Miracle-dz/UniFAR-FTUs huggingface dataset:
Comment on “Prediction of geomagnetic activity” by C. K. Goertz, Lin‐Hua Shan, and R. A. Smith

The authors comment on the previous paper by Goertz, et. al., which they had refereed prior to the authors deaths. They question a number of correlations claimed in that paper. In particular the auroral electrojet index observed, and that infered from ISEE 3 measurements. They also point out possible problems with a cutoff time filter applied to data used in the study. This filter would remove high frequency variations in the electrojet observations. Goertz, et. al. obtain equations for the ionospheric electric field which mimic the field across a resistor in an inductive circuit driven by a time varying voltage. The authors argue that their success in making predictions based on these equations is a consequence of several factors which they did not fully explore.
Drug Target Interaction Prediction using Graph Convolution based Neural Fingerprinting

The time and cost invested for development of new drugs for orphan diseases is substantial and it is not profitable as it only serves a small patient population. This process can be simplified using Drug Repurposing, a method where clinically approved drugs are predicted to have an impact in the treatment of novel diseases. A single drug may treat multiple diseases by acting on its related corresponding targets. This idea can be leveraged to repurpose drugs for better treatment of many diseases. Network models and text mining are some of the general approaches used to find repurposable drugs, but being rule based, they fail in ambiguous cases. In order to overcome these shortcomings, we propose a Deep Learning based approach to precisely predict drugs with maximum Drug Target Interaction (DTI) using KIBA dataset, which contains known drug-target interaction pairs. Having shown notable successes in Cancer treatment, drug repurposing can be extrapolated for many other conditions. In this paper, we predict drug target interaction using Neural Fingerprinting. This approach efficiently embeds the drug molecular structures and thereby predicts the binding affinity (Kd) with a MSE of 0.197 on the KIBA dataset.
Single Threshold TSO Level Converters for Ultra-Low Power Digital Designs

Applications like subthreshold sensor processor, on chip multiprocessors, etc. require robust and energy efficient level converter designs. In this work, we report single threshold transistor size optimized 22 nm level converter architectures with dual power supply of 0.6 V (VddL) and 0.8 V (VddH). We propose two five transistor (5T) LCs which consume considerably less power compared to static CMOS inverter-inverter designs. The 5T stacked NMOS LC emerges as the energy efficient architecture with significant reduction in power consumption and less Si area. Moreover, the 5T LCs presented in this study, efficiently operate at 10 MHz.
A GNSS Position Spoofing Mitigation Algorithm using Sparse Estimation

The Global Navigation Satellite Systems (GNSS) are widespread for providing Position, Velocity, and Time (PVT) information across the globe. The GNSS usually employs the Extended Kalman Filter (EKF) framework to estimate the PVT information of the receiver. The GNSS receivers PVT information is falsified by using a mimic GNSS signals is called a spoofing attack. This paper focuses mainly to combat the spoofing attack using sparse estimation theory. A generalized mathematical model is proposed for authentic and spoofed pseudoranges at the GNSS receiver. After that, a generalized pseudorange measurement model is presented by combining the authentic and spoofed pseudorange measurements. It is assumed that, only a part of satellite signals are spoofed. Further, the GNSS receiver’s state is estimated by mitigating the spoofed pseudoranges and it is formulated as a Least Absolute Shrinkage and Selection Operator (LASSO) optimization problem. The simulated results, compares the proposed LASSO based EKF algorithm with traditional EKF framework. It is observed that, the proposed algorithm suppresses the spoofing effect. Moreover, the Position Root Mean Square Error (PRMSE) of the proposed algorithm decreases by increasing the number of spoofed measurements.
Preliminary Analysis of the Influence of the Stereotype Threat on Computer Programming

Background: Workforce shortage in information technology (IT) has become a pressing issue. To increase the number of female IT professionals, it is important to improve the working environments of female IT professionals. A previous study showed that gender bias is a threat to the outcomes of mathematics tests by females. This influence is called the stereotype threat. Aim: We focused on the stereotype threat to enable female IT developers to deliver their best performance. Method: Using a subjective experiment, we analyzed the relationship between the stereotype threat and the performance of computer programming developers. In addition, we analyzed the effect of eliminating the stereotype threat on programming. Result: Focusing on the time to develop a program, neither the stereotype threat nor the elimination of the threat had any noticeable effect. This result might be affected by the nature of computer programming and a gamification effect.
Periodic Green's function for skewed 3‐D lattices using the Ewald transformation

