Miracle-dz/UniFAR-FTUs huggingface dataset:
Stock Market Strengthens Economy And Strengthened by AI to Minimize Risk

Inflation is not a friend particularly when one tries to avoid wasting money for a few major outlays, such as buying a house or financing a comfortable retirement. In the world of finance, stock commerce is one of the essential activities for the growth and prosperity of the nation by giving a good chance to the commerce and the individuals to invest capital. Earlier, buying and selling of stocks were solely based on human conscience with luck or guessing and without understanding the background details of the stock. It was more kind of betting or gambling. The main aim of this proposed work is to make people understand the importance of investing in the share market for the opulence of a nation. Moreover, this proposed method reduces the risk of losing money in the stock market by hybridizing 2 of the most popular stock market prediction techniques i.e., Auto-Regressive Integrated Moving Average (ARIMA) and Long Short Term Memory (LSTM) with conditional weights determined using the logistic regression using Artificial Intelligence. This aids the decision making process like which stocks to buy/sell at what price at that particular instance. However, no proposed work was found directly indicating the approach that combines these monetary technical indicators with more than one machine learning algorithm. Yet, efficient results were observed as compared to some other proposed approaches as we merged more than one method to predict the same stock price.
Dynamic Data Integration for Resilience to Sensor Attacks in Multi-Agent Systems

In recent years the number of security incidents affecting control systems has increased. These incidents have shown the need to develop strategies to improve system resilience to cyber-attacks. This paper presents a practical implementation of a strategy to detect cyber-attacks and mitigate their effects on sensors of a multi-agent system. The proposed approach computes, in real-time, a convex combination of measurements from main and redundant sensors, producing a trust value of the measurement and feeding it to the controller. We implemented this approach on a testbed of ground robots in formation. Experimental results to various kinds of attacks and a key performance index show that the proposed strategy reduces the effects of attacks not only on the affected agent, but also prevents the propagation of the attack over the remaining agents.
Intelligent Detection Algorithm Against UAVs' GPS Spoofing Attack

Unmanned Aerial Vehicle (UAV) technology is more and more widely used in the field of civil and military information acquisition. GPS plays the most critical part of UAVs' navigation and positioning. However, since the communication channel of the GPS signals is open, attackers can disguise as real GPS signals to launch GPS spoofing attacks on civilian UAVs. At present, the detection schemes for GPS spoofing attacks can be divided into three categories respectively based on encryption and digital signatures, the characteristics of the GPS signal and various external characteristics of UAVs. However, there are some problems in these methods, such as low computing efficiency, difficulty in equipment upgrading, and limited application scenarios. To solve these problems, we propose a new GPS spoofing attack detection method based on Long Short-Term Memory (LSTM) which is a machine learning algorithm. In order to improve the detection ratio, after the machine learning algorithm, we let the UAVs fly according to the path of a specific shape to accurately detect GPS spoofing attacks. This is also the first time machine learning has been used to detect GPS spoofing attacks. According to our algorithm, we can detect GPS spoofing attacks accurately and quickly in a short time. This paper describes in detail the algorithm we proposed to resist GPS spoofing attacks, and the corresponding experiments are carried out in the simulation environment. The experimental results show that our method can quickly and accurately detect UAV GPS spoofing attacks without requiring upgrades to existing equipment.
The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities

Digital twinning is one of the top ten technology trends in the last couple of years, due to its high applicability in the industrial sector. The integration of big data analytics and artificial intelligence/machine learning (AI-ML) techniques with digital twinning, further enriches its significance and research potential with new opportunities and unique challenges. To date, a number of scientific models have been designed and implemented related to this evolving topic. However, there is no systematic review of digital twinning, particularly focusing on the role of AI-ML and big data, to guide the academia and industry towards future developments. Therefore, this article emphasizes the role of big data and AI-ML in the creation of digital twins (DTs) or DT-based systems for various industrial applications, by highlighting the current state-of-the-art deployments. We performed a systematic review on top of multidisciplinary electronic bibliographic databases, in addition to existing patents in the field. Also, we identified development-tools that can facilitate various levels of the digital twinning. Further, we designed a big data driven and AI-enriched reference architecture that leads developers to a complete DT-enabled system. Finally, we highlighted the research potential of AI-ML for digital twinning by unveiling challenges and current opportunities.
Robust Data Predictive Control Framework for Smart Multi-Microgrid Energy Dispatch Considering Electricity Market Uncertainty

