Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Approximate Analytical Analysis of the Response of Non-Linear Damped Systems Subjected to Step Function Excitation

Abstract A new approximate technique developed by Mansour and Hussein [1] has been used to solve a non-linear differential equation model that describes the underdamped and the overdamped motion of systems subjected to step function excitation. The analytical results obtained in this work have been compared with the numerical ones obtained by performing the Butcher Runge-Kutta method, and have been shown to be accurate in comparison.
Advancements in Intrusion Detection Systems for Internet of Things Using Machine Learning

Advancement in technology leads to connecting different types of devices or things to the Internet and enables the formation of a special kind of network called the Internet of Things (IoT). Intrusion detection in an IoT is a challenging task due to its unique characteristics. Machine learning schemes possess the potential to improve intrusion detection systems in case of an IoT. In this paper, we present a survey of advancements in research on the use of machine learning approaches for intrusion detection in an IoT. Our focus is on architectures, schemes, and the types of machine learning approaches used for intrusion detection. We compare different schemes based on their basis and features.
Nonlinear Model Predictive Control of a Spacecraft Equipped with Reactions Wheels on SO(3)

In the present work, nonlinear model predictive control of a spacecraft equipped with reaction wheels as the actuators that generate the driving torque for the system is studied. The spacecraft body is also exposed to external disturbance originated from the existence of off-center in the system structure. The system configuration manifold is Lie group SO(3). Discrete equations of motion of the system, referred to as Lie group variational integrators and the NMPC formulation are much more complicated due to the terms added to the equations considering the effects of speed of the wheels and the angular momentum exchanges between the wheels and the spacecraft body and also due to the effects of external disturbances. However, the model of the system and consequently, the obtained results are much more realistic in return. The necessary conditions of optimality are extracted and solved using indirect shooting method based on sensitivity derivatives. Since the equations of motion are established based on Lie group properties, the geometric structure of the system is preserved in long time integration. In addition, the extracted equations are symplectic and momentum preserving and show good energy behavior. The system of a spacecraft with three reaction wheels with the off-center in the z-direction which impose external disturbance to the system is simulated using the extracted equations for NMPC control of spacecraft on SO(3) and the simplification of sensitivity equations is used to reduce the computation time of optimizations. The obtained results show that our method is able to bring the system to the zero position even when there is an off-center in the yaw direction of the spacecraft by structure.
Matrix Profile XXV: Introducing Novelets: A Primitive that Allows Online Detection of Emerging Behaviors in Time Series

While offline exploration of time series can be useful, time series analysis is almost unique in allowing the possibility of direct and immediate intervention. For example, if we are monitoring an industrial process and our algorithm predicts imminent failure, the algorithm could direct a controller to open a release valve or alert a response team. There now exist mature tools to monitor time series for known behaviors (template matching), previously unknown highly conserved behaviors (motifs) and unexpected behaviors (anomalies). In this work we claim that there is another useful primitive, emerging behaviors, that are worth monitoring for. We call such behaviors Novelets. We explain that Novelets are neither anomalies nor motifs but can be loosely thought of as initially apparent anomalies that are later realized to be motifs. We will show Novelets have a natural interpretation in many disciplines, including science, medicine, and industry. As we will further demonstrate, Novelet discovery can have many downstream uses, including prognostics and abnormal behavior detection. We will demonstrate the utility of our proposed primitive on a diverse set of domains.
GRANDE: a neural model over directed multigraphs with application to anti-money laundering

The application of graph representation learning techniques to the area of financial risk management (FRM) has attracted significant attention recently. However, directly modeling transaction networks using graph neural models remains challenging: Firstly, transaction networks are directed multigraphs by nature, which could not be properly handled with most of the current off-the-shelf graph neural networks (GNN). Secondly, a crucial problem in FRM scenarios like anti-money laundering (AML) is to identify risky transactions and is most naturally cast into an edge classification problem with rich edgelevel features, which are not fully exploited by the prevailing GNN design that follows node-centric message passing protocols. In this paper, we present a systematic investigation of design aspects of neural models over directed multigraphs and develop a novel GNN protocol that overcomes the above challenges via efficiently incorporating directional information, as well as proposing an enhancement that targets edge-related tasks using a novel message passing scheme over an extension of edge-to-node dual graph. A concrete GNN architecture called GRANDE is derived using the proposed protocol, with several further improvements and generalizations to temporal dynamic graphs. We apply the GRANDE model to both a real-world anti-money laundering task and public datasets. Experimental evaluations show the superiority of the proposed GRANDE architecture over recent state-of-the-art models on dynamic graph modeling and directed graph modeling.
Energy-Efficient Single Transistor Neuron for Reconfigurable Threshold Logic and Image Classification

