Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Classification of Human Stress Levels Based on Brain Wave Observation Using EEG with K-NN Algorithm

Stress is a condition that can suppress the person’s psychic state in achieving something. Stress, at some level, can harm human health since it can cause various diseases that humans often underestimate. These diseases include headaches, cramps, heart attacks, high blood pressure, and even strokes can occur. Stress levels are detected by manually filling out questionnaires or conducting self-assessment tests. However, it seems subjective because the results depend on honesty in answering the questionnaire. Therefore, in this study, we conduct a study to classify human stress levels by observing brain wave activity using an Electroencephalogram (EEG). Since EEG signals can directly reflect the brain’s electrical activity, they can be used as an objective measure for classifying stress levels. In this study, the method used in classification is K-Nearest Neighbour. The signal processing stages include pre-processing, feature extraction using Independent Component Analysis (ICA), and then classification using K-Nearest Neighbour. This research used 62 data as training and testing data. After testing the system, it was concluded that the K-Nearest Neighbour method could classify stress into normal and high levels with an accuracy of 75% with a k-value of 7.
Low Power Ternary XNOR using 10T SRAM for In-Memory Computing

To solve the bottleneck of von Neumann architecture, many researchers are looking to in-memory computing as an alternative to overcome this. Similarly, the evolution of neural networks is also emerging as binary neural networks (BNNs) to lower the memory cost. We propose new 10T-bitcell for In-memory Computing, which is capable of ternary multiplication by combining BNN's simple XNOR-based multiplication with the very widely used SRAM. The proposed circuit is simulated at 0.9 V in a 65-nm CMOS process and uses two transistors fewer compared to the conventional ternary multiplication output circuit, and the average power consumption is approximately 2 times lower.
In-group Advantage for Chinese and English Emotional Prosody in Quiet and Noise Conditions

It has been found that people recognize emotions from the same culture more accurately than recognize other languages, which is known as an in-group advantage. However, it is still unclear whether there is also an in-group advantage in Chinese, which is the most widely spoken language in the world. Therefore, this paper aims to investigate whether in-group advantage exists during emotional identification between Chinese and English and how it can be affected. To conduct the experiment, we investigated the perception of three Chinese and English emotional speech, i.e., happiness, sadness and neutrality under quiet and noise conditions by Chinese learners with high or low English proficiency. A generalized linear mixed logit model was applied to analyze the binomial response. The results revealed an existence of in-group advantage in perception of Chinese emotional speech, which is more significant in the perception of sad emotions. Besides, the in-group advantage was influenced by the environmental conditions and the listeners’ proficiency in English. This study gives evidence that in-group advantage exists in Chinese, and provides a reference for Chinese English learners to learn English emotional expressions.
Comparison Study of Collaborative Learning Techniques on Residential Short-Term Load Forecasting

The deployment of the advanced metering infrastructure provides the opportunity for performing Short-Term Load Forecasting (STLF) for a single energy user. In the last few decades, Artificial neural networks (ANNs) have been widely implemented in STLF. Conventionally, a user trains an ANN only based on her/his own historical load data. In recent years, collaborative learning techniques have been applied to facilitate multiple users to train the ANNs to enhance the STLF performance that can hardly be achieved by the users individually. This paper presents a comparison study evaluating the performances of three state-of-the-art collaborative learning STLF methods on an Australian “Smart Grid, Smart City” residential power load dataset. The work is expected to reference researchers and engineers the practical implementation of STLF systems in the residential sector.
RMDU-Net: 3-D MMW Imaging Via Range Migration Based Deep Unfolding Network

Limited by the Nyquist sampling rate, conventional matched filtering-based algorithms always confronts large-scale data dimension, causing difficulties in real-time processing. To address the issue, in this article, a 3-D millimeter-wave (MMW) radar imaging method via range migration (RM) based deep unfolding network is proposed, named as RMDU-Net, which utilizes the compressed sensing (CS) theory and unfolding networks to achieve high-resolution accurate reconstruction under a low sampling rate. The 512×512×6 large-scale imaging results can be obtained by RMDU-Net and the theoretical resolutions are up to 2.8mmx2. 8mmx4cm. Besides, the proposed method is compared with traditional range migration algorithm (RMA), and the numerical evaluations are given. The practical experiments demonstrate this imaging approach is excellent in both imaging quality and computing efficiency compared with classical algorithms.
Geophysical parameters retrieval from SMR measurements of HY-2B satellite

