Miracle-dz/UniFAR-FTUs huggingface dataset:
’ s repository of research publications and other research outputs R-classify : Extracting research papers ’ relevant concepts from a controlled vocabulary Journal Item

In the past few decades, we saw a proliferation of scientific articles available online. This data-rich environment offers several opportunities but also challenges, since it is problematic to explore these resources and identify all the relevant content. Hence, it is crucial that they are appropriately annotated with their relevant concepts so to increase their chance of being properly indexed and retrieved. In this paper, we present R-Classify, a web tool that assists users in identifying the most relevant concepts according to a large-scale ontology of research areas in the field of Computer Science.
Application of the robust discrete wavelet transform to signal detection in underwater sound

This paper applies the notion of robust discrete wavelet transform (robust DWT) to recursive density estimation as a solution to detecting multipath signals underwater. Though there are no obvious distribution assumptions that can be made to model underwater noise, a recursive density estimator of the initial background noise reconstructed by the discrete wavelet transform (DWT) can be established as an empirical model. Observations that are identified as an outlier are then flagged as potential signals. In a multipath environment, where many signal components arrive with arbitrary delays, the DWT's lack of translation invariance is a problem for processing multipath signals. We adopt a robust DWT as a solution. Using an iterative algorithm in the Zak domain, a scaling function can be turned to a robust one. By designing a robust scaling function we can obtain a robust DWT. The performance of the robust DWT, exhibiting much better shift invariance than the conventional DWT, is illustrated. Utilizing the robust DWT reconstruction to establish the recursive density estimator of the underwater background noise, the ability to detect a multipath signal in an underwater environment is improved compared to that of the conventional DWT.
Machine Learning Based Prediction Models for Spontaneous Ureteral Stone Passage

A prediction model was developed for spontaneous stone passage (SSP) using decision tree based machine learning (ML) methods which could serve as a promising tool for doctors to decide whether to administer active therapies and interventions to patients with ureteral stones, or wait for the SSP to happen. The work was performed on the medical records of 192 patients suffering from ureteral stones. A total of 20 features were taken into account including the target attribute, laboratory investigation data, radiographic results, stone characteristics, history of stones and interventions. On analysis, the dataset was found to be imbalanced with 65.1% cases of SSP occurred, and 34.9% SSP didn’t occur. The study included three data splitting approaches, namely hold-out method, 10-fold cross-validation, and stratified 10-fold cross-validation. Decision tree (DT), extreme gradient boosting trees (XGBoost), and random forest (RF) were employed as classification techniques in our work. The prediction accuracies on the test set using stratified 10-fold cross validation, 10-fold cross validation, and hold-out method respectively were 90.83%, 81.66%, and 75.00% for DT, 91.66%, 83.33%, and 83.33% for XGBoost, and 87.50%, 87.50%, and 83.33% for RF. The validation accuracies using stratified 10-fold cross validation, 10-fold cross validation, and hold-out method respectively were 85.55%, 81.66%, and 83.33% for DT, 88.89%, 83.33% and 83.33% for XGBoost, and 87.78%, 83.33%, and 77.78% for RF. It was observed that stratified k-fold cross-validation method gave the best results for our imbalanced dataset. The results are encouraging and suggest that the developed approach might be utilized to help urologists with counseling, prognosticating ureteral stone outcomes, and eventually selecting a suitable course of action or monitoring technique to deal with ureteral stones.
Ion irradiation of asphaltite as an analogue of solid hydrocarbons in the interstellar medium

We present the results of experiments of low temperature ion irradiation of pure asphaltite samples and those covered with water ice layers. Asphaltite (a natural bitumen) was used as an analogue of carbonaceous interstellar grains on which water- rich icy mantles can be formed in the dense and cold phases of the interstellar medium. We simulated irradiation by cosmic ions in the laboratory to study, by in situ infrared spectroscopy, the formation of molecular species in the asphaltite samples and at the interface between the refractory solid (asphaltite) and the water ice mantle. New molecular species were formed at the expense of water molecules and atoms (mainly carbon and hydrogen) from the asphaltite substrate. The vibration modes we detected at low temperature (16 K) include: C-H stretch (carbynoids), C-H asymmetric stretch (acetylene), C-H aromatic stretch, C=O stretch in CO2 ,C ≡O stretch in CO, and C≡C stretch (carbynoids). Some of these species remain stable after warming of the irradiated samples to room temperature.
LNA Design for LEO Space S-Band Applications

