Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Brain Metrics for Improving Multi-Agent Adaptive Human-Robot Collaboration: A Preliminary Study

When humans work closely together, they can pick up subtle cues from their team members and adapt their behavior appropriately. Humans working closely with robots may also give off cues, but the robots cannot detect these signals and therefore cannot change behavior. In this paper, we focus on heterogeneous multi-human and robot teams. Such scenarios exist frequently in search and rescue operations as well as space missions, where robots perform tasks that are unsafe or even impossible for humans. At the same time, human team members collaborate to make important decisions that influence and direct the robots’ work. These decisions often have to be made quickly with high levels of uncertainty, with simultaneous physical and mental demands on the human. In this project, we aim to explore the following questions: Can brain data provide insights that could improve team performance? Could we use these signals to detect when someone is experiencing excessive workload? Could we detect an impact on team performance caused by the robot?
BlueSky: Towards Convergence of Zero Trust Principles and Score-Based Authorization for IoT Enabled Smart Systems

Zero trust (ZT) is the term for an evolving set of cybersecurity paradigms that move defenses from static, network-based perimeters to focus on users, assets, and resources. It assumes no implicit trust is granted to assets or user accounts based solely on their physical or network location. We have billions of devices in IoT ecosystems connected to enable smart environments, and these devices are scattered around different locations, sometimes multiple cities or even multiple countries. Moreover, the deployment of resource-constrained devices motivates the integration of IoT and cloud services. This adoption of a plethora of technologies expands the attack surface and positions the IoT ecosystem as a target for many potential security threats. This complexity has outstripped legacy perimeter-based security methods as there is no single, easily identified perimeter for different use cases in IoT. Hence, we believe that the need arises to incorporate ZT guiding principles in workflows, systems design, and operations that can be used to improve the security posture of IoT applications. This paper motivates the need to implement ZT principles when developing access control models for smart IoT systems. It first provides a structured mapping between the ZT basic tenets and the PEI framework when designing and implementing a ZT authorization system. It proposes the ZT authorization requirements framework (ZT-ARF), which provides a structured approach to authorization policy models in ZT systems. Moreover, it analyzes the requirements of access control models in IoT within the proposed ZT-ARF and presents the vision and need for a ZT score-based authorization framework (ZT-SAF) that is capable of maintaining the access control requirements for ZT IoT connected systems.
Mixed precision algorithms in numerical linear algebra

Today’s floating-point arithmetic landscape is broader than ever. While scientific computing has traditionally used single precision and double precision floating-point arithmetics, half precision is increasingly available in hardware and quadruple precision is supported in software. Lower precision arithmetic brings increased speed and reduced communication and energy costs, but it produces results of correspondingly low accuracy. Higher precisions are more expensive but can potentially provide great benefits, even if used sparingly. A variety of mixed precision algorithms have been developed that combine the superior performance of lower precisions with the better accuracy of higher precisions. Some of these algorithms aim to provide results of the same quality as algorithms running in a fixed precision but at a much lower cost; others use a little higher precision to improve the accuracy of an algorithm. This survey treats a broad range of mixed precision algorithms in numerical linear algebra, both direct and iterative, for problems including matrix multiplication, matrix factorization, linear systems, least squares, eigenvalue decomposition and singular value decomposition. We identify key algorithmic ideas, such as iterative refinement, adapting the precision to the data, and exploiting mixed precision block fused multiply–add operations. We also describe the possible performance benefits and explain what is known about the numerical stability of the algorithms. This survey should be useful to a wide community of researchers and practitioners who wish to develop or benefit from mixed precision numerical linear algebra algorithms.
Offline Handwritten Text Recognition Using Hybrid CNN–BLSTM Network

Handwriting text recognition, or htr, is a computer capability to receive and understand handwritten data from various sources, like paper journals and graphics, touchpad screen. Identification of handwritten text is a new and complicated field of study that has a vast range of applications in banks and other enterprises. In this work , we use a hybrid convolutional neural network (CNN) and Hybrid Bi-directional Long Short Term Memory (BLSTM) based Recurrent Neural Network design to determine text. We deploy CNN layers to create a variety of activation maps. Activation maps highlight the image's most important characteristics. This feature is then feed to BLSTM layer for encoding and decoding visual feature present in the input image . Our CNN-BLSTM based implementation is experimented with the dataset IAM to obtain the accuracy of handwritten text. We achieve an accuracy of 97.8% in IAM word dataset.
Air Quality Analysis and Smog Detection in Smart Cities for Safer Transport using Machine Learning (ML) Regression Models

