Reviewerly/exHarmony huggingface dataset:
The transition of WRRF models to digital twin applications

Digital Twins (DTs) are on the rise as innovative, powerful technologies to harness the power of digitalisation in the WRRF sector. The lack of consensus and understanding when it comes to the definition, perceived benefits and technological needs of DTs is hampering their widespread development and application. Transitioning from traditional WRRF modelling practice into DT applications raises a number of important questions: When is a model's predictive power acceptable for a DT? Which modelling frameworks are most suited for DT applications? Which data structures are needed to efficiently feed data to a DT? How do we keep the DT up to date and relevant? Who will be the main users of DTs and how to get them involved? How do DTs push the water sector to evolve? This paper provides an overview of the state-of-the-art, challenges, good practices, development needs and transformative capacity of DTs for WRRF applications.
Perspectives of visualization onboarding and guidance in VA

A typical problem in Visual Analytics (VA) is that users are highly trained experts in their application domains, but have mostly no experience in using VA systems. Thus, users often have difficulties interpreting and working with visual representations. To overcome these problems, user assistance can be incorporated into VA systems to guide experts through the analysis while closing their knowledge gaps. Different types of user assistance can be applied to extend the power of VA, enhance the user's experience, and broaden the audience for VA. Although different approaches to visualization onboarding and guidance in VA already exist, there is a lack of research on how to design and integrate them in effective and efficient ways. Therefore, we aim at putting together the pieces of the mosaic to form a coherent whole. Based on the Knowledge-Assisted Visual Analytics model, we contribute a conceptual model of user assistance for VA by integrating the process of visualization onboarding and guidance as the two main approaches in this direction. As a result, we clarify and discuss the commonalities and differences between visualization onboarding and guidance, and discuss how they benefit from the integration of knowledge extraction and exploration. Finally, we discuss our descriptive model by applying it to VA tools integrating visualization onboarding and guidance, and showing how they should be utilized in different phases of the analysis in order to be effective and accepted by the user.
Object-Based Reliable Visual Navigation for Mobile Robot

Visual navigation is of vital importance for autonomous mobile robots. Most existing practical perception-aware based visual navigation methods generally require prior-constructed precise metric maps, and learning-based methods rely on large training to improve their generality. To improve the reliability of visual navigation, in this paper, we propose a novel object-level topological visual navigation method. Firstly, a lightweight object-level topological semantic map is constructed to release the dependence on the precise metric map, where the semantic associations between objects are stored via graph memory and topological organization is performed. Then, we propose an object-based heuristic graph search method to select the global topological path with the optimal and shortest characteristics. Furthermore, to reduce the global cumulative error, a global path segmentation strategy is proposed to divide the global topological path on the basis of active visual perception and object guidance. Finally, to achieve adaptive smooth trajectory generation, a Bernstein polynomial-based smooth trajectory refinement method is proposed by transforming trajectory generation into a nonlinear planning problem, achieving smooth multi-segment continuous navigation. Experimental results demonstrate the feasibility and efficiency of our method on both simulation and real-world scenarios. The proposed method also obtains better navigation success rate (SR) and success weighted by inverse path length (SPL) than the state-of-the-art methods.
A Decade of International Migration Research in HCI: Overview, Challenges, Ethics, Impact, and Future Directions

This article presents a thorough discussion of the trajectories of international migration research in HCI. We begin by reporting our survey findings of 282 HCI-related publications about migration from nine digital libraries between 2010–2019, summarizing how this research stream has evolved, the geographies and populations it encompasses, and the methodologies it utilizes. We then augment these findings with data from interviews with 11 skilled researchers who reflect on their working experience in this area. Our analysis reveals how the domain has evolved from the European migrant crisis to a more global agenda of migration and points towards a shifting focus from addressing immediate needs to acknowledging more complex political and emotional aspects of mobility. We also uncover the critical role of academic, local, and international politics in migration research in HCI. We discuss these findings to explore future opportunities in this area and advance HCI research discourse with the marginalized populace.
Global and local structure preserving GPU t-SNE methods for large-scale applications