We apply the Ewald acceleration technique to the efficient evaluation of periodic Green's functions (GFs) for 3‐D skewed lattices like those arising in electromagnetic/photonic band‐gap (EBG/PBG) structures and metamaterials (MTMs). We develop the expression for the optimal value of an associated splitting parameter, derive the gradient of the scalar potential GFs and address the extraction of singularity for both vector/scalar potential GF and their curl/gradient. Several numerical implementation issues are also discussed leading to further enhancement in computational speed, accuracy, and numerical stability. Finally, the accuracy of the developed GFs is verified against well established algorithms. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 1353–1357, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22429
Automated Detection of Heart Murmurs From the PCG Signal Using Stationary Wavelet Transform

Heart sound (HS) murmur detection in a phonocar-diogram (PCG) signal is essential for primary cardiac healthcare units. This work proposes a stationary wavelet transform (SWT) to automatically detect four HS murmurs, namely mitral valve prolapse, mitral regurgitation, aortic stenosis, mitral stenosis, and regular HS. First, the PCG signal is preprocessed and segmented into cardiac cycles. Second, SWT-based wavelet decomposition is carried out on each cardiac cycle to get different wavelet subband signals. Four novel features, relative wavelet subband energy, log-entropy energy, multi-scale kurtosis, and maximum absolute deviation, are extracted from each subband. Finally, these features are fed to a supervised random forest (RF) classifier for murmur detection. The efficacy of the proposed approach is verified using a multiclass heart murmur database. The proposed method shows a better performance result by achieving an overall accuracy (OA) of 99.28%, 99.27% precision, 99.27% recall, and 99.27% F1-score. The proposed method is competitive enough with the existing state-of-the-art methods. The performance results show that the proposed work is suitable for the preliminary detection of heart murmurs.
Are we in sync during turn switch?

During an interaction, people exchange speaking turns by coordinating with their partners. Exchanges can be done smoothly, with pauses between turns or through interruptions. Previous studies have analyzed various modalities to investigate turn shifts and their types (smooth turn exchange, overlap, and interruption). Modality analyses were also done to study the interpersonal synchronization which is observed throughout the whole interaction. Likewise, we intend to analyze different modalities to find a relationship between the different turn switch types and interpersonal synchrony. In this study, we provide an analysis of multimodal features, focusing on prosodic features (F0 and loudness), head activity, and facial action units, to characterize different switch types.
Exploring Activity and Contributors on GitHub: Who, What, When, and Where

Apart from being a code hosting platform, GitHub is the place where large-scale open collaborations and contributions happen. Every minute, thousands of developers are submitting code, having discussions of issues or pull requests, with all user behaviors recorded in the GitHub Event Stream (GES). Exploration of the activities in the GES could help understand who is active, the way they work, the time when they are active and even their location. To this end, a large-scale analysis was initially performed based on the 0.86 billion event records generated in 2020. We extracted 902K active contributors out of 14 million GitHub accounts by observing their activity distribution, then explored their behavior distribution, active time in the day and week, and estimated time zone distributions on the basis of their circadian activity rhythm. To go deeper, a case study of 79 projects in CNCF and contrast analyses of different project maturity levels were conducted. Our results showed that from a macro perspective, bots are increasingly more active and can serve numerous projects. Contributors work on weekdays, and are globally more inclined toward the daytime working hours in the Americas and Europe. The time zone distribution also reveals that UTC+2 and UTC-4 have the most active contributors. A critical discovery was the validation and quantification of a high bus factor risk exists in the OSS ecosystem. Whether from a large group point of view or within specific projects, a rather small group of OSS contributors (less than 20%) undertook the majority of the work. The GES can provide a wealth of information about open source software (OSS). Our findings provide insights into global GitHub collaboration behaviors and may be of help for researchers and practitioners to further understand modern OSS ecosystem.
Blockchain -Assisted Hybrid Deep Learning-Based Secure Mechanism for Software Defined Networks

Software Defined Network (SDN) has emerged as an autonomous concept for dynamic management and network configuration. Blockchain technology has the potential to enhance the security of SDN in many practical scenarios against cyber attacks. However, security lapses are realized when integrating the SDN with blockchain during the updation of the malicious traffic flows. Hence, in addition to the encryption mechanisms and machine learning models, adopting the deep learning model becomes crucial to provide a secure mechanism for the SDN-blockchain environment. Designing a deep learning model as the flow analyzer in the SDN-blockchain layers is critical due to the functioning of the blockchain and its security structure. In addition, deep learning models confront the overfitting and class imbalance constraints across various cyber attacks in the SDN. Thus, this paper develops the security mechanism for the SDN by dynamically selecting the network-centric nodes and applying Hybrid Deep Learning (HDL) to learn the spatial and temporal characteristics of network traffic for flow entry verification with the help of weight dispersive regularization and adaptive ownership weight schemes. In the proposed system, the HDL integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to perform the feature extraction with the handling of overfitting and traffic flow classification with the handling of class imbalance, respectively. The proposed HDL-SDN dynamically evaluates the traffic flows by enabling the trust measurement and resolves the gaps in the NSL-KDD dataset by enriching the NSL-KDD dataset with the features influenced by the designed blockchain-assisted SDN. Thus, the experimental results demonstrate that the HDL-SDN outperforms the existing SDN security scheme, yielding 8.55% higher accuracy while testing on the enriched NSL-KDD dataset.
Physics-based Analytical Modeling of p-GaN/AlGaN/GaN HEMTs

