating a multiqubit controlled–controlled- Z ( CCZ ) gate based on Rydberg atoms where an amplitude-modulated field is employed to induce Rydberg antiblockade. The Rydberg atoms can form a Rabi oscillation between the ground state and the collective excited state effectively, so a one-step three-qubit CCZ gate can be easily achieved through a Rabi cycle. Numerical simulation results show that the scheme has a high fidelity and robustness against errors which are caused by parameter errors, intrinsic errors, and atomic decays. Therefore, we hope that this scheme will enable fast and robust quantum computing in the near future.
Structure‐Aware Image Translation‐Based Long Future Prediction for Enhancement of Ground Robotic Vehicle Teleoperation

Predicting future frames through image‐to‐image translation and using these synthetically generated frames for high‐speed ground vehicle teleoperation is a new concept to address latency and enhance operational performance. In the immediate previous work, the image quality of the predicted frames was low and a lot of scene detail was lost. To preserve the structural details of objects and improve overall image quality in the predicted frames, several novel ideas are proposed herein. A filter has been designed to remove noise from dense optical flow components resulting from frame rate inconsistencies. The Pix2Pix base network has been modified and a structure‐aware SSIM‐based perpetual loss function has been implemented. A new dataset of 20 000 training input images and 2000 test input images with a 500 ms delay between the target and input frames has been created. Without any additional video transformation steps, the proposed improved model achieved PSNR of 23.1; SSIM of 0.65; and MS‐SSIM of 0.80, a substantial improvement over our previous work. A Fleiss’ kappa score of &gt;0.40 (0.48 for the modified network and 0.46 for the perpetual loss function) proves the reliability of the model.
A Bayesian Formulation for Estimating the Composition of Earth's Crust

Abstract Due to the inaccessibility of Earth's deep interior, geologists have long attempted to estimate the composition of the continental crust from its seismic properties. Despite numerous sources of error including nonuniqueness in the mapping between composition and seismic properties, the corresponding uncertainties have typically been estimated qualitatively at best. We propose a Bayesian approach that uses mineralogical modeling to combine prior knowledge about the composition of the crust with seismic data to give a posterior distribution of the predicted composition at any location, combined with a Monte Carlo simulation to estimate the average composition of the Earth's crust. Our approach yields an estimated composition of 59.5% silica in the upper crust (90% credible interval 58.9 %–60.1%), 57.9% in the middle crust (90% credible interval 57.2%–58.6%), and 53.6% in the lower crust (90% credible interval 53.0%–54.2%). Our estimate exhibits less compositional stratification over depth and a more intermediate composition in the upper and middle crust than previous estimates. Testing our approach on a simulated crust reveals the importance of prior assumptions in estimating the composition of the crust from its seismic properties, and suggests that future work should focus on quantifying those assumptions.
A Context Semantic Auxiliary Network for Image Captioning

Image captioning is a challenging task, which generates a sentence for a given image. The earlier captioning methods mainly decode the visual features to generate caption sentences for the image. However, the visual features lack the context semantic information which is vital for generating an accurate caption sentence. To address this problem, this paper first proposes the Attention-Aware (AA) mechanism which can filter out erroneous or irrelevant context semantic information. And then, AA is utilized to constitute a Context Semantic Auxiliary Network (CSAN), which can capture the effective context semantic information to regenerate or polish the image caption. Moreover, AA can capture the visual feature information needed to generate a caption. Experimental results show that our proposed CSAN outperforms the compared image captioning methods on MS COCO “Karpathy” offline test split and the official online testing server.
Examining the role of knowledge sharing among stakeholders and firm innovation performance: Moderating role of technology usage

Abstract Knowledge sharing is a typical activity of using different ways to share ideas, skills, expertise, and opinions among friends, family members, peers, communities, and employees. Knowledge can be shared with a firm's internal and external stakeholders, and it can improve process efficiency as well as product quality. Not many studies have examined the influence of knowledge sharing among different stakeholders of a firm and its impact on a firm's innovative performance. Also, studies that understand the role of modern technology usage in firm innovation performance are scant. Using stakeholder theory, other views, and existing literature, we have developed a theoretical model that was validated using the PLS‐SEM technique to analyze 341 respondents from different firms in India. The study demonstrates that knowledge sharing has a significant impact among stakeholders on improving firm innovation performance. The study also finds that stakeholder usage of modern technology has a significant moderating impact on the relationship between process innovation, product innovation, and innovation performance of firms.
Not-So-Low Hanging Fruit

