Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing Molecular Dynamics Simulation on Commodity Platforms

Molecular Dynamics (MD) simulation is an essential tool driving innovation in key scientific domains such as physics, materials science, biochemistry, and drug discovery. Enabling larger, longer, and more accurate MD simulations can directly impact scientific discovery and innovation. While domain-specific architectures for MD exist, they are not widely accessible, and MD performance on commodity platforms (i.e., CPUs and GPUs) remains critical for supporting broad and agile scientific progress. This paper aims at characterizing MD simulation on commodity platforms with a benchmark campaign on modern systems available in public cloud offerings. We focus on LAMMPS, one of the prevalent MD frameworks, and characterize several representative and diverse MD experiments. We find that the benchmarked CPU instance provides good scalability to many cores, while the reference LAMMPS GPU implementation struggles with scaling to multiple devices. Additionally, we evaluate the performance impact of application-specific parameters such as error threshold and arithmetic precision. Our study indicates that key drivers for further improvement of LAMMPS performance on commodity systems are: 1) improving scalability and offload efficiency in multi-accelerator systems and 2) reducing work imbalance in the CPU parallelization.
Effect of N-type Doped Layer on Side-illuminated Photoconductive Semiconductor Switch

Side-illuminated vertical photoconductive semiconductor switch (PCSS) was fabricated on high purity semi-insulating (HPSI) 4H-SiC substrate employing an n-type doped layer formed by ion-implantation. Output characteristics of the PCSS with an n-type doped layer under the anode and cathode were investigated.
Model for Optimal Feature Selection using Binary Teaching Learning Based Optimization

Improvements in the technology made to collect huge amounts of data. As there is a vast amount of data available in the present digital world, it is a difficult task to handle, manage, maintain and train the model using that large data. Large amounts of features make the machine learning model complex. So, Feature Selection is a significant and trending task in this era. In order to get best results of a predicting model, identifying the best subset of features plays a key role. Tuning of the best features seems to be an effortful task. The proposed methodology called Binary Teaching Learning Based Optimization Algorithm (BTLBO) makes it easier. The common controlling parameters for the proposed system are population size and number of iterations. The optimization technique is applied on the fitness function, which is accuracy. To prove that the proposed model is efficient, the model is applied on five different datasets like Diabetes, Thyroid, Parkinson, Breast Cancer and Heart are used with different classification algorithms like Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and K-Nearest Neighbours (KNN). The proposed model is validated with the performance metric Accuracy and compared with other algorithms. The proposed model obtained better results when compared to existing techniques.
Weld Defects Automatic Visual Recognition by Combined Application of Canny Edge Detector and Interval Fusion with Preference Aggregation

Checking the surface quality of the weld is an important step in nondestructive testing. This inspection can be performed visually, but the accuracy of the results is highly operator-dependent and time-consuming. This paper proposes a new approach to automatic visual recognition of welding defect edges from photographic images. The approach is a combination of the Canny edge detector (CED) and the method of interval fusion with preference aggregation (IF&PA). CED uses first-order derivatives to detect pixels with sudden changes in color intensity and to recognize the contours of objects in the image. For this aim, the CED needs a threshold value to recognize real edges and remove false ones. The IF&PA algorithm is proposed to detect this threshold, where the image of the weld surface is divided into equal bands. For each band, a color intensity histogram is constructed, using which the lower and upper bounds, and the average intensity values are defined for an intensity interval, which characterizes the band. All the intervals are represented by rankings forming a preference profile. Kemeny ranking rule is applied to the profile to determine the best color intensity value, which then acts as the CED threshold. The proposed approach has been tested in detecting the edge of some typical weld defects. The test results show that the proposed approach accurately recognizes the edges of weld defects and eliminates most of the noise in the input image.
Enhancing Biometric Security with Combinatorial and Permutational Multi-Fingerprint Authentication Strategies

Biometrics provide an efficient solution for user authentication as researchers and practitioners are using biometrics to increase the ease of usage and to ensure the privacy and security of applications. In this paper, we propose a method to improve the accuracy of the biometric authentication system by decreasing the False Acceptance Rates (FAR) and the False Rejection Rates (FRR). We examine the effect of having different combinations and permutations of fingerprint samples to specify a multi-factor authentication scheme. We find that our approach reduce the attack on the system by imposters and improve the accuracy of the biometric authentication systems by reducing the two used metrics of error.
3D IC Design-for-Test with Implementation of the IEEE 1149.1-2013, the IEEE 1500-2005, and the IEEE 1838-2019 Standard

