Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Pattern Recognition with Localised Surface Haptics and Apparent Motion

Surface haptics is gaining increasing interest due to the potential of the added tangibility to standard touchscreen devices, with compliance effects, different localised multitouch feedback or textures. Dynamic illusions provide further possibilities, such as apparent motion, to indicate directions or other more complex motions. It could also help feel the contours of a shape, particularly useful in the accessibility field for digital graphics. The confinement of vibrotactile stimuli is a recent method that enables not only to provide both localised and multitouch feedback but also to put the whole hand on the display. Thus, it has the potential to combine different interaction styles, from static to dynamic interaction, with a single finger to the whole hand. Yet, so far, there has been little work on tactile interactions, and particularly pattern recognition with this method. Therefore, this paper presents a preliminary investigation into the feasibility of using confined vibrotactile stimuli to identify geometric shapes using apparent motion. The results show that participants could feel continuous movements and with training reached recognition rates overall of 72.1%.
Adaptive Robust Control of an Uncertain Robotic Manipulator with Guaranteed Transient and Steady State Performance

Guaranteeing human-specified performance in robot control subject to complex uncertainties is challenging. This study takes time-varying (possibly fast), bounded with unknown bounds uncertainties into consideration in controlling an uncertain robotic manipulator system (URMS). We employ an adaptive law to on-line estimate the unknown bounds for uncertainty compensation, and prescribed transient and steady state performance (TSSP) are guaranteed by using a state transformation technique. As a result, a class of adaptive robust control with prescribed TSSP is proposed. Simulation results are provided to validate the effectiveness of the proposed approach.
Deep Learning for Spatiotemporal Modeling of Illegal, Unreported, and Unregulated Fishing Events

Illegal, unreported, and unregulated (IUU) fishing encompasses a range of fishing practices that are considered environmentally harmful and that violate national and international regulations. IUU fishing is an ongoing challenge that undermines efforts to nurture sustainable fisheries. In the United States alone, over 13% of seafood imports in 2019 were estimated to be from IUU fishing. To combat IUU fishing, various means, including legislative, law enforcement, and technological, are used to track the movement of fishing vessels and fish catches globally. However, to effectively track the prevalence of IUU fishing events geographically and over time, a highly accurate model is necessary. Therefore, in our work, we present a spatiotemporal IUU fishing forecasting algorithm to predict IUU fishing events over time and in different geographic locations. We train and evaluate our model on disjoints subsets of a dataset of IUU fishing events associated with the Atlantic bluefin tuna (Thunnus thynnus) and southern bluefin tuna (Thunnus maccoyii) species in North America over the time period 1950–2014. Our model achieves a final mean absolute error of 0.085, and is able to capture the trend of IUU fishing event severity over time. Accurate forecasting of IUU fishing events can enable effective enforcement actions to advance sustainable fisheries.
Optimising the Cuckoo Search Algorithm for Improved Quality of Service in Cognitive Radio ad hoc Networks

This study proposes an optimised routing scheme, called OCS-AODV, for Cognitive Radio Ad Hoc Networks (CRAHNs) to enhance Quality of Service (QoS). The scheme applies the Cuckoo Search (CS) algorithm optimised with a fitness function to improve the performance of the Ad Hoc On-Demand Distance Vector (AODV). The objective of the study is to evaluate the proposed scheme's performance with respect to delay, packet loss, packet delivery ratio and throughput. The literature review shows that the existing routing protocols have limitations which impact performance in dynamic environments. The proposed OCS-AODV scheme aims to address these limitations by selecting reliable paths based on a fitness function that considers the lifetime of nodes, reliability, and available buffer capacity. The simulation results have shown that the OCS-AODV scheme outperforms the CS-DSDV and ACO-AODV schemes in terms of PDR, packet loss, delay, and throughput. The study concludes that the proposed scheme improves the QoS of routing in CRAHNs. However, the use of a single fitness function may not be optimal for all network scenarios. Multiple fitness functions may be considered in future and the schemes be evaluated in real-world CRAHNs
Automatic Tuberculosis Detection using Binary Pattern of Phase Congruency

Computational solutions to assist the automatic analysis of radiographic images are already a reality. Commercial solutions for the diagnosis of tuberculosis have shown exceptional results similar to those of radiologists. However, these solutions are still far from regions of greater vulnerability, given their high costs. Researchers have shown a growing interest in developing increasingly efficient and effective solutions that require the least resources. Thus, in this work, we explore the use of chest X-ray and Phase Congruence Binary Pattern features to build a minimalist, low computational cost, and high-efficiency Feed-Forward Neural Network model to aid the diagnosis of tuberculosis. Our results showed high performance compared to related research, placing our solution as a viable alternative.
FDIO: Extended Kalman Filter-Aided Deep Inertial Odometry