A smart city is a technologically modern urban area in which different electronic methods, and multiple advanced features based on Information and Communication Technologies (ICT) are developed. The Smart Transport is one of the important functions of a smart city which facilitates a smooth movement of vehicles and allows citizens a safe and luxurious journey experience. The two major reasons that are responsible for accidents and mishaps on urban highways are the excess pollution of the air and the smog. The smog is a mixture smoke and fog, and it is composed of ozone (O3) and the particulate matter like pollen, dust, sulphur oxides, etc. Thus, if the air pollutants and their concentration is determined, we can easily detect the presence of the smog in the air. In this paper different machine learning regression models namely Polynomial Regression Model, Decision Tree Regression Model, Random Forest Regression Model, and Support Vector Regression Model are used for the prediction of Air Quality Index (AQI) from which the pollutant concentration can be determined, which will help to detect the smog. The results of the implementation show that the Random Forest Regression Model gives a better result of prediction amongst all the five models.
Propagation, blockage and coverage evaluation in 5G urban wireless networks

Using ray tracing and based on a set of realistic simulations performed in MATLAB ® and Open Street Maps, this document relates the coverage problem in urban 5G wireless networks with other relevant propagation and signal blockage stochastic models. From a statistical analysis of these results, the main characteristics of shadow fading are extracted and compared. In addition, a review of the current IMT-2020 and 3GPP standards on radio propagation for 5G has been carried out.
Design and Analysis of Interleaved Flyback Converter Topology for Photovoltaic Applications

All over the planet, there is a great demand for and need for electrical energy utilization. The market for non-conventional energy sources is increasingly growing and demand shoots up. Renewable energy resources play an important role among which Photo Voltaic (PV) cell electrical energy is the most reliable and effective one for power generation. The analysis of various converter circuits leads to show that, an interleaved fly-back converter topology in isolated grid associated inverter for high power photovoltaic applications is best even under partial shading conditions. A special mechanism known as an interleaving concept is used in this paper and it is an essential phenomenon in order to achieve high power as well as minimize the ripple components in current waveform. By the implementation of the proposed method, we acheived reliable percentage efficiency is 98.08 and the power factor for the system is 0.925.
Success Model Measuring Implementation of Customer Intention to use Virtual Booth on E-Commerce System in the Pandemic Covid-19

This study develops a virtual booth implementation measurement model in e-commerce to increase customer intention to use and then buy. This development is based on this pandemic period. The use of e-commerce has become a place of buying and selling, so new innovations in the system are needed to increase visitors to the e-commerce system to buy. Thus, in this study, we develop a success measurement model for implementing a system based on the IS success model developed by Delon and Mclean. This study uses the variables Information Quality, Service Quality, Exhibition experience, System Quality, exhibition Satisfaction, and intention to use. This variable can play a role in helping determine the effectiveness of the implemented information system. Furthermore, this research will be used to measure respondents’ quantitative analysis using the system.
Systematic Generation of XSS and SQLi Vulnerabilities in PHP as Test Cases for Static Code Analysis

Synthetic static code analysis test suites are important to test the basic functionality of tools. We present a framework that uses different source code patterns to generate Cross Site Scripting and SQL injection test cases. A decision tree is used to determine if the test cases are vulnerable. The test cases are split into two test suites. The first test suite contains 258,432 test cases that have influence on the decision trees. The second test suite contains 20 vulnerable test cases with different data flow patterns. The test cases are scanned with two commercial static code analysis tools to show that they can be used to benchmark and identify problems of static code analysis tools. Expert interviews confirm that the decision tree is a solid way to determine the vulnerable test cases and that the test suites are relevant.
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions

Machine learning is expected to fuel significant improvements in medical care. To ensure that fundamental principles such as beneficence, respect for human autonomy, prevention of harm, justice, privacy, and transparency are respected, medical machine learning systems must be developed responsibly. Many high-level declarations of ethical principles have been put forth for this purpose, but there is a severe lack of technical guidelines explicating the practical consequences for medical machine learning. Similarly, there is currently considerable uncertainty regarding the exact regulatory requirements placed upon medical machine learning systems. In this paper, we survey the technical and procedural challenges involved in creating medical machine learning systems responsibly and in conformity with existing regulations, as well as possible solutions to address these challenges. We begin by providing a brief overview of existing regulations affecting medical machine learning, showing that properties such as safety, robustness, reliability, privacy, security, transparency, explainability, and nondiscrimination are all demanded already by existing law and regulations—albeit, in many cases, to an uncertain degree. Next, we aim to illuminate the key technical obstacles to achieving these desirable properties, and important techniques to overcome these obstacles in the medical context. We notice that distribution shift, spurious correlations, model underspecification, uncertainty quantification, and data scarcity represent severe challenges in the medical context. Promising solution approaches include the use of large and representative datasets and federated learning as a means to that end, the careful exploitation of domain knowledge, the use of inherently transparent models, comprehensive out-of-distribution model testing and verification, as well as algorithmic impact assessments.
Detection and Classification of Pulmonary Lung Nodules in CT Images Using 3D Convolutional Neural Networks

