Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Low-Rank Matrix Approximation for Hyperspectral Image Spatial-Spectral Feature Extraction

It is important to take account into both the spectral domain and spatial domain information for hyperspectral image analysis. Thus, how to effectively integrate both spectral and spatial information confronts us. Motived by the least square form of PCA, we extend it to a low-rank matrix approximation form for multi-feature dimensionality redu-ction. In addition, we use the ensemble manifold regularize-ation techniques to capture the complementary information provided by spectral-spatial features of hyperspectral image. Experimental results on public hyperspectral data set demonstrate the effectiveness of our proposed method.
Prediction of Work From Home Post COVID-19 using Classification Model

This study aims to predict the continuance of the adoption of WFH after COVID-19 and other factors that also influence these preferences. This study was conducted using data mining techniques with the CRISP-DM framework based on the Decision Tree, Naïve Bayes, and Random Forest Algorithms. The dataset was taken based on a questionnaire survey distributed online via Google Forms with a target of 200 respondents and returned from 183 respondents from four divisions. The results of this study indicate the Decision Tree model has the best performance with an accuracy of 85.45%. Based on the prediction and questionnaire results, employees tend to agree to continue implementing WFH after COVID-19 with a Hybrid working model. These preferences influence work improvement, employee performance, and work environment.
Design and Development of Multi-Model Deep Learning based Approach for Scaling the Severity of Mental Disorders

Electroencephalogram (EEG) is probably the most critical technology for human brain-computer interface applications. People who have mental illnesses frequently go through substantial changes in their routines, emotions, or ideas. The idea that early identification is essential for successful treatment has led to several breakthroughs in the field of mental illness. Using pattern discovery and feature extraction techniques, it is considered that EEG data indicating brain activity are crucial for identifying mental illness. We present a hybrid model based on machine learning and deep learning techniques to categorize and diagnose various brain disorders. The acquired distinctive characteristics from the dataset were utilized in the current study to build a reliable 3D-CNN model. We investigated whether the obtained EEG dataset is adequate to identify healthy individuals and subjects who have been diagnosed with various mental disorders and impairments concerning their prescribed antipsychotic medications. In particular, we are concerned with using specific types of electrodes that can produce such distinctions.
Triaxial behaviour of transversely isotropic materials: application to sedimentary rocks

Failure and long‐term behaviour of oriented solids are studied. Transversely isotropic materials are considered and a mathematical formulation that respect the material symmetry is developed and applied to model the triaxial behaviour of sedimentary rocks. Two failure criteria and a viscoplastic constitutive model that describe, respectively, triaxial failure and triaxial creep tests are presented and discussed. The application of the developed models to describe the mechanical behaviour of Tournemire shale shows that theoretical predictions are in good agreement with the experimental data. In the present paper, the developed approach is applied to sedimentary rock materials, nevertheless, it can be generalized to any material that exhibits transverse isotropy. Copyright © 2007 John Wiley & Sons, Ltd.
Gyrofluid turbulence studies of the effect of the poloidal position of an axisymmetric Debye sheath

The field line connection of a tokamak sheared magnetic field ensures a finite parallel dynamical response for every degree of freedom available in the system. In the scrape-off layer (SOL) the flux surfaces are open, and the field line connection property is broken by the presence of a Debye sheath arising where the field lines strike boundary plates, hence allowing the existence of convective cell modes for which there is no dynamical parallel response. This leads to a major distinction in terms of turbulence character between closed and open flux surface regions. We study this using three-dimensional electromagnetic gyrofluid computations. The turbulence is found to change character from an ion temperature gradient to a generic interchange type, crossing the last closed flux surface (LCFS) radially outward. The width of the transition zone is about ten ion gyroradii. Various poloidal configurations of the Debye sheaths retain this interface property but affect the interaction between the turbulence and the slowly varying, self-consistent background. The strongest effect is found in a case with sheath plates at both the top and bottom of the SOL, allowing the high-field and low-field sides of the SOL to decouple. In these sides the curvature is favourable and unfavourable, respectively. The clear asymmetry observed between these sides of the plasma is consistent with previous experimental results and makes room for future experimental qualitative comparisons, for instance, on double null configurations of the tokamak ASDEX Upgrade.
Deep Transfer Learning and Data Augmentation for Food Image Classification

The problem of food image classification has become a prominent topic that attracts many researchers due to its multiple benefits and applications in various aspects of life, from health to marketing. Image classification applications rely heavily on recent advancements in computer vision-based object recognition. In this paper, several deep transfer learning methods were investigated for food image classification. Furthermore, we applied a data augmentation approach to expand the Food-101 dataset. The impact of applying data augmentation and transfer learning was evaluated using five different deep learning models including Mobilenet, EfficientNetB1, and ResNet. It was noted that the EfficientNetB1 classifier achieved the best results with a score of 96.13%. In addition, we found that our data augmentation process was able to improve model performance.
Deep Learning Metaphor Detection with Emotion-Cognition Association

