Reviewerly/exHarmony huggingface dataset:
A Novel 3D Reversible Data Hiding Scheme Based on Integer–Reversible Krawtchouk Transform for IoMT

To avoid rounding errors associated with the limited representation of significant digits when applying the floating-point Krawtchouk transform in image processing, we present an integer and reversible version of the Krawtchouk transform (IRKT). This proposed IRKT generates integer-valued coefficients within the Krawtchouk domain, seamlessly aligning with the integer representation commonly utilized in lossless image applications. Building upon the IRKT, we introduce a novel 3D reversible data hiding (RDH) algorithm designed for the secure storage and transmission of extensive medical data within the IoMT (Internet of Medical Things) sector. Through the utilization of the IRKT-based 3D RDH method, a substantial amount of additional data can be embedded into 3D carrier medical images without augmenting their original size or compromising information integrity upon data extraction. Extensive experimental evaluations substantiate the effectiveness of the proposed algorithm, particularly regarding its high embedding capacity, imperceptibility, and resilience against statistical attacks. The integration of this proposed algorithm into the IoMT sector furnishes enhanced security measures for the safeguarded storage and transmission of massive medical data, thereby addressing the limitations of conventional 2D RDH algorithms for medical images.
Photovoltaic Spectral Responsivity and Efficiency under Different Aerosol Conditions

While solar power applications are growing rapidly worldwide, information about solar energy availability, its characteristics and the factors that affect it are essential. Among other parameters, a reference spectrum (ASTMG-173-03) is adopted, relying on Standard Test Conditions (STC), under which Photovoltaic (PV) devices are evaluated. However, these rigorously defined conditions can vary considerably from realistic environmental conditions. The objective of the present work is to assess the impact of the variability of atmospheric composition on the spectral distribution of the incident solar spectral irradiance (SSI) and, therefore, its implication on various PV materials performance. Ground-based measurements of global horizontal SSI have been conducted using a Precision Spectroradiometer (PSR) in the framework of the ASPIRE (Atmospheric parameters affecting SPectral solar IRradiance and solar Energy) project in Athens, Greece. The gathered data in combination with spectrally resolved radiative transfer under clear-sky conditions contributed to the investigation of the atmospheric variables that attenuate irradiance (e.g., aerosols). In addition, since PV modules’ spectral absorptivity differs according to the semiconductor material used, the impact of the above-mentioned spectral features on PV performance has been investigated in order to estimate the spectral impact between the theoretical and outdoor conditions on the yield of different PV technologies. Overall, the results denote that smoke has a more significant effect than dust, while the effect on various technologies varies. The highest deviation compared to the STC was observed in the case of a-Si, reaching an absolute difference of 45% in the case of smoke particles in the atmosphere, while the maximum deviation between the different technologies reached approximately 7%.
DSF-Net: A Dual Feature Shuffle Guided Multi-Field Fusion Network for SAR Small Ship Target Detection

SAR images play a crucial role in ship detection across diverse scenarios due to their all-day, all-weather characteristics. However, detecting SAR ship targets poses inherent challenges due to their small sizes, complex backgrounds, and dense ship scenes. Consequently, instances of missed detection and false detection are common issues. To address these challenges, we propose the DSF-Net, a novel framework specifically designed to enhance small SAR ship detection performance. Within this framework, we introduce the Pixel-wise Shuffle Attention module (PWSA) as a pivotal step to strengthen the feature extraction capability. To enhance long-range dependencies and facilitate information communication between channels, we propose a Non-Local Shuffle Attention (NLSA) module. Moreover, NLSA ensures the stability of the feature transfer structure and effectively addresses the issue of missed detection for small-sized targets. Secondly, we introduce a novel Triple Receptive Field-Spatial Pyramid Pooling (TRF-SPP) module designed to mitigate the issue of false detection in complex scenes stemming from inadequate contextual information. Lastly, we propose the R-tradeoff loss to augment the detection capability for small targets, expedite training convergence, and fortify resistance against false detection. Quantitative validation and qualitative visualization experiments are conducted to substantiate the proposed assumption of structural stability and evaluate the effectiveness of the proposed modules. On the LS-SSDDv1.0 dataset, the mAP50−95 demonstrates a remarkable improvement of 8.5% compared to the baseline model. The F1 score exhibits a notable enhancement of 6.9%, surpassing the performance of advanced target detection methods such as YOLO V8.
Long-Tailed Object Detection for Multimodal Remote Sensing Images