Currently, the use of dimensionality reduction techniques such as t-distributed stochastic neighbor embedding (t-SNE) to visualize data has become essential in dealing with large-scale datasets. The state-of-the-art t-SNE-based techniques rely on a variety of methods to take advantage of GPU parallelism. The major contributions of this work consist of a new approach named simulated wide-warp anchor t-SNE (SWW-AtSNE) that combines the SWW-tSNE technique with the anchor t-SNE (AtSNE) approach, which has better preservation of global structures than SWW-tSNE and a faster execution time than AtSNE. The preservation of global structures was measured with a new metric called medium neighborhood preservation (MNP). We also propose and study the adaptations of the technique simulated wide-warp t-SNE (SWW-tSNE). The adaptations consist of using a preprocessing technique or changing the initialization method using principal component analysis (PCA). The proposal of SWW-AtSNE and the adaptations of SWW-tSNE also include the possibility of performing dimensionality reduction in two dimensions in addition to three dimensions. Furthermore, this research compares different t-SNE-based techniques using large-scale datasets. Two essential criteria are used in the comparisons: the preservation of global and local structures. Moreover, this paper compares seven methods through two AI applications: reinforcement learning and generative adversarial networks (GANs). The experimental results show that strategies such as the AtSNE method could improve dimensionality reduction quality, considering the preservation of global structures. However, it cannot achieve better results than other approaches, such as using principal component analysis in the initialization of t-SNE. Nevertheless, the ideas of both methods could be merged into a unique technique in future studies. • Fast SWW-AtSNE method for dimensionality reduction preserves Global/Local structures. • The introduction of a new metric to quantify the global structure preservation. • Analysis of GPU t-SNE based methods in real-world applications with large datasets. • PCA initialization in SWW-tSNE is fundamental to preserve global structures. • UMAP and AtSNE does not preserve global structures better than SWW-tSNE.
A PRM Approach to Path Planning with Obstacle Avoidance of an Autonomous Robot

A path planning algorithm is one of the most important parts of the system of an intelligent robot. There are many studies to avoid obstacles in a navigation route for a known environment, but it is difficult to get an optimal path. In short, knowing the environment is one of the most important parts of robotic planning and probabilistic Roadmap (PRM) uses this to direct the mobile robot to find the target while having obstacle avoidance. Mobile robots have been widely used in multiple automation-related settings. Navigation from one stage to another must be performed while avoiding current obstacles in the region to execute their duties effectively. The purpose of this research is to show the effectiveness of methods in path planning in different environments by using a PRM. This paper suggests a trajectory based on a PRM for an autonomous robot trajectory in a known environment. The robot motion path was simulated and compared to other studies.
An optimum end-to-end text-independent speaker identification system using convolutional neural network

In recent years, convolutional neural networks (CNNs) have outperformed conventional methods in end-to-end speaker identification (SI) systems. The CNN training time is considerably long due to the need for large amounts of training data and high costs of computation and memory consumption. This paper proposes a new CNN for text-independent SI inspired by the VGG-13 architecture with fewer parameters but an acceptable accuracy. In addition to the proposed CNN, the time complexity and memory cost of network training can be reduced through offline feature extraction by using a short segment of each audio sample and online data augmentation. According to the results on Voxceleb1, the proposed system is more accurate than the other state-of-the-art methods in SI. Therefore, the proposed CNN improved the accuracy and decreased the training time.
Overcoming limitation of dissociation between MD and MI classifications of breast cancer histopathological images through a novel decomposed feature-based knowledge distillation method

Magnification-independent (MI) classification is considered a promising method for detecting the histopathological images of breast cancer. However, it has too many parameters for real implementation due to dependence on input images in different magnification factors. In addition, magnification-dependent (MD) classification usually performs poorly on unseen samples, although it has lower input image sizes and fewer parameters. This paper proposes a novel method based on knowledge distillation (KD) to overcome the limitation of dissociation between MI classification and MD classification of breast cancer in histopathological images. The proposed KD method includes a pre-trained MI teacher model that is responsible for training an unprepared MD student model developed through only one magnification factor. In the proposed method, the decomposed feature maps of a teacher's intermediate layers are transferred as dark knowledge to a student. According to the experimental results, the student model developed through 40X images yielded accuracy rates of 99.41%, 99.26%, 99.14%, and 99.09% in response to unseen samples of 40X, 100X, 200X, and 400X images, respectively. Moreover, comparison results indicated the competitive performance of the proposed student model as opposed to the state-of-the-art method based on deep learning on BreakHis.
High-resolution optical flow and frame-recurrent network for video super-resolution and deblurring

