Miracle-dz/UniFAR-FTUs huggingface dataset:
Parents’ mobile relational maintenance in resource-constrained contexts: Barriers and facilitating access conditions

Mobile phones have become central to family members’ daily communication. This study investigates the material and skills access barriers that parents of adolescents in resource-constrained contexts face for mobile relational maintenance. Following an exploratory sequential mixed-methods design, interviews with parents in Bogotá, Colombia, explored how they managed to keep in touch with their children despite facing access barriers and how these barriers still affected their parent–child mobile interactions. A survey was conducted in the quantitative phase to confirm the relationship patterns suggested by qualitative findings. Financial, cognitive, and environmental resources constituted access barriers. Despite gaining material access through alternative social and environmental resources, the barriers they faced still influenced the nature and frequency of their parent–child mobile interactions. Findings also showed that cognitive barriers, observed as parents’ low levels of perceived capability to learn how to use mobile phones, affected parents’ text messaging through their acquisition of skills.
Latent Variable Structured Bayesian Network for Cyanobacterial Risk Pre-control

Cyanobacterial blooms increasingly pose threats to ecosystems and human health. This paper is aimed to propose systematic risk pre-control schemes by understanding the complex causalities between cyanobacteria and multiple influencing variables. This research remains a challenge for three reasons. Firstly, the time-series evolution of cyanobacteria is characterized by deep uncertainties and nonlinear dynamics. Secondly, latent variables with hidden information usually exist in this kind of complex aquatic system. Thirdly, it is difficult to identify an efficient pre-control scheme that specifies variables for preferential regulation. To address these problems, we propose a latent variable structured Bayesian network model and a corresponding parameter learning algorithm. The model is tested by real-time spatio-temporal data. The computational results reveal that the proposed model demonstrates better performance in terms of inference accuracy and degree of system understanding. Based on sensitivity analysis and combination-effect analysis, a systematic risk pre-control scheme is proposed for decision-makers to prevent cyanobacterial blooms under the scenario of global warming.
Wheel Center Detection From Point Cloud Based on Recurrent Neural Networks

In the intelligent parking lot, the robot has to get the accurate position of the wheels in the process of automatic parking. The traditional image processing algorithm is not proper to be employed in this application because of sensitivity to the noises. Originating from natural language processing, RNN has now been used in the computer vision for its robustness and excellent performance. This paper proposes a method to position vehicle wheels from point cloud based on the RNN model. The experimental results show that the prediction accuracy of the bidirectional RNN is obviously higher than that of the unidirectional RNN. In a small-scale training set, the effect of BiLSTM/BiGRU is close, while the training speed of BiGRU is faster, which can be used in the wheel detection system.
High order numerical algorithms based on biquadratic spline collocation for two-dimensional parabolic partial differential equations

ABSTRACT We report a new algorithm for solving linear parabolic partial differential equations in two space dimension. The algorithm employs optimal biquadratic spline collocation for space discretization and modified trapezoidal rule for time discretization. We need to solve a block tridiagonal linear system at each time step, and obtain an approximate solution with error at space-time grid points. We analyse the stability of the new algorithm, and present a stability enhanced variant. Moreover, we give an acceleration strategy based on spectral deferred correction, and the theoretical accuracy can be increased to , where k is the number of correction loops. We also analyse the stability for the accelerated algorithms. Numerical experiments are attached to demonstrate the effectiveness of the new algorithms.
A Survey of EE Optimization Methods for Primary and Secondary Users in Cognitive Radio Networks

A large scale of spectrum sensing techniques are proposed to improve the use of spectrum resources. However, the EE (energy efficiency) should be guaranteed for both primary and secondary users, especially under various detection performance constraints. In this regard, the linking between activities of PU (primary user) and dynamic access behaviors of SUs (secondary users) should be considered in an integrated way. This survey has compared different existing scenarios and frameworks on EE optimizations. The principal objective is to enhance the system throughput and to coordinate on the physical layer of both PU and SUs, in order to enable a high-quality spectrum detection and a more efficient spectrum access. In the technical part, several optimization methods are introduced under PU’s constraints, and different methods based on game theory are applied to suitable cooperative sensing scenarios for SUs’ optimal access. Finally, the complexity of algorithms is compared, to further reduce the execution time and deploy real-time adaptation for users with lower delay.
UAV-based Moving Object Detection Based on Sliding-Window Trajectories Analysis