With the rapid development of remote sensing technology, the application of convolutional neural networks in remote sensing object detection has become very widespread, and some multimodal feature fusion networks have also been proposed in recent years. However, these methods generally do not consider the long-tailed problem that is widely present in remote sensing images, which limits the further improvement of model detection performance. To solve this problem, we propose a novel long-tailed object detection method for multimodal remote sensing images, which can effectively fuse the complementary information of visible light and infrared images and adapt to the imbalance between positive and negative samples of different categories. Firstly, the dynamic feature fusion module (DFF) based on image entropy can dynamically adjust the fusion coefficient according to the information content of different source images, retaining more key feature information for subsequent object detection. Secondly, the instance-balanced mosaic (IBM) data augmentation method balances instance sampling during data augmentation, providing more sample features for the model and alleviating the negative impact of data distribution imbalance. Finally, class-balanced BCE loss (CBB) can not only consider the learning difficulty of specific instances but also balances the learning difficulty between categories, thereby improving the model’s detection accuracy for tail instances. Experimental results on three public benchmark datasets show that our proposed method achieves state-of-the-art performance; in particular, the optimization of the long-tailed problem enables the model to meet various application scenarios of remote sensing image detection.
Efficient CU Decision Algorithm for VVC 3D Video Depth Map Using GLCM and Extra Trees

The new generation of 3D video is an international frontier research hotspot. However, the large amount of data and high complexity are core problems to be solved urgently in 3D video coding. The latest generation of video coding standard versatile video coding (VVC) adopts the quad-tree with nested multi-type tree (QTMT) partition structure, and the coding efficiency is much higher than other coding standards. However, the current research work undertaken for VVC is less for 3D video. In light of this context, we propose a fast coding unit (CU) decision algorithm based on the gray level co-occurrence matrix (GLCM) and Extra trees for the characteristics of the depth map in 3D video. In the first stage, we introduce an edge detection algorithm using GLCM to classify the CU in the depth map into smooth and complex edge blocks based on the extracted features. Subsequently, the extracted features from the CUs, classified as complex edge blocks in the first stage, are fed into the constructed Extra trees model to make a fast decision on the partition type of that CU and avoid calculating unnecessary rate-distortion cost. Experimental results show that the overall algorithm can effectively reduce the coding time by 36.27–51.98%, while the Bjøntegaard delta bit rate (BDBR) is only increased by 0.24% on average which is negligible, all reflecting the superior performance of our method. Moreover, our algorithm can effectively ensure video quality while saving much encoding time compared with other algorithms.
Scaling-Invariant Max-Filtering Enhancement Transformers for Efficient Visual Tracking

Real-time tracking is one of the most challenging problems in computer vision. Most Transformer-based trackers usually require expensive computational and storage power, which leads to these robust trackers being unable to achieve satisfactory real-time performance in resource-constrained devices. In this work, we propose a lightweight tracker, AnteaTrack. To localize the target more accurately, this paper presents a scaling-invariant max-filtering operator. It uses local max-pooling to filter the suspected target portion in overlapping sliding windows for enhancement while suppressing the background. For a more compact target bounding-box, this paper presents an upsampling module based on Pixel-Shuffle to increase the fine-grained expression of target features. In addition, AnteaTrack can run in real time at 47 frames per second (FPS) on a CPU. We tested AnteaTrack on five datasets, and a large number of experiments showed that AnteaTrack provides the most efficient solution compared to the same type of CPU real-time trackers.
Defect Detection Method of Phosphor in Glass Based on Improved YOLO5 Algorithm

Phosphor in Glass (PiG) is easy to be stirred unevenly during production and processing, and improper use of instruments and other factors lead to defective products. In this paper, we propose an improved YOLOv5 target detection algorithm. Firstly, the Coordinate Attention (CA) is introduced into the backbone network to enable the network to notice detect targets in a larger range. Secondly, the Bidirectional Feature Pyramid Network (BiFPN) is used to fuse different scale information in the neck part to obtain the output feature map with rich semantic information. At the same time, the weighted bidirectional feature fusion pyramid structure adjusts the contribution of different scale input feature maps to the output by introducing weights. This optimization enhances the feature fusion effect, reduces the loss of feature information in the convolution process, and improves detection accuracy. Then, the GIOU_Loss function is replaced with the EIOU_Loss function to speed up the convergence. Finally, the comparative experiment is carried out with the self-made PiG dataset. The experimental results show that the average accuracy mAP of this method is 12.35% higher than that of the original method (YOLOv5s), with a detection speed is 53.92 FPS, aligning with the actual needs of industrial detection.
A Lightweight Modified YOLOv5 Network Using a Swin Transformer for Transmission-Line Foreign Object Detection

