Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated metadata extraction: challenges and opportunities

Proper application of the FAIR data principles is what separates a vibrant data ecosystem, in which research data are frequently shared and reused, from a lifeless data graveyard. Automated metadata extraction systems have been proposed as a means of bolstering the findability, interoperability, and reusability of data repositories with little or no human intervention. These extraction systems mine metadata by crawling a repository and applying lightweight extractors that, for various types of file (e.g., image, CSV file), extract or synthesize relevant attributes. In practice, however, the automated creation of generally useful metadata is fraught with challenges. Data consumers may have different perspectives as to what metadata representations are useful, the standards for recording metadata tend to change over time, and the software model for processing updates can introduce unnecessary human and computational effort. Thus, generalizing extraction for a broad audience of data consumers is a difficult and relatively unsolved problem. In this work, we explore these challenges faced by extraction systems in the context of constructing our own extraction system for science data. We first define the metadata extraction problem and provide context to the issues faced in generalizing metadata. Additionally, we identify potential research directions to help alleviate many of these challenges for all automated extraction systems. Ultimately, this work represents a first step in designing Ubiquitous metadata extraction systems that can maximize the value of research data while minimizing the human efforts required in doing so.
Transfer Learning for Automatic Image Orientation Detection Using Deep Learning and Logistic Regression

The number of images produced each day increased significantly. The ability to detect and correct an image’s orientation can provide several advantages in computer vision. This paper presents a new framework based on a transfer learning technique for automatically detecting image orientation. To implement the power of deep neural networks, we applied a convolutional neural network model pre-trained on the ImageNet database for feature extraction. Then, we built a multi-class logistic regression classifier to detect the four image orientation probabilities corresponding to the following orientations (0 for no orientation, 90, 180, and 270). We tested our model on the SUN-397 dataset, one of the most extensive data sets currently used for image-orientation detection tasks. We conducted a cross-dataset evaluation for in-depth testing and analysis. We also examined our model using different old and recent state-of-the-art convolutional neural network (CNN) baselines. We demonstrate that our model yields promising results based on transfer learning for feature extraction combined with a one-vs-rest logistic regression classifier. Our proposed model surpassed the state-of-the-art results in terms of accuracy and performance.
Impacts of Lighting to User's Sleep Quality: A Review

This study reviews previous studies on the impacts of lighting on the human's sleeping condition. The effects of lighting on the human condition have been the focus of research for the past decades to improve the health and wellness of its user. Current studies that include the lighting intensity, correlated color temperature, and lighting type were found to affect human's circadian rhythm related to wake and sleep patterns. They were subsequently reviewed in terms of each category, compared to the previous related experiment, and recommendations for future improvement. The result shows that adolescents are more sensitive to lighting environments than adults. In addition, the illuminance level needs to be designed according to the time to improve a significant quality of sleep.
LSTM Model based Time Series Modelling and Prognosis of Foreign Exchange Rates against US Dollar

In today’s global economy, precision in projecting macroeconomic characteristics such as the foreign exchange rate, or at the very least properly gauging the trend, is critical for any prospective investment. In recent time, application of artificial intelligence-based forecasting models for macroeconomic variables has been extremely fruitful. The global currency rate changed dramatically during the Covid-19 incident. This study examines the behaviour of the Australian dollar’s (AUD) daily exchange rates against the US dollar’s (USD) daily exchange rates from January 2016 to December 2020 and makes LSTM RNN-based predictions for the 2021 exchange rate. There are different sorts of performance metrics used in exchange rate forecasting to compute the accuracy of the projected result. This research investigates six performance metrics and discovers that the accuracy of the anticipated outcomes is satisfactory when compared to the actual data.
Edge Computing versus Cloud Computing: Impact on Retinal Image Pre-processing

With the emergence of new intelligent applications, there has been a revolution in information management, mainly including processing routines, storage, and computing capacity. Cloud computing is one of the most well-known and popular paradigms. However, there are still some barriers to cloud adoption. Edge computing serves as a decentralized extension and provides solutions that facilitate data processing at the generation source. This paper discusses the development of a retinal image pre-processing application for efficient use in screening systems and the impact that pre-processing has on network interconnection. Results show that the parallel version has reduced application execution time by up to ≈ 73%, decreasing by ≈11.5× the bandwidth used, achieving throughput above 5 images/second with Edge pre-processing, 2.57× higher than Cloud.
Sub-dB/m loss integrated 103 and 90 million Q resonators for laser stabilization at rubidium and strontium wavelengths

We report integrated silicon nitride resonators with 0.65 dB/m loss and $90\times10^{6}$ intrinsic Q at 674 nm and 0.5 dB/m loss with $103\times10^{6}$ intrinsic Q at 780 nm. We demonstrate laser stabilization with over two orders magnitude frequency noise reduction.
Neural Network based Intrusion Detection system for critical infrastructure