Moving object detection (or say change detection) for stationary cameras has been well studied by research, however such problem for cameras with general motion still remains challenging. This is because the motion of target is coupled with the motion of camera platform and the scene structure. Moreover, the problem can be further complicated if thermal cameras are used. In this work, we propose an effective moving object detection technique for image sequences captured via a UAV’s thermal camera. Specifically, in each adaptive sliding window of frames, sparse feature trajectories of the thermal images are extracted, followed by a group sparsity constrained low-rank decomposition to detect foreground in the trajectory domain. Due to the unavailability of a standard benchmark dataset for the freely moving thermal camera case, in addition to the VIVID sequence, we include three challenging thermal image sequences that reflects real-world settings for performance evaluation. The results demonstrate that our method works effectively and achieves better performance than many available methods.
Discrete wavelet face graph matching

We have designed a new face graph model, using the discrete wavelet transform, for fast elastic bunch graph matching. With fiducial points as graph nodes, and local-area power spectrum vectors estimated from a space-frequency tree as node attributes, the DWT graph achieves similar model matching performance to the Gabor graph, but with orders of magnitude faster computation.
Numerical study of the second harmonic generation of Lamb waves at an imperfect joint of plates

Lamb waves are attracting significant attention in the nondestructive evaluation for plate structures due to their capability to propagate long distances. In this study, the nonlinear interaction of Lamb waves with an imperfect joint of elastic plates is numerically investigated. In particular, the second harmonic generation behavior from the joint is examined by perturbation analysis. The imperfect joint is modeled as a spring-type interface with quadratic nonlinearity, and the perturbation analysis is carried out using the hybrid finite element method (HFEM). For the incidence of the lowest-order symmetric (S0) Lamb mode below the cut-off frequencies of the higher-order modes, the double-frequency S0 mode is generated from the imperfect joint due to the nonlinear interaction. A nonlinear parameter calculated from the amplitude of the second harmonic S0 mode shows a sharp peak at a certain incident frequency. The peak frequency increases with increasing interfacial stiffness, and the double of the peak frequency corresponds to the resonance frequency of the imperfect joint subjected to the S0 mode incidence. This result shows that the resonance of the imperfect joint leads to the amplification of the second harmonic S0 mode.Lamb waves are attracting significant attention in the nondestructive evaluation for plate structures due to their capability to propagate long distances. In this study, the nonlinear interaction of Lamb waves with an imperfect joint of elastic plates is numerically investigated. In particular, the second harmonic generation behavior from the joint is examined by perturbation analysis. The imperfect joint is modeled as a spring-type interface with quadratic nonlinearity, and the perturbation analysis is carried out using the hybrid finite element method (HFEM). For the incidence of the lowest-order symmetric (S0) Lamb mode below the cut-off frequencies of the higher-order modes, the double-frequency S0 mode is generated from the imperfect joint due to the nonlinear interaction. A nonlinear parameter calculated from the amplitude of the second harmonic S0 mode shows a sharp peak at a certain incident frequency. The peak frequency increases with increasing interfacial stiffness, and the double of the peak f.
Precise Detection of Pose and Assembly Error Based on Partial Scan Data for Fabrication of Tubular Joints

Automated and accurate determination of the pose of a roughly placed workpiece is essential for fabrication tasks such as robotic welding. In this paper, an algorithm based on cylinder fitting and Iterative Closest Point (ICP) alignment is proposed for detecting both the pose and assembly error of tubular joints in a T-, K-, or Y-shaped configuration using partial scan data. A typical TKY joint consists of a brace and a chord component. In order to also compute the assembly error, the poses of the individual joint components are determined separately. The pose information is obtained by aligning scan data points with ideal CAD models using cylinder fitting and ICP matching techniques. Due to uniformity of the chord surface, assembly error due to a translation along, or a rotation about the chord longitudinal axis are not determined. The assembly error obtained can be used to determine whether realignment is required, or combined with the pose information to re-plan paths for subsequent tasks. The effectiveness of the proposed algorithm is verified with simulation studies and application to an actual robotic welding system to locate a tubular workpiece.
An Application of Just-in-time as a Strategy for Competitive Advantage: The Case of a Non-alcoholic Company in South Africa

This paper’s main objective is to enlighten the readers on the benefits of a good application the Just-In-Time (JIT) approach in a local South African soft drink manufacturing plant with the aim of improving the company’s performance. The study followed a mixed approach, combining both the qualitative and quantitative design approach. In order to collect data, interviews with the employees that constituted the sample were conducted before they later responded to a questionnaire to collect qualitative data. Results demonstrates that the company does not use JIT as a manufacturing strategy. This explained the difficulty experienced with many aspects of the production such as: demand management and forecasting, supply chain relationship and inventory control. A recommendation made in this study was to actively apply JIT in the manufacturing process as this practice would help the company improve its performance as far as the inventory control, the relationship with suppliers and the demand management and forecasting are concerned.
A Novel Word Clustering and Cluster Merging Technique for Named Entity Recognition

