Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Emotion Recognition Through Weighted Averaging in Deep Learning Ensembles

Facial emotion recognition (FER) is a necessary in order to bridge the communication gap between people and computers since, in the complete range of face-to-face contact, facial expressions are a sort of communication that convey the majority of a person's emotional and mental state. This field's innovations also enhance how successfully computers interact with or assist people. The vast body of research in this field is focused on Deep Learning. FER has drawn interest from the research community due to its possible applications. The main objective of FER is to relate different facial expressions to the associated emotional states. Even though profile pictures from different angles are essential for a functional FER system, most current systems only take frontal photos into account, which is a severe drawback. By adapting a pre-trained DCNN model's dense top layer(s) to be FER-compatible, the model is employed. The model is then fine-tuned using emotion data, which eventually increased the FER accuracy to a larger degree. In this study, We have used ensemble method over VGG16 & VGG19. The suggested method is tested using the well-known datasets FER2013 and CKPLUS and two different DCNN models that have been pretrained (VGG16 and VGG19). The suggested technique had an accuracy of 90.38% on FER2013 and 96.53% on CKPLUS after ensemble learning.
The Impact of Air Pollution on Respiratory Health Results: An Analysis of Asthma and COPD in a Population Study

Numerous studies have connected air pollution to various health problems, including asthma, COPD, and reduced lung function. Particulate Matter (PM), Nitrogen Dioxide (NO2), and Ozone are three of the most notable pollutants that contribute significantly to worsening chronic diseases. Recent research has revealed that patients with respiratory disorders can have their COVID-19 symptoms made worse by increased levels of air pollution in their environments. U.S. scientists studying this connection will benefit from the Air Quality System (AQS) data of the Environmental Protection Agency (EPA). Air pollution in the environment and its effects on respiratory health can be studied using the tried-and-true linear regression method. Pollution in the air poses not only a risk to the natural world but also a significant danger to human health, and this situation requires quick attention.
1800 MHz and 2.45 GHz Antennas for RF Energy Harvesting Applications

The proposed antenna in this work is intended to function at GSM 900 and GSM1800 IOT applications. The last few years have seen a surge in interest in obtaining energy from environmental sources, particularly electromagnetic waves. The ability to receive microwave radiation and convert it to DC power using radio frequency rectennas(receiving antenna + rectifying circuit)has lately come to light. Our goal is to simultaneously increase bandwidth, efficiency, and gain. The proposed antenna in this work is intended to function at 1800MHz and 2.45GHz. The simulated antenna's return loss S11 reaches a bandwidth that is 75MHz@1800 MHz and 88MHz@2.45 GHz band.
TizenRT OS Based Data Compression Algorithms for IoT Devices

The paper investigates the integration and adaptation of the LZ4 compression algorithm within the context of TizenRT, in addition to existing algorithms Miniz and LZMA. TizenRT, a real-time operating system tailored for Internet of Things (IoT) devices, presents unique challenges in balancing compression efficiency and resource constraints. The study evaluates the performance of LZ4 alongside Miniz and LZMA in terms of compression ratios, processing overhead, and memory utilization. Experimental results demonstrate the feasibility of LZ4's adoption in TizenRT, highlighting its potential to enhance data compression and decompression operations while adhering to the stringent requirements of IoT devices. The findings contribute to the optimization of data management strategies within the TizenRT ecosystem.
PSO-Based Frequency and Voltage Restoration with Virtual Impedance in Droop Control for Islanded Microgrid

The focus of this work revolves around the active-reactive power sharing problem in islanded microgrids, an area where research in Inverter-Based Resources (IBRs) is currently thriving. To mitigate the active-reactive power sharing issue and enhance system stability and resilience, virtual impedance and voltage-frequency restoration are employed. However, the tuning of parameters that arise during specific state transitions, such as load addition and shedding, is accomplished through particle swarm optimization (PSO). Simulation and comparative results demonstrate the improvements introduced by the application of PSO-based frequency and voltage restoration in this paper.
A Real-Time Visual SLAM in Dynamic Scene With Segmentation, Prediction and Geometric Constraint

