Reviewerly/exHarmony huggingface dataset:
ULN: An efficient face recognition method for person wearing a mask

Although the face recognition has advanced by leaps and bounds in recent years, recognizing faces with large occlusion, e.g., masks, is still a challenging problem. In the context of the COVID-19 outbreak, wearing masks becomes mandatory, which fails numerous face attendance and surveillance systems. Therefore, a robust face recognition algorithm that can deal with facial masks is urgently needed. To build a mask-robust face recognition algorithm, we first generate numerous facial images with masks based on public face datasets, which obviously alleviates the problem of the training data shortage. Second, we propose a novel network architecture called Upper-Lower Network (ULN) to recognize the faces with masks efficiently. The upper branch of ULN with the mask-free images as input is pretrained that provides supervisory information for the training of the lower branch. Considering that the occlusion areas of masks usually appear in the lower parts of faces, we further divide the high-order semantic features into upper and lower parts. The designed loss function force the learned features of the lower branch similar to those of the upper branch with the same mask-free image inputs, but only the upper part of features similar to the mask counterparts. Extensive experiments demonstrate that the proposed method is effective for recognizing persons with masks and outperforms other state-of-the-art face recognition methods.
A conceptual model for studying the immersive mobile augmented reality application-enhanced experience

This study aims to develop and propose an integrated conceptual framework that illustrates how emerging technologies such as mobile augmented reality applications (MAR apps) stimulate a user's immersive MAR app-enhanced experience-a human psychological state of being engaged and engrossed in a virtual environment-which in turn facilitates user responses.This study draws on a literature review of related fields to develop a theoretical model showing the centrality of the immersive MAR app-enhanced experience.A conceptual model that explicates the selected antecedents and outcomes of the AR-enhanced immersive experience is proposed. The findings suggest that the traits of both the user (mental imagery, personal innovativeness) and the device (simulated physical control, environmental embedding) facilitate the immersive MAR app-enhanced experience. Moreover, the immersive MAR app-enhanced experience is identified as a key driver of customer emotions, values and behavioral responses.The integrated conceptual model provides scholars and practitioners with a general picture of the main factors affecting the immersive AR-enhanced experience, as well as the benefits available to firms; thus, theoretical and practical implications are delineated.Conceptual paper.
Adapting Semantic Similarity Methods for Case-Based Reasoning in the Cloud

Clood is a cloud-based CBR framework based on a microservices architecture which facilitates the design and deployment of case-based reasoning applications of various sizes. This paper presents advances to the similarity module of Clood through the inclusion of enhanced similarity metrics such as word embedding and ontology-based similarity measures. Being cloud-based, costs can significantly increase if the use of resources such as storage and data transfer are not optimised. Accordingly, we discuss and compare alternative design decisions and provide justification for each chosen approach for Clood.
Evolving Through the Looking Glass: Learning Improved Search Spaces with Variational Autoencoders

Abstract Nature has spent billions of years perfecting our genetic representations, making them evolvable and expressive. Generative machine learning offers a shortcut: learn an evolvable latent space with implicit biases towards better solutions. We present SOLVE: Search space Optimization with Latent Variable Evolution, which creates a dataset of solutions that satisfy extra problem criteria or heuristics, generates a new latent search space, and uses a genetic algorithm to search within this new space to find solutions that meet the overall objective. We investigate SOLVE on five sets of criteria designed to detrimentally affect the search space and explain how this approach can be easily extended as the problems become more complex. We show that, compared to an identical GA using a standard representation, SOLVE with its learned latent representation can meet extra criteria and find solutions with distance to optimal up to two orders of magnitude closer. We demonstrate that SOLVE achieves its results by creating better search spaces that focus on desirable regions, reduce discontinuities, and enable improved search by the genetic algorithm.
Uncertainty of quantum channels via modified generalized variance and modified generalized Wigner–Yanase–Dyson skew information

Uncertainty relation is a fundamental issue in quantum mechanics and quantum information theory. By using modified generalized variance (MGV), and modified generalized Wigner-Yanase-Dyson skew information (MGWYD), we identify the total and quantum uncertainty of quantum channels. The elegant properties of the total uncertainty of quantum channels are explored in detail. In addition, we present a trade-off relation between the total uncertainty of quantum channels and the entanglement fidelity and establish the relationships between the total uncertainty and entropy exchange/coherent information. Detailed examples are given to the explicit formulas of the total uncertainty and the quantum uncertainty of quantum channels. Moreover, utilizing a realizable experimental measurement scheme by using the Mach-Zehnder interferometer proposed in Nirala et al. (Phys Rev A 99:022111, 2019), we discuss how to measure the total/quantum uncertainty of quantum channels for pure states.
Non-invasive Haemoglobin Prediction Using Nail Color Features: An Approach of Dimensionality Reduction