The IETF is actively developing technical specifications for state-of-the-art cryptographic protocols that aim to improve user privacy in the areas of privacy-preserving measurement, anti-fraud systems, and related applications. The success of these protocols requires review and analysis from experts familiar with the underlying cryptographic concepts or problem areas. This paper presents research opportunities for work in this area that would help advance standardization and deployment of these technologies.
An advanced YOLOv3 method for small object detection

Small object detection has important application value in the fields of autonomous driving and drone scene analysis. As one of the most advanced object detection algorithms, YOLOv3 suffers some challenges when detecting small objects, such as the problem of detection failure of small objects and occluded objects. To solve these problems, an improved YOLOv3 algorithm for small object detection is proposed. In the proposed method, the dilated convolutions mish (DCM) module is introduced into the backbone network of YOLOv3 to improve the feature expression ability by fusing the feature maps of different receptive fields. In the neck network of YOLOv3, the convolutional block attention module (CBAM) and multi-scale fusion module are introduced to select the important information for small object detection in the shallow network, suppress the uncritical information, and use the fusion module to fuse the feature maps of different scales, so as to improve the detection accuracy of the algorithm. In addition, the Soft-NMS and Complete-IOU (ClOU) strategies are applied to candidate frame screening, which improves the accuracy of the algorithm for the detection of occluded objects. The experimental results on MS COCO2017, VOC2007, VOC2012 datasets and the ablation experiments on MS COCO2017 datasets demonstrate the effectiveness of the proposed method.The experimental results show that the proposed method achieves better accuracy in small object detection than the original YOLOv3 model.
Neural Architectures for Feature Embedding in Person Re-Identification: A Comparative View

Solving Person Re-Identification (Re-Id) through Deep Convolutional Neural Networks is a daunting challenge due to the small size and variety of the training data, especially in Single-Shot Re-Id, where only two images per person are available. The lack of training data causes the overfitting of the deep neural models, leading to degenerated performance. This article explores a wide assortment of neural architectures that have been commonly used for object classification and analyzes their suitability in a Re-Id model. These architectures have been trained through a Triplet Model and evaluated over two challenging Single-Shot Re-Id datasets, PRID2011 and CUHK. This comparative study is aimed at obtaining the best-performing architectures and some concluding guidance to optimize the features embedding for the Re-Identification task. The obtained results present Inception-ResNet and DenseNet as potentially useful models, especially when compared with other methods, specifically designed for solving Re-Id.
Multi‐label feature selection based on relative entropy and fuzzy neighborhood mutual discrimination index

Abstract Multi‐label feature selection eliminates irrelevant and redundant features, and then improves the performance of multi‐label classification models. Most multi‐label feature selection algorithms assume that the training set contains logical labels, which means that labels are equally important for instances. However, in practical applications, there are different importances with respect to labels. To solve the problem, a multi‐label feature selection method based on relative entropy and fuzzy neighborhood mutual discriminant index is proposed. Firstly, logical labels are converted to label distribution through label enhancement. Secondly, the neighborhood and relative entropy are introduced into the label distribution, the label neighborhood similarity matrix is constructed to describe the similarity of samples under label space. Finally, the fuzzy neighborhood mutual discrimination index is used to combine the candidate features with the label neighborhood similarity matrix, which is used to judge the distinguishing ability of the candidate features. Comprehensive experiment of eight multi‐label datasets shows that the proposed algorithm has better classification performance than other compared algorithms.
Generative adversarial network based data augmentation and quantum based convolution neural network for the classification of Indian classical dance forms

The study addresses the challenges of human action recognition and analysis in computer vision, with a focus on classifying Indian dance forms. The complexity of these dance styles, including variations in body postures and hand gestures, makes classification difficult. Deep learning models require large datasets for good performance, so standard data augmentation techniques are used to increase model generalizability. The study proposes the Indian Classical Dance Generative Adversarial Network (ICD-GAN) for augmentation and the quantum-based Convolutional Neural Network (QCNN) for classification. The research consists of three phases: traditional augmentation, GAN-based augmentation, and a combination of both. The proposed QCNN is introduced to reduce computational time. Different GAN variants DC-GAN, CGAN, MFCGAN are employed for augmentation, while transfer learning-based CNN models VGG-16, VGG-19, MobileNet-v2, ResNet-50, and new QCNN are implemented for classification. The study demonstrates that GAN-based augmentation outperforms traditional methods, and QCNN reduces computational complexity while improving prediction accuracy. The proposed method achieves a precision rate of 98.7% as validated through qualitative and quantitative analysis. It provides a more effective and efficient approach compared to existing methods for Indian dance form classification.
Detection of Fake Colorized Images based on Deep Learning