Security is crucial in the internet age since the number of users increases quickly. One of the major problems of the current day is cybersecurity. The rapid increase and widespread usage of Internet over the past ten years have made system security weaknesses a crucial issue. An intrusion detection system to monitor unusual attacks across network security as well as unlawful access is the need of the hour in many critical infrastructures. Recently, a number of IDS experiments have been conducted. And to be aware of how machine learning techniques are now being used to handle the intrusion detection problem. IDS is frequently used to quickly and automatically identify and detect cyberattacks at the network and host stages. Deep learning techniques have significantly improved the performance of numerous applications of practical safety tools. It is regarded as the finest option for employing them for backpropagation methods in highly dimensional data to find complicated architecture. DL is indeed a subset of machine learning that deals with artificial intelligence. A technology called artificial intelligence makes it simple for machines to mimic people. Machine learning is a method for developing data algorithms, and deep learning is a subset of ML that draws inspiration from the architecture of the human mind. A neural network made of artificial neurons is this network structure. In this study we have proposed Neural network-based system for identifying intrusions in critical infrastructure. We have evaluated them based on significant metrics.
Removing Target Defense for Internet-Controlled Systems

Internet-controlled systems provide a variety of applications, from healthcare to military and industrial applications. One of the best advantages of these systems is remotely controlling from an unlimited distance via the Internet. Another advantage is leveraging Internet Protocol (IP) with many off-the-shelf applications. Remote cyber-attacks on such systems can cause loss of life, threaten public safety/national security, or impact environmental disasters. IP address hopping, rotating IP addresses, is one of the Moving Target Defense (MTD) solutions that could prevent remote cyber-attacks during their reconnaissance step. This paper presents a novel Removing Target Defense (RTD) method that can remove any accessible IP address from the systems instead of dynamically changing those addresses. RTD is a secure and reliable network protocol for data communications between multiple devices over the Internet without having any assigned IP addresses on those devices. RTD uses multiple ideas from different fields such as steganography, covert channels, cryptography, and network protocols to eliminate the attack surface.
Thermal stability of the HfO2∕SiO2 interface for sub-0.1μm complementary metal-oxide-semiconductor gate oxide stacks: A valence band and quantitative core-level study by soft x-ray photoelectron spectroscopy

Synchrotron-radiation photoelectron spectroscopy is used to study the valence-band structure and the core-level photoemission spectra of HfO2 ultrathin films grown onto SiO2∕Si substrates by atomic layer deposition (ALD). We determine the band offsets (valence and conduction) of HfO2 to Si as a function of postdeposition annealing treatments (under an inert N2 atmosphere or in situ in ultrahigh vacuum) and find a significant evolution, the conduction-band offset remaining larger than 1.5eV. The Si2p and the Hf4f core-level spectra give detailed information on the composition and the spatial extent of the interfacial Hf silicate layer formed between the SiO2 bottom oxide and the HfO2 ALD thin film. By a quantitative treatment of the Si2p core-level intensities, we examine the thermal stability of the interface silicate after postdeposition annealing under N2 and in situ annealing in ultrahigh vacuum (UHV), both at 800°C. The as-deposited layer gives rise to a HfO2∕Hf0.35Si0. 65O2∕SiO2 stack with correspondi.
Clustering of Air Objects based on Path, Missile Path Prediction Using Deep Learning

For a given instance, the location of the object in air can be demonstrated by quadruples there are latitude, altitude, longitude and velocity. Trajectory is continuous stream time, latitude, altitude and longitude. The proposed work classifies the air objects based on trajectories and group them into meaningful objects such as flying objects, drones, helicopters, fighter, civilian aircraft, unmanned aerial vehicles, missiles, flying bombs, and so on. In this paper, the proposed work estimates the location of air objects by using their trajectory and these provides a guidance to the missiles to shoot the target based on its location where the missile is going to meets the object based on the trajectory. For calculating missile’s path, one of the most difficult challenges is applying the missile model to various related simulations is determining the missile path. The traditional way for this task is to use models and numerical methods, that also necessitates a large computational power. The data generated by the traditional system has been used to train the network and test the network, and the defect of the network prediction result is evaluated in this study. Recurrent neural network (RNN) and long short-term memory (LSTM) have been used to perform Clustering of Air Objects based on Path and for Missile Path Prediction. For clustering of air objects, LSTM gave r2 score of 0.997 and for missile path prediction, LSTM with 7 epochs gave an r2 score of 0.791.
Automated measurements of leg length on radiographs by deep learning