Much of the existing SLAM system in dynamic scenes has tended to focus more on improving the accuracy of localization at the expense of real-time performance. This paper proposes a real-time accurate dynamic SLAM system based on ORB-SLAM3. It combines the latest efficient instance segmentation network YOLOv8s-Seg and geometric constraints to extract dynamic objects and filter dynamic feature points respectively. To balance accuracy and efficiency, a new efficient strategy of performing instance segmentation on keyframes while object tracking and prediction on others is proposed in parallelized semantic threads, which greatly reduces the number of forward inferences of the network, saves computational overhead, and shortens the time delay. In addition, to avoid the disadvantages of difficulty in filtering the features due to the small motion amplitude between adjacent frames, this paper introduces an inter-frame epipolar line constraint checking mechanism based on sliding window to judge the motion state of each feature in object regions as accurately as possible, to retain more static features and improve the accuracy of pose estimation. Experimental evaluation is conducted on dynamic sequences of the TUM RGB-D dataset. The results show that the proposed method enables the system to reduce the absolute trajectory error (ATE) and relative attitude error (RPE) significantly compared to the baseline, and it also ranks high compared to several existing advanced algorithms. Further, it performs well in real-time, which fully demonstrates that the proposed system excels in both accuracy and efficiency.
Optimization Method for Remote Inspection Path Planning of Secondary Equipment Based on Chaotic Genetic Algorithm

Remote inspection of secondary equipment is an important task in the operation and maintenance of power systems. Reasonable planning of inspection paths can improve efficiency and safety. This article proposes an optimization method for remote inspection path planning of secondary equipment based on chaotic genetic algorithm. Firstly, construct a substation grid model to describe whether there are obstacles in the remote inspection workspace of secondary equipment in the substation. On this basis, using chaotic genetic algorithm, the application process in path planning includes individual encoding, population initialization, selection operation, crossover and mutation operation, as well as chaotic search process, to achieve optimization of remote inspection path planning for secondary equipment in substations. The experimental results show that this method has made significant improvements in improving the efficiency of remote inspection of secondary devices and path quality, and has better performance compared to traditional methods.
A Metaverse Intelligent Classroom Construction Method for Multi-Domain Users

With the rapid development of metaverse technologies, virtual educational environments have become an important means of meeting the demand for personalized learning. However, current virtual classroom construction often relies on specialized knowledge, which limits the use of non-specialized users. In this paper, we propose a metaverse intelligent classroom construction method for multi-field users, which introduces a Domain-specific Language (DSL), so that users do not need to have an in-depth understanding of programming or virtual environment development techniques, and can define and construct a virtual classroom through a natural language approach. Through a series of case studies and user feedback, we validate the effectiveness and feasibility of the approach. The experimental results show that non-specialist users are able to create highly personalized and interactive virtual classroom scenarios in a short period of time, and learners show more interaction and improved academic performance when learning in the created virtual smart classroom.
Effect of Work Function Modulation on Switching Current Ratio For A Dual Metal Gate Junctionless FinFET(DMG-JL FinFET)

This research work presents a new type of transis- tor called the WFMDMG-Junction-less FinFET, which stands for Dual Metal Gate Junctionless FinFET with workfunction modulation. The transistor is specifically designed to address short channel effects and enhance RF performance. The pro- posed design involves splitting the workfunction of a gate metal electrode in half. The electrode's source side is split into five equal sections, exhibiting a linear fluctuation in work-function. In contrast, the drain side remains a consistent work-function. To conduct simulations, the SILVACO TCAD tool is utilized. The junctionless device that has been presented has enhanced subthreshold features, such as a subthreshold swing (SS) of 59.8 mV/decade with an excellent switching current ratio (ION /IOFF) of 1.4 × 1013.
Aerial Target Grouping based on Sparse Subspace Clustering with the Mixed constraints of Distance and Velocity

Aerial target grouping, partitioning targets into some clusters according to the features of attribute and behavior, contributes to tactical recognition and command decision-making. For the scenario of high-dimensional data in aerial target grouping, sparse subspace clustering is expected to be one of the effective methods, where high-dimensional data are represented by low dimensional subspaces using sparsity. In order to achieve aerial target grouping by sparse subspace clustering efficiently and accurately, an improved sparse subspace clustering with the mixed constraints of distance and velocity (SSC_MC) is proposed. First, k nearest neighbours algorithm is used to filter neighbours, so as to improve the self-represention by the whole data set into only by their nearest neighbours; next, the mixed constraints of distance and velocity are introduced in sparse subspace clustering, which emphasizes the importance of these features in aerial target grouping; last, the experiments for aerial target grouping are implemented, showing the proposed algorithm SSC_MC can be used to aerial target grouping with high efficiency and accuracy.
Trajectory Planning for Spacecraft Far-field Rendezvous via Convex Optimization

Safe rendezvous operations of spacecraft require the capability to generate highly constrained trajectories timely. For far-field rendezvous operation, the trajectory planning of the spacecraft is a complex problem due to factors such as arbitrary orbits and the unignorable mass changes caused by fuel consumption. A trajectory planning method is proposed in this paper which considering low engine thrust and representative constraints such as approach corridors, relative speeds, etc. Utilizing various convexification techniques, a corresponding relaxed version of the original problem is formulated. Next, an innovative iterative solution methodology is introduced for addressing the relaxed problem. After discretization, the original nonlinear problem is restructured into a sequence of second-order cone programming problems, whose solutions can be obtained through convex optimization algorithms. Simulation experiments have provided evidence that this approach is effective, confirming its reliability and potential for practical application.
Gene Expression based Blood Cancer Classification Model using Natural Computing along with Deep Learning

