Reviewerly/exHarmony huggingface dataset:
Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach

Most facial recognition and face analysis systems start with facial detection. Early techniques, such as Haar cascades and histograms of directed gradients, mainly rely on features that had been manually developed from particular images. However, these techniques are unable to correctly synthesize images taken in untamed situations. However, deep learning's quick development in computer vision has also sped up the development of a number of deep learning-based face detection frameworks, many of which have significantly improved accuracy in recent years. When detecting faces in face detection software, the difficulty of detecting small, scale, position, occlusion, blurring, and partially occluded faces in uncontrolled conditions is one of the problems of face identification that has been explored for many years but has not yet been entirely resolved. In this paper, we propose Retina net baseline, a single-stage face detector, to handle the challenging face detection problem. We made network improvements that boosted detection speed and accuracy. In Experiments, we used two popular datasets, such as WIDER FACE and FDDB. Specifically, on the WIDER FACE benchmark, our proposed method achieves AP of 41.0 at speed of 11.8 FPS with a single-scale inference strategy and AP of 44.2 with multi-scale inference strategy, which are results among one-stage detectors. Then, we trained our model during the implementation using the PyTorch framework, which provided an accuracy of 95.6% for the faces, which are successfully detected. Visible experimental results show that our proposed model outperforms seamless detection and recognition results achieved using performance evaluation matrices.
A novel system for medical equipment supply chain traceability based on alliance chain and attribute and role access control

With the increasing sales of the medical device market, the medical incidents caused by passive medical devices are increasing, which needs to be solved from the medical device supply chain. In addition, there are complex rights retrieval and assignment problems in the medical device supply chain system, and the traditional Role-based Access Control (RBAC) model is no longer suitable for the actual application environment of the supply chain. This article combines the traditional RBAC model and the Attribute-based Access Control (ABAC) model for access control management in the medical device supply chain to overcome the limitations abovementioned, taking advantage of the ABAC model's flexibility to achieve granular and dynamic management of permissions, as also the RBAC model to simplify the permissions management of the entire system. Blockchain technology's traceability, non-tampering, and decentralization features are essential to eliminate passive and inferior medical equipment sales activities, solving the problem of mutual mistrust and 'Information Silo' between the two sides of medical device transactions. The access control model is implemented in the blockchain's smart contract, providing granular and dynamic authority management for the medical equipment supply chain and guaranteeing the security and privacy of medical device information in the supply chain. Three smart contracts on Hyperledger Fabric are designed for device management information, storage of implemented access control models, and implementation of access control policies. Experimental results show that the entire system is stable, ensuring high throughput and high security and promising.
Understanding Failures in Security Proofs of Multi-Factor Authentication for Mobile Devices

Multi-factor authentication is a promising way to enhance the security of password-based authenticated key exchange (PAKE) schemes. It is widely deployed in various daily applications for mobile devices (e.g., e-Bank, smart home, and cloud services) to provide the first line of defense for system security. However, despite intensive research, how to design a secure and efficient multi-factor authentication scheme is still a challenging problem. Hundreds of new schemes have been successfully proposed, and many are even equipped with a formal security proof. However, most of them have been shortly found to be insecure and cannot achieve the claimed security goals. Now a paradox arises: How can a multi-factor scheme that was “formally proven secure” later be found insecure? To answer this seemingly contradicting question, this paper takes a substantial first step towards systematically exploring the security proof failures in multi-factor authentication schemes for mobile devices. We first investigate the root causes of the “provable security” failure in vulnerable multi-factor authentication schemes under the random oracle model, and classify them into eight different types in terms of the five steps of conducting a formal security proof. Then, we elaborate on each type of these eight proof failures by examining three typical vulnerable protocols, and suggest corresponding countermeasures. Finally, we conduct a large-scale comparative measurement of 70 representative multi-factor authentication schemes under our extended evaluation criteria. The schemes we select range from 2009 to 2022, and the comparison results suggest that understanding failures in formal security proofs is helpful to design more secure multi-factor authentication protocols for mobile devices.
Migration to the metaverse and its predictors: Attachment to virtual places and metaverse-related threat

