Miracle-dz/UniFAR-FTUs huggingface dataset:
Remittances Economic Growth and Investment Nexus: Evidence From Nepal

This paper assesses the contribution of remittances on GDP and private gross fixed capital formation of Nepal by employing the ARDL bound test approach. The model incorporates the level of financial development, and the institutional quality of Nepal as regressors in addition to the macroeconomic regressors recognised by the literature. Perron’s (1997) innovation outlier model of breakpoint unit root test has been used to confirm the suitability of the variables in the ARDL bounds test approach. The findings show a positive effect of remittances on GDP while a negative effect on private gross fixed capital formation. The paper concludes that remittances do not act as a source of capital flows in the context of Nepal, rather they behave as compensatory transfers to the recipient households. To align remittances in productive activities such as self-employment, financial investment, etc., a remittance-focused policy is advised to reach out the recipients and provide them rigorous advisory and training supports.
Fast Stochastic Dual Dynamic Programming for Economic Dispatch in Distribution Systems

The multistage economic dispatch solution is of paramount importance for achieving the optimal unit commitment and the optimal decentralized decision-making in smart grid operations. Through the approximations of the optimal expected cost-to-go functions, the stochastic dual dynamic programming (SDDP) has been shown effective to obtain the optimum for the multistage stochastic programming (SP) problem, while without finite termination guarantee. Thus, we propose a fast stochastic dual dynamic programming (FSDDP) method to accelerate the convergence rate of the SDDP, including updating the candidate points based on the case with the maximum difference between the upper and lower bounds of the optimal value functions, adding a backward-forward-backward inner scheme in the backward pass, and initializing the bounds with finite constant values. We can verify that the FSDDP method enjoys a nice property with a finite convergence guarantee for the multistage SP problems. Numerical tests on 4-, 33-, 34-, 69-, 118- and 123-bus distribution systems have demonstrated that FSDDP can approach the optimal economic performance at significantly improved computational efficiency.
Prediction of Human Hand Motions based on Surface Electromyography

Tracking human hand motions has raised more attention due to the recent advancements of virtual reality (Rheingold, 1991) and prosthesis control (Antfolk et al., 2010). Surface electromyography (sEMG) has been the predominant method for sensing electrical activity in biomechanical studies, and has also been applied to motion tracking in recent years. While most studies focus on the classification of human hand motions within a predefined motion set, the prediction of continuous finger joint angles and wrist angles remains a challenging endeavor. In this research, a biomechanical knowledge-driven data fusion strategy is proposed to predict finger joint angles and wrist angles. This strategy combines time series data of sEMG signals and simulated muscle features, which can be extracted from a biomechanical model available in OpenSim (Delp et al., 2007). A support vector regression (SVR) model is used to firstly predict muscle features from sEMG signals and then to predict joint angles from the estimated muscle features. A set of motion data containing 10 types of motions from 12 participants was collected from an institutional review board approved experiment. A hypothesis was tested to validate whether adding the simulated muscle features would significantly improve the prediction performance. The study indicates that the biomechanical knowledge-driven data fusion strategy will improve the prediction of new types of human hand motions. The results indicate that the proposed strategy significantly outperforms the benchmark date-driven model especially when the users were performing unknown types of motions from the model training stage. The proposed model provides a possible approach to integrate the simulation models and data-driven models in human factors and ergonomics. Prediction of Human Hand Motions based on Surface Electromyography
Relation-Aware Attentive Neural Processes Model for Remaining Useful Life Prediction

