Neural architecture search, which aims to automatically search for architectures (e.g., convolution, max pooling) of neural networks that maximize validation performance, has achieved remarkable progress recently. In many application scenarios, several parties would like to collaboratively search for a shared neural architecture by leveraging data from all parties. However, due to privacy concerns, no party wants its data to be seen by other parties. To address this problem, we propose federated neural architecture search (FNAS), where different parties collectively search for a differentiable architecture by exchanging gradients of architecture variables without exposing their data to other parties. To further preserve privacy, we study differentially-private FNAS (DP-FNAS), which adds random noise to the gradients of architecture variables. We provide theoretical guarantees of DP-FNAS in achieving differential privacy. Experiments show that DP-FNAS can search highly-performant neural architectures while protecting the privacy of individual parties. The code is available at https://github.com/UCSD-AI4H/DP-FNAS

Research in quantum computing hardware has shown that quantum bits can be implemented with superconducting circuits, these qubits could be used in other applications such as quantum devices for electronic systems. In this paper a nonlinear parameterised two qubits quantum device is presented, the current - voltage characteristic of this element is similar to an adjustable memristor when its parameters are changed.

Circuit models for multiphase coupled inductors are summarized, compared, and unified. Multiwinding magnetic structures are classified into parallel-coupled structures and series-coupled structures. For parallel-coupled structures used for multiphase inductors, the relationships between a) inductance-matrix models, b) extended cantilever models, c) magnetic-circuit models, d) multiwinding transformer models, e) gyrator-capacitor models, and f) inductance-dual models are examined and discussed. These models represent identical physical relationships in the multiphase coupled inductors, but emphasize different physical aspects and offer distinct design insights. The circuit duality between the series-coupled structure and the parallel-coupled structure is explored. Design equations for interleaved multiphase buck converters based on these models are streamlined and summarized, and a simplified equation showing the relationships between current ripple with and without coupling is presented. The models and design equations are verified through theoretical derivation, SPICE simulation, and experimental measurements.

A document by Pengyuan Zhou . Click on the document to view its contents.

This paper reports the fundamentals and SPICE implementation of the dynamic memdiode model (DMM) for the conduction characteristics of bipolar resistive switching devices. The model equations are implemented in the LTSpice simulator using an equivalent circuital approach with behavioral components and sources.

This work analyses the different phases of Covid-19 outbreak in India and performs progress of the disease spread. The data is collected from John Hopkins epidemiological data providing the latest data from January 31,2020 to May 31, 2020. A simple mathematical model is developed to gather a quantitative picture of the epidemic spreading with limited reference data. Further, we performed an analysis and forecast of the disease spread in different phases of lock down in the country. The profound model predictions considering the overall data exhibit that the numbers to reach peak between 28 August 2020 to 6 Sep 2020. As the pandemic still increases the number of infected cases, different quarantine levels would serve as an effective measure in containing the spread much earlier than the other similar cases.

The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using Machine Learning (ML) techniques to build more efficient and reliable Intrusion Detection Systems (IDSs). However, the advent of larger IDS datasets has negatively impacted the performance and computational complexity of ML-based IDSs. Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues. While most of these researchers reported the success of these preprocessing techniques on a shallow level, very few studies have been performed on their effects on a wider scale. Furthermore, the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML algorithm used, which most of the existing studies give little emphasis on. Thus, this study provides an in-depth analysis of feature selection and normalization effects on various IDS models built using two IDS datasets namely, NSL-KDD and UNSW-NB15, and five different ML algorithms. The algorithms are support vector machine, k-nearest neighbor, random forest, naive bayes, and artificial neural network. For feature selection and normalization, the decision tree wrapper-based approach, which tends to give superior model performance, and min-max normalization methods were respectively used. A total of 30 unique IDS models were implemented using the full and feature-selected copy of the datasets. The models were evaluated using popular evaluation metrics in IDS modeling, intra- and inter-model comparisons were performed between models and with state-of-the-art works. Random forest achieved the best performance on both NSL-KDD and UNSW-NB15 datasets with prediction accuracies of 99.87% and 98.5%, as well as detection rates of 99.79% and 99.17% respectively, it also achieved an excellent performance in comparison with the recent works. The results show that both normalization and feature selection positively affect IDS modeling with normalization shown to be more important than feature selection in improving performance and computational time. The study also found that the UNSW-NB15 dataset is more complex and more suitable for building and evaluating modern-day IDS than NSL-KDD.

