Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Analysis Of Twitter Data Towards COVID-19 Vaccines Using A Deep Learning Approach

Social networks play a significant role in our lives, serving as a platform for the exchange of views, thoughts, and opinions. Consequently, sentiment analysis has become a valuable process for collecting and analyzing people's opinions on a wide range of issues. Given the global COVID-19 pandemic and the development of various vaccines to combat it, people have expressed a range of opinions on Twitter. By analyzing these opinions, health organizations can become more aware of people's feedback and emotions. Despite the benefits of sentiment analysis, challenges remain with respect to accurately interpreting and determining the appropriate polarity of sentiments. These issues may negatively impact people's thoughts and opinions when it comes to making informed decisions. To address this issue, we developed a model for classifying people's tweets into three categories: positive, negative, and neutral. We used a sizable dataset extracted from Twitter comprising 228,207 tweets and an architecture based on LSTM and BiLSTM-CNN models. The results obtained from the experiment indicate that each model could achieve 93.66% and 94.10% rates, respectively, which outperformed the other models.
A joint image encryption and watermarking algorithm based on compressive sensing and chaotic map

In this paper, a compressive sensing (CS) and chaotic map-based joint image encryption and watermarking algorithm is proposed. The transform domain coefficients of the original image are scrambled by Arnold map firstly. Then the watermark is adhered to the scrambled data. By compressive sensing, a set of watermarked measurements is obtained as the watermarked cipher image. In this algorithm, watermark embedding and data compression can be performed without knowing the original image; similarly, watermark extraction will not interfere with decryption. Due to the characteristics of CS, this algorithm features compressible cipher image size, flexible watermark capacity, and lossless watermark extraction from the compressed cipher image as well as robustness against packet loss. Simulation results and analyses show that the algorithm achieves good performance in the sense of security, watermark capacity, extraction accuracy, reconstruction, robustness, etc.
Seamless Redundancy for High Reliability Wi-Fi

By removing wire harness, Wi-Fi is becoming increasingly pervasive in every aspect of our lives, in both the consumer and industrial worlds. Besides flexibility, the recent high efficiency and extremely high throughput versions managed to close the performance gap with Ethernet. However, it still lags behind Ethernet for what concerns dependability. To this aim, the ultra high reliability study group has been recently formed. This paper reports on some preliminary ideas and proposals about the ways seamless redundancy can be exploited to make Wi-Fi more reliable, yet retaining a good degree of backward compatibility with existing network infrastructures.
An Attention Mechanism for Forest Tree Segmentation based on Efficient Convolutional Neural Networks

It is critical to have accurate semantic categorization of standing trees in order to extract relevant information about standing trees automatically and efficiently from digital photos. The effective categorization of several standing trees set against complicated backdrops is the goal of various classic approaches, however these methods suffer from drawbacks such as poor segmentation accuracy and the need for human intervention. This article presents Modified Convolutional Neural Network,which is based on MobileNet (CNNM), a lightweight network segmented model that is based on deep learning. The goal of this model is to accomplish correct categorization of standing tree photos in an efficient manner. For the purpose of performing multi-scale fusion of the convolutional features of standing trees in images, asemantic segmentation has been selected as the base framework. This decision was made in order to minimize the loss of image edge details that occurs during the standing tree segmentation process, as well as minimize the loss of feature data. MobileNet, which is a lightweight network, has been incorporated into the backbone network in order to minimize the complexity of the computations. The lightweight network segmentation model CNNM based on deep learning that was introduced in this study has the capability of segmenting numerous standing trees with a high degree of precision.
Anomaly Detection Using Deep CNN-ELM in Semiconductor Manufacturing

