Miracle-dz/UniFAR-FTUs huggingface dataset:
Harvester : Influence Optimization in Symmetric Interaction Networks

The problem of optimizing influence diffusion in a network has applications in areas such as marketing, disease control, social media analytics, and more. In all cases, an initial set of influencers are chosen so as to optimize influence propagation. While a lot of research has been devoted to the influence maximization problem, most solutions proposed to date apply on directed networks, considering the undirected case to be solvable as a special case. In this paper, we propose a novel algorithm, Harvester, that achieves results of higher quality than the state of the art on symmetric interaction networks, leveraging the particular characteristics of such networks. Harvester is based on the aggregation of instances of live-edge graphs, from which we compute the influence potential of each node. We show that this technique can be applied for both influence maximization under a known seed size and also for the dual problem of seed minimization under a target influence spread. Our experimental study with real data sets demonstrates that: (a) Harvester outperforms the state-of-the-art method, IMM, in terms of both influence spread and seed size; and (b) its variant for the seed minimization problem yields good seed size estimates, reducing the number of required trial influence spread estimations by a factor of two; and (c) it is scalable with growing graph size and robust to variant edge influence probabilities.
G-Prop-II: global optimization of multilayer perceptrons using GAs

A general problem in model selection is to obtain the right parameters that make a model fit observed data. For a multilayer perceptron (MLP) trained with backpropagation (BP), this means finding the right hidden layer size, appropriate initial weights and learning parameters. The paper proposes a method (G-Prop-II) that attempts to solve that problem by combining a genetic algorithm (GA) and BP to train MLPs with a single hidden layer. The GA selects the initial weights and the learning rate of the network, and changes the number of neurons in the hidden layer through the application of specific genetic operators. G-Prop-II combines the advantages of the global search performed by the GA over the MLP parameter space and the local search of the BP algorithm. The application of the G-Prop-II algorithm to several real world and benchmark problems shows that MLPs evolved using G-Prop-II are smaller and achieve a higher level of generalization than other perceptron training algorithms, such as QuickPropagation or RPROP, and other evolutive algorithms, such as G-LVQ. It also shows some improvement over previous versions of the algorithm.
Acquisition system for obtaining sharp barcode images despite motion

A system for barcode acquisition having a camera 11, 23 with a flutter shutter 17. The flutter shutter 17 may operate according to a pattern or code designed to accommodate parameters such as distance between the camera 11, 22 and a barcode 13, 23, barcode type, velocity between the camera 11, 22 and the barcode 13, 23, blur estimation base on barcode features, and other factors. The image from the camera 11, 22 may be de-blurred or decoded in accordance with the flutter shutter pattern or code. Several images may be captured for patterns or codes based on different parameters. These images may be de-blurred and the highest quality image may be selected according to barcode 13, 23 landmarks.
The investigation of legislations in the world about air pollutants from animal barns.

The inverse effects of animal feeding operations on environment are increase due to increases in capacity and rearing system used in barns. Developed and developing countries aimed to reduce these negative effects of animal barns on environment by adding animal feeding operations to their air quality protection regulations. The gas and particulate matter emissions from animal barns have reduced by setting up various rules in these air quality protection regulations. The regulations applied in United States of America and European Union Countries was looked at and rules for producer were explained in this study.
Multi-parameter Sensing Based on the Stimulated Brillouin Scattering in Ring Core Fibers

In this paper, we designed two ring core fibers (RCFs) for the simultaneous sensing of temperature and strain using stimulated Brillouin scattering. The designed fibers are referred to as step index RCF (SI-RCF) and graded index RCF (GI-RCF). Numerical simulations proved that the designed RCFs achieve temperature sensitivities of 1.928 MHz/°C for SI-RCF and 1.872 MHz/°C for GI-RCF, and strain sensitivities of 0.087 MHz/με for SI-RCF and 0.0842 MHz/με for GI-RCF. The respective optimal errors for the simultaneous sensing of temperature and strain have been evaluated and discussed for both designed RCFs. The reported results and the performed comparisons with literature confirm that the designed RCFs sensors could find applications in next generation Brillouin sensing systems for enabling temperature and strain discrimination.
A YOLOv5-Based Method for Detecting Insulator Defects in Transmission Lines