“© 20xx IEEE.Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” DOI: 10.1109/TPWRD.2020.3003250 Abstract: When a lightning strikes the top of a transmission line tower or shield wires, electromagnetic waves propagate through the tower back and forth, increasing the voltage across insulator strings. ‡is can eventually lead to a back-flƒashover (BF), which may cause damage to equipment or costly power outages. To calculate the over-voltages and predict the probability of a BF, an accurate model of the tower and its grounding system is needed in electromagnetic transient (EMT) type simulators. ‡There are a number of theoretical models for the equivalent circuit of a transmission tower. However, they either are not accurate enough or they are derived for a certain type of transmission tower, which limits their applicability. Numerical electromagnetic analyses have less simplifications compared to the theoretical solutions and are by far less expensive than field measurements. They also have the flexibility to analyze any type of tower. In this paper, the direct method for the measurement of tower impedance is implemented by NEC4 and applied to a 400-kV double circuit tower with all its details. Th‡e process of obtaining the wire network of the tower used in this paper is completely automated and it can be applied to any other type of transmission tower. Th‡e results of the numerical simulations are compared to those obtained with existing tower models. Th‡e developed model in this paper is capable of considering all the details of the tower and including the \eurofinite resistance of the ground and grounding electrodes.

In recent years, artificial neural networks (ANNs) have won numerous contests in pattern recognition, machine learning, and artificial intelligence. The basic unit of an ANN is to mimic neurons in the brain. Neuron in ANNs is expressed as f(wx+b) or f(wx).This structure does not consider the information processing capabilities of dendrites. However, recently, studies shown that dendrites participate in pre-calculation in the brain. Concretely, biological dendrites play a role in the pre-processing to the interaction information of input data. Therefore, it’s time to perfect the neuron of the neural network. This paper, add dendrite processing section, presents a novel artificial neuron, according to our previous studies (CR-PNN or Gang transform). The dendrite processing section can be expressed as WA.X. Because I perfected the basic unit of ANNs-neuron, there are so many networks to try, this article gives the basic architecture for reference in future research.

This paper, discusses the implementation of RSA (Rivest–Shamir–Adleman) algorithm of encrypting and decrypting data. This is an assymetric algorithmic approach, which implies that two different keys, one public and another private have been used to accomplish the purpose. This paper also extends the application of RSA algorithm by choosing random prime numbers for every byte of transferred data. The programs involved are entirely based on the microprocessor INTEL 8085.

According to WHO, COVID-19 is an infectious disease and has a significant social and economic impact. The main challenge in ?fighting against this disease is its scale. Due to the imminent outbreak, the medical facilities are over exhausted and unable to accommodate the piling cases. A quick diagnosis system is required to address these challenges. To this end, a stochastic deep learning model is proposed. The main idea is to constrain the deep representations over a gaussian prior to reinforce the discriminability in feature space. The model can work on chest X-ray or CT-scan images. It provides a fast diagnosis of COVID-19 and can scale seamlessly. This work presents a comprehensive evaluation of previously proposed approaches for X-ray based disease diagnosis. Our approach works by learning a latent space over X-ray image distribution from the ensemble of state-of-the-art convolutional-nets, and then linearly regressing the predictions from an ensemble of classifi?ers which take the latent vector as input. We experimented with publicly available datasets having three classes { COVID-19, normal, Pneumonia. Moreover, for robust evaluation, experiments were performed on a large chest X-ray dataset with fi?ve different very similar diseases. Extensive empirical evaluation shows how the proposed approach advances the state-of-the-art.