Deep learning algorithms can evaluate large and complex sets of data, offering various support for medical imaging analysis. Previous works have explored applications of deep learning to measure leg lengths more efficiently. These previous studies provide evidence to suggest deep-learning algorithms can improve efficiency with high levels of accuracy and speed. In this retrospective study, we utilize deep learning-based convolutional neural networks, programmed with input from a human expert, to identify key points and measure leg length. We collected frontal computed tomography (CT) scout radiographs from pre-operative CT scans of patients undergoing evaluation for knee arthroplasty from diverse sources to both train and test the model. We prepared a DenseNet121 model to predict and identify key points, which were then used to develop patch-based models. We applied separable convolutional layers to complete the analysis. The data reflects that 1) separable convolution exhibits lower mean absolute error (MAE) and increased convergence speed as compared to global average pooling layers and 2) optimal learning rates, batch size, and patch size can be achieved to present the least MAE. Our findings provide useful information and an automated tool to assist radiologists to diagnose leg length discrepancy in clinical practice.
Classification of EEG signals using Machine learning algorithms

An alternative to human expert-performed manual identification is automatic detection of epilepsy using electroencephalogram (EEG) data. Automatic epilepsy detection from EEG data need high classification performance in order to eliminate false positives. A classification strategy for automated epilepsy identification using EEG data is being proposed in this work. The signals generated form the EEG device were transformed using the DWT before feature extraction was carried out. Based on various statistical parameters and crossing frequency features, an algorithm dubbed GBMs fusion was developed to identify EEG data. As an added bonus, the significant traits were first selected using a genetic algorithm. EEG data from the University of Bonn has been used to test the suggested method's ability to distinguish between normal and ictal EEG patterns. Experimentation has shown that the suggested GBMs fusion may increase the EEG classification performance. It is also possible to identify epilepsy from EEG data with a 100% accuracy using the suggested GBMs fusion.
Intelligent Car Accident Report System (i-Cars)

One of the problems faced by many people nowadays is accident. Accident is an unforeseen event which cannot be predicted or avoided. When a victim met with an accident, he/she have no idea on what needs to be done next and it may cause trauma to the person. Moreover, car accidents are very common these days around the cities given the higher traffic density compared to rural areas. No human has ever been free of deviants and it is unlikely that society will ever be. Therefore, this research is conducted to develop a web-based system i-CARS would fundamentally bring benefits to both the people and government sector as this can be used on a large-scale accident report system. This will tremendously help victim of minor accidents where they are not required to go to a police station to report, as the police report and final verdict will automatically be sent to victim via SMS. However, for the major accident victim, the system will automatically send the report via SMS, where the victim can bring the e-report directly to the police station. Thus, reducing the time spent at the police station as the report is readily available for further investigation.
On perturbed steepest descent methods with inexact line search for bilevel convex optimization

We use a general framework for solving convex constrained optimization problems introduced in an earlier work to obtain algorithms for problems with a constraint set defined as the set of minimizers of a given function. Also, the algorithms allow the objective function to be decomposed as a sum of other convex functions that can be treated separately. We prove that the general algorithm converges to the optimum of the objective function over the set of minima of a convex Lipschitz-differentiable function chosen previously. When using orthogonal projections onto the convex constraints, we retrieve a Cimmino-like algorithm that converges to the optimum over the set of weighted least squares solutions. Furthermore, we show an important application of our approach to compressed sensing and inverse problems.
Effectively and Efficiently Supporting Predictive Big Data Analytics over Open Big Data in the Transportation Sector: A Bayesian Network Framework

Today, various types of valuable data can be collected with ease and at a rapid pace. In recent years, many governments, researchers, and organizations have been driven by open data pioneers, to make their data available for public. Transportation data, such as public bus performance data, is an example of open big data. The analyzing of these open big data can be used in social services. For example, bus service operators might get a vision into time delays in bus services by processing and mining public bus performance data. Then, making ameliorative steps (e.g., adding more buses, rerouting some bus routes, etc.) results in improving the feeling of the passenger. We provide a Bayesian framework, which is applied on big data obtained from transportation system. Specifically, a number of Bayesian networks have been used in our framework to predict whether a bus will arrive late or early at a specific bus stop. We investigate and establish the optimum network settings and/or parameter permutations for each (bus stop, bus route, arrival time)-triplet. The results demonstrate that the proposed Bayesian framework effectively supports predictive analytics on big transportation data collected from the City of Winnipeg, Manitoba, Canada.
A Survey of Quantitative Comparative Research on Intrusion Detection Algorithms in Wireless Sensor Networks

