Reviewerly/exHarmony huggingface dataset:
RSFDM-Net: Real-Time Spatial and Frequency Domains Modulation Network for Underwater Image Enhancement

Underwater images typically experience mixed degradations of brightness and structure caused by the absorption and scattering of light by suspended particles. To address this issue, we propose a Real-time Spatial and Frequency Domains Modulation Network (RSFDM-Net) for the efficient enhancement of colors and details in underwater images. Specifically, our proposed conditional network is designed with Adaptive Fourier Gating Mechanism (AFGM) and Multiscale Convolutional Attention Module (MCAM) to generate vectors carrying low-frequency background information and high-frequency detail features, which effectively promote the network to model global background information and local texture details. To more precisely correct the color cast and low saturation of the image, we introduce a Three-branch Feature Extraction (TFE) block in the primary net that processes images pixel by pixel to integrate the color information extended by the same channel (R, G, or B). This block consists of three small branches, each of which has its own weights. Extensive experiments demonstrate that our network significantly outperforms over state-of-the-art methods in both visual quality and quantitative metrics.
Intelligent Java Dataset Construction and Visualization Evaluation for Reliable Software Development

As big data and artificial intelligence continue to gain importance, the reliable quality of datasets has be-come a crucial factor in algorithm performance and result reliability. However, many datasets lack standardization and quality control, which can lead to potential issues in software development and data analysis. In this paper, we propose an objective dataset evaluation algorithm that utilizes multiple metrics, including unified naming conventions and document annotations, for statistical analysis and filtering to ensure reliable data. Our approach scores datasets using reasonable criteria, and we use visualization techniques inspired by remote sensing to compare and visualize reliable dataset quality. Our research demonstrates that our evaluation method is feasible and can assist software developers in enhancing the reliable quality and efficiency of software development by improving dataset quality. Our study concentrates on datasets obtained from GitHub using a web crawler, and our approach establishes a standardized technique for evaluating reliable dataset quality.
Traffic Intersection Vehicle Movement Counts with Temporal and Visual Similarity based Re-Identification

Vehicle movement counting and classification is one of the critical components for traffic intersection monitoring and management. Cameras can be used to determine the vehicle movement counts (left-turning, right-turning, and through movements). Typically, cameras are installed with the view focused towards a particular approach and there is no overlap or very low overlap between the different camera views. Therefore, vehicles need to be re-identified across multiple cameras to detect the complete movement trajectory of the vehicle. In this study, we proposed combining visual similarity obtained using Convolutional Neural Networks (CNN) and temporal similarity (vehicle re-appearance time in cameras) using logistic regression (LR) model to perform vehicle re-identification. The logistic regression model has been used in two stages (without and with hard-negative mining) combining visual and temporal similarity. The results showed that using the hard-negative mining based LR model, the Top@1 results improved by 22% and Top@5 results improved by 8.48%, compared to the results obtained using only visual similarity measure for generating the rankings.
Metagrad: Adaptive Gradient Quantization with Hypernetworks

A popular track of network compression approach is Quantization aware Training (QAT), which accelerates the forward pass during the neural network training and inference. However, not much prior efforts have been made to quantize and accelerate the backward pass during training, even though that contributes around half of the training time. This can be partly attributed to the fact that errors of low-precision gradients during backward cannot be amortized by the training objective as in the QAT setting. In this work, we propose to solve this problem by incorporating the gradients into the computation graph of the next training iteration via a hypernetwork. Various experiments on CIFAR-10 dataset with different CNN network architectures demonstrate that our hypernetwork-based approach can effectively reduce the negative effect of gradient quantization noise and successfully quantizes the gradients to INT4 with only 0.64 accuracy drop for VGG-16 on CIFAR-10.
Towards Query Efficient and Generalizable Black-Box Face Reconstruction Attack

In this paper, we address the black-box face reconstruction attack with two crucial requirements: query efficiency and generalizability. A practical attack must be query efficient due to limited access to the target black-box model, and the reconstructed face must be generalizable so it can be used to attack other face recognition systems. To this end, we propose a novel face reconstruction attack that optimizes the latent vector of a pre-trained StyleGAN generator. Unlike existing methods, our method is query efficient as neither training nor simultaneous updating of multiple latent vectors is required. Furthermore, we propose a simple initialization scheme that greatly enhances the generalizability of the proposed method. We demonstrate the effectiveness of our method by a thorough evaluation on LFW and CFP-FP datasets across multiple state-of-the-art face recognition models. Project Code: github.com/1ho0jin1/Black-box-Face-Reconstruction.
Conditional Privacy-Preserving Multi-Domain Authentication and Pseudonym Management for 6G-Enabled IoV