In modern society, technology is constantly evolving. This technological advancement creates new demands from consumers. To meet the needs of consumers, companies must improve the quality of their products. Semiconductor manufacturing requires many processes and fine techniques. Because of these characteristics, small defects or anomaly values have a great influence on the semiconductor yield. However, defects can be prevented if an anomaly can be determined from data collected during the semiconductor manufacturing process. In this paper, we propose an anomaly detection model that combines the deep convolutional neural network and extreme learning machine network. The proposed model provides better performance in detecting anomalies in semiconductor manufacturing data by taking advantage of the two models. The results of the proposed model are compared and analyzed with a widely used anomaly detection model.
The Adsorption and Preliminary Degradation Performance of SF6 on Mixed Terminal Ti2CTx: A DFT Study

Considering the excellent insulation and arc extinguishing characteristics, strong greenhouse gas SF6 is widely used in power equipment like Gas Insulated Substation (GIS) and Gas Insulated Transmission Line (GIL). Searching for an efficient SF6 degradation treatment has been a hotspot. With abundant active terminals on the surface, two-dimensional MXene has a considerable catalytic application prospect. Herein, gas-solid adsorption configurations were constructed based on $\mathrm{Ti}_{2} \mathrm{CT}_{\mathbf{I}}(\mathrm{T}=\mathrm{OH}, \mathrm{F}, \mathrm{O})$ MCene with complete terminal of one type and mixed terminals of three different average site occupancies. Density Functional Theory (DFT) calculations including adsorption energy, charge transfer, deformation charge density and density of states (DOS) were carried out to analyze the potential active sites on $\mathrm{Ti}_{2} \mathrm{CT}_{\mathrm{I}}$ for SF6 catalytic degradation. Results showed that hydroxyl groups possessed strong activity for preliminary degradation of SF6. On the $\mathrm{Ti}_{2} \mathrm{CT}_{\mathbf{I}}$ surface containing 44 % hydroxyl groups, SF6 had a chemisorption behavior and then degraded to form SF4 and HF. The adsorption energy reached -1.138eV level and about 0.901 electrons transferred from the surface to SF6. This study provides theoretical support for MXene as a catalyst for SF6 degradation.
ANALYTICAL DISCRETE OPTIMIZATION

Although for many years scientists and engineers h ave been faced with the problem of optimizing discrete functions, no general analytica l method has been proposed for multidimensional discrete optimization. All the met hods that have already been reported in the literature are algorithmic. In one dimensional disc rete optimization, no general analytical method has yet been proposed to find all the local minima and maxima of multimodal one dimensional discrete functions either. In this paper, for the f irst time, a general analytical method has been proposed for solving multidimensional and multimodal discrete optimization problems. Here, first we will introduce the method and present its mathematical proof, and then confirm its validity through some examples and computer simulations.
HYBRID RANS-LES MODELLING OF TURBULENT FLOW AROUND A THREE-ELEMENT AIRFOIL

An algebraic hybrid RANS-LES (HYB0) model was applied to the A310 high-lift three-element configuration at a freestream Mach number of M∞ = 0.22. Computations were conducted for two different angles of attack at α = 12.2◦ and α = 25.2◦, respectively, with or without prescribed transition around the leading edges of the slat, main wing and flap. At α = 25.2◦, wind tunnel test indicated a stalled flow with a sudden drop in lift. The computations were compared with measured pressure distributions and, at the stall angle, with the S-A DES computation as well. It was shown that the modelling may significantly be affected by the transition specified around the leading edges, for which the eddy viscosity was switched off in the laminar boundary layer. With prescribed transition, the S-A DES model pronounced undesired boundary layer separation on the slat upper surface. For full-turbulence computations, the modelling tends to pronounce a delayed stall at a relatively large incidence. Provided that the uncertainties could be ruled out at large incidences, the modelling performance should be justifiable to scrutinize the properties of high-lift flows towards and at stall. At α = 12.2◦, where the location of transition was experimentally affirmed, it was shown that hybrid modelling was able to produce reasonable predictions with appropriate grid resolution.
Design of a PI Control using Operator Theory for the nonlinear de Saint-Venant equations: Some numerical extension