The most ambitious visions of metaverse technology promise to create virtual places that offer the same possibilities as the real world. However, as any novel technology, the metaverse raises controversies and questions. Does one want to migrate to the metaverse? Does one's willingness to move to virtual worlds depend on the bonds with existing virtual places and the sense of threat related to this technology? To address these questions, we drew on the theories of place attachment and intergroup threat. In two studies – (1) among users of open-world games (N = 366) and (2) using a sample representative of the Polish population in terms of age, gender and size of the residential place (N = 995) – we observed a low level of willingness to migrate to the metaverse. The participants displayed a high level of perceived metaverse-related threat, ranging from privacy concerns to the belief that metaverse can deprive one of access to essential human experiences. However, greater attachment to virtual, as opposed to real, places was associated with both an increased willingness to migrate to the metaverse and a low level of perceived threat. The results provide a better understanding of individuals' perception of the metaverse and of how the bonds with virtual and real places translate into attitudes towards metaverse technology.
Investigating the role of psychological needs in predicting the educational sustainability of Metaverse using a deep learning-based hybrid SEM-ANN technique

Metaverse is an immersive three-dimensional (3D) virtual world inhabited by avatars beyond the physical realm. The COVID-19 pandemic has disrupted the education system and the need to accelerate the digitalization of education has received a lot of attention. Metaverse can be an alternative solution for sociocultural interaction and to continue education. Accordingly, this study aimed to identify key factors in predicting the educational sustainability of Metaverse. The study empirically tested the role of psychological needs (i.e. hedonic motivation, affiliation, dominance, achievement, and autonomy) in predicting educational sustainability. The study employed a hybrid method integrating covariance-based structural-equation-modeling (CB-SEM) and deep artificial neural network (ANN) model. The CB-SEM results indicated that the need for autonomy and hedonic motivation significantly predicted the educational sustainability of Metaverse. Further, deep ANN models indicated that hedonic motivation was the most important input factor, followed by autonomy, affiliation, dominance, and achievement. Findings have practical implications for developers of the Metaverse environments and theoretical contributions for educators who manage and implement such environments.
Neighbor-aware deep multi-view clustering via graph convolutional network

Multi-view clustering (MVC) enhances the clustering performance of data by combining correlation information from different views. However, most existing MVC approaches process each sample independently and ignore the correlation amongst samples, resulting in reduced clustering performance. Although graph convolution network (GCN) can naturally capture correlation amongst samples by integrating the neighbors and structural information into representation learning, it is used in the semi-supervised learning scenario. In this paper, we propose a neighbor-aware deep MVC framework based on GCN (NMvC-GCN) for clustering multi-view samples and training GCN in a fully unsupervised manner. In addition, we design a consensus regularization to learn the common representations and introduce a clustering embedding layer to jointly optimize the clustering task and representation learning, so that the correlation amongst samples and that between the clustering task and representation learning can be fully explored. Extensive experiments on 10 datasets illustrate that NMvC-GCN significantly outperforms the state-of-the-art MVC methods. Our code will be released at https://github.com/dugzzuli/NMvC-GCN.
SIGNFORMER: DeepVision Transformer for Sign Language Recognition

Sign language is the most common form of communication for the hearing impaired. To bridge the communication gap with such impaired people, a normal people should be able to recognize the signs. Therefore, it is necessary to introduce a sign language recognition system to assist such impaired people. This paper proposes the Transformer Encoder as a useful tool for sign language recognition. For the recognition of static Indian signs, the authors have implemented a vision transformer. To recognize static Indian sign language, proposed methodology archives noticeable performance over other state-of-the-art convolution architecture. The suggested methodology divides the sign into a series of positional embedding patches, which are then sent to a transformer block with four self-attention layers and a multilayer perceptron network. Experimental results show satisfactory identification of gestures under various augmentation methods. Moreover, the proposed approach only requires a very small number of training epochs to achieve 99.29 percent accuracy.
Using empirical wavelet transform and high-order fuzzy cognitive maps for time series forecasting

Many studies on time series forecasting have employed fuzzy cognitive maps (FCMs). However, it is required to develop techniques capable of effective responses and great accuracy for large-scale non-stationary time series, such as sharp-fluctuated datasets. For forecasting such data, the present study introduces an accurate, precise, and efficient combined forecasting framework with ridge regression, high-order FCM (HFCM), and empirical wavelet transform (EWT). The proposed learning method named as EWTHFCM. This model employs EWT for non-stationary time series transformation into a multivariate sequence. The HFCM then models each multivariate time series through a node. For the optimized learning part, ridge regression is employed to perform the process for the HFCM representation of large-scale time series. The main reasons to choose the ridge regression are its circle-like phenomenon which makes it a perfect fit for being combined with EWT. This fitness is due to the optimal filter bank selection behavior and adaptivity feature of EWT, eventually making the ridge regression fast, efficient, and accurate. Subsequently, HFCM models and forecasts the multivariate time series and helps find the trend pattern. Then, the multivariate time series is reconstructed by inverse EWT to predict time series at all time steps. The root mean square error (RMSE) is measured on 15 real-life standard datasets, of which 8 are utilized to compare the proposed framework to 11 state-of-art algorithms. According to the experimental results and comparison with other research papers, we recommend using EWTHFCM as a new, superior, and accurate method for many forecasting purposes and circumstances.
Face Mask Detection on Photo and Real-Time Video Images Using Caffe-MobileNetV2 Transfer Learning