Lung Cancer is a disease that mainly rises cancer death. Early detection and accurate diagnosis can enrich the patient's life. Precise cancer segmentation assists specialists in comprehending the position as well as magnitude of cancer and henceforth take worthwhile diagnostic judgment. However, manual segmentation among numerous cancer-related medical images is a very weariness task. A deep learning architecture V-net is provided in the proposed study for detecting the nodule with fewer false positives (FP). Following the detection of candidate nodules, the data is prepared for 3D ResNet to determine whether a candidate nodule is an FP candidate or a genuine nodule. The Hounsfield u nit(HU) preprocesses t he input CT scans. Since the process of computation of the raw CT scans are expensive and difficult, t hey a re separated into numerous patches of size 96X96X16 after preprocessing. This proposed approach gave the precision and recall of 98.7% and 98.2 % respectively when applied on LUNA16 data-set.
A Compact MIMO Antenna with High Isolation and Gain-Bandwidth Product for Wireless Personal Communication

The multiple-input-multiple-output (MIMO) system consisting of multiple antenna elements faces issue of isolation performance between antenna elements. In this paper, isolation is improved by inserting a thin parasitic slit between antennas on the upper side and a plus-shaped stub in the ground plane. The proposed MIMO antenna consists of two bevelled circular antennas with a common partial ground. It is designed on FR-4 substrate having 12.5 mm x 24 mm x 1.6 mm dimensions. Bevelling is applied at the bottom of a circular patch to attain wideband performance. This antenna achieved bandwidth from 5.22 GHz to 14.89 GHz and offers isolation better than 15 dB in most of the operational bands while isolation more than 20 dB in the entire X-band. The Envelope Correlation Coefficient (ECC) of the antenna is less than 0.017 and DG is more than 9.9175. It offers an acceptable gain between 1.12 to 3 dBi and radiation efficiency between 57 to 75 %. The simulation and measurement results show good agreement. The designed MIMO antenna is compatible with WLAN (5.2 GHz to 5.8 GHz), upper C-band (5.22 GHz to 8 GHz), X-band (8 GHz to12 GHz) and lower Ku-band (12 GHz to 14.89 GHz) applications.
Automatic Labeling and Data Enhancement System for Sample Collection Based on Deep Learning

In this paper, sample collection based on deep learning is used to automatically label the problem, and the prior knowledge of the image label is used as the supervision information of the deep neural network. On the basis of obtaining the basic label vocabulary, the dependency relationship and prior distribution of the original image label vocabulary are used to improve the labeling result of the image. The advanced data augmentation method as the benchmark has shown unparalleled appeal at present. GAN learns the simulated distribution of images through game training and generates fake and real images. It also has broad prospects in the field of image migration and image editing.
TESRAC: A Framework for Test Suite Reduction Assessment at Scale

Regression testing is an important task in any large software project, however as codebase increases, test suites grow and become composed of highly redundant test cases, thus greatly increasing the time required for testing. To solve this problem various test suite reduction tools have been proposed, however their absolute and relative performance are unclear to their prospective users, since there is a lack of a standardized evaluation or approach for choosing the best reduction tool. This work proposes TESRAC, a framework for assessing and comparing test suite reduction tools, which allows users to evaluate and rank a customizable set of tools in terms of reduction performance according to criteria (coverage, dimension, and execution time), and which can be configured to prioritize specific criteria. We used TESRAC to assess and compare three test suite reduction tools and one test suite prioritization tool that has been adapted to perform test suite reduction, across eleven projects of various dimensions and characteristics. Results show that a test suite prioritization tool can be adapted to perform a adequate test suite reduction, and a subset of tools outperforms the remaining tools for the majority of the projects. However, the project and test suite being reduced can have a strong impact on a tool's performance.
IT Attack Detection and Classification using Users Event Log Feature And Behavior Analytics through Fourier EEG Signal

As the documentary action of every single enterprise and government agencies are being digitalized and several industries are heading towards complete Migration towards virtualization the risk of security threat, malicious attacks, criminal mindset are arising. Deep learning and AI have always been setting a new paradigm in one step above detection and classification of several attacks based on its file and features. Several enterprises utilize the historic data of their employees for storing cookies and understanding business processes. In this paper, we are proposing a new framework in which Malicious attacks can be classified using anomaly detection based on event users' log similarities and summaries of operations performed virtually. Also, the Classification of these relevant features log using Decomposed EEG signal is shown for detecting behavior more precisely using Kruskal- Wallis algorithm has been employed to select explicit static features. These selected features are used to classify the given EEG dataset into different categories using a supervised EEG decomposition algorithm.
Atomic Force Microscopy: an innovative technology to explore cardiomyocyte cell surface in cardiac physio/pathophysiology