Transmission lines are often located in complex environments and are susceptible to the presence of foreign objects. Failure to promptly address these objects can result in accidents, including short circuits and fires. Existing foreign object detection networks face several challenges, such as high levels of memory consumption, slow detection speeds, and susceptibility to background interference. To address these issues, this paper proposes a lightweight detection network based on deep learning, namely YOLOv5 with an improved version of CSPDarknet and a Swin Transformer (YOLOv5-IC-ST). YOLOv5-IC-ST was developed by incorporating the Swin Transformer into YOLOv5, thereby reducing the impact of background information on the model. Furthermore, the improved CSPDarknet (IC) enhances the model’s feature-extraction capability while reducing the number of parameters. To evaluate the model’s performance, a dataset specific to foreign objects on transmission lines was constructed. The experimental results demonstrate that compared to other single-stage networks such as YOLOv4, YOLOv5, and YOLOv7, YOLOv5-IC-ST achieves superior detection results, with a mean average precision (mAP) of 98.4%, a detection speed of 92.8 frames per second (FPS), and a compact model size of 10.3 MB. These findings highlight that the proposed network is well suited for deployment on embedded devices such as UAVs.
DBENet: Dual-Branch Brightness Enhancement Fusion Network for Low-Light Image Enhancement

In this paper, we propose an end-to-end low-light image enhancement network based on the YCbCr color space to address the issues encountered by existing algorithms when dealing with brightness distortion and noise in the RGB color space. Traditional methods typically enhance the image first and then denoise, but this amplifies the noise hidden in the dark regions, leading to suboptimal enhancement results. To overcome these problems, we utilize the characteristics of the YCbCr color space to convert the low-light image from RGB to YCbCr and design a dual-branch enhancement network. The network consists of a CNN branch and a U-net branch, which are used to enhance the contrast of luminance and chrominance information, respectively. Additionally, a fusion module is introduced for feature extraction and information measurement. It automatically estimates the importance of corresponding feature maps and employs adaptive information preservation to enhance contrast and eliminate noise. Finally, through testing on multiple publicly available low-light image datasets and comparing with classical algorithms, the experimental results demonstrate that the proposed method generates enhanced images with richer details, more realistic colors, and less noise.
MIFM: Multimodal Information Fusion Model for Educational Exercises

Educational exercises are crucial factors in the successful implementation of online education as they play a key role in assessing students’ learning and supporting teachers in instruction. These exercises encompass two primary types of data: text and images. However, existing methods for extracting exercise features only focus on the textual data, neglecting the rich semantic information present in the image data. Consequently, the exercise characterization vector generated by these methods struggles to fully characterize the exercise. To address these limitations, this paper proposes a multimodal information fusion-based exercise characterization model called MIFM. The MIFM model tackles the challenges of current exercise modeling methods by performing extraction and fusion operations on the heterogeneous features present in exercises. It employs a dual-stream architecture to separately extract features from images and text, and establishes connections between heterogeneous data using cross-modality attention methods. Finally, the heterogeneous features are fused using a Bi-LSTM combined with a multi-head attention mechanism. The resulting model produces a multimodal exercise characterization vector that fuses both modalities and incorporates three knowledge elements. In the experiments, by using the model to replace the exercise characterization modules in the three educational tasks, specifically, it achieves an increased ACC value of 72.35% in the knowledge mapping task, a heightened PCC value of 46.83% in the exercise difficulty prediction task, and an elevated AUC value of 62.57% in the student performance prediction task.
MCDC‐Net: Multi‐scale forgery image detection network based on central difference convolution

Abstract Generative Adversarial Networks (GANs) emerged thanks to the development of deep neural networks. Forgery images generated by various variants of GANs are widely spread on the Internet, which may be damage personal credibility and cause huge property losses. Thus, numerous methods are proposed to detect forgery images, but most of them are designed to detect forgery faces. Therefore, a method to detect forgery images of various scenes is proposed. In this work, central difference convolution and vanilla convolution (CDC‐Mix) are mixed after considering the depth and width features of neural networks and analyzing the influence of attention on network performance. Based on CDC‐Mix, a separable convolution (SeparableCDC‐Mix) is proposed. The proposed method consists of three parts: (1) CDC‐Mix and SeparableCDC‐Mix are used to extract the gradient information and texture features; (2) CDCM is used to extract the multi‐scale information of the image; (3) multi‐scale fusion module (MS‐Fusion) is used to fuse the multi‐scale information from different locations of the network. A large number of experiments have been carried out on several datasets generated by GAN, and the experimental results show that the proposed method has a great improvement compared with the existing advanced methods.
Proof of Flow: A Design Pattern for the Green Energy Market

