Reviewerly/exHarmony huggingface dataset:
Robust JPEG Image Steganography Using Wavelet Domain SVD and Adaptive QIM

Massive images in Internet networks provides rich cover objects for covert communication using digital steganography. However, the lossy processing such as image compressing and image scaling often take place in some network platforms, which makes the existing image steganography algorithms designed for lossless environment unusable for lossy network platforms. Based on singular value decomposition (SVD) in stationary wavelet domain, a robust and adaptive image steganography algorithm is proposed, which can resist lossy image compression and extract the embedded secret message correctly. First, stationary wavelet transformation is performed for cover image, and the low-frequency subband is divided into non-overlapping blocks, and SVD is performed for each block; second, considering the stability of maximum singular value, the cover elements are extracted by applying a adaptive quantization index modulation (QIM) process on the maximum singular values; third, the embedding distortion function is defined according to the influence of the cover element change on the inter-block and intra-block wavelet coefficients, and the secret messages are embedded using syndrome-trellis code (STCs); finally, the adaptive QIM is used to embed the secret elements and generate the stego image. The experimental results show that the proposed steganography algorithm has better robustness and stronger anti-detection capabilities.
Review of Cross-Age Face Recognition in Discriminative Models

The purpose of cross-age face recognition is to identify people with a large age difference. It is of great significance in security, finance, fighting criminals and other fields. In order to organize the development process of Convolutional Neural Networks (CNN) in the field of cross-age face recognition, the main discriminant methods in the field in recent years are summarized. First of all, the evolution and performance of cross-age face recognition based on CNN are systematically described, and summarized. Secondly, the advantages and disadvantages of the most commonly used cross-age face recognition data sets are introduced. Finally, combined with practical application, the existing challenges and bottlenecks of discriminant models are discussed, and the future research direction of cross-age face recognition is prospected.
Micro-motion Signal Enhancement via Convolutional Autoencoder Equipped with Multi-scale Feature Pyramid

Due to the time-varying and non-stationary characteristics of micro-motion signals, micro-Doppler spectrograms have been widely used for micro-motion signature analysis, which plays an important role in civil and military fields. However, the background noise in the spectrogram severely restricts subsequent applications such as feature extraction and parameter estimation. In this paper, we propose a novel micro-motion signal enhancement method based on the multi-scale feature pyramid structure and convolutional autoencoder. Compared to the traditional micro-Doppler spectrogram enhancement, the proposed method can restore micro-motion components and suppress background noise even under low signal-to-noise (SNR) conditions by integrating high-level semantic features and low-level detail information. Experimental results demonstrate the effectiveness and robustness of our method.
Parabola-Based Artificial Neural Network Activation Functions

The paper deals with the issues of constructing logical functions using artificial neurons with various activation functions. In particular, we propose a method for constructing parabola-based nonlinearity that allows expanding the logical capabilities of neurons and neural networks based on them. Alternative nonlinearity, like the sigmoid, is suitable for configuring a neural network by error backpropagation. We give an algorithm for such an XOR function adjustment using two neurons. The paper also shows the possibility of the XOR function building using one neuron by applying a rotated parabola (solving the XOR problem). We conduct experimental research on neural network adjustment based on standard sigmoid, s-parabola, and rotated parabola activation functions, which showed prospects of the proposed approach. The new nonlinearity makes it possible to reduce the overall computational complexity of setting up a neural network and speed up calculations in tasks that require the use of a large number of neurons.
Detection and Recognition of Personal Protection Equipment Wearing Based on an Improved YOLOv5 Algorithm

An improved YOLOv5 algorithm for small target detection and recognition imposed in this paper, which was deployed in an embedded platform to complete testing experiments. Our experimental evidence shows that the enhanced YOLOv5 algorithm improves detection efficiency. This paper adopts the improved YOLOv5s algorithm to detect and recognize whether workers on site are wearing helmets, masks, or reflective clothing during their operations, ensuring safty. The final model achieves an mAP50 of 0.847, a computational load of 8GFlops, and a model size of 7.4mb. The inference latency on the Ubuntu operating system with an i7-7850cpu is approximately 60ms. Applying the improved YOLOv5s algorithm in the field of construction site safety enables effective monitoring of the proper wearing of helmets and masks by workers, meeting the requirements of real-world applications in terms of detection speed and the performance requirements of portable devices.
A Conceptual Model of a Smart Medical Ward for Patient Care in Inpatient Facilities