For the remaining useful life (RUL) prediction task, the temporal information under the acquired data is crucial for prediction accuracy. The typical deep learning model for RUL prediction is mainly achieved by the recurrent neural network (RNN) or convolutional neural network (CNN) with a time window. However, the CNN model cannot explicitly extract the global temporal information of the sequence. The RNN model suffers from the slow forward computation speed due to its sequential structure. This article proposes the relation-aware attentive neural processes (R-ANPs) model to solve the RUL prediction problem. The local relation-aware self-attention model first processes the input time series, which explicitly fuses the local temporal information between adjacent sequence points. Then, the RUL is obtained by feeding the processed data into the attentive neural processes (ANPs) model. The relation-aware self-attention model and ANPs model can be trained and inferred in parallel to accelerate the training process. The proposed R-ANPs model has three innovative advantages: 1) the relation-aware self-attention model is used to fuse local temporal information into each data point explicitly; 2) the output of the model contains the standard deviation, which offers the uncertainty measurement for prediction results; and 3) the training data are served as context for the ANPs model to exploit their valuable information at the prediction stage. The effectiveness of the proposed model is validated on a run-to-failure dataset. The results demonstrate that the proposed model outperforms recent RNN- and CNN-based models.
Budget-Feasible Mechanisms in Two-Sided Crowdsensing Markets: Truthfulness, Fairness, and Efficiency

In a crowdsensing platform, users are invited to provide data services, and multiple requesters compete for desired services. Due to users’ costs of providing services, it is critical to design incentive mechanisms to incentivize users with (monetary) rewards. Meanwhile, requesters may have individual budgets and compete for services with different procurement abilities. Such a setting falls into the budget-feasible mechanism design. However, most of the existing budget-feasible mechanisms focus on one-sided markets with a single requester rather than the two-sided markets with multiple requesters having different procurement abilities. Moreover, requesters and users can be selfish and strategic with their private information, which requires preventing information manipulation on both requesters’ and users’ sides. In this paper, we investigate budget-feasible mechanisms in two-sided crowdsensing markets where multiple strategic requesters come with private budgets to obtain services from the strategic users. We also consider the fairness on the requesters’ side, i.e., a requester with more budget should obtain more service. We propose budget-feasible mechanisms for two models by distinguishing the types of services, i.e., the homogeneous or heterogeneous services. All proposed mechanisms satisfy fairness, budget feasibility, truthfulness on both users’ and requesters’ sides, and the constant approximation ratio. Numerical experiment results further demonstrate the efficiency of our proposed mechanisms.
Attitude control of ultra-low orbit satellite based on RBF neural network

Ultra-low-orbit satellites have the advantages of high resolution, high efficiency and low launch costs; however, atmospheric drag may lead to complex external interference, and continuous orbital fuel consumption may cause uncertain satellite rotation inertia. In view of the attitude control problem of ultra-low orbit satellite, this paper puts forward an adaptive attitude control method based on RBF neural network, which approaches the ideal slip mode controller through RBF neural network and adjusts neural network parameters according to external disturbance adaptation. The paper is designed to prove the progressive stability of the controller by Lyapunov theory and carried out the simulation verification. The simulation results show that the designed attitude controller can effectively overcome the influence of uncertainty disturbance in the system and improve the accuracy of attitude control.
CP+: Camera Poses Augmentation with Large-scale LiDAR Maps

Large-scale colored point clouds have many advantages in navigation or scene display. Relying on cameras and LiDARs, which are now widely used in reconstruction tasks, it is possible to obtain such colored point clouds. However, the information from these two kinds of sensors is not well fused in many existing frameworks, resulting in poor colorization results, thus resulting in inaccurate camera poses and damaged point colorization results. We propose a novel framework called Camera Pose Augmentation (CP+) to improve the camera poses and align them directly with the LiDAR-based point cloud. Initial coarse camera poses are given by LiDAR-Inertial or LiDAR-Inertial-Visual Odometry with approximate extrinsic parameters and time synchronization. The key steps to improve the alignment of the images consist of selecting a point cloud corresponding to a region of interest in each camera view, extracting reliable edge features from this point cloud, and deriving 2D-3D line correspondences which are used towards iterative minimization of the re-projection error.
Synthesis of a bifunctional EDTA–carboxymethyl chitosan derivative and its potential as an adsorbent for the removal of Cu2+ ions from aqueous solutions