It is well established that utilizing gene expression data to classify cancer holds the solution to the basic issues with cancer diagnosis and treatment innovation. It's critical to create deep learning techniques that can accurately identify between various cancer subtypes or healthy and malignant samples. In this paper, gene expression-based Blood Cancer Classification (BCC) Model using Deep Learning with Genetic Algorithm (GA) is proposed. Here we used statically computed features and based on the statically calculated features to classify the cancer using Modified Deep Neural Network (MDNN) as a deep learning. Our developed work aims to offer a novel classification technique for gene expression human blood data that may be used to other high dimensional data issues and improve classification performance. The outcomes of the data trials demonstrate that the suggested BCC model performs well in comparison to lots of state-of-the-art works in terms of its effectiveness like as precision, recall (true positive rate), f-measure, and accuracy. Achieved average precision, average recall, average $\mathrm{f}$-measure, and average accuracy is 96.8%, 93.8%, 95.2% and 97.8% respectively.
Assessing the Performance of Machine Learning Algorithms for Credit Risk Assessment

Assessment of credit risk is crucial for modern economic systems. Historically, methods from statistics and manual auditing have been used for evaluating it. The latest advances in financial AI are a consequence of an influx of models which are made by machine learning for assessment of risk in credit that have garnered a massive interest from business and also academia. To solve issues of credit risk, a number of significant academic contributions over the previous eight years (76 publications) have used ML, statistical modeling, and DL techniques. In particular, utilizing open datasets, we provide an interactive methodology of categorization for credit which is driven by ML credit threat methodologies and their resulting ranking. We go on to explore other issues such data inequalities, inconsistent datasets, modeling accountability, and insufficient use of the deep learning methods. Our review's findings indicate that 1) the majority deep learning algorithms perform better at estimating credit risk than traditional machine learning and analytical algorithms, and 2) collective approaches offer more accuracy when compared to individual models. Lastly, we provide tables that summarize the data sets and recommended models.
Advancing COVID-19 Inquiry Responses Through Transfer Learning-Based Question Entailment Methodology

The COVID-19 pandemic underscores the urgency for efficient solutions addressing critical questions, given the prevalence of misinformation and ambiguous answers that necessitate a dependable automated system. Public Health Question Answering (QA) within Natural Language Processing (NLP) and Artificial Intelligence (AI) poses unique challenges, demanding both linguistic comprehension and domain expertise. Recent advancements in Deep Learning (DL) and Deep Neural Networks (DNN) have revolutionized QA systems, leading to improved performance. In this paper, we propose a novel question entailment approach named CoviBioBERT. It employs finely-tuned ensemble neural networks to enhance precision in addressing COVID-19-related queries. CoviBioBERT is an optimized QA system based on the BioBERT pre-trained model, customized for COVID-19 inquiries, and evaluated on a benchmark dataset of question-answer pairs. The system aims to provide dependable and accurate responses to contextually relevant COVID-19 queries. Experimental findings underscore the superiority of CoviBioBERT over foundational models, exhibiting a notably higher Exact Match (EM) score and ensuring more trustworthy responses to COVID-19-related inquiries. This paper makes a significant contribution by introducing a state-of-the-art QA system for retrieving COVID-19 information. It thereby equips the general public and healthcare practitioners with reliable insights to bolster global efforts against the pandemic.
Li-Fi Data Transmission Using Visible Light Communication Technology

Light fidelity, also known as optical Wi-Fi, is abbreviated as Li-Fi. One of the most pressing issues was how to handle data transfer for radio signals, which gave rise to Light as a crucial alternative. Data transfer has become more efficient thanks to Visible Light transfer (VLC). One of the VLC technologies, Light Fidelity (Li-Fi), is a novel method for transmitting data from a source to a destination using light signals. It promises a number of advantages and can get beyond some Wi-Fi technology drawbacks like security concerns, media restrictions, and radio interference. Li-Fi technologies are used for testing purposes only; they are not widely used in industry. As Li-Fi technology becomes more widely used in business, it's important to gauge how well it transmits different sorts of data. This paper's goal is to examine the effectiveness of VLC based data transfer. This study is based on a Li-Fi implementation for various kinds of data transfer. This study does, in fact, demonstrate a successful audio and text transmission using VLC technology.
Design and Characterization of a Herringbone Actuator with Variable Chamber Height