Insulators are a crucial part of transmission lines and are essential for the reliable operation of power grids. Low insulator defect detection accuracy in complicated settings is an issue. To do this, an improved YOLOv5m model-based insulator defect detection approach is proposed in this study. We first add a dilated convolution during the feature extraction stage. Next, we optimize the loss function using the SimOTA positive and negative sample matching technique. Finally, we use Swin Transformer to refine the attention computation. The results demonstrated that our YOLOv5m model enhanced the mAP0.5 by 2.4% compared to the original network for the detection of pollution flashover defects, by 2.6% compared to the original network for the detection of broken defects. In conclusion, the experimental findings demonstrate that our method can detect insulator defects with excellent detection accuracy and stability in both single- and multi-defect situations.
Modeling and Optimisation of a Hybrid Energy Harvesting System for IoT Nodes Through PSO-MPPT

The principal contribution of this research paper is to propose an efficient hybrid solar-wind energy harvesting approach that can be used as a solution to the limited battery energy problem of IoT nodes. Ideally, the IoT Network nodes using optimized Solar-wind Energy Harvesting should function for many years. In order to obtain the optimal power for a hybrid system, the Maximum power point tracking (MPPT) based on Particle Swarm Optimization (PSO) controller is proposed. This is by adjusting the duty cycle of the boost converter for both Solar and wind system based on voltage and current measurements. During this work, the modeling and simulation of the proposed hybrid system with PSO−MPPT technique under various wind speeds and various irradiation levels are done using MATLAB/SIMULINK. The Battery Condition of Charge (SoC), battery voltage and battery Current are evaluated. Simulation results prove that the proposed hybrid system with MPPT using PSO can provide better performance as compared to the Perturb and Observe (P&O) method. The proposed system has the potential to meet the energy demand of IoT nodes. increases the durability, and total efficiency of the IoT devices, making them perpetually powered.
Software Testing Combining the Fusion of Surrogate Model and Evolutionary Algorithm

The primary challenge is the extensive number of iterations required to converge on suitable solutions in using Genetic Algorithms (GA) for test data generation. Each iteration involves executing candidate solutions and evaluating their fitness values, resulting in prolonged time consumption. This limitation can impede the scalability and efficiency of the evolutionary algorithm, particularly in large-scale or time-critical software testing scenarios. Addressing this issue is crucial for enhancing test data generation efficiency. The method we proposed to mitigate this problem is by fusing surrogate models that approximate the fitness values of candidate solutions instead of executing them on the actual software system. The proposed method typically begins with initial candidate solutions (test data), which along with their fitness values, can be used as a training set to develop the surrogate model. Additional samples may be collected during the evolutionary process to enhance the model’s accuracy. Subsequently, the surrogate model is employed to evaluate the fitness values of new candidate solutions. Solutions with high predicted fitness values are then selected as the initial population to execute the program and undergo the genetic operations. The preliminary experimental results show that by utilizing surrogate models, the need for numerous evaluations on the actual system can be significantly reduced, enabling faster convergence and improved efficiency in test data generation.
Compact and Robust Silicon Waveguide to Hollow Metallic Waveguide Coupling using a Quarter-Wave Dielectric Slot Waveguide for mmW and THz Waves