The Scanning Microwave Radiometer (SMR) onboard Chinese HY-2B satellite launched on 25 October 2018, is a multi-frequency conical-scanning microwave sensor, which operates on 6.925, 10.7, 18.7, 23.8 and 37 GHz. The SMR has the capability to measure accurate sea surface temperature, sea surface wind speed, total columnar water vapour and cloud liquid water simultaneously. In this paper, using an optimal estimation method, we retrieved these four geophysical parameters and compared the SMR retrievals with the WindSat products. The results show that the SMR performs well in the inversion of all the four physical parameters, which results in a good agreement with WindSat products.
An Analysis of Insider Attack Detection Using Machine Learning Algorithms

Among the greatest obstacles in cybersecurity is insider threat, which is a well-known massive issue. This anomaly shows that the vulnerability calls for specialized detection techniques, and resources that can help with the accurate and quick detection of an insider who is harmful. Numerous studies on identifying insider threats and related topics were also conducted to tackle this problem are proposed. Various researches sought to improve the conceptual perception of insider risks. Furthermore, there are numerous drawbacks, including a dearth of actual cases, unfairness in drawing decisions, a lack of self-optimization in learning, which would be a huge concern and is still vague, and the absence of an investigation that focuses on the conceptual, technological, and numerical facets concerning insider threats and identifying insider threats from a wide range of perspectives. The intention of the paper is to afford a thorough exploration of the categories, levels, and methodologies of modern insiders based on machine learning techniques. Further, the approach and evaluation metrics for predictive models based on machine learning are discussed. The paper concludes by outlining the difficulties encountered and offering some suggestions for efficient threat identification using machine learning.
Resilience Assessment and Enhancement Strategies of Transmission System under Extreme Ice Disaster

Ice storm event with high impact and low probability causes a huge challenge to the normal operation of the transmission system. To assess and enhance the resilience of the transmission system under an ice disaster, this paper constructs a resilience assessment and enhancement method for the transmission system. Firstly, the failure rate model of the transmission line is established according to the characteristics of the ice disaster scenario. Then, the resilience assessment metrics are constructed by analyzing the whole process of the system resilience under an ice disaster. On this basis, a resilience enhancement method under the ice disaster is proposed by using the transfer entropy of power flow to screen the lines that need deicing. Finally, the IEEE-30 bus transmission system is utilized to assess the resilience of the transmission system and verify the effectiveness of the proposed resilience enhancement method.
Repulsive knot energies and pseudodifferential calculus for O’Hara’s knot energy family E(α), α ∈ [2, 3)

We develop a precise analysis of J. O’Hara’s knot functionals E(α), α ∈ [2, 3), that serve as self‐repulsive potentials on (knotted) closed curves. First we derive continuity of E(α) on injective and regular H2 curves and then we establish Fréchet differentiability of E(α) and state several first variation formulae. Motivated by ideas of Z. ‐X. He in his work on the specific functional E(2), the so‐called Möbius Energy, we prove C∞‐smoothness of critical points of the appropriately rescaled functionals \documentclass{article}\usepackage{amssymb}\begin{document}\pagestyle{empty}$\tilde{E}^{(\alpha )}= {\rm length}^{\alpha -2}E^{(\alpha )}$\end{document} by means of fractional Sobolev spaces on a periodic interval and bilinear Fourier multipliers.
Research on Residual Power Reconfiguration of Hybrid Energy Storage System Based on Microgrid

In the photovoltaic hybrid energy storage microgrid system, in order to reduce the unreasonable value of decomposition mode number (K) and secondary penalty factor (a) in VMD affect the accuracy of system reconstruction power. A new intelligent algorithm called sooty tern optimization algorithm(STOA) is proposed for the K and a optimization analysis. The parameters of VMD are optimized by STOA to obtain the [K, a] optimal combination quickly and stably, and then the result is applied in VMD to decompose the residual power of microgrid system. So as to improve the coincidence degree between the reconstructed power and the original residual power signal and can allocate the residual power to hybrid energy storage system reasonably, which will be beneficial to optimize the initial power allocation and capacity allocation of hybrid energy storage. This paper analyzes the algorithm and compares it with the results of particle swarm optimization and gray wolf algorithm to verify the effectiveness and superiority of the method.
Knowledge and Skill Sets for Big Data Profession: Assessing Student's Quality using Exploratory Factor Analysis

Recently, several higher education institutions in Malaysia announced discontinuing some courses to ensure employability post-graduation. Finding a job that fits their qualifications is a hurdle that graduates frequently face. The International Labor Organization states that when the education and training system does not deliver the skills the labour market needs, there is a mismatch between skills and jobs. This paper presents research on big data analytics knowledge and skills acquired by students throughout their studies. A sample of 185 UiTM students from various campuses participated. These students were among those who had formally taken big data courses during their studies. Data analysis was done using exploratory factor analysis (EFA) to identify the knowledge and skills obtained. Those are important to UiTM students' preparedness for the big data profession. From the exploratory factor analysis, 26 of the 40 items are included in the six constructs with factor loadings above 0.60: teamwork, student awareness and university readiness, programming language, student's effort, data storytelling and visualization, and data organization. These factors align with the finding made by [26], which identified the key competencies the employer needs for big data professions. In conclusion, higher education institutions need to focus on these skills in improving the existing program to meet better market demand and satisfy employer expectations since the score of factor loadings obtained are just satisfactory.
Analysing Wireless Capsule Endoscopy Images Using Deep Learning Frameworks to Classify Different GI Tract Diseases