Low-Noise Amplifiers are key components in every communication receiving end. The LNAs primary purpose is to amplify the weak input signal, adding additional little noise. A SiGe Low-Noise amplifier at design frequency of 3.5GHz is presented. This paper includes the bias network design, Stability analysis, Matching Network Design and EM Co-simulation of the LNA. Gain of ≥15dB, Noise Figure of ≤1dB is achieved to make sure noise contribution of the amplifier is as low as possible. Keysight's Advance Design Software tool has been used for simulation.
Lightweight Cost Effective Deep Learning Model for COVID-19 Detection using CXR Images

In the field of medical imaging, deep learning techniques have already proven to be quite a success.The global population’s health and well being continue to be severely impacted by the Coronavirus Disease 2019 (COVID-19) pandemic, healthcare systems are unable to examine and diagnose patients as soon as they ought to. The various post COVID complications which may have been dealt in a better way if the virus was detected at an earlier stage and given appropriate clinical support. Chest radiography imaging is essential for detecting and tracking COVID-19 because of its effects on pulmonary tissues. Chest X-Ray(CXR) imaging is even more readily available than chest computed tomography(CT) imaging, especially in developing countries where CT scanners are too costly due to high equipment and maintenance costs. In this work we propose a very lightweight convolutional neural network (CNN), in which the chest X-Ray samples comprising of COVID-19, Non-COVID and Normal cases are analyzed without any human intervention. Our model gives comparable accuracy to other COVID-19 detection models proposed earlier while having significantly fewer parameters than them, which makes our model optimal for deployment on machines with low computing power.
Design and Analysis of ITAE Tuned Robust PID Controller for Brushed DC Motor

Robust control is one of the most evergreen control techniques which has practical significance in actual engineering applications primarily because almost all systems have uncertainties and to deal such uncertain systems there is no better control than robust techniques. In this regard PID controllers have been a long time favourite in the industry but for robustness, suitable tuning of PID controller is essential. This has always been a subject of interest and been evolving ever since its inception. In this paper a well established technique of Integral Time Absolute Error (ITAE) has been used to develop a robust PID controller for a brushed DC motor speed control. The DC motor model is first analysed using LFT technique to obtain the various system transfer functions. Later the robustness of the controller is experimented and established through simulations for parametric variations.
Formation Control of Multi-quadrotors Based on Deep Q-learning

The purpose of this study is to address a model-free formation problem for a team of quadrotors. A cascade controller including a tracking controller and an attitude controller, is developed. The assumptions preserve the nonlinearity and the under-actuation of the model. The tracking controller uses reinforcement learning to develop a model-free online controller. Moreover, the attitude controller is equipped with an actor-critic neural network to solve the nonlinearity issue. The whole formation leads with a virtual leader in the center of the predesigned formation. Simulation results of multiaerial vehicles including four heterogeneous quadrotors, demonstrate the effectiveness of the proposed controller.
LIME Explainability on Flower Classification

Artificial Intelligence is becoming more advanced with increasing complexity in generating the predictions and as a result it is becoming more challenging for the users to understand and retrace how the algorithm is predicting the outcomes. Artificial intelligence has also been contributing in making decisions. There are many flowers in the world so the botanist scientists need help in identifying or recognizing which type of flower. The paper presents an x-ray diagnostic model and the explained with Local interpretable model-agnostic explanations LIME method. The model is trained with various COVID as well as non-COVID images. Whereas chest X-rays are segmented to extract the lungs and the model predictions are tested with perturbated images that are generated using LIME. This paper opens a wide area of research in the field of XAI.
Design of Autonomous Marine Fauna Deterring Device Employed during an Oil Spill

Oil Spill is the release of Liquid petroleum hydrocarbon into the marine environment, usually in big water bodies such as seas, oceans and rivers. It’s caused mainly by human error and equipment malfunction. This paper discusses the working of a model whose primary objective is to deter birds, fishes and other marine fauna from the affected area with different highly application-specific and configurable modules that improve the existing solutions. As part of our secondary goal, we aim to locate and rescue the ones already affected by the wreckage. The product will reduce bird deaths by effectively dissuading movement near the spillage. The said developments will be elucidated in the upcoming sections
Performance Analysis of Intelligent Reflecting Surfaces in comparison to MIMO

Intelligent Reflecting Surfaces(IRS) provide improved energy efficiency and consumes less transmit power in wireless communication systems, when the direct path between source to destination is weak. IRS surfaces contain an array of elements that reflect the incoming signal to the destination with beamforming. The new IRS technology is compared with MIMO technology and simulation results are plotted for transmit power, energy efficiency, and Bit Error Probability. It is seen that the for different performance metrics IRS performs better than MIMO.
Resilient Control of Vehicular Platoons in The Presence of False Data Injection Attack