Chitosan is a well-studied biomaterial which has been widely used for environmental applications as an efficient natural polymer for the adsorption and removal of metal ions. Owing to its unique properties, chitosan shows good metal-binding behavior toward several different metal ions such as Cu2+, Zn2+, Cd2+, Ni2+, Co2+, and Ca2+. Chemical modifications with the introduction of functional groups have been carried out extensively and thereby producing various chitosan derivatives to increase the selectivity and adsorption capacity toward metal ions. The present work focuses on two such monofunctional derivatives, namely, carboxymethyl chitosan (CMC) and ethylenediaminetetraacetic acid chitosan (EDTA-CS) which have been recognized as excellent adsorbents for metal removal. The main objective of this study was to synthesize a new bifunctional chitosan derivative, namely, ethylenediaminetetraacetic acid–carboxymethyl chitosan (EDTA-CMC) by attaching both carboxymethyl and EDTA functional groups on the polymer backbone and thereby enhancing its metal-binding properties. The bifunctional derivative synthesis was conducted by combining the procedures of synthesis of CMC and EDTA-CS. Newly synthesized EDTA-CMC derivative was characterized by Fourier-transform infrared (FT-IR) spectroscopy, scanning electron microscope analysis, and thermogravimetric analysis. Adsorption properties of EDTA-CMC were investigated with Cu2+ ions which produced an adsorption capacity of 111.90 mg g−1 for 1000.0 mg/L and 12.20 mg g−1 for 10.00 mg/L Cu2+ solutions. The preliminary results revealed that EDTA-CMC is an effective adsorbent than CMC to remove Cu2+ in aqueous samples. The effects of pH, initial concentration, and mass of the adsorbent in the adsorption process were studied. Under the optimized parameters of an adsorbent dosage of 10.00 mg and pH 5.5, a comparable maximum adsorption capacity up to 112.44 mg g−1 was achieved with a 150.00 mg/L of Cu2+ solution. Furthermore, EDTA-CMC showed good adsorption performance even after five cycles of regeneration.
A Process and Data Variations Tolerant Capacitive Coupled 10T1C SRAM for In-Memory Compute (IMC) in Deep Neural Network Accelerators

Advanced processors suffer from Von-Neumann bottleneck and In-Memory Compute (IMC) based solutions are proposed to improve performance and reduce power consumption by bypassing this bottleneck. In this work, we present a 10T1C SRAM cell that enables process and data variation tolerant robust compute operation. The cell is 33% more linear than competing architectures and also enables much higher parallelism (higher throughput) due to 40X lesser variability. A sample IMC SRAM instance in 65nm Low Standby Power Technology with 4-bit flash ADC and digital adder operates at an energy efficiency of 350 TOPS/W.
Stability analysis for fractional‐order neural networks with time‐varying delay

This paper focuses on the stability analysis for fractional‐order neural networks with time‐varying delay. A novel Lyapunov's asymptotic stability determination theorem is proved, which can be used for fractional‐order systems directly. Different from the classical Lyapunov stability theorem, constraint condition on the derivative of Lyapunov function is revised as an uniformly continuous class‐K function in the fractional‐order case. Based on this novel Lyapunov stability theorem and free weight matrix method, a new sufficient condition on Lyapunov asymptotic stability of fractional‐order Hopfield neural networks is derived by constructing a suitable Lyapunov function. Moreover, two numerical examples are provided to illustrate the effectiveness of these criteria.
A Novel Hybrid Sentiment Analysis Classification Approach for Mobile Applications Arabic Slang Reviews