— This paper considers the control design of a nonlinear distributed parameter system in infinite dimension, described by the hyperbolic Partial Differential Equations (PDEs) of de Saint-Venant. The nonlinear system dynamic is formulated by a Multi-Models approach over a wide operating range, where each local model is defined around a set of operating regimes. A Proportional Integral (PI) feedback was designed and performed through Bilinear Operator Inequality (BOI) and Linear Operator Inequality (LOI) techniques for infinite dimensional systems. The authors propose in this paper to improve the numerical part by introducing weight µi not only equal to {0,1}, but µi ∈ [0, 1].
TSN Scheduler Benchmarking

Time-Sensitive Networking (TSN) disrupts realtime communication technology by making IEEE Ethernet realtime-capable. For time-triggered, hard realtime traffic, TSN provides standardized mechanisms to reserve communication paths as well as individual transmission time slots for data frames. By leveraging these means in a precomputed network schedule, TSN allows for bounded end-to-end delays and minimal jitter. Not being part of the IEEE standard, corresponding scheduling algorithms are an active field of research. Unfortunately, due to differing model assumptions, evaluation setups, and key metrics, a fair comparison of schedulers is impossible so far. In this paper, we present a systematic and reproducible approach to benchmark TSN schedulers. First, we provide a scheduler taxonomy that enables to cluster schedulers by their characteristics. Second, we analyze interactions of input parameters and scheduler results to derive a benchmarking parcour for quantitative comparisons. Finally, we use the approach to benchmark existing schedulers and show subtle interaction effects. This way, our approach enables—for the first time-comparability between schedulers, fueled by the public availability of our benchmarking scenarios.
Optimal Design of Iron Core Loss for Ultra-high-speed Motor Based on Kriging Surrogate Model

The increase in rotor temperature caused by eddy current loss significantly affects the performance of ultra-high-speed rotor. Therefore, the rotor eddy current loss must be comprehensively considered with the stator core loss during the optimal design process. The paper establishes a finite element model of ultra-high-speed motor core loss, uses the sensitivity analysis method based on Spearman rank correlation to systematically analyze the sensitivity of design parameters and reduce the number of design parameters, and establishes an optimization model based on rotor eddy current loss and stator core loss. The Latin square experimental design is adopted to obtain high-quality experimental samples, the Kriging surrogate model is established, and the genetic algorithm is applied to complete the optimal design. Finally, the optimization results are verified by finite element models. Results show that the stator core loss and rotor eddy current loss of the motor are greatly reduced.
TopSort: A High-Performance Two-Phase Sorting Accelerator Optimized on HBM-Based FPGAs

The emergence of high-bandwidth memory (HBM) brings new opportunities to boost the performance of sorting acceleration on FPGAs, which was conventionally bounded by the available off-chip memory bandwidth. However, it is nontrivial for designers to fully utilize this immense bandwidth. First, the existing sorter designs cannot be directly scaled at the increasing rate of available off-chip bandwidth, as the required on-chip resource usage grows at a much faster rate and would bound the sorting performance in turn. Second, designers need an in-depth understanding of HBM's characteristics to effectively utilize the HBM bandwidth. To tackle these challenges, we present TopSort, a novel two-phase sorting solution optimized for HBM-based FPGAs. In the first phase, 16 merge trees work in parallel to fully utilize 32 HBM channels’ bandwidth. In the second phase, TopSort reuses the logic from phase one to form a wider merge tree to merge the partially sorted results from phase one. TopSort also adopts HBM-specific optimizations to reduce resource overhead and improve bandwidth utilization. TopSort can sort up to 4 GB data using all 32 HBM channels, with an overall sorting performance of 15.6 GB/s. TopSort is 6.7× and 2.7× faster than state-of-the-art CPU and FPGA sorters.
Electro-Optic Barium Titanate Modulators on Silicon Photonics Platform