This article examines the performance of a platoon which permits different vehicles merging and splitting in the presence of cyber-attacks. In this system, the dynamics of the vehicles are longitudinal and a graph has been used for the network simulation. To lessen the impact of the attack, one of the resilient algorithms called W-MSR is applied to the system. The condition for using this algorithm for a network whose f agents have undergone a cyber-attack is that the relationship between agents should be 2f + 1-robust. After utilizing MATLAB for the simulation, it will be concluded that the system has reached a consensus even after being attacked, and consequently, adding and subtracting new agents will not disrupt the network.
Jaccard Median for Ego-network Segmentation

An ego-network is a graph representing the interactions of a node (ego) with its neighbors and the interactions among those neighbors. A sequence of ego-networks having the same ego can thus model the evolution of these interactions over time. We introduce the problem of segmenting a sequence of ego-networks into k segments, for any given integer k. Each segment is represented by a summary network, and the goal is to minimize the total loss of representing k segments by k summaries. The problem allows partitioning the sequence into homogeneous segments with respect to the activities or properties of the ego (e.g., to identify time periods when a user acquired different circles of friends in a social network) and to compactly represent each segment with a summary. The main challenge is to construct a summary that represents a collection of ego-networks with minimum loss. To address this challenge, we employ Jaccard Median (JM), a well-known NP-hard problem for summarizing sets, for which, however, no effective and efficient algorithms are known. We develop a series of algorithms for JM offering different effectiveness/efficiency trade-offs: (I) an exact exponential-time algorithm, based on Mixed Integer Linear Programming and (II) exact and approximation polynomial-time algorithms for minimizing an upper bound of the objective function of JM. By building upon these results, we design two algorithms for segmenting a sequence of ego-networks that are effective, as shown experimentally.
Music Genre Classification using Deep learning - A review

A music genre is a term used to categorize certain types of music according to a shared tradition or set of rules. There are many ways to categorize music into distinct genres. Pop, electronic dance music, Metal, Jazz, Blues, Country, Rock, and Hip-Hop. Sorting music files into categories based on their genres is a difficult task in the field of information on music retrieval (MIR). Automatically classifying the genre of music helps various music platforms like Spotify, wynk, saavn, etc. Nowadays, businesses classify music to offer recommendations to their clients or simply as a product. The initial step in the music suggestion process is identifying music genres. Deep learning techniques are one of the current methods for categorizing music genres. Techniques of Deep learning have shown to be quite effective at identifying trends and patterns from the vast collection of data. In this paper, the different music genre classification techniques were studied and implemented using CNN.
Deep-HFID: Deep Neural Network Based Hands and Face bio-metric Identification System Using Metric Learning

This paper presents a device for person identification based on hand bio-metrics. The main objective of a person recognition device is to perform accurate identification at a reasonable production cost. The so-called Deep-HFID device is designed to meet these objectives as well as to provide users with ease of use. For the purpose of identifying individuals, this device uses hand and face bio-metrics. In terms of dimensions, this device has been designed to facilitate the use of a variety of individuals. For ease of deployment, the Deep-HFID system relies on a server for most of its processes. The routine tasks, like showing the graphical user interface are handled by a Raspberry Pi board. A dataset from the palm side of both hands from 110 individuals has been created. Using transfer learning, the six pre-trained state-of-the-art neural network architecture was fine-tuned and calibrated based on the F1 score and precision criteria. An augmented dataset containing samples from the palm side of 30 individuals with between fingers angles variation is collected. By applying the main and augmented dataset and using the center-based separation index as a complexity measure algorithm to rank pre-trained deep neural networks, a compression on the last layer of the best performed neural network, EfficientNetV2B1, is done which results in a more robust system without reducement in accuracy.
MedSkim: Denoised Health Risk Prediction via Skimming Medical Claims Data

Health risk prediction is a challenge task that aims to predict whether patients would suffer from a certain disease/condition in the near future based on their historical EHR data. Although existing approaches can achieve better performance, none of them can deal with the noise existing in the EHR data explicitly. In this paper, we hypothesize that automatically removing noise from EHR data should help the models further improve the performance. Correspondingly, we propose a novel model named MedSkim, which is able to automatically rule out irrelevant visits and codes by effectively skimming through the EHR data. In particular, the proposed model has a code selection module that can directly make a skipping decision to each individual diagnosis codes and then remove the target-irrelevant ones. A backward probing RNN (BPRNN) is designed to reversely process the EHR data and provide a coarse grained representation learning for visits. Besides, a forward skipping RNN (FSRNN) is proposed to read the EHR in a preceding way and dynamically select important visits and codes based on the results of previous two modules. Finally, the risk prediction module uses the output hidden states from FSRNN for generating the final representation to make predictions. Additionally, we also design an extra regularization term based on the skip rate of the model and combine it with standard cross entropy loss to train the model in an end-to-end setting. Experimental results show that MedSkim achieves the best performance on three real-world datasets compared with the state-of-the-art baselines in terms of PR-AUC, F1 and Cohen’s Kappa. Moreover, the ablation study and case study confirm that the proposed MedSkim is reasonable and effective for removing noise from EHR data 1.1The source code of the proposed MedSkim is available at https://github.com/SH-Src/MedSkim
Analysis and Automatic Detection of Microaneurysms in Diabetic Retinopathy using transfer learning