Over the last years, advances in deep learning have brought huge developments to the studying of super-resolution reconstruction. However, most super-resolution methods only deal with simply down-sampled sharp images, which may lose efficacy when encountering severe blur. The severe motion blur caused by the rapid movement of an object or the large shake of the lens is common in video captured by cameras. However, existing super-resolution algorithms often bring a large amount of artifacts and are difficult to achieve satisfactory results when reconstructing such blurred video sequences. In this paper, a novel convolutional neural network is proposed that jointly processes video super-resolution (SR) and deblurring (DB) to deal with severe motion blur and recover sharp high-resolution (HR) frames. In particular, a pyramid optical flow module is introduced to estimate the sharp latent image in the blurred frame and generate HR optical flow in a coarse-to-fine way. Then, the frame-recurrent is used to warp the previous SR frame to achieve motion compensation and make full use of the previous sharp features and temporal information to help restoration of the subsequent frames. Next, to further overcome the destruction caused by motion blur in the final reconstruction, a parallel-fusion module was designed to extract and fuse the SR and DB features, finally reconstructing the output frame. Experimental results obtained in this study confirm that, compared with other advanced SR algorithms, the proposed method is both effective and efficient in dealing with videos that contain real motion blur.
Design and Application of Machine Learning-Based Evaluation for University Music Teaching

Teaching evaluation (TE) is an operational means of music education and management, a value judgment on university-related music teaching activities, with the fundamental purpose of promoting and improving the overall development of the cultivated subjects. With the deepening of quality education, the types and contents of music teaching evaluation (MTE) are being updated and changed, and many universities are gradually introducing music classroom teaching quality evaluation systems. Teaching quality evaluation plays an important role in the development of music teaching and affects the quality of educational evaluation, and is an important guarantee for the continuous reform and development of university music teaching (UMT), as well as an important means for scientific and reasonable management of schools, which plays an irreplaceable role in the development of schools. The evaluation of the quality of university music classroom teaching is a systematic process of analyzing and evaluating the teaching programs, teaching effects, and teaching processes of the music classroom within the school, which is conducive to improving the quality of university music classroom teaching. This article will focus on the overview of university music classroom teaching quality evaluation, analyze its main problems and causes, develop effective improvement measures to solve them, and build a machine learning (ML)-based UMT quality evaluation system. The model quantifies the concept of MTE indexes into definite data as the input of the network, and the comprehensive evaluation results as the output. The method overcomes the subjective factors of the evaluation subject in the evaluation process, but also obtains satisfactory evaluation results with wide applicability.
Generalized robust training scheme using genetic algorithm for optical neural networks with imprecise components

One of the pressing issues for optical neural networks (ONNs) is the performance degradation introduced by parameter uncertainties in practical optical components. Hereby, we propose a novel two-step ex situ training scheme to configure phase shifts in a Mach–Zehnder-interferometer-based feedforward ONN, where a stochastic gradient descent algorithm followed by a genetic algorithm considering four types of practical imprecisions is employed. By doing so, the learning process features fast convergence and high computational efficiency, and the trained ONN is robust to varying degrees and types of imprecisions. We investigate the effectiveness of our scheme by using practical machine learning tasks including Iris and MNIST classifications, showing more than 23% accuracy improvement after training and accuracy (90.8% in an imprecise ONN with three hidden layers and 224 tunable thermal-optic phase shifters) comparable to the ideal one (92.0%).
Multi-Scale Attention 3D Convolutional Network for Multimodal Gesture Recognition

Gesture recognition is an important direction in computer vision research. Information from the hands is crucial in this task. However, current methods consistently achieve attention on hand regions based on estimated keypoints, which will significantly increase both time and complexity, and may lose position information of the hand due to wrong keypoint estimations. Moreover, for dynamic gesture recognition, it is not enough to consider only the attention in the spatial dimension. This paper proposes a multi-scale attention 3D convolutional network for gesture recognition, with a fusion of multimodal data. The proposed network achieves attention mechanisms both locally and globally. The local attention leverages the hand information extracted by the hand detector to focus on the hand region, and reduces the interference of gesture-irrelevant factors. Global attention is achieved in both the human-posture context and the channel context through a dual spatiotemporal attention module. Furthermore, to make full use of the differences between different modalities of data, we designed a multimodal fusion scheme to fuse the features of RGB and depth data. The proposed method is evaluated using the Chalearn LAP Isolated Gesture Dataset and the Briareo Dataset. Experiments on these two datasets prove the effectiveness of our network and show it outperforms many state-of-the-art methods.
Double Attention Based on Graph Attention Network for Image Multi-Label Classification