Estimation of blood haemoglobin level is essential for evaluating health condition related to anaemia and its associated diseases. The invasive way of haemoglobin estimation is costly as well as it needs trained professionals along with a good infrastructural and administrative support. Due to the lack of specialized equipment, trained professionals, and laboratory facilities, non-invasive haemoglobin estimation approaches are in high demand, specially, in the rural areas where there is lack of resources. This paper reports a smartphone camera-based non-invasive haemoglobin prediction model that analyses the video photography of pallor changes of the nail bed due to pressure application and release. Unlike different color space features extracted directly from the video, a reduced dimensional latent space representation of those features is considered to train a model for haemoglobin level estimation. A Gaussian-neighborhood function in error calculation of autoencoder is incorporated to get a reduced dimensional latent structure that holds all the properties of input patterns. The mean root-mean-squared (RMSE) error between the actual and predicted output derived from the different state-of-the-art regression models and a weighted sum rule over the output of regression models are compared to prove the efficacy of the proposed model.
Rapid alteration of cortical bone in fresh- and seawater solutions visualized and quantified from the millimeter down to the atomic scale

It is widely known that post mortem diagenetic alteration processes cause modifications and overprinting of the chemical and isotopic proxies incorporated in vivo in bone apatite and collagen. Understanding the processes occurring during the interaction between fluids and bones in an early diagenetic setting is fundamental to determining the extent to which the commonly-used geochemical proxies in bone get modified during fossilization. The present study experimentally investigates the structural and chemical changes in bone induced by controlled in vitro aqueous alteration experiments under simulated early diagenetic conditions. It is intended to derive a deeper phenomenological and quantitative understanding of the transport and reaction processes that occur in the early stage of fossilization. For this purpose, 3.5 mm-sized cylinders were drilled from modern ostrich cortical bone and immersed in different experimental solutions enriched with tracers such as Zn, Sr, rare earth elements, and U. The experiments ran for several hours to weeks at 30, 60, and 90 °C - the latter two temperatures were chosen to accelerate anticipated early diagenetic modifications of the bone samples. Both the bone samples and the experimental solutions were analyzed using micro-analytical techniques such as Raman spectroscopy, electron microprobe, high-resolution inductively coupled plasma-mass spectrometry, nanoscale ion microprobe, and atom probe tomography to assess mineralogical, chemical, and structural changes from the millimeter to the atomic scale. The results show that element uptake into the bone samples occurs within hours after they have been exposed to an aqueous solution instead of years, as previously assumed. Additionally, distinct modifications of the organic phase were observed, accompanied by the growth of new apatite phases by dissolution-reprecipitation and recrystallization processes. Carbonate-poor or -free hydroxylapatite formed in the sample center and more stable carbonated fluorapatite in the sample rim. From these data, a phenomenological model is derived that explains the interaction between bone and aqueous solutions during the earliest stages of fossilization. This study also demonstrates the importance of using a comprehensive methodological approach when investigating alteration processes whose effects range from the millimeter down to the atomic scale.
Phishing website detection: How effective are deep <scp>learning‐based</scp> models and hyperparameter optimization?

Abstract Phishing websites are fraudulent websites that appear legitimate and trick unsuspecting users into interacting with them, stealing their valuable information. Because phishing attacks are a leading cause of data breach, different anti‐phishing solutions have been explored for cybersecurity management including machine learning‐based technical approaches. However, there is a gap in understanding how robust deep learning‐based models together with hyperparameter optimization are for phishing website detection. In this vein, this study pursues the tasks of developing parsimonious deep learning models and hyperparameter optimization to achieve high accuracy and reproducible results for phishing website detection. This paper demonstrates a systematic process of building detection models based on three deep learning algorithm architectures (Long Short‐Term Memory‐based detection models, Fully Connected Deep Neural Network‐based detection models, and convolutional neural network‐based detection models) that are built and evaluated using four publicly available phishing website datasets, achieving the best accuracy of 97.37%. We also compared two different optimization algorithms for hyperparameter optimization: Grid Search and Genetic Algorithm, which contributed to 0.1%–1% increase in accuracy.
Multi-Population Differential Evolution Algorithm with Uniform Local Search

