Reviewerly/exHarmony huggingface dataset:
Implementation of an Embedded Masked Face Recognition System using Huskylens System-On-Chip Module

Globally, Facial recognition systems have been increasingly adopted, by governments, as a viable means of identification and verification in public spaces such as the airport, train stations, and stadiums. However, in the wake of the COVID-19 outbreak, the World Health Organization (WHO) declared that wearing face masks is an essential safety precaution. As a result, current facial recognition systems have difficulties recognizing faces accurately, which motivated this study. This research aims to implement an embedded masked face recognition system using the HuskyLens SoC module to identify people, even while wearing a face mask. The developed method was actualized using the Kendryte K210 chip embedded in the HuskyLens module. This system-on-chip design was integrated with other peripherals using an Arduino Pro-mini board. The results of testing and evaluating the system's performance show that the system's facial recognition accuracy with masked and without masks faces was 90% and 95%, respectively. Implementing this solution in our environment would enable accurate real-time recognition of masked and unmasked faces
Augmented PIN Authentication through Behavioral Biometrics

Personal Identification Numbers (PINs) are widely used today for user authentication on mobile devices. However, this authentication method can be subject to several attacks such as phishing, smudge, and side-channel. In this paper, we increase the security of PIN-based authentication by considering behavioral biometrics, specifically the smartphone movements typical of each user. To this end, we propose a method based on anomaly detection that is capable of recognizing whether the PIN is inserted by the smartphone owner or by an attacker. This decision is taken according to the smartphone movements, which are recorded during the PIN insertion through the built-in motion sensors. For each digit in the PIN, an anomaly score is computed using Machine Learning (ML) techniques. Subsequently, these scores are combined to obtain the final decision metric. Numerical results show that our authentication method can achieve an Equal Error Rate (EER) as low as 5% in the case of 4-digit PINs, and 4% in the case of 6-digit PINs. Considering a reduced training set, composed of solely 50 samples, the EER only slightly worsens, reaching 6%. The practicality of our approach is further confirmed by the low processing time required, on the order of fractions of milliseconds.
Разработка архитектуры универсального фреймворка федеративного обучения

The paper researches the technology of federated learning that allows collective machine learning on distributed training datasets without transferring them to a single central storage.The relevance of the technology is determined by the long growing trend towards using machine learning methods to solve many applied problems on the one hand, and by the growth of requests for privacy and data processing closer to the data source or directly at the source, including legislative ones, on the other hand.The main problems in creating federated learning systems are the lack of flexible frameworks for various federated learning scenarios: the majority of the existing solutions focus on training artificial neural networks in a centralized computing environment.The subject of the research is the common framework architecture for developing applied federated learning systems, which allows building systems for different scenarios, parameters and topologies of the computing environment, various models, and machine learning algorithms.The article considers the federated learning subject area, gives the main definitions, describes the process of federated learning, presents and analyzes various scenarios of possible applied tasks for federated learning.It contains the analysis of the most well-known federated learning frameworks at the time of writing, as well as their application for possible cases that were described previously.As a result, there is a description of the architecture of a universal framework that, unlike the existing ones, can be used to develop applied federated learning systems of various types and different cases.
Statistical Shape Model of the Temporal Bone Using Segmentation Propagation

Automated image registration techniques can successfully determine anatomical variation in human temporal bones with statistical shape modeling.There is a lack of knowledge about inter-patient anatomical variation in the temporal bone. Statistical shape models (SSMs) provide a powerful method for quantifying variation of anatomical structures in medical images but are time-intensive to manually develop. This study presents SSMs of temporal bone anatomy using automated image-registration techniques.Fifty-three cone-beam temporal bone CTs were included for SSM generation. The malleus, incus, stapes, bony labyrinth, and facial nerve were automatically segmented using 3D Slicer and a template-based segmentation propagation technique. Segmentations were then used to construct SSMs using MATLAB. The first three principal components of each SSM were analyzed to describe shape variation.Principal component analysis of middle and inner ear structures revealed novel modes of anatomical variation. The first three principal components for the malleus represented variability in manubrium length (mean: 4.47 mm; ±2-SDs: 4.03-5.03 mm) and rotation about its long axis (±2-SDs: -1.6° to 1.8° posteriorly). The facial nerve exhibits variability in first and second genu angles. The bony labyrinth varies in the angle between the posterior and superior canals (mean: 88.9°; ±2-SDs: 83.7°-95.7°) and cochlear orientation (±2-SDs: -4.0° to 3.0° anterolaterally).SSMs of temporal bone anatomy can inform surgeons on clinically relevant inter-patient variability. Anatomical variation elucidated by these models can provide novel insight into function and pathophysiology. These models also allow further investigation of anatomical variation based on age, BMI, sex, and geographical location.
Self-Supervised Denoising for Real Satellite Hyperspectral Imagery