The task of image multi-label classification is to accurately recognize multiple objects in an input image. Most of the recent works need to leverage the label co-occurrence matrix counted from training data to construct the graph structure, which are inflexible and may degrade model generalizability. In addition, these methods fail to capture the semantic correlation between the channel feature maps to further improve model performance. To address these issues, we propose DA-GAT (a D ouble A ttention framework based on the G raph A ttention ne T work) to effectively learn the correlation between labels from training data. First, we devise a new channel attention mechanism to enhance the semantic correlation between channel feature maps, so as to implicitly capture the correlation between labels. Second, we propose a new label attention mechanism to avoid the adverse impact of a manually constructed label co-occurrence matrix. It only needs to leverage the label embedding as the input of network, then automatically constructs the label relation matrix to explicitly establish the correlation between labels. Finally, we effectively fuse the output of these two attention mechanisms to further improve model performance. Extensive experiments are conducted on three public multi-label classification benchmarks. Our DA-GAT model achieves mean average precision of 87.1%, 96.6%, and 64.3% on MS-COCO 2014, PASCAL VOC 2007, and NUS-WIDE, respectively, and obviously outperforms other existing state-of-the-art methods. In addition, visual analysis experiments demonstrate that each attention mechanism can capture the correlation between labels well and significantly promote the model performance.
Underwater image enhancement by color correction and color constancy via Retinex for detail preserving

In underwater, light attenuation causes non-uniform illumination that degrades underwater image. To enhance the degraded image, we propose an underwater image enhancement method that includes color correction, color constancy, multi-scale retinex (MSR), edge-aware and texture smoothing filters, and contrast stretching. The formulated color correction compensates the red and blue channels by masking. Color constancy transforms non-uniform illumination into uniform illumination. Furthermore, the gamma correction in the color constancy improves the contrast of the image. The distorted colors are restored using the MSR with contrast limited adaptive histogram equalization (CLAHE). The edges are preserved using a fast local laplacian filter (FLLF), while the detailed texture of the image is smoothed using a rolling guidance filter (RGF) The contrast stretching enhances the image by stretching the pixel values. The qualitative and quantitative evaluation of the proposed algorithm outperforms the existing methods in terms of naturalness, contrast, visibility and enhancement of low-light images.
Spatial, spin, and charge symmetry projections for a Fermi-Hubbard model on a quantum computer

We propose an extended version of the symmetry-adapted variational-quantum-eigensolver (VQE) and apply it to a two-component Fermi-Hubbard model on a bipartite lattice. In the extended symmetry-adapted VQE method, the Rayleigh quotient for the Hamiltonian and a parametrized quantum state in a properly chosen subspace is minimized within the subspace and is optimized among the variational parameters implemented on a quantum circuit to obtain variationally the ground state and the ground-state energy. The corresponding energy derivative with respect to a variational parameter is expressed as a Hellmann-Feynman-type formula of a generalized eigenvalue problem in the subspace, which thus allows us to use the parameter-shift rules for its evaluation. The natural-gradient-descent method is also generalized to optimize variational parameters in a quantum-subspace-expansion approach. As a subspace for approximating the ground state of the Hamiltonian, we consider a Krylov subspace generated by the Hamiltonian and a symmetry-projected variational state, and therefore the approximated ground state can restore the Hamiltonian symmetry that is broken in the parametrized variational state prepared on a quantum circuit. We show that spatial symmetry operations for fermions in an occupation basis can be expressed as a product of the nearest-neighbor fermionic swap operations on a quantum circuit. We also describe how the spin and charge symmetry operations, i.e., rotations, can be implemented on a quantum circuit. By numerical simulations, we demonstrate that the spatial, spin, and charge symmetry projections can improve the accuracy of the parametrized variational state, which can be further improved systematically by expanding the Krylov subspace without increasing the number of variational parameters.
RideVR: Reducing Sickness for In-Car Virtual Reality by Mixed-in Presentation of Motion Flow Information

Humans spend a significant portion of their daily life in cars.In this study, we investigate a method to reduce motion sickness and allow people to use virtual reality (VR) while riding in cars.As the sickness arises primarily from the sensory conflict between visual and actual (or vestibular) motions, the proposed approach attempts to resolve the mismatch by mixing in and visualizing the estimated information of the actual motion, which is sensed by the on-board diagnostics and inertial measurement unit modules attached to the vehicle.We conduct a pilot experiment to validate our approach by comparing the sickness levels before and after implementing the approach in three in-car VR usage conditions:(1) Default -using the VR content without modification; (2) transparent wall -using the VR content with its background scene changing depending on the car motion; (3) particle flow -mixing in the VR content with the estimated motion flow of the car visualized as moving particles.Our experimental results show that motion sickness is reduced significantly (but not eliminated to a negligible level) using our approach.