Abstract In this paper, we present a novel word clustering technique to capture contextual similarity among the words. Related word clustering techniques in the literature rely on the statistics of the words collected from a fixed and small word window. For example, the Brown clustering algorithm is based on bigram statistics of the words. However, in the sequential labeling tasks such as named entity recognition (NER), longer context words also carry valuable information. To capture this longer context information, we propose a new word clustering algorithm, which uses parse information of the sentences and a nonfixed word window. This proposed clustering algorithm, named as variable window clustering, performs better than Brown clustering in our experiments. Additionally, to use two different clustering techniques simultaneously in a classifier, we propose a cluster merging technique that performs an output level merging of two sets of clusters. To test the effectiveness of the approaches, we use two different NER data sets, namely, Hindi and BioCreative II Gene Mention Recognition. A baseline NER system is developed using conditional random fields classifier, and then the clusters using individual techniques as well as the merged technique are incorporated to improve the classifier. Experimental results demonstrate that the cluster merging technique is quite promising.
A novel energy efficient electro-hydraulic actuation system and its force control design

A novel energy efficient hydraulic configuration is proposed for force control applications. It can achieve rapid dynamic closed loop response similar to the valve controlled system while maintaining high energy efficiency as well as the displacement controlled system in the steady state. Based on the proposed electro-hydraulic configuration, the force control problem is addressed. Firstly, A simplified mathematical model used for control design is proposed. To deal with inherited system uncertainties and unmodelled dynamics, a disturbance observer is used to approximate lumped disturbance. In addition, mid-ranging strategy is used to allocate virtual command between the pump and the proportional valve. To handle the problem of actuator saturation, a reference governor was used to regulate the transient pressure trajectory when future actuator saturation limits are predicted. The proposed nonlinear controller provide excellent tracking performance during the transient period in the numerical simulations.
Wavelet coding of polygonal mesh data based on triangular lattice structure with expanded nodes

Efficient representations of a 3D object shape and its textural data have attracted wide attention for the transmission of computer graphics data and for interactive design in manufacturing. Polygonal mesh data, which consist of connectivity information, geometric data and textural data are often used for representing a 3D object in many applications. We propose a compression method for polygonal mesh data by structuring its geometric and textural data on a triangular lattice with expanded nodes. The structuralized geometric and textural data are compressed using a 2D signal coding method. Experimental results following from the application of the new method to polygonal mesh data show that the proposed scheme performs efficiently.
Distributed-based Hierarchical Clustering System for Large-scale Semiconductor Wafers

In this paper, we propose a Distributed-based Hierarchical Clustering System for Large-Scale Semiconductor Wafers (DHCSSW). By applying the bigdata framework to existing hierarchical clustering algorithm, the proposed system makes it feasible to cluster large-scale wafers with up to 320,000 wafers. To verify the performance of our approach, we used simulated wafer maps. The experimental results show that our system outperformed the existing hierarchical clustering in processing large-scale wafers, suggesting that currently used hierarchical clustering is insufficient in analyzing large-scale wafer maps. In addition, some failure patterns, which the existing approach is not able to detect, can be found with the DHCSSW. We anticipate that the DHCSSW will contribute in identifying the failure patterns in large-scale semiconductor wafers.
Gender and Organization Science: Introduction to a Virtual Special Issue

Gendered processes and outcomes are pervasive in organizational life. They shape how individuals perceive their career prospects, which types of opportunities they pursue, how they get work done within organizations, and how they balance this work with the rest of their life. Organizations themselves also shape and are shaped by gender dynamics, from the ways they design jobs and performance evaluation systems to the assumptions managers make about individuals’ preferences and motivations. This virtual special issue collects together 14 papers published in Organization Science that challenge common understandings about the sources of gender differences in career outcomes, the effects of balancing work–life obligations, and the ways that gender dynamics play out in teams and organizations. An important insight that emerges from a comparison of these studies is that demand effects are often confused for supply effects. What looks like a supply problem—we think that women choose not to aspire to top positions or to jobs in top paying fields—might actually be a demand problem—organizations or jobs look unappealing to women because of past histories of not hiring or promoting women into leadership roles or of making work–life balance appear to be impossible. These studies suggest that essentialist explanations that attribute gendered outcomes to inherent characteristics or choices of women might be too simplistic or inaccurate. Instead, future research would benefit from examining the complex interactions between supply-side and demand-side drivers of gender inequality.
Prospective medical applications of Nonlinear Time Reversal Acoustics