With the emerging technologies for Energy Intent (EI) and data-driven applications, the conventional power grid network is undergoing a radical modernization. An efficient energy management and electricity price forecasting remains a challenging task. In this paper, a new Robust Data Predictive Control framework for Energy Management System (RDPC-EMS) is developed to overcome the uncertainty of the electricity retail price market and minimize the total operating costs for the multi-microgrids (MMG) system. The proposed framework solves the economic energy dispatch based on an accurate Electricity Price Forecasting (EPF) by an Outlier-Robust Extreme Learning Machine (OR-ELM) algorithm and a two layers cooperative Distributed Model Predictive Control (DMPC). The First level provides an optimal energy scheduling between the Distribution System Operator (DSO) and cooperative microgrids systems to minimize the operating cost based on the forecasted electricity price. In contrast, second level maintains the supply-demand balance by applying the optimal energy scheduling from the first layer through an adjustment of the distributed energy resources (DER). The electricity retail price prediction is assessed using real dataset from the Iso New England electricity market. The OR-ELM regression method shows a significant forecasting performance in terms of error metrics. For instance, the mean absolute error in the training stage 2.05% for OR-ELM with a comparison of 4.17% and 6.29% for Support Vector Regression (SVR), and Artificial Neural Network (ANN) models respectively. Finally, simulation results demonstrate the efficiency RDPC-EMS for daily operating cost reduction, with decrease of 15% for MG 1 and 16% for MG 2.
Attentional Behavior of Children With ASD in Response to Robotic Agents

Autism spectrum disorder (ASD) affects a variety of social skills in children. One of the most important social skills that are affected by ASD is the ability to pay attention; ASD introduces unnecessary temporal delays in the attentional behavior of children toward external stimuli. This research focuses on identifying the effect of different social stimuli from robotic agents to assess the attentional behavior of children with ASD. Latencies in shifting attention and the number of attentions paid to a stimulus are defined as indexes of attentional behaviors for children with ASD. Nine children with ASD (seven males and two females, mean: 7.57 ± 2.57 years) that had minimal to no symptoms of autism participated in this study. Two robotic agents presented visual, speech, and motion social stimuli to the participants. Regarding social stimuli, a significant effect on the latency in shifting attention (<inline-formula> <tex-math notation="LaTeX">$\chi ^{2}(2)=9.55$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\text {p} = 0.008$ </tex-math></inline-formula>) and the number of attentions paid (<inline-formula> <tex-math notation="LaTeX">$\chi ^{2}(2)=9.56$ </tex-math></inline-formula>, p = 0.008) was observed in children with ASD. The speech stimulus captured the children’s attention faster than the visual and motion stimuli in terms of latency in shifting attention and the number of attentions paid. Moreover, significant positive correlations were found for some pairs of stimuli among visual, speech, and motion stimuli for latencies in shifting attention, as well as the number of attentions paid using Pearson’s correlation analysis. This study contributes to the literature in terms of the impact of robotic stimuli on the attentional behavior of children with ASD.
Multi-Objective based Congestion Management considering Generation Rescheduling and Cost Minimization using Multi-Verse Optimizer

In this present study of congestion management, the problem is demonstrated in a multi-objective manner by generator rescheduling and cost minimization. Power transfer capability is limited in such cases of increasing load due to the thermal limitation of the transmission line. The power generation from different buses is often adjusted to meet the load requirement. For confronting the congestion management problem, the operation of the system has to deal with system operational constraints which are the equality and the inequality constraints. The power balance constraint can be termed as the equality constraint and the inequality constraints consists of the power generation limit, voltage limit for both generator and load bus. A Multi-Verse Optimization algorithm for a multi-objective problem is utilized to solve the problem of congestion management.
Higher-Order Graph Convolutional Networks With Multi-Scale Neighborhood Pooling for Semi-Supervised Node Classification

Existing popular methods for semi-supervised node classification with high-order convolution improve the learning ability of graph convolutional networks (GCNs) by capturing the feature information from high-order neighborhoods. However, these methods with high-order convolution usually require many parameters and high computational complexity. To address these limitations, we propose HCNP, a new higher-order GCN for semi-supervised node learning tasks, which can simultaneously aggregate information of various neighborhoods by constructing high-order convolution. In HCNP, we reduce the number of parameters using a weight sharing mechanism and combine the neighborhood information via multi-scale neighborhood pooling. Further, HCNP does not require a large number of hidden units, and it fits a few parameters and exhibits low complexity. We show that HCNP matches GCNs in terms of complexity and parameters. Comprehensive evaluations on publication citation datasets (Citeseer, Pubmed, and Cora) demonstrate that the proposed methods outperform MixHop in most cases while maintaining lower complexity and fewer parameters and achieve state-of-the-art performance in terms of accuracy and parameters compared to other baselines.
Deep Learning Based Automated Inspection of Weak Microscratches in Optical Fiber Connector End-Face