Differential evolution (DE) is a very effective stochastic optimization algorithm based on population for solving various real-world problems. The quality of solutions to these problems is mainly determined by the combination of mutation strategies and their parameters in DE. However, in the process of solving these problems, the population diversity and local search ability will gradually deteriorate. Therefore, we propose a multi-population differential evolution (MUDE) algorithm with a uniform local search to balance exploitation and exploration. With MUDE, the population is divided into multiple subpopulations with different population sizes, which perform different mutation strategies according to the evolution ratio, i.e., DE/rand/1, DE/current-to-rand/1, and DE/current-to-pbest/1. To improve the diversity of the population, the information is migrated between subpopulations by the soft-island model. Furthermore, the local search ability is improved by way of the uniform local search. As a result, the proposed MUDE maintains exploitation and exploration capabilities throughout the process. MUDE is extensively evaluated on 25 functions of the CEC 2005 benchmark. The comparison results show that the MUDE algorithm is very competitive with other DE variants and optimization algorithms in generating efficient solutions.
Interpretable Artificial Intelligence through Locality Guided Neural Networks

In current deep learning architectures, each of the deeper layers in networks tends to contain hundreds of unorganized neurons which makes it hard for humans to understand how they interact with each other. By organizing the neurons using correlation as the criteria, humans can observe how clusters of neighbouring neurons interact with each other. Research in Explainable Artificial Intelligence (XAI) aims to all alleviate the black-box nature of current AI methods and make them understandable by humans. In this paper, we extend our previous algorithm for XAI in deep learning, called Locality Guided Neural Network (LGNN). LGNN preserves locality between neighbouring neurons within each layer of a deep network during training. Motivated by Self-Organizing Maps (SOMs), the goal is to enforce a local topology on each layer of a deep network such that neighbouring neurons are highly correlated with each other. Our algorithm can be easily plugged into current state of the art Convolutional Neural Network (CNN) models to make the neighbouring neurons more correlated. A cluster of neighbouring neurons activating for a class makes the network both quantitatively and qualitatively more interpretable when visualized, as we show through our experiments. This paper focuses on image processing with CNNs, but can theoretically be applied to any type of deep learning architecture. In our experiments, we train VGG and WRN networks for image classification on CIFAR100 and Imagenette. Our experiments analyse different perceptible clusters of activations in response to different input classes.
An Automated Tool for Upgrading Fortran Codes

With archaic coding techniques, there will be a time when it will be necessary to modernize vulnerable software. However, redeveloping out-of-date code can be a time-consuming task when dealing with a multitude of files. To reduce the amount of reassembly for Fortran-based projects, in this paper, we develop a prototype for automating the manual labor of refactoring individual files. ForDADT (Fortran Dynamic Autonomous Diagnostic Tool) project is a Python program designed to reduce the amount of refactoring necessary when compiling Fortran files. In this paper, we demonstrate how ForDADT is used to automate the process of upgrading Fortran codes, process the files, and automate the cleaning of compilation errors. The developed tool automatically updates thousands of files and builds the software to find and fix the errors using pattern matching and data masking algorithms. These modifications address the concerns of code readability, type safety, portability, and adherence to modern programming practices.
A new modification and application of item response theory‐based feature selection for different machine learning tasks

Abstract Feature selection (FS) is an important step of the existing machine learning (ML) methodology since it often makes it possible to obtain better results using a lower number of features. Hence, in the literature, there exist many studies aiming at proposing a new FS method or improving an existing one in the context of ML. Accordingly, this study presents a new lossy modification of a feature selector which is a specific type of filter‐based FS and depends on item response theory. This method computes feature importance in a supervised manner and is previously employed for classical text categorization (TC) task, where it was shown that the selector provided satisfying results on high dimensional and benchmark text datasets. As such, this paper introduces a new modification of this selector along with its new variants and investigates its applicability for different ML tasks other than TC. Experimental results are obtained on 35 different datasets, of which nine are well‐known and real‐world datasets from the UCI ML repository. Our comparative results with the most popular filter‐based FS methods show that it is possible to obtain better results with this new modified selector or one of its variants on the majority of both binary and real‐world datasets compared to its well‐known peers.
Active Federated YOLOR Model for Enhancing Autonomous Vehicles Safety