This paper presents a novel technique for efficient waveguide coupling between a hollow metallic waveguide and a silicon dielectric rod waveguide in the 75–110 GHz range. The technique uses a short section of dielectric slot waveguide as a quarter-wave matching section to enhance robustness, reliability, compactness and yield for all-silicon terahertz integrated devices and systems, compared to those that employ conventional tapered-spike couplers. The transmission and reflection responses of the quarter-wave matched and tapered dielectric rod waveguides are presented, showing less than 2 dB loss. Reflection below -10 dB is achieved over the 75-110 GHz, reaching the lowest reflection magnitude of -25 dB at 97 GHz. The strong localization of the E-field with quarter-wave matching enables contactless coupling of mm waves, even in cases where there is a small separation.
Energetic ion confinement studies using comprehensive neutron diagnostics in the Large Helical Device

Understanding energetic particle (EP) confinement is one of the critical issues in realizing fusion reactors. In stellarator/helical devices, the research on EP confinement is one of the key topics to obtain better confinement by utilizing the flexibility of a 3D magnetic field. A study of EP transport in the Large Helical Device (LHD) has been performed by means of escaping EP diagnostics in hydrogen plasma operation. By starting deuterium operation of the LHD, the confinement study of EPs has progressed remarkably using newly developed comprehensive neutron diagnostics providing information for EPs confined in the core region. The total neutron emission rate (Sn) increases due to the relatively low deviation of the beam ion orbit from the flux surface with the inward shift of the magnetic axis. The Sn has a peak around the electron density of 2 × 1019 m−3 to 3 × 1019 m−3, as predicted. It is found that the fraction of beam–beam components in Sn is evaluated to be approximately 20% by the Fokker–Planck models TASK/FP in the plasma with both co- and counter-neutral beam injections. The equivalent fusion gain in DT plasma achieved 0.11 in a negative-ion-based neutral beam heated plasma. Time evolution of Sn following the short pulse neutral beam injection into the electron–cyclotron-heated low-beta plasma is reproduced by drift kinetic simulation, indicating that transport of a beam ion injected by a short pulse neutral beam can be described with neoclassical models in magnetohydrodynamic quiescent low-beta plasmas. The vertical neutron camera works successfully, demonstrating that in the co-neutral beam-injected plasma, the neutron emission profile shifts according to the magnetic axis position. The shift of the neutron emission profile is reproduced by orbit-following models. The triton burnup study is performed for the first time in a stellarator/heliotron to understand the alpha particle confinement. It is found that the triton burnup ratio, which largely increases at inward-shifted configurations due to the better triton orbit and better plasma performance in the inward-shifted configuration, is similar to that measured in a tokamak having a similar minor radius to the LHD. We study the confinement capability of EPs toward a helical reactor in the magnetohydrodynamic quiescent region and expansion of the energetic ion physics study in toroidal fusion plasmas.
A Generative AI-driven Application: Use of Large Language Models for Traffic Scenario Generation

Artificial Intelligence (AI) has revolutionized the way we interact with technology. One of its forefronts is Natural Language Processing (NLP), an essential sub-field that focuses on human language understanding and generation. Large Language Models (LLMs) such as PaLM, BERT, GPT have emerged as the powerhouses of NLPs with remarkable capabilities. Today, we also utilize AI in other industries like automotive. While using AI-backed powerful instruments in autonomous vehicle technologies, we are still suffering to create virtual traffic scenarios for special purposes such as safety testing and validation, autonomous driver training and regulatory compliance. In this study, we introduce a solution which harnesses the power of LLMs to overcome scenario creation difficulties. Thus, our approach provides creation of the traffic scenarios in linguistic way rather than using user interfaces of the editors or writing special formatted structured scenario files.
Dual-output microwave photonic frequency up-converter based on a dual-polarization dual-parallel Mach-Zehnder modulator

A dual-output microwave photonic frequency up-converter with optically frequency-doubled local oscillator based on a dual-polarization dual-parallel Mach-Zehnder modulator is proposed. Simulation and experimental results show that the proposed structure can simultaneously up-convert the IF signal to X-band and Ku-band, with a fixed 6 GHz LO signal, and the undesired signal suppression ratio is about 19 dB in X-band and in Ku-band.
A Comparison of Different Chest X-ray Datasets with Fast Pre-Trained CNN Models in the Detection of Covid-19 Infection