In this paper, a physics based analytical model is reported for potential variations at the various interfaces of pGaN cap AlGaN/GaN HEMT. This model uses solution of the Poisson’s equation self consistently for the calculation of potential drop at different regions. The 2D-density of states and the FermiDirac (FD) distribution are used to calculate the charge densities at various interfaces. The suggested model fits the TCAD and experimental data for the p-GaN cap HEMT and is explicit in nature providing physical insight of voltage drops at each layer and the correlation with the device parameters. An analysis is presented regarding the effect of out-diffused Mg dopants, polarization charges at various layers as well as the doping concentration of acceptor ions in p-GaN cap layer.
Verification of the Influence of Multiple Virtual Runners on Rival Recommendation for Acoustic AR Running Assistance System

In recent years, the number of people running to improve their health and physical fitness has been increasing. However, it is not easy to stay motivated and keep running as regular activity. Therefore, we believe that it is very significant to develop a running support system. In a previous study, a running assistance system that enables users to run with a virtual runner based on their own running records in an acoustic augmented reality space was constructed. In order to make the running experience more realistic and keep users motivated it is important to develop a system that allows users to compete with virtual runners who can serve as good rivals. In this study we examined how multiple virtual runners competing against the user affect the user's motivation.
Defeating Modern Day Anti-Viruses for Defense Evaluation

A system without an antivirus is just like a house with an open door. The majority of the attacks aim to compromise the endpoint. Anti-virus (AV) is used at the endpoint in conjunction with the firewall. With the increase in sophisticated attacks, many advancements have been done in AV. Now we see modern AV in the form of Endpoint Detection & Response (EDR). However, threat actors are still successful in evading EDR. Past research focuses on preventive measures in security rather than investigating how attack surface is increasing and AV won't help in defending our system. In this paper, we will present some techniques that can be used to evade modern-day next-generation AV. This research aims to help penetration testers and security researchers, to see how an advanced AV can be bypassed.
Discrete Pseudo-Control Sets for Optimal Control Problem

*Rocket-Space Corporation "Energia", Korolev, Moscow Region, 141070, Russia Methods of the optimal control problem solution based on a new concept of pseudocontrol sets are presented. The approach concerning with significant increasing of the decision variables by introducing artificial variables or pseudo-variables is used. Largescale linear programming algorithms and well-known discretization of the continuous system dynamics on small segments are applied. The sets are considered independently for each segment. The every set is expressed as a discrete mesh approximation of an admissible control space. Terminal conditions are presented as a linear matrix equation. An extension of the matrix equation for the sums of the pseudo-controls is used to transform the problem into a linear programming form. A linear matrix inequality is formed for the interior-point inequality constraints. The resulting linear programming form is characterized by matrices that are very large and sparse. The number of decision variables is on the order of tens of thousands. In modern linear programming methods there are interior-point algorithms to solve such problems. Minimum path-planning problem with nonlinear constraints and reentry trajectory optimization with maximum crossrange are considered as application examples.
Life Cycle Analysis of Hydrogen Production and Fuel Cell Electric Vehicle in Indian Conditions

This study presents Life Cycle Assessment (LCA) of hydrogen production from Proton Exchange Membrane Water Electrolysis (PEMWE). The emission factor of Fuel Cell Electric Vehicle (FCEV) has also been calculated based on hydrogen produced from PEMWE. Though there are many methods for hydrogen production like Steam Methane Reforming (SMR), PEMWE is a leading edge method with less carbon emissions, especially if electricity generated from renewable sources are used for electrolysis. The Energy Pay Back Time (EPBT) of the PEMWE system has been calculated to be 3.54 years in Indian conditions. The emission factor is 52.3 kgCO2e/kg of H2 if the system is supported by the present generation mix, but reduced to 20.95 kgCO2e/kg of H2 if supported by solar power. The Vehicle Emission Factor (VEF), considering only driving phase is 0.52 and 0.21 kgCO2e/km respectively. The study shows that the integration of renewables in the generation mix will have significant impact on the GHG emission footprint of H2 production in India. It has also been concluded that in projected generation scenarios for India with more renewable penetration, there shall be notable reduction in the emission values.
Reinforcement Learning based Control of a Quadruped Robot