The paper presents a design for a testable 3D integrated circuit (IC), and more specifically the Design-for-Test (DfT) method of its stacked layers. It describes how it implements the IEEE 1149.1-2013 Standard, the IEEE 1500-2005 Standard, and the IEEE 1838-2019 Standard. Finally, it presents the instructions that allow for the combined implementation of these Standards, and an analysis of its additional required footprint.
Inverse-designed, normal incidence grating couplers for multi-core fiber I/O

We experimentally demonstrate normal incidence grating couplers designed using topology optimization to couple into multi-core fiber. The insertion loss of polarizing and polarization demultiplexing gratings was 5.5 dB and 7.7 dB, which is within 0.8 dB of the performance obtained using a conventional fiber array.
Android Malware Evasion Framework For Auditing Anti-Malware Resistance Against Various Obfuscation Technique And Dynamic Code Loading

Malware is one of the biggest threats to the Android system today. Anti-malware researchers and malware developers are constantly competing to produce their best product. In this study we tried to evaluate the robustness of anti-malware on the market by auditing it from the point of view of malware authors. By understanding how an attack technique is developed, hopefully we can come up with better defenses in the future. Several studies have shown that transformation with code obfuscation and the use of dynamic code loading has proven to be effective in avoiding detection. But the research so far has only focused on how to bypass anti-malware. Existing studies have not discussed how to bypass Play Protect and permission based checks on the android system and get permission from the user. We propose a framework for auditing anti-malware using various obfuscation techniques and dynamic code loading. Malware will be embedded into legitimate apps to bypass play protect detection. This framework has a mechanism to check and add a number of permissions that malware needs into the application and force the user to grant permissions at runtime. In addition we also added a feature to randomize the order of the obfuscation process and also randomly generate certificates. From the experiments we conducted, each of these features was able to reduce the detection rate of the virustotal by 50%. The overall implementation and testing of our framework shows a significant reduction in detection. The malware was also successfully installed on the real device and carried out its functions properly without being detected.
Subject Wise Motor Imagery Classification from EEG Data Using Transfer Learning

Machine learning (ML) has no doubt virtually helped in nearly all fields of life, including medical sciences. ML models are now being trained, tested and developed with the help of information gained from Electroencephalogram (EEG) Signals. Neural Networks (NN) are being used specifically in this regard to exploit their image classification ability. A special class of NN called Transfer Learning (TL), is used to enhance the capability of NNs. In this paper, EEG signals are extracted and used to classify Left or Right Motor Images of the brain using Inception V3 and VGG 16 models. We try to enhance the accuracy of these TL Models by exploiting a different methodology as compared to other available statistical methods available in the research community. For the said purpose, a dataset from Brain-Computer Interface (BCI) Competition IV 2b was used. EEG signals are extracted and transformed into Short Time Fourier Transform (STFT) images. These STFT images are labeled with either Left or Right Motor Imagery (MI) Class. The transfer learning models are trained using these STFT images and results are also compared with a state-of-the art research, implementing Capsule Networks.
High Throughput FPGA Implementation of LDPC Decoder Architecture for DVB-S2X Standard

This paper is devoted to the design of a decoder structure for Quasi-Cyclic (QC) Low-Density Parity-Check (LDPC) codes of the DVB-S2X standard. The quasi-cyclic form of the matrix representation is the most convenient for the hardware implementation of the LDPC decoder, so the matrices of the DVB-S2X standard are converted to the QC form. The implementation of an LDPC decoder for the DVB-S2X standard is a challenging task, especially because of the long code words and large parity-check matrices. We propose a highly parallel FPGA implementation of LDPC decoder design based on the layered architecture and Min-Sum decoding algorithm. The structure of the decoder consists of 360 parallel cores responsible for the sequential calculation of variable-to-check (VTC), check-to-variable (CTV) messages and update posterior probabilities (APPs). Block RAM memory was used to store intermediate values of APP and CTVs. The decoder design is applicable to all code rates and allows to achieve throughput up to 500 Mbps at a clock frequency of 250 MHz
Event Detection in Financial Markets

Price prediction in the stock market is challenging since it is affected by several factors and the price shows a semi-random behavior. One important factor that is affecting the stock price, is the news associated with the companies. If a news article affects the market, it is called an Event, otherwise it is called non-important news or noise. Due to the semi-random behavior of the price and the high number of news articles, finding the correlation between the stock price and news articles and detecting the events among the news articles become very sophisticated, and consequently, predicting the price change regarding news articles gets challenging. In this paper, we propose a model to predict the impact of news articles on future stock prices. The model can capture the relationship between the price change and the news articles by an event detection method. The event detection method improves the prediction model by extracting the events among all news articles published in history and removing non-important news articles, or in another word, it decreases the noise. It helps to determine if a newly published news article is similar to an event in history and as a result, improves the performance of the prediction model. The provided results confirm that the event detection method improves the prediction.
Estimating the Required Resources in a Vehicular Ad Hoc Network Using Queueing Theory Models