Surface defects on optical fiber end-face, such as scratches, cause heavy loss and low data-transfer rate. Therefore, the inspection of optical fiber end-face defect cannot be neglected. Most optical fiber defect detection methods ignore the striped pattern of scratches and easily cause incomplete segmentation. In this article, a novel method based on deep convolutional neural networks (DCNNs) for weak microscratches inspection in the optical fiber end-face is proposed. First, a new feature pyramid structure with criss-cross max-pooling (CCMP) block is developed to extract precise boundary features in all directions and generate feature maps. Second, these feature maps are fed into the spatial attention upsample (SAU) module to recover a full-resolution feature map and localize the defects. Finally, multiple scratches with various types can be detected in the images. This article uses many real optical fiber images as experimental images. The experimental results demonstrate that the proposed method yields good comprehensive performance with a mean pixel accuracy (mPA) of 92.70% and a mean intersection over union (mIoU) of 85.35%.
Intelligent unmanned aerial vehicle (UAV) system for aircraft surface inspection

Aircraft surface inspection is critical to the safe flight of aircraft. At present, aircraft surface inspection is still mainly conducted by manual visual inspection. In order to improve the efficiency of inspection efficiency, in this paper, we designed an unmanned aerial vehicle (UAV) intelligent system to collect aircraft surface images and used deep learning object detection algorithm to independently and efficiently solved aircraft surface inspection include:(1) Used unmanned aerial vehicle (UAV) to collect the whole surface image of the aircraft wholly and independently according to the pre-planning information. (2) A high-performance algorithm for real-time detection and a high-precision algorithm for accurate detection were designed to realize intelligent inspection of aircraft surface damage based on deep learning object detection. Our experiments showed that this system could quickly and accurately inspect the entire aircraft surface with meeting the safety requirements.
Data-driven model of Photovoltaic Module by Machine Learning Regression for Power Maximization

Sun, as the energy source is abundant in nature, is a potential renewable energy source. In this paper, a five parameter model Solar Photovoltaic (PV) module is simulated to produce data. Machine Learning Regression algorithm using R software is applied to predict the electrical parameters of the cells while varying the independent parameters from the simulation. The study has an impact on reliable and maximum power dispatch considering model parameter change and the variable nature of PV power in a standalone system. The proposed approach is mathematical model-independent and has extensive use.
Constrained Clustering Approach in Wireless Sensor Networks to Minimize the Energy Consumption

The applications of Wireless Sensor Networks (WSNs) are massive in the range that starts from the defense, civil, military to our day to day routine life. Energy conservation in WSNs is a crucial issue to make the network operational for a long time. It is entirely worth noting that most of the energy consumption occurs during the data transmission. Routing protocol plays a significant role in carrying and forwarding the data to the destination. Clustering is one such branch of routing technique that increases the Network Lifetime unexpectedly through minimal energy consumption. In this regard, this research paper aims to formulate an exciting clustering problem in WSNs where the coordinates of patterns are random variables. The clustering problem is solved using the CENTROID approach, such as Cluster Head (CH) selection is done with an optimization interpretation. The proposed protocol is verified through the ns-2 simulator, and the results obtained from the simulations show that the proposed constrained clustering LEACH (CC-LEACH) offers its superiority over the existing protocols such as LEACH and LEACH-C in terms of various performance parameters.
Review on QoS Aware Resource Management in Fog Computing Environment

The ever-increasing demand for computing power and the high penetration of the Internet of Things(IoT) in our daily life interactions undoubtedly enhance the quality of life. To fulfill the heterogeneous demand of these IoT applications Quality of service(QoS) is an important performance measurement metric that must be considered. The traditional computing paradigm is not effective to address the latency sensitive applications generated through the IoT devices. To overcome these limitations of traditional computing a novel computing paradigm Fog computing emerges as a viable solution to provide cloudlike services. However moving the computation, networking, and storage resources to the edge of the network near to source do not guarantee to solve all the challenges. Due to the limited storage capacities of fog nodes, the optimal distribution of resources is an important key to enhance the overall performance of the fog computing paradigm. The resources available in the fog infrastructure are constrained and heterogeneous so there is a critical need to optimize the resources in the fog layer to handle and allocate the resources efficiently to the different applications in an IoT network. To manage the resources efficiently we need to study different resource management issues, objectives, and approaches. Different authors classify the resource management policy into different methods like service placement, resource scheduling, resource allocation, and resource provisioning and adopted different heuristic and meta-heuristic strategies to optimize the available resources in fog layers like computation, communication, storage, data, and energy from the end-user. In this paper we discuss the functionality and importance of fog orchestration node in the generic three-tier architecture that is primarily designed for efficient workload distribution like the number of virtual machines required, the number of different resources required, etc. to execute in the dynamic IoT environment. Also, we introduce some challenges in resource management that must be included in order to provide better QoS to clients.
A simple model for cellulose solubility in supercritical water