Soft actuators have since been widely recognized for specific tasks due to their advantages against rigid robots such as lightweight and safe human/product interaction. However, they are widely characterized for single-directional bending thereby limiting their application in the same way. As a proposed solution to this course, we developed an actuator with dual directional bending capability by further optimizing the herringbone-structured design through integration of chamber height variation to better improve its performance. Prototypes of the actuator were created, and subsequent characterization experiments were done to determine the validity of these optimizations on actuator performance. Result of the study shows promising results on the novel design's performance output for both dual directional bending and force characterizations as induced by the structural optimization of the chamber height.
Explainable AI for Prioritizing and Deploying Defenses for Cyber-Physical System Resiliency

The adoption of digital technology in industrial control systems (ICS) enables improved control over operation, ease of system diagnostics and reduction in cost of maintenance of cyber physical systems (CPS). However, digital systems expose CPS to cyber-attacks. The problem is grave since these cyber-attacks can lead to cascading failures affecting safety in CPS. Unfortunately, the relationship between safety events and cyber-attacks in ICS is ill-understood and how cyber-attacks can lead to cascading failures affecting safety. Consequently, CPS operators are ill-prepared to handle cyber-attacks on their systems. In this work, we envision adopting Explainable AI to assist CPS oper-ators in analyzing how a cyber-attack can trigger safety events in CPS and then interactively determining potential approaches to mitigate those threats. We outline the design of a formal framework, which is based on the notion of transition systems, and the associated toolsets for this purpose. The transition system is represented as an AI Planning problem and adopts the causal formalism of human reasoning to asssit CPS operators in their analyses. We discuss some of the research challenges that need to be addressed to bring this vision to fruition.
Broadband Encoding Stochastic Terahertz Filters Based on Inverse-Designed Spatial Light Modulation

Computational spectroscopic instruments with broadband encoding stochastic (BEST) filters are capable of accurately reconstructing a spectrum using only a few filters. However, this method has not yet been applied in the terahertz (THz) band. On the other hand, spatial light modulators (SLMs) play a crucial role in modulating various aspects of light, including phase, amplitude, and polarization. With the recent advancements in inverse design, it is now possible to flexibly design different patterns of SLMs, which can then serve as encoders with higher flexibility and robustness. This development provides a promising avenue for the creation of high-performance THz computational spectrometers. In this work, we employ the principles of compressed sensing and optimization algorithms to design encoders with varying transmission coefficients. Subsequently, we utilize the electromagnetically induced transparency (EIT) effect to design the THz encoders. This inverse design approach effectively combines optimization algorithms with multiphysics simulation methods and achieves carefully designed THz spectral modulation through simulation. The results demonstrate the feasibility of constructing THz encoders using SLMs through inverse design. In addition, this method holds potential for the design of high-performance THz computational spectrometers.
Multi-View Consistency for Correspondence Pruning

Distinguishing true correspondences between image pairs is a basic problem in 3d vision. Previous works mostly concentrate on image pairs, while information beyond two views is relatively less exploited. In this paper, we propose a novel method to prune putative correspondences via the concept of cycle-consistency in multiple views. With the help of a created triangle-based hypergraph to record two-hop paths, we design a Multi-View Consisency-Ensured Module (MVCEM) to refine noisy correspondences. Thanks to the simple binary opeartion of MVCEM, our proposed method is efficient and scales to thousands of images. Besides, as a post-processing procedure, MVCEM is flexible and naturally fits into any generic pipeline relying on local feature matching. Experimentally we show that our approach greatly enhances the keypoints matching results and downstream tasks, like triangulation and Structure-from-Motion.
Link-Aware Industrial Wireless Sensor Sleeping Scheme via Graph Signal Processing in Fog Enabled Industrial Internet of Things

As the advantage of lower latency and energy consumption, fog computing has been seen as an important enabler to meet the harsh requirements of Industrial Internet of Things (IIoT), which links multiple industrial wireless sensors (IWSs) with limited energy and computing ability, via offloading computation and analysis tasks to Fog Access Points (F-APs). However, F-APs often transmit all information-packages of IWS in traditional methods, making long delay in transmitting massive data-sets. To overcome this issue, we propose a link-aware IWSs sleeping scheme via graph signal sampling and reconstruction strategy in a local IIoT network scenario with multiple IWSs and one F-AP, aiming at prolonging the IIoT network lifetime and spreading out maintenance time. The simulation results demonstrate the effectiveness of the proposed scheme from the perspective of reconstruction performance, robustness on classical graph structures and the graph cutoff frequency bound. It can be seen that the proposed scheme could reduce the subsampling rate while keeping the reconstruction convergence rate compared with the baseline algorithm.