“This paper is a postprint of a paper submitted to and accepted for publication in IET Generation, Transmission & Distibution (GTD) and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.” Abstract: Grounding electrodes have an important role in electric power transmission and distribution systems. They are used to prevent excessive hazardous voltages between metallic structures and ground in the case of system faults or lightning surges. It is important that they provide a low impedance path for the current in to the ground. The electrical properties of soil, which vary substantially with geographical location and time of year, affect the process considerably along with the properties of the grounding electrode itself, such as its dimensions. In order to have an accurate estimation of the developed overvoltages and the backflashover rate of the transmission lines due to a lightning strike, one has to take into account the effect of the value of the soil electrical parameters, such as the electrical conductivity and dielectric constant. This paper investigates the high frequency behavior of the grounding electrodes by solving a full-wave electromagnetic problem using the Finite Element Method (FEM). The focus is on taking into account the effect of the variation of soil relative permittivity which has been neglected in the previous studies of the grounding systems. This allows an evaluation of the response of grounding systems due to seasonal changes and specifically change of the water content of the soil, which would cause its electrical properties to vary significantly. This study demonstrates the importance of considering the variation of relative permittivity of the soil especially in the modeling of electrodes buried in highly resistive soil.

The COVID-19 is a highly contagious viral infection which played havoc on everyone’s life in many different ways. According to the world health organization and scientists, more testing potentially helps governments and disease control organizations in containing the spread of the virus. The use of chest radiographs is one of the early screening tests to determine the onset of disease, as the infection affects the lungs severely. This study will investigate and automate the process of testing by using state-of-the-art CNN classifiers to detect the COVID19 infection. However, the viral could of many different types; therefore, we only regard for COVID19 while the other viral infection types are treated as non-COVID19 in the radiographs of various viral infections. The classification task is challenging due to the limited number of scans available for COVID19 and the minute variations in the viral infections. We aim to employ current state-of-the-art CNN architectures, compare their results, and determine whether deep learning algorithms can handle the crisis appropriately. All trained models are available at https://github.com/saeed-anwar/COVID19-Baselines

The 3rd Generation Partnership Project (3GPP) specification of the fifth generation (5G) New Radio (NR) allows for a highly scalable and flexible radio access technology to cater to network operators with different requirements. Such scalability and flexibilities in network configurations inevitably translate to complications in the design and implementation of 5G-NR systems. Radio access in 5G-NR is much more complex and involved than its predecessor, 4G long term evolution (LTE) and LTE-Advanced technology. Therefore, the 5G-NR specifications turn out to be quite dense. Specifically, the specifications are concise, design motivations rarely explained, and the information can be convoluted or distributed across several documents. Moreover, there are several key design details associated with the access layer procedures for any given physical layer channel, which are often omitted in the specifications. For example, design motivation aspects of initial access channels or signal generation can be quite difficult to follow or understand in 5G-NR. In this paper, all the design details associated with initial access channels and signal generation in 5G-NR specifications are laid out. The contributions of the paper are three folds. First, the design details and justifications associated with both downlink and uplink access channels are presented along with signal generation details. Secondly, receiver design aspects of NR PRACH short formats are discussed. Lastly, PRACH receiver implementation aspects and performance reports from different network operators are presented and compared with 3GPP specified Radio Performance and Protocol aspect requirements[1] for millimeter wave (mmW) access. The work in this paper is of significant value to researchers and system engineers looking to design and build initial access algorithms as part of 5G NR systems.   [1] Radio Performance and Protocol aspect requirements are specified by the 3GPP Radio Access Network working group 4, also known as RAN4.