Single crystal barium titanate with both c-axis and a-axis orientation have been epitaxially integrated on silicon-on-insulator wafers. Electro-optic Mach-Zehnder modulators with both X-cut and Z-cut configurations are fabricated that exhibit Pockels coefficients of 180 and 130 pm/V, respectively, with Vpi-length values of <5 V-mm.
Surface plasmon-induced enhancement of the magneto-optical Kerr effect in magnetoplasmonic heterostructures

We developed a self-consistent analytical approach to describe the optical response of a magnetoplasmonic heterostructure upon surface plasmon polariton (SPP) excitation. The approach is based on the effective susceptibility concept in the frame of the Green’s function method and accounts for the local-field effects in the system. The formalism was applied to describe the polar Kerr effect in magnetoplasmonic bilayers consisting of Co and Au on an attenuated total reflection prism. It is demonstrated that the excitation of the SPP in the plasmonic film can lead to an enhancement of the Kerr effect. Analysis performed for the s- and p-polarized incident radiation showed that the enhancement of the Kerr effect is of resonant character, and it is observed when the incident angle of the probing light is close to the angles of the SPP excitation. Thickness-dependent study revealed that the largest enhancement of the Kerr effect is observed for rather thin magnetic layers in the range of couple of nanometers.
Effective and Efficient Generation of Candidate Networks for Top-k Keyword Search in Relational Database

Todays’ data over web are retrieved from Relational Database Management Systems (RDBMS). Web retrieval and content analysis by keyword search in relational database has become the preferential way to search in todays’ era of Big Data. Any keyword search system has two main parts including candidate network generation and evaluation. Most research’s attention is on providing an efficient candidate network evaluation algorithm and relies on non-efficient candidate network generation paradigm. In this work, a novel probabilistic candidate network generation algorithm is proposed. From the outcome of our investigation on benchmark databases including new ISC database, it is possible to conclude that the proposed candidate network generation approach enhances the efficiency of databases keyword search.
Private 5G Networks for Cyber-Physical Control Applications in Vertical Domains

The introduction of the Factory of the Future, Artificial Intelligence, Big Data and the cloud computing paradigm in industrial use cases means that new communication systems to support their requirements must be developed. The fifth generation of cellular networks (5G) has become one of the most relevant alternatives in this field. Within 5G network infrastructures, there are public networks along with private 5G networks, which allow a private entity to have its own local 5G network to cover its needs. However, the regulations in place in Europe, and the spectrum reserved for private 5G, mean that it cannot meet the requirements of some of the industrial applications for which it was intended. Therefore, it is necessary to analyze the parameters of the 5G network to find the operating thresholds according to the requirements set by 3GPP.
Transfer Learning for Bird Species Identification

Monitoring and conservation of bird species play a crucial role in preserving biodiversity and maintaining the balance of the ecosystem. To address this, we have developed an automatic bird recognition system, known as the birdhouse, using the Arduino and Keras deep learning frameworks. The system is equipped with a PIR sensor that activates an ESP-32 camera to capture an image of the bird and send it to the server for processing. The deep learning model, trained using transfer learning with the MobileNetV2 architecture, is deployed with the python flask framework and is able to accurately predict the bird species with 95% test accuracy. The identified bird species is then notified to the users via the telegram application, along with the captured image of the bird. MobileNetV2 is a powerful deep learning architecture that is well-suited for deployment on resource-constrained devices such as the ESP-32 camera used in the birdhouse system. The use of transfer learning allows the model to be trained on a large dataset and then fine-tuned for the specific task of bird species recognition.
Hybrid Simulation in Healthcare: A Review of the Literature