With the emergence of the sixth-generation (6G) communication technologies, the Internet of Vehicles (IoV) is rapidly developing with the coordination between intelligent networked vehicles, road infrastructures, and the cloud. However, the openness and dynamic nature of the IoV raise significant security and privacy concerns, highlighting the need for efficient authentication schemes. Conventional authentication schemes are no longer suitable for 6G-enabled IoV due to high latency, single point of failure, and heavy management costs. Additionally, existing literature on multi-domain authentication mainly investigates vehicle mobility, ignoring the challenges posed by vehicle heterogeneity. To fill this gap, we propose a multi-domain authentication scheme with conditional privacy preservation (MACPP) that considers administrative domains ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AD</i> ) and geographic domains ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GD</i> ) in the IoV. In MACPP, we design a novel identity-based signature scheme without requiring bilinear pairing for efficient authentication. Additionally, we propose a blockchain-assisted pseudonym management scheme (BAPM) to further improve system security by designing a dynamical sparse Merkle tree structure (DSMT). We demonstrate that the proposed MACPP satisfies the security requirements through an in-depth security analysis. Moreover, the experimental results demonstrate the effectiveness and efficiency of both MACPP and BAPM.
High Dynamic Range Imaging with Multi-Exposure Binning on Quad Bayer Color Filter Array

This paper presents a high dynamic range (HDR) imaging method using multi-exposure (ME) binning on a multi-exposure Quad Bayer color filter array (MEQB-CFA). In order to save power consumption aggressively, image sensors for modern smartphones provide an operation to read raw data directly at a reduced resolution, called binning. Despite its high practical demand, such an operation has not been discussed in previous works of HDR imaging. Our ME binning is an extension of the binning for HDR imaging and can be justified under the assumption of locally-homogeneous radiance. We also implement the HDR imaging process after ME binning using a deep neural network. Experiments show outstanding efficiency and competitive imaging quality as compared with a state-of-the-art HDR imaging method.
Depth Map Estimation from Multi-View Images with Nerf-Based Refinement

In this paper, we propose a method to refine depth maps estimated by Multi-View Stereo (MVS) with Neural Radiance Field (NeRF) optimization to estimate depth maps from multi-view images with high accuracy. MVS estimates the depths on object surfaces with high accuracy, and NeRF estimates the depths at object boundaries with high accuracy. The key ideas of the proposed method are (i) to combine MVS and NeRF to utilize the advantages of both in depth map estimation, (ii) not to require any training process, therefore no training dataset and ground truth are required, and (iii) to use NeRF for depth map refinement. Through a set of experiments using the Redwood-3dscan dataset, we demonstrate the effectiveness of the proposed method compared to conventional depth map estimation methods.
BS-YOLOv5s: Insulator Defect Detection with Attention Mechanism and Multi-Scale Fusion

With the rapid development of deep learning, the use of object detection algorithms for aerial insulator image defect detection has become the main way. To address the problems of low detection accuracy for small targets, weak representation ability of feature maps, insufficient extracted key information, and the shortage of aerial insulator defect datasets, this paper proposes an improved insulator defect detection method named BS-YOLOv5s based on 3-D attention mechanism and Bi-Slim-neck using YOLOv5s as the base network. Additionally, to solve the problem of the shortage of aerial insulator datasets, this paper proposes a new aerial insulator dataset Weather-Insulator (WI) containing a variety of defect scenarios. The experimental results demonstrate that the proposed method not only greatly improves the detection accuracy, but also maintains a high detection speed, satisfying the engineering requirements for insulator defect detection. The dataset and code for this paper are publicly available at https://github.com/jspron/insulator-defect.
GMML is All You Need

Vision transformers (ViTs) have generated significant interest in the computer vision community because of their flexibility in exploiting contextual information, whether it is sharply confined local, or long range global. However, they are known to be data hungry and therefore often pretrained on large-scale datasets, e.g. JFT-300M or ImageNet. An ideal learning method would perform best regardless of the size of the dataset, a property lacked by current learning methods, with merely a few existing works studying ViTs with limited data. We propose Group Masked Model Learning (GMML), a self-supervised learning (SSL) method that is able to train ViTs and achieve state-of-the-art (SOTA) performance when pre-trained with limited data. The GMML uses the information conveyed by all concepts in the image. This is achieved by manipulating randomly groups of connected tokens, successively covering different meaningful parts of the image content, and then recovering the hidden information from the visible part of the concept. Unlike most of the existing SSL approaches, GMML does not require momentum encoder, nor relies on careful implementation details such as large batches and gradient stopping. Pretraining, finetuning, and evaluation codes are available under: https://github.com/GMML.
Interpreting Convolutional Neural Networks by Explaining Their Predictions