This work explores the applications of germanium based energy-efficient single transistor neuron for reconfigurable threshold logic. The implementation is done with the help of single device without using any external circuitry. The nature of bias current applied at the drain of the MOSFET, and the weights associated with the inputs decide the logic that is to be implemented. A three-layer spiking neural network has also been designed using the proposed neuron to verify image classification efficiency of the network for multiclass MNIST dataset. The maximum accuracy of 96.88% was achieved from the designed network.
Investigation of the charge transport mechanism and subgap density of states in p-type Cu2O thin-film transistors

We investigate the charge transport mechanism and subgap density of states (DOS) in p-type Cu2O thin-film transistors (TFTs) using the bias and temperature dependence of the drain currents. Among several charge transport mechanisms, the experimental data are well matched with a multiple trapping and release model, which suggests that the charge transport in the Cu2O TFT is mainly limited by trap states at grain boundaries or dielectric/semiconductor interface. The subgap DOS is extracted based on the Meyer-Neldel rule. Large density of subgap states is extracted, which is considered to be the reason of low mobility in fabricated Cu2O TFTs.
A Novel Parameters Linearization Algorithm for n-Links Robot Manipulators

In recent years, adaptive control has been widely used due to its acceptable performance in systems with uncertain parameters. One of the limitations of adaptive control methods is the calculation of parameter linearization. Until now, this work has been done manually, and a systematic approach has not been introduced. In this study, an algorithm is presented that can separate the regressor terms of a dynamical system from kinematic parameters. This method has no restrictions on the number of degrees of freedom(DOF) of the system and can add any uncertainty to the system. In the end, for evaluation of the algorithm, the inverse dynamic adaptive control method has been performed for a 4-link robot. The simulation results show that despite all uncertainties in dynamics (e.g., mass and product moments of inertia) and kinematics terms (e.g., length and center of mass), the controller has been able to follow the reference signal. This adaptive controller has 0.5% error in the joint variable while tracking the sine reference. Moreover, the regressor terms obtained from the algorithm for 2-link robots are presented and compared to the manual separation. the results prove that all of the regressor terms have been identified.
Online Fake News Detection using Pre-trained Embeddings

Fake news is a word, often heard. We often come across this term and its dangerous consequences with respect to society. To curb the menace of fake news is a big challenge in today's world. Many efforts have been made through different approaches to detect the fake news on online media. In this study, we propose a fake news detection system that utilizes pre-trained embeddings for the detection of fake news. We use a benchmark dataset of social media to carry our experiments. This study uses pre trained GloVe embedding based embedding matrix to generate the embeddings for the text as an input to our deep learning model. We also compare the performance of simple deep neural network based model with the LSTM based deep neural network on the same input embeddings. The results are indicative of good performance of the models to detect fake news.
Stress Assessment with Convolutional Neural Network Using PPG Signals

Stress is one of the main issues of nowadays lifestyle. If it becomes chronic it can have adverse effects on the human body. Thus, the early detection of stress is crucial to prevent its hurting effects on the human body and have a healthier life. Stress can be assessed using physiological signals. To this end, Photoplethysmography (PPG) is one of the most favorable physiological signals for stress assessment. This research is focused on developing a novel technique to assess stressful events using raw PPG signals. To achieve this goal, an adaptive convolutional neural network (CNN) combined with Multilayer Perceptron (MLP) has been utilized to realize the detection of stressful events. This research will use a dataset that is publicly available and named wearable stress and effect detection (WESAD). This dataset will be used to simulate the proposed model and to examine the advantages of the proposed developed model. The proposed model in this research will be able to distinguish between normal events and stressful events. This model will be able to detect stressful events with an accuracy of 96.7%.
Cardiovascular Disease Prediction using Machine Learning and Deep Learning

One of the leading causes of death is CVD(Cardiovascular disease) which is communally referred to as heart disease. CVD is the summation of disorders that affect the heart's ability to function. Every year, over 18 million people worldwide die as a result of heart disease. One in three deaths from cardiovascular disease in them is preventable, and heart attacks can be predicted months in advance by assessing the patient's risk factors. Obesity, Blood Pressure, Cholesterol, and Glucose levels are some of the risk factors. The aim is to predict CVD based on risk factors of patients, who are affected by their habits and patients' basic health information, using the Cardiovascular Disease Dataset from Kaggle using Machine Learning and Deep Learning Algorithms. Smoking and Alcohol consumption are some practices that will maximize the possibility of getting cardiovascular disorders, and doing workouts will reduce the risk of getting cardiovascular disease. In our work, we have implemented four models for predicting CVD namely Logistic Regression, Naive Bayes, Deep Learning Model and Random Forest. The DL model, with an accuracy of 73.78 %, outperformed the other three models.
Experimentelle Untersuchung über harmonische Schwingungen in rotierenden Flüssigkeiten