Smartphone-based deep inertial odometry has recently gained great research interest, which utilizes the inertial measurement unit (IMU) and deep learning technique for relative states estimate. In this paper, we propose FDIO: a Filter-aided Deep Inertial Odometry, which utilizes an extended Kalman filter (EKF) for orientation tracking following by a learning module for position estimation. Our inertial odometry requires only the inertial signals without relying on external device orientation information. In the position learning module, this paper proposes a novel representation of the pedestrians' velocity and a robust loss for regression. The proposed FDIO is validated by using the public RoNIN dataset. Experimental results show that our model outperforms state-of-the-art deep inertial odometry architectures.
FDIA Detection Methods on a Navy Smart Grid AMI Data Set using Autoenocoder Neural Networks: A Case Study

In 2019, the Naval Facilities Engineering Command (NAVFAC) deployed its first smart grid infrastructure in Norfolk, VA, enabling shore commands to meet energy goals set by the Secretary of the Navy. However, with increased functionality and control comes increased vulnerability to malicious cyber activity. In this paper we aim to address anomaly detection in the Navy smart grid using autoencoder neural networks. Our specific focus is on anomalies in the sensor data originating within the advanced metering infrastructure (AMI) of the Navy smart grid. An efficient autoencoder model was developed through benchmarking experiments with open source Modbus data sets. We then used NAVFAC provided AMI data to train the autoencoder model to detect 14 different false data injection attacks (FDIA). Twenty six AMI data features were manipulated to simulate FDIAs. Accuracy, precision and recall scores were used to quantify model performance. We compare the performance of our autoencoder with that of a deeper neural network model to show that a smaller, leaner model can be equally effective in classifying and identifying FDIAs. Our experiments showed that our autoencoder model achieved an average 90%-95% on precision, accuracy and recall scores over multiple combinations of optimizers and activation functions. This work is a case study on the use of unsupervised machine learning methods for data anomaly detection on the Naval smart grid.
Similarity Solution for Magnetogasdynamic Shock Waves in a Weakly Conducting Perfect Gas by Using the Lie Group Invariance Method

Under axial and azimuthal magnetic inductions, the similarity solutions for a cylindrical shock wave in a weakly conducting ideal gas are determined using the Lie group invariance method. The axial and azimuthal magnetic inductions and density are presumed to vary in an ambient medium. This study determines the form of expression for axial and azimuthal magnetic inductions in the ambient medium. The ambient density is considered to be varying according to the power law of the shock radius. The weakly conducting medium causes inadequate magnetic freezing. We have numerically solved the system of ordinary differential equations that resulted from applying the Lie group invariance method to the system of partial differential equations. The impact of the variation in the ambient density exponent, the ratio of specific heats, magnetic Reynolds number, or the inverse square of axial and azimuthal Alfven Mach numbers on the shock strength and the flow variables behind the shock front is discussed. It is found that the shock strength decreases with an increase in the ratio of specific heats, magnetic Reynolds number, or the inverse square of axial and azimuthal Alfven Mach numbers.
Baselining a Functional Architecture for a Power Electronic Power Distribution System for Navy Vessels

The transition to the electric ship requires evolving from passive power regulation to active state anticipation with data linkage and controls between machinery and mission systems to become an agile power and energy system. Toward this, the Navy sponsored research initiatives are currently developing a new Power Electronic Power Distribution System (PEPDS) paradigm. A key enabler for a successful PEPDS R&D program is a well-developed and documented functional system architecture as the basis for collaborate research and subsequent future performance studies. The technical approach is to employ Model-Based Systems Engineering (MBSE), to capture stakeholder needs, behaviors, structures, and measures for PEPDS in a system model. This work has recently baselined a functional architecture, which is a solution-independent description of what a system does, defining the trade space for design. The present paper provides an overview of the process, product, and prospects of that functional architecture.
Radar-Based Human Behavior Model Construction and Modulation Pattern Analysis

Micro-Doppler features in human radar echoes can be applied for behavior recognition, particularly the detection of abnormal behaviors, which is valuable for reducing casualties in the elderly. This paper proposes a method for constructing human behavior model in order to realize the simulation of human behavior. The method designs the human body as a structure with 17 joints and 16 parts, and the joints where angular rotations occur and the rotation order of the joints are determined according to various behaviors. The rotation matrix is applied to generate the joint coordinates at different moments. This method is used to construct three categories of human behavior models, and the micro-Doppler spectrum of each type of behavior model are obtained using the short-time Fourier transform. Based on micro-Doppler spectrum, the Doppler frequency modulation pattern of various behaviors on radar echoes are summarized in detail.
Soliton trapping and harmonic oscillations due to nonlinear interactions in optical fibers