In this paper, we evaluate the performance of a Vehicular Ad Hoc Network (VANET) exploiting the M/M/c and the M(N)/M/c queueing models in order to determine the number of the resources that are required to satisfy the Quality of Service (QoS). To evaluate the system’s performance we examine various quantities, such as the system’s waiting time, the throughput and the number of users being served. These performance metrics are important for a VANET as they directly affect the QoS. The queueing models are employed in the following cases: a) when a vehicle communicates directly with a gNodeB (vehicle-to-infrastructure communication) and b) when a vehicle communicates with other vehicles on the road (vehicle-to-vehicle communication) in order to transmit indirectly its request to the destination gNodeB. Based on our study, we show that when the minimum possible resources are exploited, the number of users being served may be highly reduced and consequently the system’s throughput can be decreased. Therefore, this condition should be adopted only when it is guaranteed that the system’s overall performance will not be dangerously affected.
Design and Simulation of 9 Level Hybrid MLI with Cascaded Structure

This paper proposes a new hybrid 9 level multi-level inverter (MLI), which combines flying capacitor multilevel inverter (FCMLI) topology and cascaded H-bridge MLI topology which is connected in series with each other which provides nine level output. This reduces the voltage sources required by nine level cascaded H-Bridge multilevel inverter (CHBMLI) structure and also the flying capacitor voltage balancing control is eliminated as the proposed switching method provides both charging and discharging paths for the flying capacitor in equal intervals for the switching cycle. The number of levels can be further extended to 11 by extending the modules of cascaded topology. The proposed topology is being simulated in MATLAB/Simulink software.
Utilisation Profiles of Bridging Function Chain for Healthcare Use Cases

On the road towards personalised medicine, one of the main challenges is to enforce security and network low-level policies to secure data-sharing. The proposed dynamic framework defines the topology of the service chains to enforce network and security policies by instantiating Virtual Network Functions (VNF's) on the fly via light-weight and easily-deployable containers. In this paper, we profile the resource utilisation of chained VNF's deployed to enable data movement within different healthcare use cases. We provide example configurations that map to a couple of use cases (e-Health record query and heath data streaming), then we monitor and collect CPU utilisation of the different VNF compositions. In the considered policies we can: discard flow, protect (encrypt) and transmit, or allow with no protection. To enforce each policy, we deploy a firewall function (relatively heavy-weight function), encryption function, and a decryption function (light-weight stream cipher). As a result, we analyse the behaviour of the VNF services with various setups, and then we aim to further use this analysis to build the placement heuristic according to available and trusted clusters resources. Subsequently, we will recommend heuristic-based placement based on collected profiling data of the resource usage and limits for high availability, optimal performance, and minimal resource waste.
A Bibliometrics-Based Systematic Review on Automated Essay Scoring in Education

Advances in information technology and computer science have brought significant changes to various sectors of human life. In education, computers have helped ease a variety of human tasks, especially for large-scale and repetitive tasks such as essay grading. Current's computer capabilities make it possible to do essay scoring automatically. Research related to automatic essay scoring continues to develop by trying certain techniques and methods to improve the accuracy of automatic scoring close to that of human scoring. This systematic review aims to explore the 21st century applications of automated essay grading used in education. We carried out a bibliometric analysis of the meta data obtained from the SCOPUS database using the Bibliometrix and VOSviewer software reporting the main themes of implementing automated essay scoring applications in educational studies, providing an overview of the current state of the art and future directions of research and applications. Based on bibliometric analysis, we found that research related to automatic essay assessment in education has a fluctuating development trend and the current direction of AES research has used deep learning techniques such as long short-term memory, transfer learning and bert to build a better AES.
Paddy Crop Disease Detection using Deep Learning Techniques

Agriculture productivity plays a key role in the economy development. The availability of food is being exposed to crop diseases. Due to the spread of technology worldwide, it is now technically possible to use image processing techniques to identify the kind of plant disease from a straightforward approach. The use of an automatic method for crop disease detection is advantageous due to the less effort and in identifying the disease signs at an early stage. In the presented work a deep convolutional network and semi-supervised techniques are trained to distinguish crop species and the condition of illness in leaf. The technique for identifying paddy illness contains two key phases: the first is training the model, and the second is spotting the disease from the provided picture. The proposed work has used the CNN, VGG19 and DenseNet models to classify the Paddy Crop Disease. This work shows that the DenseNet model achieves the highest accuracy of 99% compared to other models.
A Smart and Secure IoT devices Using Machine Learning Algorithm