—Arabic language incurs from the shortage of accessible huge datasets for Sentiment Analysis (SA), Machine Learning (ML), and Deep Learning (DL) applications. In this paper, we present MASR, a simple Mobile Applications Arabic Slang Reviews dataset for SA, ML, and DL applications which comprises of 2469 Egyptian Mobile Apps reviews, and help app developers meet user requirements evolution. Our methodology consists of six phases. We collect mobile apps reviews dataset, then apply preprocessing steps, in addition perform SA tasks. To evaluate MASR datasets, first we apply ML classification techniques: K-Nearest Neighbors (K-NN), Support vector machine (SVM), Logistic Regression (LR), and Random Forest (RF), and DL classification technique: Multi-layer Perceptron Neural Network (MLP-NN). From the examination for pervious classification techniques, we adopted a hybrid classification approach combined from the top two ML classifier accuracy results (LR, RF), and DL classifier (MLP-NN). The findings prove the adequacy of a hybrid supervised classification approach for MASR datasets.
High-Quality Signal Denoising and Deep Feature Representation and Its Application to the ENPEMF

The Earth’s natural pulsed electromagnetic field (ENPEMF) signal is released by the instantaneous disturbance of the Earth’s naturally varying magnetic field, which contains a large amount of information about the changing geological structures and their kinetic principles. The ENPEMF signals acquired by the equipment are often accompanied by a great deal of noise, and effective noise reduction is required for accurate signal analysis. We propose a new noise reduction method for a very low signal-to-noise ratio (SNR), called the Blaschke unwinding adaptive Fourier decomposition (AFD), and use synthetic signals to demonstrate the high accuracy of the proposed noise reduction method. Meanwhile, the root-phase deep feature representation (RPDFR) of the ENPEMF signal acquired during the Lushan Ms7.0 earthquake is explored using the roots obtained by Blaschke unwinding. The results show that the time-based RPDP can accurately reveal the deep feature of the ENPEMF signal before and after the earthquake and extract the anomalous changes, potentially revealing the electromagnetic anomaly information contained in the ENPEMF signal.
Image Detection and Classification of Oil Palm Fruit Bunches

Many computer vision approaches, such as deep learning and machine learning, can be employed in agriculture in this era of artificial intelligence (AI). These computer vision techniques are frequently utilized in product categorization, identification, and estimation. Implementing computer vision in agriculture will boost agricultural output and quality by assisting farmers in monitoring agricultural activities. Currently, the oil palm harvester judges oil palm maturity manually using natural signs like oil palm colour appearance and the number of oil palm loose fruit drops under the tree. This paper focuses on developing automated detection systems using a deep learning model to detect and classify oil palm fruit bunch based on their ripeness level. The ripeness of oil palm can be classified into four maturity levels: unripe, under ripe, ripe, and over ripe. There are two approaches in this paper namely detection of oil palm fruit bunches and classification of oil palm fruit bunches. This paper employes several types of YOLO algorithms such as YOLOv3, YOLOv3-tiny, YOLOv4, and YOLOv4-tiny to compare the performance and accuracy of different versions of YOLO. It is shown that YOLOv4 has a higher accuracy of 98.70% in detecting and classifying oil fruit bunches based on their ripeness level compared to YOLOv3, YOLOv3-tiny, and YOLOv4-tiny.
Epileptic Seizure Prediction Based on EEG by Auto-Machine Learning

The sudden epileptic seizures may not only cause accidental injuries to the patient, but also lead to psychological trauma. It is crucial to predict the onset of a seizure before it occurs. Although the current researches could achieve relatively high prediction performance, there still remain some challenges in the practical scenes, such as class-imbalance problem between pre-ictal and inter-ictal samples, manual hyperparameter tuning problem, etc. This paper proposes a feature-enhancing strategy combining automatic machine learning method to solve these problems. Firstly, the EEG signals are divided into preictal and interictal stages, and then separated into five sub-bands by the pre-processing stage. Then, the features are extracted from the preprocessed signals, followed by feature smoothing and feature augmentation process, which we employ conditional tabular generative adversarial network (CTGAN) to generate the preictal samples. Finally, the processed features are fed into the automatic machine learning (Auto-ML) for seizure prediction. The CHB-MIT EEG dataset is used in this study to evaluate the performance of our proposed method. The combination CTGAN and K-nearest neighbors (KNN), logistic regression (LR), Naive Bayes (NB) classifier and multilayer perceptron (MLP) achieved an average precision of 0.97, 0.94, 0.87 and 0.95, respectively. Auto-ML combined with CTGAN outperforms traditional machine learning models in seizure prediction, with an average accuracy of 99%. Results show that feature augmentation strategy and automatic machine learning can improve the epileptic seizures prediction performance.
Self-Supervised Learning for Audio-Visual Relationships of Videos With Stereo Sounds