A precise and real-time object detection system is crucial to ensuring the safety, smoothness, and trust of Autonomous Vehicles (AVs). Several machine learning techniques have been designed to improve vehicle detection capabilities and reduce the shortcomings caused by limited data and by transferring these data to a central server, which has shown poor performance under different conditions. In this paper, we propose an active federated learning-integrated solution over AVs that capitalizes on the You Only Learn One Representation (YOLOR) approach, a Convolutional Neural Network (CNN) specifically designed for real-time object detection. Our approach combines implicit and explicit knowledge, together with active learning and federated learning with the aim of improving the detection accuracy. Experiments show that our solution achieves better performance than traditional solutions (i.e., Gossip decentralized model and Centralized model).
An Improved Matting-SfM Algorithm for 3D Reconstruction of Self-Rotating Objects

The 3D reconstruction experiment can be performed accurately in most cases based on the structure from motion (SfM) algorithm with the combination of the multi-view stereo (MVS) framework through a video recorded around the object. However, we need to artificially hold the camera and stabilize the recording process as much as possible to obtain better accuracy. To eliminate the inaccurate recording caused by shaking during the recording process, we tried to fix the camera on a camera stand and placed the object on a motorized turntable to record. However, in this case, the background did not change when the camera position was kept still, and the large number of feature points from the background were not useful for 3D reconstruction, resulting in the failure of reconstructing the targeted object. To solve this problem, we performed video segmentation based on background matting to segment the object from the background, so that the original background would not affect the 3D reconstruction experiment. By intercepting the frames in the video, which eliminates the background as the input of the 3D reconstruction system, we could obtain an accurate 3D reconstruction result of an object that could not be reconstructed originally when the PSNR and SSIM increased to 11.51 and 0.26, respectively. It was proved that this algorithm can be applied to the display of online merchandise, providing an easy way for merchants to obtain an accurate model.
Implementing Micro Frontends Using Signal-based Web Components

Micro frontends are an emerging architectural style that involves combining independently created frontend applications to develop a larger application. In JavaScript, the high modularity required for micro frontends can be achieved using the Web Components API, but passing data between components requires the use of event mechanisms and their corresponding callback functions, which make the code difficult to understand. In this paper, we propose using signals in reactive programming (RP) as an API to solve this problem and enable declarative dataflow, as well as maintain high modularity. To demonstrate the effectiveness of the proposed approach, we have developed a JavaScript extension that can express the web components themselves as signals and used it to implement a simple single-page application (SPA), which is then compared with the implementation using the Web Components API. We have also conducted a similar implementation in the existing reactive frontend application framework called React and argue that the implementation using signals has better modularity.
Improved YOLOv5 Network with Attention and Context for Small Object Detection

Object detection is one of the most important and challenging branches in computer vision. Although impressive progress have been achieved on large or medium scale objects, detecting small objects from images is still difficult due to the limited image size and feature information. To deal with the small object detection problem, we explore how the popular YOLOv5 object detector can be modified to improve its performance on detecting small objects. To achieve this, we integrate Coordinate Attention (CA) and Context Feature Enhancement Module (CFEM) in YOLOv5 network. Coordinate Attention is based on attention mechanism and it embeds positional information into channel attention, which enables deep neural network to augment the representations of the objects of interest. Context Feature Enhancement Module explores rich context information from multiple receptive fields and only contains several additional layers. Extensive experimental results on the VisDrone-Detection dataset demonstrate that our approach can improve the performance of the proposed method for small object detection.
Outsourced Data Integrity Checking with Practical Key Update in Edge-Cloud Resilient Networks

The edge-cloud continuum is an advanced paradigm for cloud computing, which brings data storage and compute power closer to users or devices, and as a result, eliminates lag time and saves bandwidth. However, this new paradigm is harsh for secure storage and computation due to the separation of data ownership and control. Data security, especially outsourced data integrity in the edge-cloud resilient network, becomes one of the most fundamental challenges. To check the outsourced data integrity and address the efficient key update issue in this scenario, in this article, we propose a framework of outsourced data integrity checking with a practical key update in the edge-cloud resilient network. We first review the existing outsourced data integrity checking algorithms and then put forward a potential solution to achieving outsourced data integrity checking with a practical key update, which is composed of three phases, namely key request and update, local data upload, and outsourced data integrity auditing. We implement a prototype system for our proposal as well, which demonstrates its practicality.