Image editing technologies have been advanced that can significantly enhance the image, but can also be used maliciously. Colorization is a new image editing technology that uses realistic colors to colorize grayscale photos. However, this strategy can be used on natural color images for a malicious purpose (e.g. to confuse object recognition systems that depend on the colors of objects for recognition). Image forensics is a well-developed field that examines photos of specified conditions to build confidence and authenticity. This work proposes a new fake colorized image detection approach based on the special Residual Network (ResNet) architecture. ResNets are a kind of Convolutional Neural Networks (CNNs) architecture that has been widely adopted and applied for various tasks. At first, the input image is reconstructed via a special image representation that combines color information from three separate color spaces (HSV, Lab, and Ycbcr); then, the new reconstructed images have been used for training the proposed ResNet model. Experimental results have demonstrated that our proposed method is highly generalized and significantly robust for revealing fake colorized images generated by various colorization methods.
An information fusion method for meta-tracker about online aerospace object tracking

The aerospace target tracking is difficult to achieve due to the dataset is intrinsically rare and expensive, and the complex space background, and the large changes of the target in the size. Meta-learning can better train a model when the data sample is insufficient, and tackle the conventional challenges of deep learning, including the data and the fundamental issue of generalization. Meta-learning can quickly generalize a tracker for new task via a few adapt. In order to solve the strenuous problem of object tracking in aerospace, we proposed an aerospace dataset and an information fusion based meta-learning tacker, and named as IF-Mtracker. Our method mainly focuses on reducing conflicts between tasks and save more task information for a better meta learning initial tracker. Our method was a plug-and-play algorithms, which can employ to other optimization based meta-learning algorithm. We verify IF-Mtracker on the OTB and UAV dataset, which obtain state of the art accuracy than some classical tracking method. Finally, we test our proposed method on the Aerospace tracking dataset, the experiment result is also better than some classical tracking method.
Global optimization via optimal decision trees

Abstract The global optimization literature places large emphasis on reducing intractable optimization problems into more tractable structured optimization forms. In order to achieve this goal, many existing methods are restricted to optimization over explicit constraints and objectives that use a subset of possible mathematical primitives. These are limiting in real-world contexts where more general explicit and black box constraints appear. Leveraging the dramatic speed improvements in mixed-integer optimization (MIO) and recent research in machine learning, we propose a new method to learn MIO-compatible approximations of global optimization problems using optimal decision trees with hyperplanes (OCT-Hs). This constraint learning approach only requires a bounded variable domain, and can address both explicit and inexplicit constraints. We solve the MIO approximation to find a near-optimal, near-feasible solution to the global optimization problem. We further improve the solution using a series of projected gradient descent iterations. We test the method on numerical benchmarks from the literature as well as real-world design problems, demonstrating its promise in finding global optima efficiently.
Trajectory Optimization for Multi-Sensor Multi-Target Search and Tracking with Bearing-Only Measurements

This paper proposes a trajectory optimization approach for multi-sensor multi-target search and tracking using bearing-only sensors. Based on the framework of the joint integrated probabilistic data association (JIPDA) filter, the intensity of potential unknown targets is updated according to the trajectories of the UAVs. The performance indices for target search and tracking are constructed based on, respectively, the intensity of unknown targets in the search area and the tracking error covariance. A dimensionless criterion, evaluating the search and tracking performance, is formulated and leveraged as the objective function of the UAV trajectory optimization problem. Simulations were carried out in different search and tracking scenarios to demonstrate the effectiveness of the proposed approach.
Approaches to designing a model for developing students’ universal competencies within the framework of developing and implementing the Smart University concept

Problem statement. The authors deal with the components of universal competences in terms of applying technological innovations in the learning process. A fragment of the model for shaping universal competencies of students based on the concept of Smart University is described. The study is aimed at substantiating the approaches to designing a model for the formation of universal competencies, presenting their theoretical analysis based on the concept of Smart University and considering their features, structure, and classification. Methodology. The approach to designing a model of universal competence formation is based on the analysis of acronym SMART decoding and the structure of the concept Smart University. The matrix of overview of the concept of universal competence in a modern context is proposed. Results. The universal competences are understood as smart competences united by five types of competences: professional, motivational, adaptive, key, and digital. The formation of six groups of universal competences is justified: system and critical thinking; project development and implementation; teamwork and leadership; civic-patriotic behavior; self-organization and self-education; technological solution. The created model defines the structure of universal competences and serves as a criterion for assessing students' abilities. Conclusion. Smart competences are a response to the current economic situation. The approach is structured for educational organizations that share a development policy in combination with technology. The results may be of some interest to scholars in the field of education informatization, teachers and university managers on innovation and education policy.