GI Tract related diseases are one of the most prevalent in today's society. Studies have shown that continuous monitoring, early detection, and treatment of these diseases are imperative in improving patients' recovery rate. Wireless Capsule Endoscopy (WCE) is an innovative imaging technology that enables invasive imaging of the GI Tract. Convolutional Neural Networks (CNN) and Image Processing have become very sought-after solutions in the process of developing a Computer Aided Diagnosis (CAD) system for many medical applications. The study aims to design and develop a generalized multiclass CNN classification algorithm to be used in CAD system for diagnosis of various GI tract diseases by analyzing WCE GI tract images with varying tract lining lesions. CNN classification-based solution framework encompassing various network architectures, image processing enhancement techniques and data augmentation methods are proposed. Three histogram stretching based enhancement techniques were introduced to enhance the quality of the raw image prior to performing classification. Data augmentation was performed as well. Different network architectures of self-developed architectures, transfer learning feature extraction, fine tuning and an ensemble of models were developed. The results were analyzed, putting emphasis on the generalization capability of the developed solutions. Results showed that image processing enhancement improved the CNN models' capability in performing accurate classification. In terms of individual network architectures, the transfer learning fine tuning models performed better as compared to the rest of the architectures. CNN networks trained on the dataset with augmentation are more generalized as compared to CNN networks trained on non-augmented data. The final proposed solution for GI tract CAD CNN network is the ensemble model which managed to achieve an overall accuracy of 97.03% when tested and compared to other proposed architectures across 4 phases of result analysis.
Deconvolution Technique for InSAR Phase Restoration

Interferometric phase generated from coherent processing from Synthetic Aperture Radar (SAR) plays a vital role in large-scale deformation monitoring and digital elevation model (DEM) generation. Due to the coherence loss between acquisitions, the produced interferometric phases contain noise, which affects the application performance of interferometric SAR (InSAR) phases. Most state-of-the-art InSAR phase restoration methods are generally based on the nonlocal filtering processing scheme. In this paper, we propose a data-driven approach which formulates the InSAR phase restoration problem from a deconvolution perspective. Then, based on the learned convolutional filters from the data, we then obtain the denoised InSAR phases via solving a sparse coding problem. The proposed method can not only suppress interferometric phase noise, but also avoid the staircase effect and preserve the details. The experimental results on synthetic and one RADARSAT-2 dataset, respectively, demonstrate that the proposed method achieves the best performance among those previous state-of-the art nonlocal InSAR filter-based methods.
Performance Evaluation of Recently Proposed Metaheuristics Algorithms on Solving Job Shop Scheduling Problem

Metaheuristic optimization approaches can be effectively used to model and solve real-world scheduling problems that include making decisions about how to allocate a certain amount of capacity or resources (machinery, workers, and workspace) to tasks or customers over time. Many metaheuristic techniques are introduced yearly, and their correct assessment and improvement can aid in solving different real-life optimization problems. In this paper, the performance comparison study of the three recently proposed metaheuristic techniques, namely the African Vultures Optimization Algorithm (AVOA), the Coot Bird Optimization Algorithm (COOT), and the Mayfly Optimization Algorithm (MA), on a real-life scheduling problem of combinatorial complexity is done. The current scheduling problem considered for this article consists of a set of dissimilar parallel machines. Jobs with different release and due dates need to be assigned to the machines. The problem aims to minimize the processing cost of jobs assigned. Based on 750 unique trials (5 problems x 2 datasets x 25 runs x 3 algorithms), it was observed that COOT outperformed the other two algorithms for scheduling problems.
Disease Identification in Potato Leaves using Swin Transformer

One of Indonesia's mainstay agricultural products is the potato. Disease prevention is essential for maintaining stable potato production. One technique for detecting disease in potatoes is to determine whether potato leaves are diseased (early blight or late blight) or healthy. Deep Learning models have been widely developed and used to classify disease recognition in potato leaves in the industrial era 4.0. Swin Transformer is a deep learning model based on transformers that is more efficient and accurate at solving classification problems. The Swin Transformer, a transformer based deep learning approach, is used in this study to identify diseases of the potato leaf. Moreover, several metrics including Precision, Recall, Accuracy, and F1 score, are used to assess the experimental results of the model we use. In terms of accuracy, the value obtained when training with this model is 97.70%. These findings indicate that using the Swin Transformer model to identify potato leaf diseases could be a new trend in agricultural research.
What Do Audio Transformers Hear? Probing Their Representations For Language Delivery & Structure