The use of Hybrid Simulation (HS) increased as problems have become more complex and multidimensional, with a particular focus on healthcare systems. Such complexities make it challenging for single simulation models to provide the right support for decision-making. This article reports on a preliminary review of the literature and investigates the prevalence and utilization of HS in healthcare. Thirty-three relevant papers were found in the literature, including application papers, frameworks, and review papers. Our review categorizes the M&S techniques employed and analyses the application type, software packages, trends, opportunities, and challenges of HS in healthcare. Findings show that combining Discrete Event Simulation and System Dynamics is the most common approach to developing HS models in healthcare. However, the popularity of combining Agent-Based Simulation with others is on the rise. Current limitations of the literature and opportunities for future research are discussed.
EVAding Interference: A Hybrid Architecture Approach for Substrate-Integrated Evanescent-Mode Cavity Filters

In today’s crowded RF spectrum, receivers must be highly selective to mitigate any interference, whether intentional or from adjacent channels. The upper part of the C band (4–8 GHz) has been designated for space-to-Earth satellite communications and space exploration [1]. This long-range communication demands sensitive, low-loss receivers that can recover weak signals. Customarily, a tradeoff between size and performance presents itself, which poses a challenge to the microwave community as size is often critical in satellite payloads. Therefore, a compact, radiation-impervious filtering solution is needed.
Autonomous Diagnosis Method for Defects of Cable Accessories Based on YOLOv3 and Mean-Shift Algorithm by Infrared Images

In daily inspection, infrared images are often used to measure the temperature of cable accessories. However, in the face of a large number of inspection images, traditional manual diagnosis is time-consuming and laborious, and relies too much on manual experience. Therefore, an automated infrared infrared image diagnosis method for cable accessory defects based on YOLOv3 and Mean Shift is proposed. Firstly, the method takes YOLOv3 as the basic model, and adds Mosaic technology to the input to enhance the training effect of the model, realize the recognition and positioning of diagnostic targets, and eliminate the impact of complex background images and non diagnostic foreground images on subsequent processing. Then the Mean Shift clustering algorithm is used to segment the image for the diagnosis object, so as to extract the overheated area quickly and accurately. Finally, the temperature information of overheated area and non overheated area is extracted, and the status diagnosis of cable accessories is realized according to the corresponding diagnostic criteria. The research has certain reference value for the defect diagnosis of cable accessories in practical projects.
Human Sales Ability Estimation Based on Interview Video Analysis

In this paper, we introduce our proposed method for estimating the potential sales ability of a person based on interview analysis. A deep learning model was built and trained to estimate the major personality traits according to Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism in the (OCEAN) model. The model utilizes Convolutional Neural Networks (CNNs) and Long Sort-Term Memory (LSTMs) to extract and learn features from the sequence of the frames for each video. Base knowledge has been built to define the decision rules for classifying the participants depending on their personality analysis and to give the final classification score. The decision rules depend on the relation between the Big Five Factors that describe salesmen represented by high levels of (Extraversion, Conscientiousness, and Openness) and low levels of (Neuroticism and Agreeableness). We collected our dataset by building our website to let participants record self-introduction videos while answering customized questions for our task. The model was trained on two datasets: the Computer Vision and Pattern Recognition (CVPR) First Impressions V2 was used to train the model to be able to estimate personality traits and our dataset that were used to fine-tune the model to our task and capture the potentials of a salesman. The proposed method has been tested on a variety of samples including video samples for real sales managers and achieved good results in classifying them.
Deep Reinforcement Learning-based Energy Management Strategy for a Microgrid with Flexible Loads

In this paper, an intelligent deep reinforcement learning -based energy management strategy is investigated for the microgrid to minimize the operation cost. The thermostatically controlled loads are considered in the microgrid to ensure the operation flexibility. The energy management problem is first formulated as a Markov decision process, and the state-of-the-art deep reinforcement learning method, namely proximal policy optimization, is applied to solve the decision-making problem. Meanwhile, the system uncertainties including wind power generations, electricity prices and electricity loads are considered. A comparison study with respect to other methods is carried out to illustrate the effectiveness of the proposed method.