Learning cross-modal features is an essential task for many multimedia applications such as sound localization, audio-visual alignment, and image/audio retrieval. Most existing methods mainly focus on the semantic correspondence between videos and monaural sounds, and spatial information of sound sources has not been considered. However, sound locations are critical for understanding the sound environment. To this end, it is necessary to acquire cross-modal features that reflect the semantic and spatial relationship between videos and sounds. A video with stereo sound, which has become commonly used, provides the direction of arrival of each sound source in addition to the category information. This indicates its potential to acquire a desired cross-modal feature space. In this paper, we propose a novel self-supervised approach to learn a cross-modal feature representation that captures both the category and location of each sound source using stereo sound as input. For a set of unlabeled videos, the proposed method generates three kinds of audio-visual pairs: 1) perfectly matched pairs from the same video, 2) pairs from the same video but with the flipped stereo sound, and 3) pairs from a different video. The cross-modal feature encoder of the proposed method is trained on triplet loss to reflect the relationship between these three pairs ( $1>2>3$ ). We apply this method to cross-modal image/audio retrieval. Compared with previous audio-visual pretext tasks, the proposed method shows significant improvement in both real and synthetic datasets.
Multivariable Calculus and Mathematica: With Applications to Geometry and Physics

Benefits of Mathematical Software. - What's in This Book. - Descriptions. - What's Not in This Book. - Required Mathematica Background. - How to Use This Book. - A Word About Versions of Mathematica. - Problem Set A: Review of One-Variable Calculus. - Vectors and Graphics. - Vectors. - Applications of Vectors. - Parametric Curves. - Graphing Surfaces. - Parametric Surfaces. - Problem Set B: Vectors and Graphics. - Geometry of Curves. - Parametric Curves. - Geometric Invariants. - Arclength. - The Frenet Frame. - Curvature and Torsion. - Differential Geometry of Curves. - The Osculating Circle. - Plane Curves. - Spherical Curves. - Helical Curves. - Congruence. - Two More Examples. - The Astroid. - The Cycloid. - Problem Set C: Curves. - Kinematics. - Newton's Laws of Motion. - Kepler's Laws of Planetary Motion. - Studying Equations of Motion with Mathematica. - Problem Set D: Kinematics. - Directional Derivatives. - Visualizing Functions of Two Variables. - Three-Dimensional Graphs. - Graphing Level Curves. - The Gradient of a Function of Two Variables. - Partial Derivatives and the Gradient. - Directional Derivatives. - Functions of Three or More Variables. - Problem Set E: Directional Derivatives and the Gradient. - Geometry of Surfaces. - The Concept of a Surface. - Basic Examples. - The Implicit Function Theorem. - Geometric Invariants. - Curvature Calculations with Mathematica. - Problem Set F: Surfaces. - Optimization in Several Variables. - The One-Variable Case. - Analytic Methods. - Numerical Methods. - Newton's Method. - Functions of Two Variables. - Second Derivative Test. - Steepest Descent. - Multivariable Newton's Method. - Three or More Variables. - Problem Set G: Optimization. - Multiple Integrals. - Automation and Integration. - Regions in the Plane. - Viewing Simple Regions. - Polar Regions. - Viewing Solid Regions. - A More Complicated Example. - Cylindrical Coordinates. - More General Changes of Coordinates. - Problem Set H: Multiple Integrals. - Physical Applications of Vector Calculus. - Motion in a Central Force Field. - Newtonian Gravitation. - Electricity and Magnetism. - Fluid Flow. - Problem Set I: Physical Applications. - Appendix: Energy Minimization and Laplace's Equation. - Mathematica Tips. - Sample Notebook Solutions.