For the quick identification of severe disorders like diabetic retinopathy (DR), the recognition of retinal microaneurysms (MA) is absolutely necessary. As a result of diabetics, where it creates lesions on the retina of the eyes, DR is a degenerative condition which affects the eyes. The challenging task is the detection of lesions in retinopathy in retinal images. To properly maintain the patient's eyesight, early identification of diabetic retinopathy is essential. The biggest problem with DR detection is that physical detection requires an ophthalmologist to examine retinal fundus pictures of the eye, which takes a lot of time, money, and effort. Various machine learning techniques have been used in literature reviews to identify DR. Classification and feature extraction are the two main steps involved. Blood vessels segmentation and Identification of lesions are two approaches that are involved in the detection of DR. Based on metrics including sensitivity, specificity and accuracy this study evaluates the experimental outcomes of several machine learning algorithms. The proposed method is combination of Convolutional Neural Network along with a machine learning algorithm (Random Forest). To compare the findings, the best of 4 approach has been analyzed with 2 different datasets E-Ophtha and DIARETDB1. The model with InceptionV3 which has soft-max classification without transfer learning acquires the best accuracy, sensitivity and specificity.
On-the-Fly Data Race Detection for MPI RMA Programs with MUST

MPI Remote Memory Access (RMA) provides a one-sided communication model for MPI applications. Ensuring consistency between RMA operations with synchronization calls is a key requirement when writing correct RMA codes. Wrong API usage may lead to concurrent modifications of the same memory location without proper synchronization resulting in data races across processes. Due to their non-deterministic nature, such data races are hard to detect. This paper presents MUST-RMA, an on-the-fly data race detector for MPI RMA applications. MUST-RMA uses a race detection model based on happened-before and consistency analysis. It combines the MPI correctness tool MUST with the race detector ThreadSanitizer to detect races across processes in RMA applications. A classification quality study on MUST-RMA with different test cases shows a precision and recall of 0.95. An overhead study on a stencil and a matrix transpose kernel shows runtime slowdowns of 3x to 20x for up to 192 processes.
Projection Dual Averaging Based Second-order Online Learning

Most existing online learning methods focus on mining ever-evolving streaming data based on the principle of first-order optimization. However, one drawback of these methods is the slow convergence rate in each iteration, resulting in sub-optimal solutions and deteriorated performance. Second-order methods, while are able to provide faster convergence, have been under-studied due to the high cost of computing the curvature information. To address this problem, in this paper, we develop a second-order projection dual averaging based online learning (SPDA) method to effectively handle high-throughput streaming data. By fully exploiting the regularized dual averaging optimization, the second-order information, and an optimal projection operator, SPDA converges fast with fairly optimal solutions. Two speed-up versions of SPDA, i.e., SPDA-diag and SPDA-sketch, are developed via the diagonal operator and Hessian sketch, respectively. Theoretical derivations on the regret bound of SPDA establish a solid convergence guarantee for this method. Extensive experiments demonstrate the efficacy of the proposed algorithms on large-scale online learning tasks, such as online binary and multi-class classification and online anomaly detection, shedding light on their potential wide applications.
Decentralized Property Registration and Management Platform

The existing property registry management does not have a well-defined protocol for verifying and validating transactions that occur within the domain. These transactions rely on handwritten signatures, an unreliable methodology for determining an asset’s ownership. The legal system governs this process. However, several disputes have occurred due to improper validation and verification when registering properties, changing custody, and maintaining the chain of ownership. Trades have been made by including a lower value than the actual asset value, which will reduce the tax owed to the government and will lead to the failure of these departments. There are no appropriate mechanisms to resolve common disputes that arise within the domain. The courts must resolve these disputes using the same recurring traditional procedure, which will take years or decades to conclude. The main objective of this research is to develop a secure property registration mechanism by creating a digital protocol using a decentralized blockchain network. In addition, the research will focus on developing a minimum asset value calculator using machine learning and geographic information system, verifying the authenticity of the generated digital documents, and creating digital deeds for new and old paper-based records.
Analytical relationship between polarized reflectivities on the specular surface

An analytical expression of the relationship between polarized reflectivities over the planar surface is obtained. A comparison with the reflectivities calculated using the Fresnel equations for a given refractive index and viewing angle demonstrated that the ‘Hong approximation’, which causes erroneous results, is no longer necessary and should be replaced by an analytical expression.