A simple model for the hydration of a polar molecule is developed in order to provide a description of cellulose solubility in water under ambient and supercritical conditions. The change in free energy upon hydration is regarded as the sum of the energy cost of forming a cavity and a polar contribution. The model is able to predict the existence of an optimal density for dissolution of polar solutes in supercritical water. Those results are in line with earlier experiments and simulations showing that water at high temperature and pressure dissolves cellulose, and that an optimal density for dissolution exists. The present study shows that the density dependence comes from the fact that both the cavity formation energy and the polar energy are highly density dependent but with opposing behaviour. The cavity formation energy increases with density, whereas the polar energy decreases. Based on the present model, it is possible to rationalize a few basic strategies regarding cellulose dissolution in aqueous media. To increase solubility, one can either increase the polar/electrostatic contribution, or more importantly, one can decrease the cost of cavity formation, e.g. by introducing co-solvents, changing temperature and/or pressure.
Scheduling Rechargeable UAVs for Long Time Barrier Coverage

We consider barrier coverage applications where a set of UAVs are deployed to monitor whether intruders pass through a line, i.e., the barrier. Due to limited energy supply of UAVs, a charging pile is used to recharge UAVs. The problem is to place UAVs on top of the barrier and schedule them to the charging pile such that the barrier is seamlessly covered and the total number of UAVs is minimized. We decompose the problem into subproblems by dividing the barrier into disjoint subsegments and covering each subsegment independently. We prove a theoretical lower bound on the minimum number of UAVs required to cover the barrier forever. We then propose two scheduling strategies. In the first strategy, only fully recharged backup UAVs will be scheduled to take over UAVs running out of energy. If there are enough UAVs, this strategy can cover the barrier forever. The second strategy is proposed to deal with the situation that the number of UAVs is insufficient. Under the strategy, if no backup UAV is fully recharged, the one with the most battery energy will be selected to take over the monitoring task. When the number of UAVs is insufficient, the barrier can still be covered for a long time. We analytically derive the number of UAVs required by the first strategy, and the monitoring duration of the second strategy in case of insufficient UAVs. Simulations verify the effectiveness of the proposed solutions.
Familial analysis of interface and program targeting noise contained malware using image processing

Malware variants are increasing at a rapid pace and their detection is becoming a cumbersome task. A lot of work detect malware using the behavioural signatures of the binary executables. Existing malware detectors are inefficient due to the noise that is embedded within the malware. The exact localization of the target of the malware is still in development. The recognition of the exact family of the malware is still improving, and it helps in improving the features required for their proper identification. In this paper, we execute the familial analysis of interface and program targeting noise contained malware using image processing. We use the system call dataset generated from NITRSCT (NITR System Call Tracer) and use cuckoo sandbox for generating API calls. Representation of the binary executables are in the form of grayscale image with noise within them. We apply image-processing technique to identify the target of the malware, i.e. interface or program. This is being done in the first phase of detection. After the target is identified, we send interface targeting malware to system callbased detector and program targeting to the API-based. The familial analysis is accomplished by both the detectors and exact family is identified. Upon extensive experiments, it is illustrated that our model can achieve higher accuracy, 99.4%, 77.9% target localization accuracy and 1.03s detection time. Our classification accuracy is 91.3%, which is far better than present state-of-the-art approaches.
Smart Control of Traffic Light Using Artificial Intelligence

Traffic congestion is becoming one of the critical issues with increasing population and automobiles in cities. Traffic jams not only cause extra delay and stress for the drivers, but also increase fuel consumption and air pollution. Although it seems to pervade everywhere, megacities are the ones most affected by it. And its ever-increasing nature makes it necessary to calculate the road traffic density in real-time for better signal control and effective traffic management. The traffic controller is one of the critical factors affecting traffic flow. Therefore, the need for optimizing traffic control to better accommodate this increasing demand arises. Our proposed system aims to utilize live images from the cameras at traffic junctions for traffic density calculation using image processing and AI. It also focuses on the algorithm for switching the traffic lights based on the vehicle density to reduce congestion, thereby providing faster transit to people and reducing pollution.