Transformer models across multiple domains such as natural language processing and speech form an unavoidable part of the tech stack of practitioners and researchers alike. Au-dio transformers that exploit representational learning to train on unlabeled speech have recently been used for tasks from speaker verification to discourse-coherence with much success. However, little is known about what these models learn and represent in the high-dimensional latent space. In this paper, we interpret two such recent state-of-the-art models, wav2vec2.0 and Mockingjay, on linguistic and acoustic features. We probe each of their layers to understand what it is learning and at the same time, we draw a distinction between the two models. By comparing their performance across a wide variety of settings including native, non-native, read and spontaneous speeches, we also show how much these models are able to learn transferable features. Our results show that the models are capable of significantly capturing a wide range of characteristics such as audio, fluency, supraseg-mental pronunciation, and even syntactic and semantic text-based characteristics. For each category of characteristics, we identify a learning pattern for each framework and conclude which model and which layer of that model is better for a specific category of feature to choose for feature extraction for downstream tasks.
Design and Analysis of Novel Flux Reversal Permanent Magnet Motors for Hub Motor

In this paper, two new type of flux reversal permanent magnet (FRPM) motors for hub motor are proposed to reduce flux leakage and increase utilization of permanent magnet (PM). Both motors have the same rotor and asymmetric stator, and only PM placement is different. This paper analyzes and compares the airgap field under PM and armature of the two motors and reveals the contribution of dominating field harmonics to the torque. In addition, the effect of dimension parameters on the motor performance is investigated. Finally, the no-load and load performance of the two motors are compared. It is also compared with a surface-mounted permanent magnet (SPM) motor to verify the advantages of the proposed motor with high PM utilization.
Effect of various stress-relief annealing treatments on permeability and aging of grain-oriented electrical steel strips

A series of controlled experiments was performed to investigate the effect of various stress-relief annealing treatments on magnetic permeability and tendency to aging of grain-oriented electrical steel strips M5, 0.30 mm. The best quality is produced by stress-relief annealing in a continuous roller type furnace. However, wound transformer cores have to be stress-relief annealed in a batch type furnace. Stress-relief annealing in batch type furnaces demands an appropriate temperature treatment and a protective atmosphere. Special care should be taken to prevent contamination by carbon. Carbon content in an amount greater than 0.001 percent considerably deteriorates magnetic properties at low inductions and increases the tendency of grain-oriented electrical steel strips to aging. Plastic deformation of electrical steel strips occurring during manufacturing of magnetic circuits should be avoided as much as possible. Magnetic properties impaired by them can never be fully recovered by stress-relief annealing, and they remain deteriorated with the effect being higher for the higher degree of plastic deformation.
Automatic target recognition of SAR images based on Transformer

In order to solve the overfitting problem of synthetic aperture radar automatic target recognition (SAR-ATR) at small samples scenarios, this paper proposes a non-subsampling laplace pyramid decomposition (NSLP) based vision transformer (VIT) model, namely NSLP-VIT, for SAR-ATR task. First, the SAR images are preprocessed by feature decomposition via NSLP; Second, a three-channel image is merged as the input of the network using three feature sub-maps; Finally, the preprocessed SAR images are as inputs for VIT network training. The experimental results on Moving and Stationary Target Acquisition and Recognition (MSTAR) data sets show that the proposed method can effectively solve the overfitting problem of SAR-ATR when the data is scarce.
Medical System for Tests Analysis and Symptom Checker

The covid-19 pandemic brought the need to reduce human interaction so in order to facilitate the interaction with the doctors but reduce unnecessary trips to a medical specialty, and have a better understanding of the investigation result, we present our approach of a medical test interpreter, integrated with a symptom checker. Our system aims to provide a temporary solution for diagnostic a condition until the patient reaches a doctor, so a proper diagnose can be made. This system can also be used in low-income countries were access to healthcare is limited and people discover diseases too late.
An FPGA Implementation of CNN-based Compression Artifact Reduction

This paper proposes a convolutional neural network (CNN) based compression artifact reduction hardware. The proposed CNN architecture is applied to re-parameterization and INT8 quantization methods for efficient inference in edge devices. As a result of applying the optimization methods, the model size was reduced by $\times 5.62$, and the number of operations was reduced by $\times 1.72$. The proposed hardware achieves a frame rate of 33.33 FPS when implemented on a Xilinx ZCU104 SoC.