The focal point of this work is to automatically detect metaphor instances in short texts. It is the study of extricating the most optimal features for the task by using a deep learning architecture and carefully hand-crafted contextual features. The first feature set is created using a Convolutional Neural Network (CNN) architecture. Then, three other feature sets are manually hand-crafted using contextual justifications. Next, all of the feature sets are combined. Finally, the combined feature sets undergo the classification process using Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbour and Discriminatory Analysis. These well-known ma-chine learning classification algorithms are used at the same time for the purpose of comparison. The best algorithm for this task is found to be Support Vector Machine (SVM). The outcome of all the experiments using SVM are good in all the metrics used, with F1-measure of 0.83. Finally, comparison to existing works and performance of each feature sets are given. It is also found that a few sets performed well when they are used independently. However, even the sets that are not useful separately is proven to be very useful after the combination process.
Bidirectional LSTM and Attention for Depression Detection on Clinical Interview Transcripts

Existing studies using interviews focus on the automated depression detection task. However, it remains challenging to diagnose depression- requiring time-intensive interviews. Therefore, automated methods could help psychiatric professionals make faster, more informed diagnostic decisions by linguistic patterns in these interviews. We propose a novel method that analyzes interview transcripts to detect depression. We employ bidirectional LSTM to capture contextual information and self-attention to obtain the important information for the participants. Then a pooling layer is introduced to obtain the subject's state representation. Experimental results on the DAIC-WOZ dataset show the F1 performance improvement of our model compared with other methods.
Performance Analysis of Least Square Linear Regression with Various Classifiers for Cardiovascular Respiratory Detection from Capnography

In this study, the PPG signal was taken from a capnography data source and used along with statistical characteristics and machine learning techniques to diagnose respiratory disorders. After the statistical properties of the data have been extracted using a method called least square linear regression, the signal is then processed by a number of different classification tasks, and the outcomes of the classification algorithm are examined. Results show that the linear regression and nonlinear classifiers gives the best accuracy of 88.11% and 85.73% for both normal and respiratory disease cases.
Intelligent Garlic Routing for Securing Data Exchange in V2X Communication

The rise of the Internet of Things (IoT) has transformed the conventional vehicular network into smart vehicular communication, such as vehicle-to-vehicle, vehicle-to-pedestrians, and vehicle-to-infrastructure that are categorized under the umbrella of vehicle-to-everything (V2X) ecosystem. It offers colossal applications for road safety, such as hazard warning, intersection assistance, and vehicle prioritization by sharing vehicular data between different entities of the V2X systems. However, V2X communication is imperiled by various security vulnerabilities, such as man-in-the-middle, data injection, and session hijacking attacks that jeopardize the data exchange of V2X communication. To respond to the aforementioned security issues, we proposed an artificial intelligence (AI) and garlic routing (Gr)-based secure and intelligent architecture that improves the security enhancements of the V2X systems. First, AI-based classifiers are employed to efficiently classify the attack and non-attack data of the V2X communication. Further, the Gr network is adopted to process the non-attack data and offers ElGamal + advanced encryption standard (AES)-based encryption to secure the vehicular data. To further improvise the Gr network, each gateway of Gr is associated with the blockchain technology for secure storage. The performance of the proposed architecture is evaluated by considering different performance metrics, such as accuracy, scalability, and probability of data compromisation rate. The results show that the random forest (RF) outperforms in classifying data by achieving 98.2% accuracy. Further, with blockchain technology, the probability of data compromisation rate decreases, i.e., 14.3% and increases the scalability, i.e., 52.63% of the proposed architecture.
A Head Stabilizer of A Snake Robot Crawling in Serpentine Locomotion

Stabilizing the head module of snake robots is challenging, because these robots implement swinging locomotion to move. In this paper, a snake head stabilizer is designed based on Serpentine locomotion, which is similar to the head stabilizer of a real snake. In this stabilizer model, some feature points, namely inflection points, are defined on a Serpenoid curve through deriving the parametric equations of Serpenoid curve. These inflection points are proved that all of the points are on a certain circle, whose radius and position of center are determined by the parameters of Serpenoid curve. The desired direction of the snake head module can be obtained by the positional relationship between the circle center and the head joint. To verify the effect of stabilizer, simulation and experiments are conducted. The results prove that the stabilizer effectively reduces oscillation amplitudes of the head module during movement, which is worthy to be popularized in industry.
Design and Simulation of RF MEMS Switch using Uniform Serpentine Flexure Four Meander Type Beam