The paper considers the task of automation of patient treatment in medical facilities. For this purpose a conceptual model of a smart ward is developed that include specifications of actors, components, situations and their properties in the medical cyber-physical environment to implement the entire complex within a single framework. The basic set of entities and their relationships are specified, some particular submodels are described to illustrate data processing in smart wards for diagnosing a specific syndrome. The modeling shows that the time of personnel work is significantly (more than 2 times) reduced due to the automation of data processing in the smart ward.
Improved White Blood Cell Image Classification Using Deep Convolutional Neural Networks with Attention Mechanism

White blood cells (WBC) are a pivotal component of the immune system, with each subtype possessing unique functionalities in the body. Therefore, swift and accurate identification of WBC subtypes from microscopic images is of utmost importance in biological research. With the advent and progression of neural networks in machine learning, cell image classification through neural networks has gained widespread adoption. In this paper, a deep convolutional neural network (CNN) combined with attention mechanism is proposed to classify white blood cells in microscopic images. The pre-training of CNN on the ImageNet dataset helps to improve the training and convergence speed of the network, while enhancing the accuracy of the weight parameters. The attention mechanism enables the network to focus on important regions in the cell image, allowing for more accurate extraction of cell features and details. The model proposed in this paper is compared against other CNN models including ResNet, VGG16, Inception, etc. The results show that the model combining deep convolutional network and attention mechanism demonstrates superior stability in accuracy across various white blood cell image classification tasks. The introduction of the attention mechanism led to 88% accuracy in blood cell image classification, resulting in improved accuracy in the classification of white blood cells and effective utilization of critical information in the image.
Development and Evaluation of an Explainable Diagnostic AI for Alzheimer's Disease

Alzheimer's Disease (AD) is a progressive neurode-generative disease that is estimated to affect 24.3 million people worldwide, and with an expected rise in cases, an accurate and efficient diagnosis method is necessary. Machine learning has been implemented for diagnosing AD in previous studies utilizing various modalities of data. More specifically, deep learning has been shown to perform at very high accuracy rates. However, many healthcare applications of deep learning paradigms lack trust due to their lack of interpretability. To provide evidence for the potential of deep learning to meet established standards of accurate diagnosis, we have designed an explainable diagnostic machine learning model for predicting AD severity levels. Using two open-source Magnetic Resonance Imaging (MRI) datasets, we develop and evaluate a Convolutional Neural Network (CNN). An accuracy rate of 99.9% was achieved using the CNN model, which outperforms other models trained on the same dataset. To improve model transparency, we leverage the explainable AI technique SHapley Additive exPlanations (SHAP) to interpret the predictions of the CNN. The implementation of explainable AI demonstrates that the predictions of the model are influenced by well-known pathological indicators of AD.
Image Restoration Based on Blur Kernel Estimation Using Vibration Data

Remote sensing images are usually blurred due to platform vibrations and camera defocus, which seriously limit its application. To solve the problem of blurring remote sensing images caused by in-orbit angular vibration, an image restoration approach is proposed. Magnetohydrodynamics (MHD) angular rate sensor is used to measure the angular vibration experiencing by the camera. The blur kernel is estimated with detected vibration and defocusing radius. A regularized image deblurring algorithm with total variation (TV) regularization is used to restore the blurred image. The modulation transfer function (MTF) is used to evaluate quality of the restored image. Simulations and experiments are conducted to validate the proposed approach. Results indicate that the proposed method can improve the image quality while preserving the detail information.
Decentralized Machine Learning for Face Recognition

As the fields of machine learning and computer vision are developing, facial recognition systems are becoming increasingly popular and are slowly being widely used in various fields like security, surveillance and medicine. This paper presents the design and development of a facial recognition solution that works in a distributed context, given that the devices used for capturing the images do not have the ability to train models capable of achieving good enough accuracy on large amounts of data. Thus, a method is presented in which the detection of human faces and the characteristics extraction are done locally based on a pre-trained FaceNet model. These characteristics are sent to a strong processing unit where a global model is trained and then transferred back to the clients, where it can be used for recognition. Through experimental evaluation, we show that our solution is efficient and exhibits high accuracy values.
Dynamic Theater: Location-Based Immersive Dance Theater, Investigating User Guidance and Experience