Satellite hyperspectral remote sensing has gradually become an important means of Earth observation, but the existence of various types of noise seriously limits the application value of satellite hyperspectral images. With the continuous development of deep learning technology, breakthroughs have been made in improving hyperspectral image denoising algorithms based on supervised learning; however, these methods usually require a large number of clean/noisy training pairs, a target that is difficult to meet for real satellite hyperspectral imagery. In this paper, we propose a self-supervised learning-based algorithm, 3S-HSID, for denoising real satellite hyperspectral images without requiring external data support. The 3S-HSID framework can perform robust denoising of a single satellite hyperspectral image in all bands simultaneously. It first conducts a Bernoulli sampling of the input data, then uses the Bernoulli sampling results to construct the training pairs. Furthermore, the global spectral consistency and minimum local variance are used in the loss function to train the network. We use the training model to predict different Bernoulli sampling results, and the average of multiple predicted values is used as the denoising result. To prevent overfitting, we adopt a dropout strategy during training and testing. The results of denoising experiments on the simulated hyperspectral data show that the denoising performance of 3S-HSID is better than most state-of-the-art algorithms, especially in terms of maintaining the spectral characteristics of hyperspectral images. The denoising results for different types of real satellite hyperspectral data also demonstrate the reliability of the proposed method. The 3S-HSID framework provides a new technical means for real satellite hyperspectral image preprocessing.
Detecting Code Smells with AI: a Prototype Study

Artificial intelligence is one of the key advances in computing. AI has applications in improving the quality of software, supporting software development tools, and providing smartness to the systems we develop. In this paper, we study the adaptability of AI in a simple software engineering-related problem, a code smell analysis. Code smells are characteristics in the source code that indicate that there is a deeper problem, and they are a long-term nuisance for the developers. The code smells are not syntax or semantics errors, they simply are a product of too many fixes, changes and additions grinding down the code coherence. While it might be easy to refactor to get rid of code smells, first they need to be discovered. In this paper, our research group demonstrates a prototype for detecting these code smells. The prototype was implemented in Python, using machine learning, neural networks, and deep learning as a basis. Training and testing data were taken from external data storages. As a result, the prototype was able to detect two different code smells successfully, with a relatively small amount of training data. In addition, we identified issues that need to be addressed when the line of research and prototype development is pursued further.
<scp>PGWO‐AVS‐RDA</scp>: An intelligent optimization and clustering based load balancing model in cloud

Summary Load balancing and task scheduling in cloud have gained a significant attention by many researchers, due to the increased demand of computing resources and services. For this purpose, there are various load balancing methodologies are developed in the existing works, which are mainly focusing on allocating the tasks to Virtual Machines (VMs) based on their priority, order of tasks, and execution time. Still, it facing the major difficulties in finding the best tasks for allocation, because the sequence of patterns are normally used to categorize the relevant tasks with respect to the load. Thus, this research work intends to develop an intelligent group of mechanisms for efficiently allocating the tasks to the VMs by finding the best tasks with respect to the scheduling parameters. Initially, the user tasks are given to the load balancer unit, where the Probabilistic Gray Wolf Optimization (PGWO) technique is used to find the best fitness value for selecting the tasks. Then, the Adaptive Vector Searching (AVS) methodology is utilized to cluster the group of tasks for efficiently allocating the tasks with improved Quality of Service (QoS). Finally, the Recursive Data Acquisition (RDA) based scheduler unit can allocate the clustered tasks to the appropriate VMs in the cloud system by analyzing the properties of storage capacity, balancing load of VM, CPU usage, memory consumption, and execution time of tasks. During evaluation, the performance of the proposed load balancing model is validated by using various measures. Then, the obtained results are compared with some state‐of‐the‐art models for proving the betterment of the proposed scheme.
Lessons learned on using High-Performance Computing and Data Science Methods towards understanding the Acute Respiratory Distress Syndrome (ARDS)