In this paper four meander Serpentine flexures based movable beam structure has been used for design and development of capacitive contact RF MEMS switch for high frequency and space application. The developed MEMS switch is design on silicon wafer having the high resistivity ($\rho \gt 8 k\omega-cm$), high dielectric constant ($\varepsilon_{\mathbf{r}}= 11.8$) and low loss tangent ($tan\delta=0.001$,). Meander techniques are used to achieve the low pull-in voltage. The simulated value of pull-in voltage for four meander beam is 2.5 V for initial air height of 2.5 $\mu {\mathrm {m}}$. The developed switch exhibits the simulated RF parameters i.e. Insertion loss in Upstate (S21), return loss (S11), and Isolation (S21) are greater than 0.2, 20, and 30 dB respectively, across the complete frequency range (DC-40 GHz). The Method of Moments based ADS (advanced design system) and Finite element Method based coventorware and ANSYS HFSS full-wave simulator has been used for electromagnetic and electromechanical analysis of the 3D structure. The simulated Figure of merit (ratio of down state capacitance to up state capacitance) of the proposed switch is greater than 85. Further, The Simulated RF data are validated with equivalent circuit modal data over the entire frequency range.
Surface anisotropy of polyimide film irradiated with linearly polarized ultraviolet light

Using polarized infrared (IR) absorption, we have investigated the surface anisotropy of a poly [4, 4′-oxydiphenylene-pyromellitimide] (PMDA-ODA) film that arises from anisotropic decomposition of the polyimide chain during irradiation with linearly polarized ultraviolet (LPUV) light. To monitor the surface anisotropy, we designed the sample structure so that the polyimide films decomposed uniformly over the entire film thickness. The surface anisotropy has a maximum at an irradiation energy of 105 J/cm2. For PMDA-ODA, the maximum surface anisotropy is significantly smaller than the surface anisotropy generated by rubbing. By analyzing the irradiation energy dependence of an IR absorption band, we found that the decomposition rate of the polyimide chain oriented parallel to the polarization direction of the LPUV light is greater only by ∼23% than that oriented perpendicular to it. This is the reason for the small surface anisotropy induced by the LPUV light irradiation.
Secure Provisioning of OPC UA Applications Using the Asset Administration Shell

Modern Industry 4.0 environments are highly interconnected and have therefore an increased need for security. Protocols for machine-to-machine communication, like OPC UA, can provide this security. However, before using their security mechanisms, a trust relationship between the application and operator network first needs to be established. This trust and key exchange is performed during secure provisioning, a process often performed manually and without security in mind. In OPC UA, there are already automated provisioning mechanisms planned and partly existing, but they either involve high manual effort or cannot provide mutual authentication between application and operator network. The Asset Administration Shell (AAS), as a realization of the digital twin concept, already provides the possibility of a secured data and information exchange between manufacturer and operator. In this work, we combine the security mechanisms of OPC UA and AAS to provide a more secure and automated provisioning of new OPC UA applications.
Prediction of Heart Failure by using Machine Learning and Feature Selection

Heart attacks are one of the foremost causes of death in the world. While doctors can carry out multiple tests to diagnose it, it may go undetected for a long time which can prove fatal. However, it is possible to predict the potential risk of a heart attack using Machine Learning (ML) algorithms. This is done using patients' historical data and has found some success in the literature. However, not all features are equally important in such data which may result in low accuracy of prediction. In this study, we use feature selection techniques to select a subset of features with contribution to improve upon the prediction accuracy of ML models. Inforation theory based models are used for feature selection in combination with popular ML classifiers including Random forest (RF), Decision Trees (DTs), Logistic Regression (LR), k-nearest neighbors (kNN), Gradient Boosting, Bagging, Ensemble, Neural Network (NN) and Support Vector Machines (SVM). The model is tested the real-world and publicly available Framingham dataset. Results show that the proposed approach improves the prediction of heart disease compared to baseline models.
A Post-Correction Method for Terahertz Nonlinear Distortion with Dual-Band Carrier Aggregation

Terahertz (THz) band is a highly advantageous potential wireless spectrum resource for 6G communications due to its large bandwidth. Nevertheless, the inherent nonlinear distortion of THz devices such as power amplifiers would degrade the transmitted signal quality. After carrier aggregation (CA) process, the peak-to-average ratio of synthetic signals is stronger and the nonlinear distortion would be even worse. Conventional digital pre-distortion (DPD)-based nonlinear correction method requires the analog-to-digital converter (ADC) and the digital-to-analog converter (DAC) with a conversion rate of 5-7 times the signal bandwidth, making it difficult to support such ultra-large bandwidth application scenarios. In this paper, a digital post-correction (DPC) model is proposed for nonlinear correction of dual-band carrier aggregation application at the receiver side, and a corresponding correction method with low sampling rate is proposed. The simulation results demonstrate that compared with the single-band DPC, the proposed method optimizes the signal Error Vector Magnitude(EVM) by 11. 9dB in the case of 2GHz transmission signal bandwidth and 2. 6GHz carrier spacing, providing an effective solution to the application of higher rate modulation of THz communication.
Powerful Maximum Power Point Tracking for PV Systems: An Artificial Intelligence Paradigm