It is difficult to construct an early and effective prediction system that helps to limit the current coronavirus disease pandemic from spreading. Combining X-rays and Artificial Intelligence (AI), particularly Convolutional Neural Networks (CNNs), accelerates the COVID-19 diagnosis. Nevertheless, choosing the optimal model and dataset from the many options available can be challenging. This paper presents the analysis and creation of a large dataset to improve precision and reduce errors in the classification, overfitting, and bias, so as to enhance generalization. The larger the training dataset, the better the model learns on a larger number of images, which enables it to discover and classify new X-ray images that have not been seen before. The number of layers in each model is used to compare computational complexity. Finally, this work presents a comprehensive performance analysis of five datasets. It highlights the use of transfer learning approaches based on four models, including ResNet50V2, NASNetMobile, MobileNetV2, and EfficientNetB0, and how the dataset size, image preprocessing, and overfitting affect the accuracy of COVID-19 case identification using Chest X-ray (CXR) images. The best average accuracy of 96.6% was obtained by the ResNet50V2 model across all five datasets.
Integration of Lightweights Blocks Ciphers as an Extension into Microprocessor for IoT Security

The Internet of Things today is in all areas of daily life. It changes all sectors including industry, agriculture, and smart homes. Therefore, IoT devices require secure microprocessors for data security. However, these devices are limited in terms of computing power, surface, memory, and in terms of energy consumption. In this article, we will present a new customized version of LEON3, the RISCV (Reduced Instruction Set Computer-five) processor, dedicated to IoT applications with a powerfully built security mechanism integrated at the design stage. Also, we presented the proposed lightweight cryptographic algorithm instructions (LED and SIMON), which are integrated into the new architecture of the ReonV processor. After integrating the lightweight encryption instructions to ensure security, we have implemented our custom processor on FPGAs and are evaluated for slice LUT plus FF pairs and frequency. The obtained results show that our proposed designs can achieve good encryption results with high performance and low cost for our new RenoV processor architecture Compared to previous works.
Multi-modal Chinese Fake News Detection

The growth of fake news has been accelerated by the popularity of the Internet, creating a fertile ground for its dissemination. With the advent of diverse social platforms, fake news has evolved beyond textual content, extending to multimodal formats like images and videos. Consequently, there is an urgent need to develop fake news detection methods that are effective for the current multi-modal information landscape. This paper presents a model called MMCFND for detecting multimodal fake news in the Chinese context. The proposed MMCFND model leverages both textual and visual features extracted from news articles and accompanying images. To enhance cross-modal semantic understanding, image-text alignment learning and contrastive learning techniques are employed. Additionally, a hybrid expert system and cross-attention mechanism are incorporated to enhance detection performance in tasks involving multiple domains and modalities. The experimental results demonstrate the superiority of the proposed model compared to existing single-modal detection models. This showcases its effectiveness in tackling the challenges introduced by multi-modal fake news detection in the Chinese language. By combining textual and visual information, the model achieves improved accuracy and robustness in identifying fake news across various domains.
Linear Quadratic Regulator Design for Wheelchair Control Using Monte-Carlo Simulation Method

In this paper, we present Monte-Carlo simulation for a linearized wheelchair model that use Linear Quadratic Controller. The controller has two weighting matrices which are Q and R. These weighting matrices are important to select optimal gain matrix value. Trial and Error method has been usually used in Linear Quadratic Controller because of simplicity but very difficult to choose the best values that have good control performances. Without giving up the simplicity of the Trial and Error method, we propose Monte-Carlo method. So, many closed loop simulations using Monte-Carlo method, optimal gain matrix value is calculated. Effectiveness of the both method is discussed comparatively. The results indicate that the Monte-Carlo method are more powerful to select optimal gain matrix and better performance for the stability of the system.
Enhancing the Characteristics of the UWB Antenna Using Meta-Material and Slot