The increased penetration of Distributed Energy Resources (DERs) in electricity markets has given rise to a new category of energy players, called Aggregators, whose role is to ensure fair remuneration for energy supplied by DERs, and support the smooth feeding of the intermittent energy produced into the distribution network. This paper presents a software solution, described as a design pattern, that governs the interaction between an Aggregator and DERs, leveraging blockchain technology to achieve a higher degree of decentralization, data integrity and security, through a properly designed, blockchain-based, smart contract. Thus, the proposed solution reduces the reliance on intermediaries acting as authorities, while affording transparency, efficiency and trust to the energy exchange process. Thanks to the underlying blockchain properties, generated events are easily observable and cannot be forged or altered. However, blockchain technology has inherent drawbacks, i.e., mainly the cost of storage and execution, hence our solution provides additional strategies for limiting blockchain usage, without undermining its strengths. Moreover, the design of our smart contract takes care of orchestrating the players, and copes with their potential mutual disagreements, which could arise from different measures of energy, providing an automatic decision process to resolve such disputes. The overall approach results in lower fees for running smart contacts supporting energy players and in a greater degree of fairness assurance.
A general framework for the practical disintegration of PAC-Bayesian bounds

PAC-Bayesian bounds are known to be tight and informative when studying the generalization ability of randomized classifiers. However, they require a loose and costly derandomization step when applied to some families of deterministic models such as neural networks. As an alternative to this step, we introduce new PAC-Bayesian generalization bounds that have the originality to provide disintegrated bounds, i.e., they give guarantees over one single hypothesis instead of the usual averaged analysis. Our bounds are easily optimizable and can be used to design learning algorithms. We illustrate this behavior on neural networks, and we show a significant practical improvement over the state-of-the-art framework.
Dealing with unique minerals in petrophysical logs

Most petrophysical equations and methods are designed to deal with hydrocarbons found within mineralogies, such as quartz and feldspars or calcite and dolomite. Petrophysical tools and techniques have also largely been designed to investigate rocks containing these minerals. Hence, when log analysts encounter a field containing unique mineralogies, determining an appropriate petrophysical evaluation process for such unconventional reservoirs can be a challenge. In this paper, we aim to discuss our learnings using two unique case studies. The first is a well which had intersected opalines and logged with conventional modern “western style” tools. The second is a well which has intersected tuffs and volcanics and which was logged with older “Russian style” tools. For opalines, we discuss the potential mineral signatures observed on the well log data and the reservoir potential of the rocks. We find that the unique microporous structure created by opal-CT microspheres/lepispheres, combined with either fracture or matrix-porosity dominant properties, potentially allows for storage of hydrocarbons within the rock. Hence, we hypothesize that opaline reservoirs could be classed as “Low Resistivity Low Contrast” (LRLC) pay and should not be glossed over just because typical hydrocarbon indicators are not seen. For tuffs and volcanics, we first share some of the challenges faced and methods used in conducting modern petrophysical interpretation with data from older Russian-style logs. Again, we discuss the potential mineral signatures observed on the logs and look at properties such as pore throat radii, size, and mineral composition and how that affected reservoir potential. Our findings indicate that the distribution of pore sizes allowed for greater connectivity within the rock, where the pore geometry effects aided in low residual oil saturations.
The Role of Organizational Culture in Cybersecurity: Building a Security-First Culture

In today's digital age, organizations have harnessed unprecedented connectivity and technological advancements, leading to enhanced efficiency and productivity. However, this progress has also exposed businesses to a multitude of cyber threats, including data breaches, ransomware attacks, and social engineering exploits. This research explores the relationship between organizational culture and cybersecurity practices, emphasizing the importance of fostering a security-first culture within organizations. While technical measures are crucial, neglecting the role of organizational culture can hinder effective cybersecurity. The study is grounded in the Theory of Planned Behavior and the Cultural Dimensions Theory, providing a solid theoretical foundation. Moreover, the investigation delves into the Denison Organizational Culture Model, particularly focusing on the role of participation in nurturing a security-first culture. This becomes particularly relevant when assembling collaborative, inclusive, and communication-driven multi-disciplinary teams. Leadership emerges as a pivotal aspect in establishing a security-first culture. The onus lies with executive leadership at the highest echelons of the organization. However, it is concerning that in certain well-established companies, some senior executives continue to perceive cybersecurity as the sole responsibility of the IT department, overlooking its leadership significance.