Face detection systems have generally been used primarily for non-masked faces, which include relevant facial characteristics such as the ears, chin, lips, nose, and eyes. Masks are necessary to cover faces in many situations, such as pandemics, crime scenes, medical settings, high pollution, and laboratories. The COVID-19 epidemic has increased the requirement for people to use protective face masks in public places. Analysis of face detection technology is crucial with blocked faces, which typically have visibility only in the periocular area and above. This paper aims to implement a model on complex data, i.e., by taking tasks for the face detection of people from the photo and in real-time video images with and without a mask. This task is implemented based on the features around their eyes, ears, nose, and forehead by using the original masked and unmasked images to form a baseline for face detection. The idea of performing such a task is by using the Caffe-MobileNetV2 (CMNV2) model for feature extraction and masked image classification. The convolutional architecture for the fast feature embedding Caffe model is used as a face detector, and the MobileNetV2 is used for mask identification. In this work, five different layers are added to the pre-trained MobileNetV2 architecture for better classification accuracy with fewer training parameters for the given data for face mask detection. Experimental results revealed that the proposed methodology performed well, with an accuracy of 99.64% on photo images and good accuracy on real-time video images. Other metrics show that the model outperforms previous models with a precision of 100%, recall of 99.28%, f1-score of 99.64%, and an error rate of 0.36%. Face mask detection was originally a form of computing application, but it is now widely used in other technological areas such as smartphones and artificial intelligence. Computer-based masked-face detection belongs in the category of biometrics, since it includes using a person’s unique features to identify them with a mask on.
A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar energy is one of the most common and well-known sources of energy in existing networks. But because of its non-stationary and non-linear characteristics, it needs to predict solar irradiance to provide more reliable Photovoltaic (PV) plants and manage the power of supply and demand. Although there are various methods to predict the solar irradiance. This paper gives the overview of recent studies with focus on solar irradiance forecasting with ensemble methods which are divided into two main categories: competitive and cooperative ensemble forecasting. In addition, parameter diversity and data diversity are considered as competitive ensemble forecasting and also preprocessing and post-processing are as cooperative ensemble forecasting. All these ensemble forecasting methods are investigated in this study. In the end, the conclusion has been drawn and the recommendations for future studies have been discussed.
Analysis of Bitcoin Price Prediction Using Machine Learning

The research purpose of this paper is to obtain an algorithm model with high prediction accuracy for the price of Bitcoin on the next day through random forest regression and LSTM, and to explain which variables have influence on the price of Bitcoin. There is much prior literature on Bitcoin price prediction research, and the research methods mainly revolve around the ARMA model of time series and the LSTM algorithm of deep learning. Although it cannot be proved by the Diebold–Mariano test that the prediction accuracy of random forest regression is significantly better than that of LSTM, the prediction errors RMSE and MAPE of random forest regression are better than those of LSTM. The changes in the variables that determine the price of Bitcoin in each period are also obtained through random forest regression. From 2015 to 2018, three US stock market indexes, NASDAQ, DJI, and S&amp;P500 and oil price, and ETH price have impact on Bitcoin prices. Since 2018, the important variables have become ETH price and Japanese stock market index JP225. The relationship between accuracy and the number of periods of explanatory variables brought into the model shows that for predicting the price of Bitcoin for the next day, the model with only one lag of the explanatory variables has the best prediction accuracy.
Memory, evolutionary operator, and local search based improved Grey Wolf Optimizer with linear population size reduction technique