We propose a method that exploits the feedback provided by visual explanation methods combined with pattern mining techniques to identify the relevant class-specific and class-shared internal units. In addition, we put forward a patch extraction approach to find faithfully class-specific and class-shared visual patterns. Contrary to the common practice in literature, our approach does not require pushing augmented visual patches through the model. Experiments on two CNN architectures show the effectiveness of the proposed method.
AAFACE: Attribute-Aware Attentional Network for Face Recognition

In this paper, we present a new multi-branch neural network that simultaneously performs soft biometric (SB) prediction as an auxiliary modality and face recognition (FR) as the main task. Our proposed network named AAFace utilizes SB attributes to enhance the discriminative ability of FR representation. To achieve this goal, we propose an attribute-aware attentional integration (AAI) module to perform weighted integration of FR with SB feature maps. Our proposed AAI module is not only fully context-aware but also capable of learning complex relationships between input features by means of the sequential multi-scale channel and spatial sub-modules. Experimental results verify the superiority of our proposed network compared with the state-of-the-art (SoTA) SB prediction and FR methods.
Image Dehazing Guided by Low-Pass Reinforced Airlight

We introduce a simple but robust method to restore the visibility of hazy images. Our non deep-learning strategy refines a simplistic approximation of the airlight by taking advantage of the O-HAZE dataset that contains also the corresponding haze-free images. Knowing that the transmission is generally characterized by small values in hazy scenes, based on the optical model, we first assumes zero transmission, and approximate the airlight by the input hazy image. Then, using hazy and corresponding haze-free images available from the O-HAZE dataset, a second airlight estimate can be computed by solving the optical model assuming a simplified transmission map derived from dark channel prior. Observing that the difference between these two airlight estimates, primarily contains the low frequencies of the hazy image, we refine the airlight approximation derived from a zero transmission by reinforcing its low frequency component. We extensively tested our approach on real world hazy images. The qualitative and quantitative evaluations demonstrate that our approach yields better results than previous physically-based image dehazing techniques, and favorably compares with the deep learning dehazing approaches.
Fast-Convergent Federated Learning via Cyclic Aggregation

Federated learning (FL) aims at optimizing a shared global model over multiple edge devices without transmitting (private) data to the central server. While it is theoretically well-known that FL yields an optimal model – centrally trained model assuming availability of all the edge device data at the central server – under mild condition, in practice, it often requires massive amount of iterations until convergence, especially under presence of statistical/computational heterogeneity. This paper utilizes cyclic learning rate at the server side to reduce the number of training iterations with increased performance without any additional computational costs for both the server and the edge devices. Numerical results validate that, simply plugging-in the proposed cyclic aggregation to the existing FL algorithms effectively reduces the number of training iterations with improved performance.
Zero-Shot Hyperspectral Image Denoising With Self-Completion with Patterned Masks

Hyperspectral images (HSI) are data with higher spectral resolution than RGB images and are used for various tasks. HSI images are prone to degradation due to noise during imaging, and the high cost of imaging makes it very time-consuming to prepare large amounts of data. Therefore, it is often difficult to learn HSI using general deep learning-based methods that use degraded and non-degraded image pairs. Many methods that do not require training images have been proposed. With the recent development of self-supervised learning in image restoration tasks, learning-based image restoration methods that do not require non-degraded images for RGB images have been successful. This paper proposes a zero-shot HSI deep denoising method based on self-supervised image restoration. The feature of this method is that it learns only from a single observed HSI. Numerical experiments show that this method has higher restoration accuracy than conventional zero-shot methods.
HOKEM: Human and Object Keypoint-Based Extension Module for Human-Object Interaction Detection

Human-object interaction (HOI) detection for capturing relationships between humans and objects is an important task in the semantic understanding of images. When processing human and object keypoints extracted from an image using a graph convolutional network (GCN) to detect HOI, it is crucial to extract appropriate object keypoints regardless of the object type and to design a GCN that accurately captures the spatial relationships between keypoints. This paper presents the human and object keypoint-based extension module (HOKEM) as an easy-to-use extension module to improve the accuracy of the conventional detection models. The proposed object keypoint extraction method is simple yet accurately represents the shapes of various objects. Moreover, the proposed human-object adaptive GCN (HO-AGCN), which introduces adaptive graph optimization and attention mechanism, accurately captures the spatial relationships between keypoints. Experiments using the HOI dataset, V-COCO, showed that HOKEM boosted the accuracy of an appearance-based model by a large margin.
Representation Learning of Vertex Heatmaps for 3D Human Mesh Reconstruction From Multi-View Images