In this paper, a new antenna is proposed for the ultra-wide band (UWB) in 5G applications of low band frequency regions (FR1) 3.4-9.4GHz, using the meta-materials (MTM). The proposed antenna was designed and simulated step by step using with Octagonal Split Ring Resonator (OSRR), wire, and slot, by means of the computer simulation technology (CST) microwave studio. The proposed antenna has dimensions of 25x23x1.6mm3, and the simulated frequency bandwidth covers the frequency range 3.4 GHz to 9.4 GHz with bandwidth improvement. The S-parameter of less than -10 dB has been found to compatible with the 5G requirements. The proposed antenna has been manufactured and tested by means of the vector network analyzer (VNA) device. A constructed prototype of the planned antenna demonstrated an exceptional agreement between the measurements and the simulation findings.
A Study on Spiking Neural Network Design

A Spiking Neural Network (SNN) is defined as an Artificial Neural Network (ANN) that has developed rapidly in the last decade. There are some difficulties in implementing these networks, especially in supervised machine learning. The main limitation is that classical learning approaches such as backpropagation cannot be directly applied to SNNs. We created a model to enable backpropagation, using a biological neuron model instead of the activation function of the ANN network. This study, a deep neural network that can work in both ANN and SNN modes was designed to classify the feature vectors obtained from the wavelet coefficient of magnetoencephalography signals taken from the human brain. Thus we propose a hybrid network that can operate in both conventional and firing modes by replacing the activation function of the traditional neural network with Izhikevich neurons. Our proposed network has been tested in classifying 4-class motor imaginary signals and the results are presented comparatively. We hope this work, which blends computational neuroscience and machine learning, will bring a different perspective to fired network design.
FDCN-ML: Forecasting Directional Changes in the NASDAQ Market using Machine Learning

Identifying significant price movements is crucial and challenging in Financial Markets as it is difficult to identify and spot the best time and the most profitable price for trading. Directional Changes (DC) is a unique approach to summarize market prices and price movements in a financial market. DC discovers the trend of the market based on the magnitude of price changes. In this paper, we aim to build a strategy based on the DC approach (FDCN-ML) that tells whether the trend of the stock will continue for a certain percentage before the trend changes. Machine learning (ML) models typically outperform traditional statistical and econometric models. Furthermore, superior performance has been demonstrated in the literature by ensemble ML models. We evaluate the accuracy of the approach by training and comparing the effectiveness of tree-based ML and ensemble models (Decision Tree, Random Forest, XGBoost and Voting Classifier). The best result was achieved by Random Forest classifier with an accuracy rate of 87.97%. The obtained accuracy proves that the directional changes of stocks are predictable.
Poster: Efficient Video Instance Segmentation with Early Exit at the Edge

Video instance segmentation has emerged as a critical component in enabling connected vehicles to comprehend complex driving scenes, thereby facilitating navigation under various driving conditions. Recent advances focus on video-based solutions, which leverage temporal and spatial information to achieve superior performance compared to the traditional image-based approaches. However, these video-based solutions present challenges for efficient deployment at the edge due to their high computational and memory demands, making them inefficient for deployment on edge devices, such as intelligent vehicles. Furthermore, the large size of video data makes it impractical to upload to cloud servers. To address the latency challenge during on-device inference, we propose to incorporate early exits into the model. While the early exit strategy has been successful in image classification and natural language processing tasks, our study is the first to explore its application in video instance segmentation. Specifically, we incorporate early exits into the transformer-based video instance segmentation model, VisTR. Our experimental results on the YouTube-VIS dataset demonstrate that early exit can significantly speed up the inference by up to 4.83x with a minimal trade-off of only 3% in the averaged precision scores. Furthermore, our qualitative analysis confirms the satisfactory quality of the generated segmentation masks.