Wireless Sensor Network (WSN) is vulnerable to malicious attacks by third parties due to limited communication capabilities and unstable channels. Moreover, WSN nodes have limited energy, small storage space, and poor computing power, which restrict traditional intrusion detection methods. In recent years, people have tried to apply many traditional intrusion detection algorithms to WSN. Meanwhile, experimental results compared several intrusion detection algorithms. However, the experimental comparison of these intrusion detection algorithms is not comprehensive enough. Deep Forest and ELM algorithms are not considered. Therefore, this paper evaluates eight intrusion detection algorithms on four datasets and details the selection of algorithms, datasets, and evaluation indicators. The final experimental results show that the comprehensive performance of Deep Forest on WSN is better. The intrusion detection algorithm research in this paper provides more ideas.
The Effects of a Mountain on the Propagation of a Preexisting Convective System for Blocked and Unblocked Flow Regimes

Abstract Observations and previous research of squall lines impinging on mountain ranges have revealed that the squall lines sometimes stall upstream of the mountains for several hours leading to copious accumulations of precipitation. It has been hypothesized that squall-line stagnation may be more prone to occur in flows where the Froude number (F = U/Nh, where U is the basic-state wind, N is the Brunt–Vaisala frequency, and h is the mountain height) is low. This hypothesis is tested herein through a series of idealized, two-dimensional experiments where a convective system was triggered upstream of a mesoscale mountain in conditionally unstable flow. For simulations with relatively low Froude numbers, stagnation of the preexisting convective system was not observed. In the simulations with high values of F, squall lines were noted to stagnate between 100 and 200 km upstream of the mountain. This result indicates that squall-line stagnation may be more favored for moderate to large values of F for condi.
Partial Transfer Fault Diagnosis by Multiscale Weight-Selection Adversarial Network

Most of domain adaptation (DA) methods just focus on the case that the label spaces of two domains are identical. However, it is more valuable for studying partial DA in practical engineering. Whereupon, a novel multiscale weight-selection adversarial network (MWSAN) is proposed to enhance the effect of partial DA. MWSAN is constructed via the designed multiscale domain adversarial network (MDAN) and the multiscale weight-selection mechanism of instance and class. MDAN is innovatively built to avoid the overfitting of a single classifier and strengthen the domain confusion. For the weight-selection of instance, the instance weights are directly obtained by the probability output of MDAN. For the weight-selection of class, the possible Gaussian distributions of target-domain samples is estimated by Gaussian mixture model, and the Gaussian distribution of each class of source-domain samples is computed by maximum likelihood estimation, then the Wasserstein distance is employed to computed the class weights. With the multiscale backbone network and weighting mechanism, MWSAN can achieve the partial DA to the larger degree. The proposed method is applied to the partial transfer fault diagnosis of planetary gearboxes with the unlabeled target-domain samples, and the experimental results indicate that MWSAN outperforms other typical DA methods.
Design Of A 48V 13S Battery Management System For Enhanced Electric Vehicle Performance

State of Charge (SOC) estimation plays a vital role in electrical vehicles’ performance. With the help of estimated SOC the driving range, State of Health (SOH), energy and power limits can be determined. The conventional way of estimating SOC doesn’t include the sensor and process noise measurements. In this light, the model and data-driven- based methods served better accuracy in estimating SOC considering all the effects. Besides, the primary step will be the modeling of the battery with consideration of all the behavioral effects of the battery. Further, using MATLAB Simulink, the model is developed and executed under New European Drive Cycle (NEDC). The performance of the battery is analyzed using the coulomb counting method. Later the analysis can be extended in the direction of Kalman filter algorithms
A Low Power High-Speed 12-Bit SAR ADC using Split Capacitor Based DAC at 45 nm CMOS Technology

High-performance Integrated Analog-to-Digital Converters (ADCs) are considered an essential part of Digital Signal Processing (DSP) and Microcontroller which connect both analog and digital systems. Furthermore, combining the Successive Approximation Register (SAR) with ADC has attained additional attention in terms of its digital responsiveness and power efficiency. As a result, DSP and Microcontrollers are used globally in electronic products such as biometric sensors and medical equipment. This paper presents a 12-Bit Analog to Digital Converter (ADC) and its encoder is designed with 2 Transistor MUX and 10 Transistor Full adders (12B-2TM-10TFA). The presented design is executed in Cadence Virtuoso 45 nm CMOS (Complementary Metal Oxide Semiconductor) technology. Furthermore, WL (Width/Length) ratio is considered as 2 for designing the full adder. For N-Channel Metal Oxide Semiconductor (NMOS), the width is considered as 120 nm, while for Positive-Channel Metal Oxide Semiconductor (PMOS) is considered as 240 nm, so it can produce better results while designing the proposed 10-T Full Adder. The performance measurements of proposed designs are calculated through power, area, current, and delay and the simulation results displayed that the proposed 12B-2TM-10TFA architecture reduced 39.59% of power, 9.8 % of the area, 18.42% of delay, and 33.39 % of current when compared to the existing folding flash ADC.