Beschreibung einer Apparatur, welche die Charakteristiken nachzuweisen gestattet, die bei erzwungenen, kleinen harmonischen Schwingungen in einer gleichformig rotierenden, reibungsfreien und inkompressiblen Flussigkeit entstehen. Die beobachteten Erscheinungen stehen in Ubereinstimmung mit den theoretischen Ergebnissen. The characteristic lines produced in a uniformly rotating non-viscous incompressible liquid by forced small harmonic oscillations are investigated with the help of an experimental set-up described in the paper. The observed phenomena agree with the theoretical predictions.
Energy and Operations Optimization for Effective Greenhouse Management

IoT technology-based process automation that can be applied to a greenhouse leads to making condition management and status monitoring more robust while leading to saving energy and resources. The proposed system which is based on IoT technology and MQTT protocol can set optimal growth conditions for plant and seed growth within the greenhouse. The sensor-based inputs are to be transformed into the processed values based on the defined logic and the standard benchmarks gathered from the local agricultural authorities. The key areas of condition monitoring to be done via temperature, humidity, soil moisture, and lighting can ultimately yield an increased harvest having supported both the plant and seeds-based implementations for multiple types of plants. One of the most important factors to consider is that the farmers can have energy savings through the proposed solution by controlling the actuators in an optimal manner and reducing manual intervention by a considerable amount. The excess usage of electricity by lights and cooling fan usage in the greenhouse can be controlled with real-time data tracking and better analytics. The use of water can be properly maintained for the plants by putting only the required amount will make the soil wet and spraying the required amount to air will make better humidity control. Thus, the real-time condition-based controlling of the actuators leads to making the greenhouse operations more optimal and better utilization of resources and energy which ultimately results in financial benefits for the greenhouse owner. Based on the evaluated power consumption of the greenhouse power usage before and after the system was installed, the newly introduced system can save energy by having optimal control of actuators by performing algorithmic calculations to meet only the required level of weather conditions. This is to be proven experimentally by implementing the proposed system for a defined period of time under the monitoring of energy usage.
LAWSUP - A Smart Platform to Assist Stakeholders of Business Law

Corporate law, sometimes known as business law, is the body of law that governs the rights, relationships, and behavior of persons, corporations, organizations, and businesses. Business Organizations, employees/laborers, and the public are involved in this area of the law accompanying lawyers, and legal advisors. Business organizations need legal advice. Employees face many difficulties and injustices at their workplaces. People who wish to start a new business, search for legal guidance. When one of these parties needs support, they must seek a lawyer, go to the lawyer, and get legal support. When delivering legal support to clients, lawyers are still going through a manual process. There are very few systems that have been implemented for the law domain so far, and those only search engine types of systems that are unable to support every stakeholder of this domain. There is no common platform for all these stakeholders to find solutions, connect with a good lawyer and get support. We have identified the main issues faced by business organizations, employees that need legal support, the general public, and lawyers, and developed a web solution by implementing Machine Learning, Classification Algorithms, Text mining, Natural Language Processing, and Web Crawlers.
An Approximate Ternary Full Adder using Carbon nanotube field effect transistors

Multi-valued logic (MVL) is becoming popular because it has potential to reduce area, interconnect, pin count and hence energy than binary logic. The energy efficiency and chip area of ternary circuits can be further improved by employing approximate computing principles in error-tolerant applications. This paper proposes a dynamic approximate ternary full adder that uses only 64 and 66 Carbon nanotube field effect transistors (CNTFET) by exploiting its multi-threshold capabilities through the control of diameter of its nanotubes. The circuit has low transistor count, power, delay and PDP as compared to the exact ternary adder at the cost of inaccurate Sum output for some of the input combinations. This approximate full adder may be used in the lower significant trit positions of an n-bit adder in applications which tolerate small errors without significant degradation in performance.
Containerized Bioinformatics Ecosystem for HPC