IoT devices are various types of hardware such as appliances, sensors, machines, or actuators that are programmed for specific applications such as data transmission over a network or the Internet. They are embedded in other industrial devices, biological sensors, mobile devices, and medical devices. There are approximately 7.62 billion people in our world, with a growing graph of IoT devices. So, the amount of data released from these IoT devices also increases, and there may be a chance of leakage of data from these devices. These IoT devices used in our home is exposed to different types of attacks like eavesdropping, brute force attack, leakage of information, cyber attacks etc. In detecting these types of attacks the machine learning algorithms play an important role and which improves the security of these IoT devices and help in making these IoT devices more secure; that is the primary goal of this project. Machine learning models can help in finding the spam in the data. Smart home datasets are available, which includes different weather conditions is used for finding spam. The main models like Long Short-Term Memory (LSTM), Time Series and Recurrent Neural Network are used in finding the spam in the dataset. The proposed model is able to find out the spam in the data of the IoT devices.
A Solar Module Power Production Model Predictor for Automated Solar Module Manufacturing

Sunlight is one of the most abundant and freely available energy resources on our planet. The capacity to transform this solar energy in electrical power depends on the quality and materials of solar photovoltaic modules. The production process of cells and modules consists of a chain of physical and chemical steps. A solar module is a set of solar cells, that are the core of energy conversion technology interconnected together. These are classified considering their efficiency in terms of the maximum power that a cell can produce with a specific calibrated light exposition. Considering these factors this paper proposes a neural network model, trained with a real dataset, capable of predicting the final maximum power of solar modules by using a set of solar cells as input. Furthermore, the proposed model works also with more than one class of solar cells. Finally, the accuracy of prediction has reached an acceptable value and a final software has been developed to perform these predictions.
PLC Based Smart & Structured Car Parking System

Growing population of the world has led to a tremendous increase in the field of transport system. Parking spaces in many expanding cities are facing increase in vehicles day-by-day. To make parking spaces hassle-free and more structured, PLC based smart parking system has been proposed. The developed smart system makes the entire parking task much simple and understandable. Automation has been developed with PLC ladder logic system with the help of sensors, timers, and counters. For developing the ladder logic, Codesys software has been used in the back end and for simulating the process Coppeliasim has been used in the front end. Considered is a two-story parking space with 8 cars that can be parked and each floor with 4 cars. Back end and the front end i.e. codesys-coppeliasim are linked through linker and the complete parking system has been developed. Simulation has been carried out with virtual real-time parking model and a LCD display through which the details of exact available parking spaces in a building. Thus the idea demonstrates smart parking system to address the problems pertaining to parking issue and to make parking spaces more structured.
Cancer Detection and Classification Using 3D-Convolutional Neural Networks

Lung cancer is one of the leading causes of mortality compared with widespread of the diseases. However, with early detection and diagnosis of the lung cancer survival rate of the person can be improved. At present, Computed Tomography (CT) is effective tool for imaging tumour visibility regions and portion. In this research, proposed an automated segmentation and classification model of lung cancer image dataset in which the tumor position has been identified with higher accuracy. The proposed model is stated as 3D-CNN_U-Net_DenseNet model for the lung nodule segmentation. Here the input lung image has been pre-processed for noise removal and image resizing. Then automatic segmentation has been carried out using 3D convolutional neural network (3D-CNN) in which the cancer part has been segmented automatically. This region-based segmentation part is integrated with kernel for the effective segmentation and extraction. The processed images are classified with the Q-learning based classification technique. The performance of the proposed 3D-CNN_U-Net_DenseNet is comparatively examined with the existing technologies such as MA Detection System, GATM, Automated nodule detection and MRCNN_KT. The performance analysis expressed that the proposed 3D-CNN_U-Net_DenseNet exhibits ~3% - 5% improved performance than the existing techniques.
Interpretable Evaluation of Diabetic Retinopathy Grade Regarding Eye Color Fundus Images

This paper reports an interpretable automated grading system for diabetic retinopathy using color fundus images. First, we develop shallow learners as baselines. Second, we pre-train deep neural networks to extract high-dimensional features and complex patterns from fundus images and utilize ensemble models to do automatic grading. Then we develop several explainable artificial intelligence models to visualize the extracted deep features and to interpret the predicted outcomes. We investigate the robustness of our system over two publicly available diabetic retinopathy fundus imaging datasets. In addition, we displayed both local and global explainable results to further illustrate the clinical decision-making process with deep models. The innovations of our work include (1) using ensemble models to boost the performance of diabetic retinopathy grading system, and (2) providing transparency of ensemble models using explainable artificial intelligence. The result has shown the potential to improve the effectiveness and accessibility of diabetic retinopathy screening in clinical practice and research settings.