When the fiber dispersion is normal, there exists a pair of bright and dark solitons with orthogonal polarizations. When the power of the dark soliton (pump) on one polarization is stronger than that of the bright pulse (signal) on the other polarization, we found that the bright pulse is trapped into the dip of the dark soliton, and it undergoes harmonic oscillations if the mean frequency of the bright pulse is lower than a certain critical frequency. Otherwise, it escapes the potential well formed by the dark soliton. Analytical expressions derived for the frequency of harmonic oscillations and the asymptotic value of the trapped energy are in good agreement with numerical experiments. This result could have potential applications for ultra-fast all-optical switching and logic gates.
A Visually Interpretable Convolutional-Transformer Model for Assessing Depression from Facial Images

The accuracy and availability are the most critical and challenging problems for major depressive disorder (MDD) diagnosis. Limited receptive field and inaccurate visual interpretation always weaken the clinical application of deep learning-based depression recognition model. Thus, we propose a visually interpretable depression monitoring model termed Transformer and Convolutional with slot-attention (TC-slot) to assess depression from facial images. Specifically, this approach stands upon the intersection of convolution and transformer, combines self-attention mechanism and deep convolution, and uses a well-designed stem structure to explore the global and local relationships. Moreover, in TC-slot, a classifier built on slot-attention mechanism directly involved in the decision-making process further localizes salient regions of facial depression patterns and provides precise and meaningful explanations. The results indicate that the proposed approach effectively improves the classification and recognition performance compared with other state-of-the-art approaches, with guaranteed favorable visual interpretability, providing clinical insights into the assessment of the assessing depression.
An Improved AdaBoost Algorithm for Highly Imbalanced Datasets in the Co-Authorship Recommendation Problem

The co-authorship recommendation problem is attractive since it helps researchers extend collaboration to improve the quality of scientific articles as well as promote innovation. This problem involves suggesting authors join research groups based on their research interests, areas of expertise, and past collaborative experiences to write scientific articles together. In this paper, we tackle the co-authorship recommendation problem by modeling it as a co-authorship network, where each author is represented as a vertex, and each collaboration between two authors is represented as an edge. Since the number of author pairs without collaboration is much larger than those with collaboration, datasets created from co-authorship networks are typically two-class imbalanced datasets. Accordingly, we propose an improved algorithm of AdaBoost combined with the W-SVM algorithm, called Im.AdaBoost.W-SVM, to solve the classification problem with two-class imbalanced datasets. To evaluate the performance of our Im.AdaBoost.W-SVM algorithm for the co-authorship recommendation problem, we collected author and article data from the website https://www.sciencedirect. com through ScienceDirect APIs and created two-class imbalanced datasets. Our experimental results for our self-built co-authorship datasets with different sizes and imbalance ratios showed that our Im.AdaBoost.W-SVM algorithm outperforms the AdaBoost.DecisionTree and AdaBoost.W-SVM algorithms for the co-authorship recommendation problem.
Implicit Neural Distance Optimization for Mesh Neural Subdivision

Surface subdivision is crucial in 3D mesh presentation and storage. Existing classical subdivision methods adopt manually-defined rules and fail to precisely restore a high-resolution mesh. Recently, a data-driven subdivision method delivers neural networks to learn complex non-linear subdivision schemes, but it suffers from time-consuming training data preparation and limited subdivision levels. In this paper, we propose a new data-driven surface subdivision framework, namely Implicit Neural Distance Optimization Neural Subdivision (INDONS), aiming to subdivide coarse meshes to restore high-resolution meshes. Our method subdivides coarse meshes once in each forward process while can be recursively applied to obtain multilevel subdivision results, which is the same as the classical subdivision mechanism. Moreover, we introduce a new loss function, named implicit neural distance, to provide precise supervised signals for training the subdivision network. The implicit neural distance is defined as the distance between a mesh and an implicit neural field. Extensive experiments demonstrate that our INDONS framework is flexible to be deployed, and INDONS outperforms classical subdivision methods and existing data-driven subdivision method in restoring high-resolution meshes.
A Variational Method with Kernel Estimation and Low Rank Prior for Pansharpening