This paper proposes a mathematical model to perform optimal operational planning of large-scale energy systems with high share of renewable energy. Furthermore, it analyses the influence of different unit commitment modelling approaches on the operational planning outcomes. The value of co-optimisation of electricity and heating sector is emphasized in this paper. The results show the influence of massive renewable penetration in the energy sector towards 2050, and how this influences generation from other sources such as thermal and hydro. Including unit commitment constraints with integer variables leads to more realistic behaviour of the units, at the cost of increasing considerably the computational time. Relaxing integer variables reduces significantly the computational time, without highly compromising the accuracy of the results. Neglecting the unit commitment constraints leads to inaccurate results in terms of underestimation of costs, curtailment, wind's and solar PV's average revenue per energy unit sold, price volatility, and to overestimation of the flexibility of thermal units. Hence, depending on the purpose of the analysis, it is recommended to consider carefully the choice of unit commitment modelling approach and acknowledge the limitations. When the focus is on prices and revenues, using unit commitment constraints with integer variables is preferable.

Deep learning (DL) has seen great success in the computer vision (CV) field, and related techniques have been used in security, healthcare, remote sensing, and many other fields. As a parallel development, visual data has become universal in daily life, easily generated by ubiquitous low-cost cameras. Therefore, exploring DL-based CV may yield useful information about objects, such as their number, locations, distribution, motion, etc. Intuitively, DL-based CV can also facilitate and improve the designs of wireless communications, especially in dynamic network scenarios. However, so far, such work is rare in the literature. The primary purpose of this article, then, is to introduce ideas about applying DL-based CV in wireless communications to bring some novel degrees of freedom to both theoretical research and engineering applications. To illustrate how DL-based CV can be applied in wireless communications, an example of using a DL-based CV with a millimeter-wave (mmWave) system is given to realize optimal mmWave multiple-input and multiple-output (MIMO) beamforming in mobile scenarios. In this example, we propose a framework to predict future beam indices from previously observed beam indices and images of street views using ResNet, 3-dimensional ResNext, and a long short-term memory network. The experimental results show that our frameworks achieve much higher accuracy than the baseline method, and that visual data can significantly improve the performance of the MIMO beamforming system. Finally, we discuss the opportunities and challenges of applying DL-based CV in wireless communications.

Waste collection and management is an integrated part of both city and village life. Lack of optimized and efficient waste collection system vastly affect public health and costs more. The prevailing traditional waste collection system is neither optimized nor efficient. Internet of Things (IoT) has been playing a great role in making human life easier by making systems smart, adequate and self sufficient. Thus, this paper proposes an IoT based efficient waste collection system with smart bins. It does real-time monitoring of the waste bins and determines which bins are to emptied in every cycle of waste collection. The system also presents an enhanced navigation system that shows the best route to collect wastes from the selected bins. Four waste bins are assumed in the city of Mount Pleasant, Michigan at random location. The proposed system decreases the travel distance by 30.76% on an average in the assumed scenario, compared to the traditional waste collection system. Thus it reduces the fuel cost and human labor making the system optimized and efficient by enabling real-time monitoring and enhanced navigation.

Most cyber-attacks and data breaches in cloud infrastructure are due to human errors and misconfiguration vulnerabilities. Cloud customer-centric tools are lacking, and existing security models do not efficiently tackle these security challenges. Novel security mechanisms are imperative, therefore, we propose Risk-driven Fault Injection (RDFI) techniques to tackle these challenges. RDFI applies the principles of chaos engineering to cloud security and leverages feedback loops to execute, monitor, analyze and plan security fault injection campaigns, based on a knowledge-base. The knowledge-base consists of fault models designed from cloud security best practices and observations derived during iterative fault injection campaigns. Furthermore, the observations indicate security weaknesses and verify the correctness of security attributes (integrity, confidentiality and availability) and security controls. Ultimately this knowledge is critical in guiding security hardening efforts and risk analysis. We have designed and implemented the RDFI strategies including various chaos algorithms as a software tool: CloudStrike. Furthermore, CloudStrike has been evaluated against infrastructure deployed on two major public cloud systems: Amazon Web Service and Google Cloud Platform. The time performance linearly increases, proportional to increasing attack rates. Similarly, CPU and memory consumption rates are acceptable. Also, the analysis of vulnerabilities detected via security fault injection has been used to harden the security of cloud resources to demonstrate the value of CloudStrike. Therefore, we opine that our approaches are suitable for overcoming contemporary cloud security issues