Quadruped robots are four legged robots having potential to roam almost in all the earth surfaces in different terrains. A quadruped robot which is passively stable with 12 DOF is considered in this paper. The 12 DOF quadruped model is developed in Fusion 360 and later imported to ROS. The mathematical modelling of the quadruped system is complex and conventional control requires the complete knowledge of the system. Hence, Reinforcement Learning based algorithm can be used for the control of a quadruped system. The steps involved in making the robot walk include stabilizing the robot at neutral position, planning footsteps, generating body trajectory, planning swing leg trajectory and solution of inverse kinematic model to find the joint angles. A deep deterministic policy gradient (DDPG) agent is used to train the system to walk in straight line. The DDPG agent uses a critic value function representation to approximate the long-term reward given observations and actions. This quadruped is capable of walking amidst disturbances unlike a quadruped with conventional control.
Study on Optimization of Ventilation and Virus Transmission in Large Classroom Buildings

In large classroom buildings, people are crowded and the flow field of air is complex. It is important to study the diffusion pattern of aerosol-borne viruses in large classrooms to prevent the spread of infectious diseases and viruses. The distribution and dispersion of aerosol particles with viruses in a classroom were analyzed by numerical simulation using a large classroom in a school. For the structural diversity of classrooms, the effects on particle distribution under two working conditions, natural ventilation and central air conditioning ventilation, were studied separately. The numerical simulation results show that the aerosol particles are effectively suppressed in the central air conditioning air distribution case, and the ventilation pressure of 50 Pa has the best virus aerosol discharge effect.
Artificial Intelligence for Predicting Mortality Due to Sepsis

Objective: Sepsis is a life-threatening organ dysfunction caused by a systemic host response to infection, and lead to multi organ failure and septic shock. The incidence of sepsis and sepsis-related death were reported approximately 48.9 million and 11.0 million in the worldwide. Specially, the mortality of the patients who were diagnosed severe sepsis and septic shock was 30 - 50%, and its can cause a tremendous loss for medical resource and socioeconomic cost. Although various clinical indicators were developed for the predicting mortality, these indicators need to apply plenty of the features, or confusing due to ambiguous criteria. To solve this topic, we focused on a deep learning (DL) model predicting mortality due to sepsis. Methods: We developed a prediction model employing the multilayer perceptron (MLP), and built the dataset with 577 survival patients and 208 mortality patients to train and evaluate. Results: To evaluate the proposed model performance, we applied 5 metrics such as the accuracy, sensitivity, specificity, F1-score and area under the receiver operating characteristic (AUROC) curve. The proposed model predicted mortality due to sepsis in 164 out of 208 mortality group (sensitivity: 73.81%) and survival in 438 out of 577 survival group (specificity: 74.14%). The F1-score was 60.19% and the AUROC was 0.835. Through the comparison with the clinical indicator and machine learning models, the proposed model showed outperforming. Conclusion: Although the MLP model is not a novel technique in the artificial intelligence field, we confirmed a possibility of the DL by this study that is first step for clinicians and patients.
Capsule-based Autoencoder Network for Pointcloud Reconstruction

The theory of capsule networks and the dynamic routing mechanism for capsules was introduced by Geoffrey Hinton and his research team. In this new approach, they tried to solve typical problems of classical convolutional neural networks. For example, that the efficiency of neural networks degrades when a geometric transformation is applied on the input image, or when the data is far away from the training dataset. It became clear early on that capsule networks are state-of-the-art solutions for visual data classification tasks. For other tasks their use is less common and in many cases difficult to apply. For example image segmentation or object detection and localization. The efficiency of the capsule networks theory in the field of pointcloud processing is also an open question. In this work we investigated the pointcloud reconstruction capability of capsule networks. In this approach, three different complexity autoencoder networks was selected. We created a decoder network based on capsules theory, which was fitted to the existing autoencoder networks. The efficiency of the networks was tested using four different datasets. As a result of our work, we show the effectiveness of capsule networks in the field of pointcloud reconstruction compared with the selected autoencoder networks.
Detection of Landslide Based on Convolutional Neural Networks

Human activities are one of the main causes of geological disasters. From the perspective of visual observation, the degree of environmental damage can be reflected by detecting the exposed area of vegetation in the image along the road. In order to verify the applicability of the deep learning method in the detection of landslide, the EfficientDet model is applied to objects detection. This objects detection model is based on convolutional neural networks. A data set of landslide is established. The training samples are marked by labelme software. The object detection of landslide is carried out by EfficientDet model. The result of Precision, Recall, F1, AP, Average training time of each epoch (s) and FPS are 0.8158, 0.3240, 0.46, 0.5132, 27.17s, 12.2, respectively. The method can detect landslide objects well.