Dynamic Theater explores the use of augmented reality (AR) in immersive theater as a platform for digital dance performances. The project presents a locomotion-based experience that allows for full spatial exploration. A large indoor AR theater space was designed to allow users to freely explore the augmented environment. The curated wide-area experience employs various guidance mechanisms to direct users to the main content zones. Results from our 20-person user study show how users experience the performance piece while using a guidance system. The importance of stage layout, guidance system, and dancer placement in immersive theater experiences are highlighted as they cater to user preferences while enhancing the overall reception of digital content in wide-area AR. Observations after working with dancers and choreographers, as well as their experience and feedback are also discussed.
Sickness Reduction in FPV Drone Control: Improved Effects of Reverse Optical Flow with Static Landmarks Only

Drones are controlled remotely through the on-board live camera often using a headset to immersively (without external distraction) situate the drone operator with a first-person view. The highly dynamic imagery of drone piloting can elicit significant VR sickness. In this poster, we demonstrate the application of mixing the reverse optical flow pattern into the drone piloting imagery for mitigating VR sickness, using only the features of the static landmarks in the scene. We compare it to the cases of applying no mitigation technique (baseline) and mixing in the optical flow pattern from "all" object features. The results show that the suggested method was significantly more effective in reducing sickness than when considering all object features, and had a higher preference for improved visibility and controllability.
Enhancing the Resolution of Micro-CT Images of Rock Samples via Unsupervised Machine Learning based on a Diffusion Model

Objectives/Scope X-ray Micro-Computer Tomography (μ-CT) has been widely adopted in earth science and petroleum engineering due to its non-destructive characteristic. Meanwhile, this three-dimensional-imaging method can be integrated with computer simulation to investigate petrophysical properties of reservoir rocks at pore scales. However, the application of μ-CT is limited by the trade-off between field of view and resolution, and it is challenging to indicate the pore structure of rocks, especially for shale or carbonate rocks. To address this issue, deep-learning-based super-resolution techniques have rapidly developed in the past few years. Methodology In this study, a super-resolution algorithm based on the state-of-the-art (STOA) diffusion model is proposed to generate super-resolved CT images for carbonate rocks. The proposed method adapts denoising diffusion probabilistic models to conditional image generation and performs super-resolution through a stochastic denoising process. Cascaded diffusion model is utilized to increase the training speed and generate high fidelity CT images. This method exhibits superior performance in the resolution-enhancement of CT images at various magnification factors (with a large scaling factor of up to 16) without the occurrence of image-noise and image-blurring issue, and the super-resolved CT images performs well for the calculation of petrophysical properties of carbonate rocks. Results This algorithm is applied to the carbonate rock and the performance of the diffusion model is evaluated by quantitative extraction and qualitative visualization. In addition, this method is compared with other methods, such as GAN, Variational Autoencoder, and Super-Resolution Convolutional Neural Networks (SRCNN). The results indicate that the built model shows excellent potential in enhancing the resolution of heterogeneous carbonate rocks. To be specific, the super-resolved images exhibit clear and sharp edges and a detailed pore network. In addition, it performs well on different upscaling factors (up to 16) and is superior to the existing super-resolution approaches (for both supervised and unsupervised algorithms). This study provides a novel deep-learning-based method using a diffusion model to enhance the resolution of μ-CT images of carbonate rocks (up to 16). Novelty The novelty of this study is three-fold. First, this method belongs to unsupervised learning, indicating that pairs of high-resolution and low-resolution CT images are no longer needed. Second, a large scaling factor (up to 16) is reached without an image-blurring issue, which normally occurs in other deep-learning-based super-resolution algorithms. Third, the quality of super-resolved images is promising and faithful when compared with other generated learning methods, such as Generative Adversarial Networks (GAN).
Learning a Locally Unified 3D Point Cloud for View Synthesis

In this paper, we explore the problem of 3D point cloud representation-based view synthesis from a set of sparse source views. To tackle this challenging problem, we propose a new deep learning-based view synthesis paradigm that learns a locally unified 3D point cloud from source views. Specifically, we first construct sub-point clouds by projecting source views to 3D space based on their depth maps. Then, we learn the locally unified 3D point cloud by adaptively fusing points at a local neighborhood defined on the union of the sub-point clouds. Besides, we also propose a 3D geometry-guided image restoration module to fill the holes and recover high-frequency details of the rendered novel views. Experimental results on three benchmark datasets demonstrate that our method can improve the average PSNR by more than 4 dB while preserving more accurate visual details, compared with state-of-the-art view synthesis methods. The code will be publicly available at https://github.com/mengyou2/PCVS.
CHEESE: Distributed Clustering-Based Hybrid Federated Split Learning Over Edge Networks