In this article, a new variational pansharpening method based on kernel estimation and regional extended low rank is proposed, which aims to generate a high resolution multispectral (HRMS) image by fusing the panchromatic (PAN) and multispectral (MS) image. First, an estimated blurring kernel is generated for the spectral constraint term, which can build the relationship between the MS and HRMS image more accurately and improve the spectral quality of the HRMS image. Second, a spatial constrain term is designed by adopting the proportional relationship of the PAN and HRMS image in gradient domain, which preserves the geometric information of the PAN image well. Third, according to sensor imaging principle, a prior constraint term is proposed based on regional extended low rank, which can improve the spatial clarity of HRMS image. The above three constraint terms are combined to form the proposed variational pansharpening method, and the ADMM method is applied for solving it. Finally, experiments show the effectiveness of the proposed method through comparing with other state-of-art pansharpening methods.
Belief propagation for marginal probabilities in multiple hypothesis tracking

This paper explores evaluation of association marginals in multiple hypothesis tracking. The work builds upon recent results where loop belief propagation (LBP) has been used in single-hypothesis cases. There are two contributions in the paper. The first is a novel factor graph representation of the joint multi-hypothesis association posterior. The second contribution is two algorithms that both use LBP to evaluate association marginals. The first method uses total probability in conjunction with hypothesis-conditioned LBP, and is called PHD-LBP. The second method is an LBP algorithm running directly on the full multi-hypothesis association graph with novel, specialized message definitions that are derived in this paper and efficient to compute and store in memory, and is called MH-LBP. Results show that both algorithms perform well with high correlation with the exact marginals for the majority of the cases.
DEMDE: Decision Making Design based on Bayesian Network for Personalized Monitoring System

Personalized monitoring systems (PMS) are used for Decision Making (DeM) to support humans and fully autonomous decision-making in several applications, such as health monitoring and management. However, it is still challenging to design decision-making in PMS. In this work, we propose a systematic modeling approach, called DEMDE, for decision-making design in PMS during the design phase. DEMDE guides the design of a specific Bayesian network (BN) from an instantiated domain model for PMS based on context-aware data fusion using a probabilistic domain model (general BN). We evaluated our proposal by developing a BN for decision-making about sending an alert of high contamination risk in cell culture. The case study demonstrated the application of the DEMDE concepts and the modeling process, including model evaluation through sensitivity analysis to assess the robustness and reliability of the modeled BN.
Force Calibration and Prediction of Soft Stretch Sensor Based on Deep Learning

Soft stretch sensors are increasingly used in wearable devices and flexible exoskeleton. This paper presents a novel sensing-actuation integrated unit for elastic tension transmission and force estimation. The unit consists of a capacitive sensor, four elastic bands, which can provide enough stiffness, and two stretchable paper-cut fabric shielding layers, which can greatly shield the external interference. The mechanical and electrical properties of the unit were tested on a universal material testing machine and then a simulation test platform was designed to generate the sine curve with different travels and stretch rates. A great amount of data with a total of 35 cases were collected to train our models. Results demonstrated mean square error (MSE) less than 0.21 N2, normalized root mean square error (NRMSE) less than 1.7% for the selected calibration model, and MSE less than 0.28 N2, NRMSE less than 2.0% for the selected prediction model. Our unit together with its calibration and prediction methods in this paper holds great promise in applications such as lightweight flexible exoskeletons.
Manipulation of Body Sway Interpretation through Kinesthetic Illusion Induced by Ankles Vibration

Numerous studies have explored the body tilt and sway elicited by vibratory stimuli, which are thought to be related to reflex adjustments or kinesthetic illusions. However, prior studies have not thoroughly explored the conditions that change the interpretation of self and environmental factors. In the present study, a subjective body sway was induced through alternating vibrations that were applied to the antagonist ankle muscles. Results indicated that a low switching frequency inclined the interpretation of self-sway, while a high switching frequency favored the interpretation of environmental sway.
Qualitative Models of Data Generation Processes: Facilitating Data-Intensive AI Solutions

AI-based decision support solutions require life cycles that adequately address critical steps, such as (i) finding suitable machine learning (ML) methods for the problem at hand, (ii) preparing and executing adequate data acquisition processes and (iii) tractable evaluation of the overall solution. Understanding the data generating processes is key in achieving this. Training and test data can be seen as a result of a causal data generation process, a sampling process in which the data is collected from different sources that are influenced by multiple interdependent phenomena. This is represented by a Qualitative Model of Data Generation Processes (QM-DGP), a causal graphical model. QM-DGP facilitates analysis of the complexity of the underlying data generating processes that can inform the development of trustable ML-based solutions in multiple ways. Firstly, this analysis is the basis for the determination of the required complexity of the ML models. Secondly, it facilitates the determination of the quantities of training data supporting good learning results. Thirdly, it can provide guidance for a systematic simplification of the models, supporting tractable solutions without significantly reduced performance. The construction of QM-DGP and the analysis benefit from sound theoretical concepts, such as d-separation and I-Maps. Experimental results with simulated data indicate that the approach can be effective in predicting the required quantities of training data and the determination of the modelling complexity using different types of models.