Atomic Force Microscopy (AFM) has emerged these recent years as a multifunctional toolbox for studying biological samples in physiological conditions. Although its use has spread among biologists community, cardiology remains a scientific field where not been extensively used yet. Heart diseases are nowadays a major human threat, and cause the death of millions of people each year. A convergent point to all heart diseases seems to be related to the defect of the cardiomyocyte, the contractile unit of the he reason, many scientists got interested in this cell type. However, very few studies use a technology such as AFM and its derivatives (force spectroscopy, multiparametric imaging) to explore this cell. The aim of this review is thus to give a comprehensive an interest of the biophysical approach made possible by AFM studies. We will show how AFM has been and can be used to study fix living cardiomyocytes, and, how combined with other types of microscopy, it can help getting a better understanding o pathologies or drugs. This review is the first dedicated to the use of AFM technics in cardiology, and gives new insights in fundamental questions surrounding cardiomyocytes, that can be answered using such a technology.
DCMC: Decentralized and Cellular Mechanism for improving fault management in Clustered wireless sensor networks

Due to the shared wireless communication medium and harsh environments in which sensor nodes are deployed, Wireless Sensor networks (WSN) are inherently fault-prone. Energy is one of the most constraining factors and node failures due to crash and energy exhaustion are commonplace. In order to avoid degradation of service due to faults, it is necessary for the WSN to be able to detect faults early and initiate recovery actions. In this paper we propose a cellular and decentralized cluster based method for any fault detection and recovery which is energy efficient namely DCMC. Simulation Results show that the performance of proposed algorithm is more efficient than previous ones.
Secrecy Outage Performance Analysis of UAV-Assisted Relay Communication Systems With Multiple Aerial and Ground Eavesdroppers

In this article, we study the secrecy performance of an unmanned aerial vehicle (UAV)-assisted relay communication system, where a ground base station (GBS) intends to send confidential information to the ground legitimate user Bob with the help of a UAV relay in the presence of multiple aerial and ground eavesdroppers. We consider the height-dependent probabilistic line-of-sight and the general <inline-formula><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> shadowed channel fading model in the system. To enhance the secrecy performance, the GBS and UAV relay apply directional beamforming transmission while implementing protection zones around their intended receiving destinations. We then derive the approximate closed-form expressions of the secrecy outage probability (SOP) under different aerial and ground eavesdropperscolluding strategies. To get insights, we also obtain the SOP expression over Rayleigh fading as a special case. After discussing the impacts of transmitting and multiple aerial and ground eavesdropping parameters on the SOP, we show that the optimal altitude of UAV relay exists with the minimal SOP value under different urban environments. We also show that UAV eavesdroppers have a larger effect on the system performance than ground eavesdroppers.
DDoS Attack Detection Using Ensemble Machine Learning Models with RFE Algorithm

There are several types of Cyber-Security Attacks today. In this paper, we focus on Distributed Denial of Service Attack (DDoS-Attack). DDoS-Attack is a vicious attempt to disrupt normal internet traffic by using DDoS traffic to the target server. Today, Artificial Intelligence is used to solve many problems. Therefore, this research introduces the ensemble machine learning models with RFE algorithm to enhance the detection efficiency of DDoSAttack floods. We compare the performance of the proposed algorithm with the other well-known algorithms in terms of time efficiency, accuracy and CPU-usage. Four types of accuracy are considered. Proposed ensemble machine learning algorithm usually saves more on computing resources than other techniques that use neural networks. From the results, we found that the proposed algorithm offers better performance than other algorithms in term of accuracy, precision, testing time and CPU-usage.
Unscented Kalman Filter-Based Bayesian Filtering of RC Circuit

In this paper, we present output voltage estimation of second order resistor capacitor (RC) low pass filter (LPF) circuit using the unscented Kalman filter (UKF). Firstly, with the use of state space depiction we formulated a deterministic model of RC LPF circuit. The deterministic model is then transformed into the stochastic model by introducing noise to circuit components and input source. Finally, the MHE method and UKF method have been simulated to obtain the optimal state estimate when the circuit elements are affected by the Gaussian noise process. The derived mathematical expressions, so obtained, are simulated in MATLAB software. Then the comparison is shown between the estimated output values and PSPICE simulated values (actual output values) and digression of estimated output is expressed in the context of root mean square error (RMSE) to represent a loss function. The RMSE values validates the efficacy of proposed method with respect to the latter method.