Federated learning (FL) and split learning (SL) have emerged as two promising distributed machine learning paradigms. However, implementing either FL or SL over clients with limited computation and communication resources often faces the challenge of achieving delay-efficient model training. To overcome this challenge, we propose a novel distributed <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> lustering-based <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</u> ybrid f <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> d <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> rated <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> plit l <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> arning ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CHEESE</i> ) framework, consolidating distributed computation resources among clients by device-to-device (D2D) communications, which works in an intra-serial inter-parallel manner. In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CHEESE</i> , each learning client (LC) can form a learning cluster with its neighboring helping clients via D2D communications to train an FL model collaboratively. Specifically, inside each cluster, the model is split into multiple model segments via a model splitting and allocation (MSA) strategy, while each cluster member trains one segment. After completing intra-cluster training, a transmission client (TC) is determined from each cluster to upload a complete model to the base station for global model aggregation under allocated bandwidth. Based on this, an overall training delay cost minimization problem is formulated, which involves the following subproblems: client clustering, MSA, TC selection, and bandwidth allocation. Due to its NP-Hardness, the problem is decoupled and solved iteratively. The client clustering problem is first transformed into a distributed clustering game based on potential game theory, where each cluster further investigates the remaining three subproblems to evaluate the utility of each clustering strategy. Specifically, a heuristic algorithm is proposed to solve the MSA problem under a given clustering strategy, and a greedy-based convex optimization approach is introduced to solve the joint TC selection and bandwidth allocation problem. Finally, we propose an overall algorithm to tackle the joint problem iteratively, to reach a Nash equilibrium. Extensive experiments on practical models and datasets demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CHEESE</i> can significantly reduce training delay costs, as compared with conventional FL and vanilla SL.
DEQSVC: Dimensionality Reduction and Encoding Technique for Quantum Support Vector Classifier Approach to Detect DDoS Attacks

Distributed Denial of Service (DDoS) attacks pose a significant threat to the security of networking systems, as they can cause widespread disruption and even bring down entire distributed systems platforms. In this paper, we propose an approach called the DEQSVC that leverages quantum machine learning techniques to detect DDoS attacks with high accuracy. The DEQSVC integrates the most efficient dimensionality reduction techniques, a robust feature map method, and an efficient kernel estimation technique to improve data encoding, learning process, and detection accuracy. To evaluate the performance of the proposed DEQSVC, we conducted simulations using the Qiskit platform and executed the approach on an IBM quantum computer. Our results demonstrate that the DEQSVC outperforms several benchmark algorithms commonly used in intrusion detection systems. Specifically, the DEQSVC achieves a detection accuracy of 99.49, indicating its effectiveness as a highly accurate and efficient method for detecting DDoS attacks.
Bayesian Analysis of Two-Part Latent Variable Model with Mixed Data

Abstract In analyzing semi-continuous data, two-part model is a widely appreciated tool, in which two components are enclosed to characterize the mixing proportion of zeros and the actual level of positive values in semi-continuous data. The primary interest underlying such a model is primarily to exploit the dependence of the observed covariates on the semi-continuous variables; as such, the exploitation of unobserved heterogeneity is sometimes ignored. In this paper, we extend the conventional two-part regression model to much more general situations where multiple latent factors are considered to interpret the latent heterogeneity arising from the absence of covariates. A structural equation is constructed to describe the interrelationships between the latent factors. Moreover, a general statistical analysis procedure is developed to accommodate semi-continuous, ordered and unordered data simultaneously. A procedure for parameter estimation and model assessment is developed under a Bayesian framework. Empirical results including a simulation study and a real example are presented to illustrate the proposed methodology.
The Meta-Fusion

Blockchain technology and the metaverse are revolutionizing digital experiences by merging secure, cryptographic systems with shared virtual spaces. These advancements have immense potential across various sectors, including social interaction, education, and commerce. Despite these enhancements, current research remains fragmented. Thus, a comprehensive exploration of this domain is needed. This article conducts a systematic mapping study to understand research trends, challenges, and opportunities in the blockchain-based metaverse. The authors scrutinized 43 articles, categorizing them based on research themes and methodologies. Their analysis revealed a significant focus on technical aspects like blockchain implementation in virtual worlds and the design of virtual economies. The authors also found research that explores the social and cultural impacts of the blockchain-based metaverse. This study, thus, underscores the potential of the evolving field, emphasizing the need for research to fully understand its technical and societal implications. It also stresses the importance of evaluating the long-term sustainability, scalability, and potential risks associated with this promising, complex domain.