Acute Respiratory Distress Syndrome (ARDS), also known as noncardiogenic pulmonary edema, is a severe condition that affects around one in ten-thousand people every year with life-threatening consequences. Its pathophysiology is characterized by bronchoalveolar injury and alveolar collapse (i.e., atelectasis), whereby its patient diagnosis is based on the so-called ‘Berlin Definition‘. One common practice in Intensive Care Units (ICUs) is to use lung recruitment manoeuvres (RMs) in ARDS to open up unstable, collapsed alveoli using a temporary increase in transpulmonary pressure. Many RMs have been proposed, but there is also confusion regarding the optimal way to achieve and maintain alveolar recruitment in ARDS. Therefore, the best solution to prevent lung damages by ARDS is to identify the onset of ARDS which is still a matter of research. Determining ARDS disease onset, progression, diagnosis, and treatment required algorithmic support which in turn raises the demand for cutting-edge computing power. This paper thus describes several different data science approaches to better understand ARDS, such as using time series analysis and image recognition with deep learning methods and mechanistic modelling using a lung simulator. In addition, we outline how High-Performance Computing (HPC) helps in both cases. That also includes porting the mechanistic models from serial MatLab approaches and its modular supercomputer designs. Finally, without losing sight of discussing the datasets, their features, and their relevance, we also include broader selected lessons learned in the context of ARDS out of our Smart Medical Information Technology for Healthcare (SMITH) research project. The SMITH consortium brings together technologists and medical doctors of nine hospitals, whereby the ARDS research is performed by our Algorithmic Surveillance of ICU (ASIC) patients team. The paper thus also describes how it is essential that HPC experts team up with medical doctors that usually lack the technical and data science experience and contribute to the fact that a wealth of data exists, but ARDS analysis is still slowly progressing. We complement the ARDS findings with selected insights from our Covid-19 research under the umbrella of the European Open Science Cloud (EOSC) fast track grant, a very similar application field.
DC-VAE, Fine-grained Anomaly Detection in Multivariate Time-Series with Dilated Convolutions and Variational Auto Encoders

Due to its unsupervised nature, anomaly detection plays a central role in cybersecurity, in particular on the detection of unknown attacks. A major source of cybersecurity data comes in the form of multivariate time-series (MTS), representing the temporal evolution of multiple, usually correlated measurements. Despite the many approaches available in the literature for time-series anomaly detection, the automatic detection of abnormal events in MTS remains a complex problem. In this paper we introduce DC-VAE, a novel approach to anomaly detection in MTS, leveraging convolutional neural networks (CNNs) and variational auto encoders (VAEs). DC-VAE detects anomalies in time-series data, exploiting temporal information without sacrificing computational and memory resources. In particular, instead of using recursive neural networks, large causal filters, or many layers, DC-VAE relies on dilated convolutions (dc) to capture long and short term phenomena in the data, avoiding complex and less-efficient deep architectures, simplifying learning. We evaluate dc-vae on the detection of anoma-lies on a large-scale, multi-dimensional network monitoring dataset collected at an operational mobile internet service provider (isp), where anomalous events were manually labeled during a time span of 7-months, at a five-minutes granularity. Results show the main properties and advantages introduced by VAEs for time-series anomaly detection, as well as the out-performance of dilated convolutions as compared to standard VAEs for time-series modeling.
18‐2: Assessment of Image Quality in Augmented Reality Displays Using a Computational Model of Target Detectability

We characterized augmented reality (AR) displays and developed a computational task‐based model to evaluate their image quality. The model enables fast assessment of image quality and evaluation of tradeoffs between display configurations such as display luminance response, uniformity, resolution, and noise. This work supports the development and optimization of AR displays without extensive measurements or resource‐intensive perception studies.
Method of Interaction between Blockchain and the World outside the Chain based on Oracle Machine

Blockchain is a decentralized distributed ledger where data is produced and stored in units of blocks, and is connected end to end in chronological order to form a chain structure. However, at present, the blockchain has a certain degree of closure, that is, the problem of interaction between the blockchain and the outside of the chain, and the blockchain cannot access the state of the external closed world system. In order to overcome this limitation, various platforms have introduced the concept of oracles in different forms and purposes. However, so far, there is still a lack of elaboration on the classification, analysis and research of basic Oracle methods. Based on the two basic perspectives of Oracle, this article studies and expresses the basic Oracle model of the blockchain to solve the following problems: From the perspective of the blockchain, the data flow is divided into inbound data flow and outbound data flow; From the perspective of the originator of the data flow, the communication direction is divided into push-based communication and pull-based communication. This paper describes the structural design of these four methods in detail, which can provide researchers with some method inspiration and design ideas for the interaction between the blockchain and the off-chain world.
Detecting Phishing Websites Using Machine Learning

Phishing, a cybercriminal's attempted attack, is a social web-engineering attack in which valuable data or personal information might be stolen from either email addresses or websites. There are many methods available to detect phishing, but new ones are being introduced in an attempt to increase detection accuracy and decrease phishing websites ‘ success to steal information. Phishing is generally detected using Machine Learning methods with different kinds of algorithms. In this study, our aim is to use Machine Learning to detect phishing websites. We used the data from Kaggle consisting of 86 features and 11,430 total URLs, half of them are phishing and half of them are legitimate. We trained our data using Decision Tree (DT), Random Forest (RF), XGBoost, Multilayer Perceptrons, K-Nearest Neighbors, Naive Bayes, AdaBoost, and Gradient Boosting and reached the highest accuracy of 96.6using X G Boost.
A Combined Method for Object Detection under Rain Conditions Using Deep Learning