Container technologies such as Docker and SingularityCE wrap the application together with everything it needs to run into an isolated environment. This enables containerized applications to always run the same regardless of the environment in which they are running, which positions container technology as a critical tool for data reproducibility in science. In high-performance computing (HPC) environments, SingularityCE has been widely used, and the primary reason for its popularity is that it can significantly reduce system administrators' work of deploying applications. One such domain where we see potential for this technology is in the deployment of bioinformatics applications. Bioinformatics is an interdisciplinary scientific field combining biology, chemistry, computer science, mathematics, statistics, and other areas of science. Traditionally, HPC system administrators may need thousands of hours to compile, install, and deploy a broad stack of bioinformatics applications for users. HPC-friendly container technologies have the potential to transform traditional methods of installing and managing applications. This paper introduces how our HPC center used SingularityCE to provide over 600 containerized bioinformatics applications that were tested by staff with expertise in bioinformatics, on 6 campus production systems as well as ACCESS Anvil. This paper will also explore how, leveraging Lmod, containerization was made transparent to users through environment modules for these container images. Finally, it will discuss how we deployed applications with a graphical user interface (GUI) to Open OnDemand as interactive applications, how we modified Python-based container images to support Jupyter notebooks, and how we generated detailed usage documentation for each application on the ReadTheDocs platform. The sum of these contributions provides a robust and reproducible computing ecosystem for life science researchers. The general approach outlined in this paper is easily adaptable to utilize any underlying container technology for any collection of applications.
On the asymmetry of ripple induced losses in ICRF heated Tore Supra discharges

Toroidal asymmetries in the measured ripple losses during ion cyclotron range of frequencies (ICRF) heating have been observed on the Tore Supra tokamak. Such asymmetries have been detected in plasmas heated by low levels of ICRF power. In contrast, the ripple losses appear to be almost symmetrical (more precisely periodically symmetric) during high power ICRF heating. A theoretical analysis of the losses indicates that asymmetries can arise because a larger fraction of the ICRF power is absorbed in the ripple well region during low power ICRF heating.
Self-consistent calculation of subband occupation and electron–hole plasma effects: Variational approach to quantum well states with Hartree and exchange-correlation interactions

We present a numerically stable computational method to calculate multiple subband states in quantum wells based on the variational minimization of the total Hamiltonian involving the nonlinear interaction terms such as the Hartree and exchange-correlation interactions. Application of a nonlinear multidimensional minimization procedure to the total Hamiltonian enables us to realize multiple state computations of improved convergence. As an example of the multiple state calculation, subband states of interacting electrons and holes in single quantum wells are computed. We demonstrate that the dependence of the overlap integral of electron and hole wave functions on the electron–hole density is significantly modified by the exchange-correlation interaction. Furthermore, we show the effects of the nonlinear interactions on the carrier population and chemical potential in multiple subbands at various temperatures and carrier concentrations. This method is expected to be a convenient tool for investigating the.
MASTER EQUATIONS FOR HIMALAYAN VALLEY WINDS

Data from a recent field campaign in the Himalayan Kali Gandaki Valley are used to construct master equations for the along-valley flow component as observed at a station and the pressure difference between this location and another one in the valley. The inspection of these equations reveals a pronounced difference between the flow dynamics during the day when strong up-valley winds prevail and those during the night when weak down-valley winds are observed. This difference is not captured by a corresponding regression model. Predictions by the master equation cease to be useful after one hour. The relation of master equations to data based Fokker–Planck equations is briefly discussed.
Coherence of the exciton condensate luminescence

A new experiment to detect Bose-Einstein condensation of excitons is suggested. The experiment is a photon-counting measurement of the exciton luminescence with two detectors. As a result, the number of photons N(t) and the second-order coherence g(2)(t) emitted during the time interval t can be obtained. These quantities contain contributions from both the normal component and the condensate. It is shown in the paper that the number of photons emitted by a condensate, N(2)(t), can be found from the expression [N(2)(t)]2 = 2[N(t)]2 − g(2)(t). The integration of N(2)(t) over the spectrum gives the value of the order parameter.
High‐Frequency Conductivity and the Emission and Absorption Coefficients of a Fully Ionized Plasma

The problem of the ac conductivity of a fully ionized plasma is investigated for frequencies embracing the plasma frequency. The finite duration of encounters is taken into account in a self‐consistent fashion which includes collective effects. The concomitant processes of absorption and emission of electromagnetic radiation are investigated and in particular the bremsstrahlung emission and absorption coefficients near the plasma frequency are given. The conversion of longitudinal to transverse waves by scattering from ions is discussed.
TEM observations of dislocations in plastically deformed diamond

Type Ib synthetic diamonds from De Beers with a low dislocation density have been deformed under confining pressure under pressure of 5 GPa in a multianvil apparatus between 900 and 1200 °C and in a D‐DIA experimental set up at 1100 °C and strain rate of 7 × 10−5 s−1. TEM observations of diamond deformed at 1100 °C, the lowest deformation temperature where plasticity was unambiguously evidenced are reported in this work. Two types of dislocations have been found to coexist: dissociated and perfect. No large stacking faults resulting from the dissociation splitting blow up under large stress have been observed. Numerous dipoles are found in the microstructure which can be built with perfect or dissociated dislocations. These results are discussed in relation with dislocation core configuration calculations.