Time Reversal Acoustic (TRA) focusing methods provide precise focusing and acoustic energy to chosen areas of the body, where various medical applications of these methods are under investigation. Here, we consider three prospective applications of the Nonlinear effects in TRA. Preliminary experiments for two of them were conducted several years ago, but were not published. A new third method combines TRA focusing with nonlinear acoustic imaging of ultrasonic contrast agents (UCA) for the following applications: 1. Nonlinear TRA method for blood pressure measurements directly in the blood stream. This method is based on the measurement of the second harmonic signal from an Ultrasonic Contrast Agent injected in the blood. 2. Nonlinear TRA for osteoporosis assessment. This is based on the concentration of acoustic energy of two signals with different frequencies in a small volume of bone. Measurements are made with a signal that sums the frequencies. Tests were conducted with a human calcanei bone sample. 3. Nonlinear TRA focusing to UCA for precise treatment of target areas and enhancement drug and gene delivery. This method is based on the reception of signals of nonlinear scattering from UCA and their frequency transformation and application of transferred signals for TRA focusing. Time Reversal Acoustic (TRA) focusing methods provide precise focusing and acoustic energy to chosen areas of the body, where various medical applications of these methods are under investigation. Here, we consider three prospective applications of the Nonlinear effects in TRA. Preliminary experiments for two of them were conducted several years ago, but were not published. A new third method combines TRA focusing with nonlinear acoustic imaging of ultrasonic contrast agents (UCA) for the following applications: 1. Nonlinear TRA method for blood pressure measurements directly in the blood stream. This method is based on the measurement of the second harmonic signal from an Ultrasonic Contrast Agent injected in the blood. 2. Nonlinear TRA for osteoporosis assessment. This is based on the concentration of acoustic energy of two signals with different frequencies in a small volume of bone. Measurements are made with a signal that sums the frequencies. Tests were conducted with a human calcanei bone sample.
Developing Customer Perception Based Organization Performance Measurement Framework for Healthcare Service

We have developed a conceptual framework that can be used as customer perception based organization performance measurement framework for healthcare service. The conceptual framework consists of customer satisfaction, perceived sacrifice, and perceived service quality. This paper aims to validate the conceptual framework empirically. A survey was conducted in order to gather the data. The survey involved 123 healthcare service (HS) users in Jakarta, Indonesia. Structural Equation Modeling analysis was employed. The research result showed that the measurement model of our framework has good psychometric properties. In other words, the measurement model of our framework is valid and reliable. The research result also showed that the structural model of our framework has good goodness of fit.
Hierarchical iterative Bayesian approach to automatic recognition of biological viruses in electron microscope images

The diagnosis of biological viruses appearing in electron microscope images is currently based on time consuming visual examination by highly trained and experienced medical specialists. To reduce the diagnosis time and to de-skill the diagnosis task by allowing the use of non-specialist medical staff, an automatic virus recognition method is presented. The method is based on a hierarchical approach to decompose the multi-category multi-feature classification problem into a set of two-category classification sub-problems, with each classification sub-problem solved based on an iterative Bayesian approach. Probability of misclassification is minimised by searching an optimum set of features selected from virus spectra and projecting them to the first principal component axis. The proposed method and the reliability are described and demonstrated using four different biological viruses.
Bandit-Based Automated Machine Learning

Machine Learning (ML) has been successfully applied to a wide range of domains and applications. Since the number of ML applications is growing, there is a need for tools that boost the data scientist's productivity. Automated Machine Learning (AutoML) is the field of ML that aims to address these needs through the development of solutions which enable data science practitioners, experts and non-experts, to efficiently create fine-tuned predictive models with minimum intervention. In this paper, we present the application of the multi-armed bandit optimization algorithm Hyperband to address the AutoML problem of generating customized classification workflows, a combination of preprocessing methods and ML algorithms including hyperparameter optimization. Experimental results comparing the bandit-based approach against Auto ML Bayesian Optimization methods show that this new approach is superior to the state-of-the-art methods in the test evaluation and equivalent to them in a statistical analysis.
Object Detection for Texture-less Tubular Joints using Hierarchical CAD Tree

Object detection for large tubular joints is one of the major challenges for robotic welding. These tubular joints are texture-less objects with a shiny and strongly reflective surface. Additionally, they are attached with large fixtures resulting in dense self-occlusion. Due to this, it is difficult to plug-and-play existing 3D object recognition pipelines. This paper presents a hierarchical CAD tree (HCT), generated using a 3D CAD model at the root node, partial views as successive layer nodes and segmented-partial views, as leaf nodes of the tree. This hierarchical approach stores the assembly information of the leaf nodes that is used for our hypothesis verification pipeline. The complete method is tested using a RGB-D sensor mounted on a robotic manipulator on a gantry, to detect tubular joints in a shipyard environment. The framework demonstrates that the proposed approach can detect horizontal and diagonal configurations of tubular joints despite high levels of occlusion.