This study introduces an artificial intelligence (AI) method to evaluate and track the operating point of maximum power in a stand-alone photovoltaic (PV) system. A perturb and observe method (P\O) is first employed to test the scheme model for different temperatures and irradiation conditions. The data accumulated from this stage are then leveraged to train a suggested neural network (NN). The suggested model can enrich the dynamic and steady-state performance of the PV scheme. In addition, it provides a precise analysis of the ultimate operating point and precise analysis of the maximum power from the PV panel. A conventional (P\O) method is utilized as a baseline to prove the merit of the suggested scheme. According to the obtained results of the system, abrupt irradiation input, the suggested scheme delivers better precise outcomes that could enhance the voltage of the system in the order of 3.52% and the power of the system in the order of 5.13% in comparison with the baseline method (the P\O MPPT tracking.) for certain circumstances.
ISA-PredRNN: An Improved Self-Attention PredRNN Network for Spatiotemporal Predictive Learning

Due to the ability to deal with both temporal and spatial information, spatio-temporal prediction models are used in numerous fields. Currently, the mainstream approach is to use a combination of CNN and RNN for spatio-temporal prediction, in which the PredRNN family of networks is an excellent representative. However, CNNs are only good at dealing with local dependencies and RNNs are poor at capturing long-term dependencies. These factors can affect the accuracy of the prediction results. In order to enhance the predictive power of the model, some improvements are proposed in this paper to overcome the above shortcomings. We introduce the self-attention mechanism and the long-term memory into the ST-LSTM, the basic structural unit of PredRNN-V2, which can handle global and long-term dependencies respectively. To bridge the gap between training and inference, we use the sampling strategy with a combination of Reverse Scheduled Sampling and Scheduled Sampling. As precipitation involves relatively complex spatio-temporal factors, we design the special loss function with weights to better capture extremes. Finally, our new model ISA-PredRNN achieves the best results on the three datasets MovingMNIST, KTH and Radar echo dataset in the comparison experiments.
A Hybrid Stacked Ensemble Technique to Improve Classification Accuracy for Neurological Disorder Detection on Reddit posts

Sentiment analysis helps in the early detection of depression as identify unpleasant mental states in people who are at risk for developing mental disorders. By utilizing both syntactic and semantic information, modelling approaches for sentiment analysis rely on machine learning algorithms. In this paper, a hybrid stacked ensemble learning approach has been used for the detection of depression as a neurological disorder. With the help of pre-trained word embeddings, the Word2Vec, GloVe and Fasttext are chosen for data preprocessing and feature extraction. Then, to identify depressed and non depressed identities, we integrate a hybrid stacked ensemble learning approach over Random Forest (RF), Support vector machines (SVM), K-Nearest Neighbor (KNN) and Catboost classifier (CBC) as base models whereas logistic regression (LR) as meta model classifier. The results of the experiments show that suggested model performs best with our proposed model than individual models. It is also found that with Word2Vec word embedding model, the proposed model achieved the higher accuracy as 99% in comparison to GloVe and Fasttext that categorizes depressed over non depressed users on the social media platforms.
Hierarchical Task Analysis of Human-Robot Collaboration Scenario in Space Station

With the construction and development of China’s aerospace industry and the continuous expansion of new space missions, astronauts will face more and more space station operation tasks. Astronauts' extravehicular activity is an important part of the key technical verification in the assembly and construction stage of the space station. In this paper, we propose a task process analysis and optimization method suitable for extravehicular human-robot collaboration tasks. This method based on the hierarchical task analysis method, decomposes the operation process into the event layer, the operation layer, the operation unit layer and the basic therbligs layer, and classifies the minimum therbligs unit in the basic therbligs layer, summarizes the typical therbligs, forms a set of standardized operation processes. The method proposed in this study has formed a set of analysis software and has been verified in the real extravehicular mission, which lays a solid theoretical and practical foundation for the process design and planning of astronauts' extravehicular mission in the future.
Relation between critical current of domain wall motion and wire dimension in perpendicularly magnetized Co/Ni nanowires

We investigated the relation between critical current of domain wall motion and wire dimension by using perpendicularly magnetized Co/Ni nanowires with different widths and thicknesses. The critical current, Ic, became less than 0.2 mA when w<100 nm, suggesting that magnetic random access memory with domain wall motion can replace conventional embedded memories. In addition, in agreement with theory, the critical current density, jc, decreased as wire width decreased and became much less than 5×107 A/cm2 when w<100 nm. We also performed a micromagnetic simulation and obtained good agreement between the experiment and simulation, although a few discrepancies were found.