Optimization of multi-modal functions is challenging even for evolutionary and swarm-based algorithms as it requires an efficient exploration for finding the promising region of the search space, and effective exploitation to precisely find the global optimum. Grey Wolf Optimizer (GWO) is a recently developed metaheuristic algorithm that is inspired by nature with a relatively small number of parameters for tuning. However, GWO and most of its variants may suffer from the lack of population diversity, premature convergence, and the inability to preserve a good balance between exploratory and exploitative behaviors. To address these limitations, this work proposes a new variant of GWO incorporating memory, evolutionary operators, and a stochastic local search technique. It further integrates Linear Population Size Reduction (LPSR) technique. The proposed algorithm is comprehensively tested on 23 numerical benchmark functions, high dimensional benchmark functions, 13 engineering case studies, four data classifications, and three function approximation problems. The benchmark functions are mostly taken from the CEC 2005 and CEC 2010 special sessions, and they include rotated, shifted functions. The engineering case studies are from the CEC 2020 real-world non-convex constrained optimization problems. The performance of the proposed GWO is compared with popular metaheuristics, namely, particle swarm optimization (PSO), gravitational search algorithm (GSA), slap swarm algorithm (SSA), differential evolution (DE), self-adaptive differential evolution (SADE), basic GWO and its three recently improved variants. Statistical analysis and Friedman tests have been conducted to thoroughly compare their performance. The obtained results demonstrate that the proposed GWO outperforms the algorithms compared for the benchmark functions and engineering case studies tested.
Robust image clustering via context-aware contrastive graph learning

Graph convolution networks (GCN) have recently become popular for image clustering. However, existing GCN-based image clustering techniques focus on learning image neighbourhoods which leads to poor reasoning on the cluster boundaries. To address this challenge, we propose a supervised image clustering approach based on contrastive graph learning (CGL). Our method generates an influential graph view (IGV) and a topological graph view (TGV) for each class to represent its global context from different viewpoints. These generated graph views are used to reason the inter-cluster relationships and intra-cluster boundaries from the local context of each node in a contrastive manner. Our method considers each class as a fully connected graph to explore its characteristics and strategically generate directional graph views. This enhances the transferability of the proposed approach to handle data with a similar structure. We conduct extensive experiments on open datasets such as LFW, CASIA-WebFace, and CIFAR-10 and show that our method outperforms state-of-the-art including deep GRAph Contrastive rEpresentation learning (GRACE), GraphCL, and Graph Contrastive Clustering (GCC).
BCINet: Bilateral cross-modal interaction network for indoor scene understanding in RGB-D images

Depth cue has proven to be useful information in the indoor scene understanding of RGB-D images for providing a geometric counterpart to RGB representation. However, because of the differences between RGB-D image pairs, utilizing cross-modal data effectively is a key issue. Most methods exclusively leverage depth data to unilaterally complement RGB data for better feature representation; they invariably ignore the fact that RGB and depth data can bilaterally complement each other. Herein, a novel RGB-D scene-understanding network called BCINet is presented, in which RGB and depth data bilaterally complement each other via a proposed bilateral cross-modal interaction module (BCIM). The BCIM helps to capture cross-modal complementary cues by crossly fusing enhanced features from one modality to the counterpart modality through a feature enhanced module. Meanwhile, exploiting the long-range dependencies of RGB-D features is also significant for accurate scene understanding. Specifically, we design a hybrid pyramid dilated convolution module to enlarge the receptive fields along both the vertical and horizontal spatial directions to adaptively capture diverse contexts with different shapes. Additionally, we propose a context-guided module to aggregate these diverse higher-level contexts with lower-level features in the encoder to guide the information flow for progressively refining the segmentation map. Experimental results on two indoor scene datasets demonstrate the superiority and effectiveness of the proposed BCINet over several state-of-the-art approaches.
YOLO-HR: Improved YOLOv5 for Object Detection in High-Resolution Optical Remote Sensing Images

Object detection is essential to the interpretation of optical remote sensing images and can serve as a foundation for research into additional visual tasks that utilize remote sensing. However, the object detection network currently employed in optical remote sensing images underutilizes the output of the feature pyramid, so there remains potential for an improved detection. At present, a suitable balance between the detection efficiency and detection effect is difficult to attain. This paper proposes an enhanced YOLOv5 algorithm for object detection in high-resolution optical remote sensing images, utilizing multiple layers of the feature pyramid, a multi-detection-head strategy, and a hybrid attention module to improve the effect of object-detection networks for use with optical remote sensing images. According to the SIMD dataset, the mAP of the proposed method was 2.2% better than YOLOv5 and 8.48% better than YOLOX, achieving an improved balance between the detection effect and speed.