The process of object detection utilizing deep learning is one of the most important deep learning applications and computer vision techniques, where one can learn image features in normal weather conditions and different rain conditions. Therefore, a deep convolutional neural network (DCNN) has become more important for object detection. Rain is a common and maj or factor in degrading image quality and decreasing object detection reliability. The main aim of this work is to remove rain streaks to get high reliability in detection process and decrease the error rate, in normal conditions and different rain conditions (light, medium and heavy). Firstly, the quality of the images is improved and removed rain streaks by de-raining algorithm that use the Deep Detail Network (DDN) method. Then the way deep learning is the main object detector, through use the YOLO to detect objects and determine its type. YOLOv3 and tiny-YOLOv3 have been determine from the literature review as the most suitable and efficient algorithms for detecting objects in real time after improving the image quality. The performance of these algorithms has been calculated and compared with each other. To evaluate the effectiveness of the devised approach (De-raining+YOLOv3), Fl-score, Recall, and Precision were computed. Using the proposed method combined from DDN with YOLOv3 technique (De-raining+YOLOv3), the mean of Fl-score of 95.02%, Recall of 97.22%, and a Precision of 92.92% were attained. Our presented approach is more resilient and accurate in object detection under rain conditions according to the findings of the results of the experiments. It is considered the best way for object detection under rain conditions with high reliability.
Unsupervised simple Siamese representation learning for blind super-resolution

Deep convolutional neural networks have made unprecedented achievements in image super-resolution (SR) and dominated the field due to their remarkable performance. When the degradation pattern of the test images is inconsistent with the training images, it leads to poor model performance. For example, the degradation could happen after a dimensional stretching. In this case, the most common method is to take blurry, noise, and low-resolution (LR) images and reconstructs SR images by degradation estimation. However, the SR results for this method are highly dependent on the estimation accuracy. To overcome the difficulty with the degradation estimation, this paper designs a degradation representation attention network (DRAN) for image SR. In which, we propose the use of a simple Siamese representation learning to extract the degradation information from various LR images. Specifically, DRAN distinguishes degradation information instead of performing degradation estimation, which can greatly reduce the difficulty. In other words, DRAN can avoid pixel-level operations, transform degradation computation problems into degradation classification problems and flexibly process LR images through degradation representation learning. Finally, DRAN also introduces a channel attention mechanism to enhance the performance of SR. Experimental results show that the proposed scheme can distinguish different degradation modes and obtain accurate degradation information. Meanwhile, experiments on synthetic and real images show that the DRAN achieves remarkable performance on blind SR tasks with good visual effects.
A Stack Based Multimodal Machine Learning Model for Breast Cancer Diagnosis

Breast cancer is the most frequent type of cancer, and it has a dismal prognosis. It represents about 30 % (or 1 in 3) of all new female cancers each year. As a result, there is a pressing need to create efficient and quick computational approaches for breast cancer prognosis. In this study, a multimodal deep learning model that enables decision-making on data from multiple data sources is proposed and used with three different classifiers. We achieved 82% accuracy in decision trees, 90% in random forests and 88% in support vector machines. We have seen that the results we get from the combined data are more successful than the distinct convolutional neural network models we have run separately before. Combining diverse data sources for the successful application of multimodal deep learning algorithms appears to be an effective strategy to improve human breast cancer prediction performance.
High-resolution Solar Image Reconstruction Based on Non-rigid Alignment

Suppressing the interference of atmospheric turbulence and obtaining observation data with a high spatial resolution is an issue to be solved urgently for ground observations. One way to solve this problem is to perform a statistical reconstruction of short-exposure speckle images. Combining the rapidity of Shift-Add and the accuracy of speckle masking, this paper proposes a novel reconstruction algorithm-NASIR (Non-rigid Alignment based Solar Image Reconstruction). NASIR reconstructs the phase of the object image at each frequency by building a computational model between geometric distortion and intensity distribution and reconstructs the modulus of the object image on the aligned speckle images by speckle interferometry. We analyzed the performance of NASIR by using the correlation coefficient, power spectrum, and coefficient of variation of intensity profile (CVoIP) in processing data obtained by the NVST (1m New Vacuum Solar Telescope). The reconstruction experiments and analysis results show that the quality of images reconstructed by NASIR is close to speckle masking when the seeing is good, while NASIR has excellent robustness when the seeing condition becomes worse. Furthermore, NASIR reconstructs the entire field of view in parallel in one go, without phase recursion and block-by-block reconstruction, so its computation time is less than half that of speckle masking. Therefore, we consider NASIR is a robust and high-quality fast reconstruction method that can serve as an effective tool for data filtering and quick look.