This study addresses the problem of 3D human mesh reconstruction from multi-view images. Recently, approaches that directly estimate the skinned multi-person linear model (SMPL)-based human mesh vertices based on volumetric heatmap representation from input images have shown good performance. We show that representation learning of vertex heatmaps using an autoencoder helps improve the performance of such approaches. Vertex heatmap autoencoder (VHA) learns the manifold of plausible human meshes in the form of latent codes using AMASS, which is a large-scale motion capture dataset. Body code predictor (BCP) utilizes the learned body prior from VHA for human mesh reconstruction from multi-view images through latent code-based supervision and transfer of pretrained weights. According to experiments on Human3.6M and LightStage datasets, the proposed method outperforms previous methods and achieves state-of-the-art human mesh reconstruction performance.
Teachers’ Agency in Technology for Education in Pre- and Post-COVID-19 Periods: A Systematic Literature Review

Technology has become an indispensable element in education that challenges conventional teaching. The pandemic significantly forced a paradigm shift in education from traditional methods to digital platforms. Emerging technology expanded the teacher’s role faster than predicted, and technology has become a significant criterion in defining 21st-century teachers. Teachers had to upgrade education and act as change agents in creating and managing technology-enhanced learning environments requiring teachers to be digitally literate. Considering teachers as significant stakeholders, this paper investigates how literature contributes to the knowledge of their perceptions of digital literacy in education by systematically investigating 59 research articles searched in EBSCO discovery services, covering commonly included 80 different databases in the default search. The review focuses on how teachers use technology, their challenges, and what teachers expect from successful technology integration. Results revealed a contradiction between expectations for technology integration into education before the pandemic and the experience after the lockdown. Even though teachers are confident using basic technology, many have observed a disconnect between technology and pedagogy that emphasises digital literacy’s need. Teachers struggle with technology integration due to lack of knowledge, accessibility, cost, disconnection, infrastructure, time, workload, and technology anxiety Therefore, teachers expect institutions to play a significant role in integrating technology by assisting them in mitigating challenges that require defining the institutional role of a technology-integrated learning environment.
Memory Tensor for Non-Markovian Dynamics with Random Hamiltonian

In the theory of open quantum systems, the Markovian approximation is very widespread. Usually, it assumes the Gorini–Kossakowski–Sudarshan–Lindblad (GKSL) equation for density matrix dynamics and quantum regression formulae for multi-time correlation functions. Nevertheless, now, quantum non-Markovianity is being actively studied, especially the non-Markovianity of multi-time correlations. In this work, we consider dynamics with a random Hamiltonian, which can lead to GKSL dynamics of the density matrix for some special cases, but correlation functions generally do not satisfy the quantum regression formulae. Despite the fact that random Hamiltonians have been actively studied, dynamics with such Hamiltonians has been little discussed from the viewpoint of multi-time correlations. For specific models with a random Hamiltonian, we provide the formulae for multi-time correlations which occur instead of the usual regression formulae. Moreover, we introduce and calculate the memory tensor, which characterizes multi-time correlations against the Markovian ones. We think that, despite being applied to specific models, the methods developed in this work can be used in a much broader setup.
Review of mapping analysis and complementarity between solar and wind energy sources

This review aims to identify the available methodologies, data, and techniques for mapping the potential of solar and wind energy and its complementarity and to provide significant research and patents regarding these issues. The review shows that the mapping methodologies vary in space and time, going from a global scale to a microscale. Studies use observed data, such as land-based monitoring stations, upper-air stations, wind towers, and satellite data. Additionally, they use meteorological modeling, such as analysis and reanalysis from global models, and mesoscale and microscale models. The complementarity analysis uses mainly correlation techniques such as Pearson's, Spearman, or Kendall's correlation. However, some studies pointed out that these techniques are unsuitable for analyzing wind and solar time series. This brings additional research and methods to assess and evaluate the complementarity between energy sources, reinforcing that there is no standard or common methodology to perform complementarity analyses. The patent survey has shown that most patents are regarding power plant components, configuration, energy storage in hybrid systems, computational fluid dynamics, and artificial intelligence techniques for improving power plant operation. A case study was established to illustrate the methodology of mapping the solar and wind potential and their complementarity.