Reviewerly/exHarmony huggingface dataset:
Digitalization and Artificial Intelligence in Migration and Mobility: Transnational Implications of the COVID-19 Pandemic

Digitalization and artificial intelligence (AI) technologies in migration and mobility have incrementally expanded over recent years. Iterative approaches to AI deployment experienced a surge during 2020 and into 2021, largely due to COVID-19 forcing greater reliance on advanced digital technology to monitor, inform and respond to the pandemic. This paper critically examines the implications of intensifying digitalization and AI for migration and mobility systems for a post-COVID transnational context. First, it situates digitalization and AI in migration by analyzing its uptake throughout the Migration Cycle. Second, the article evaluates the current challenges and, opportunities to migrants and migration systems brought about by deepening digitalization due to COVID-19, finding that while these expanding technologies can bolster human rights and support international development, potential gains can and are being eroded because of design, development and implementation aspects. Through a critical review of available literature on the subject, this paper argues that recent changes brought about by COVID-19 highlight that computational advances need to incorporate human rights throughout design and development stages, extending well beyond technical feasibility. This also extends beyond tech company references to inclusivity and transparency and requires analysis of systemic risks to migration and mobility regimes arising from advances in AI and related technologies.
Proposal of a remote education model with the integration of an ICT architecture to improve learning management

University education is at a critical moment due to the pandemic generated by the Coronavirus Disease 2019. Universities, to guarantee the continuity of education, have considered it necessary to modify their educational models, implementing a transition towards a remote education model. This model depends on the use of information and communication technologies for its execution and the establishment of synchronous classes as a means of meeting between teachers and students. However, moving from face-to-face classes to online classes is not enough to meet all the needs of students. By not meeting the needs and expectations of students, problems are generated that directly affect learning. In this work, Big data and artificial intelligence are integrated as a solution in a technological architecture that supports the remote education model. This integration makes it possible to identify the state of learning and recommend immediate actions to its actors. Teachers, knowing the variables that affect academic performance, have the ability to change the components of learning or the method used. Improving learning and validating the capacity of information technologies to generate digital environments suitable for the generation of knowledge. In addition to improving the functionality of educational models and their adaptability to the new normal.
Deep Learning-Based Context-Aware Recommender System Considering Contextual Features

A context-aware recommender system can make recommendations to users by considering contextual information such as time and place, not only the scores assigned to items by users. However, as a user preferences matrix is expanded in a multidimensional matrix, data sparsity is maximized. In this paper, we propose a deep learning-based context-aware recommender system that considers the contextual features. Based on existing deep learning models, we combine a neural network and autoencoder to extract characteristics and predict scores in the process of restoring input data. The newly proposed model is able to easily reflect various type of contextual information and predicts user preferences by considering the feature of user, item and context. The experimental results confirm that the proposed method is mostly superior to the existing method in all datasets. Also, for the dataset with data sparsity problem, it was confirmed that the performance of the proposed method is higher than that of existing methods. The proposed method has higher precision by 0.01–0.05 than other recommender systems in a dataset with many context dimensions. And it showed good performance with a high precision of 0.03 to 0.09 in a small dimensional dataset.
Smooth Pursuit Study on an Eye-Control System for Continuous Variable Adjustment Tasks

Smooth pursuit is a physiological phenomenon of human eyes, which can be used as eye-control interaction to avoid the “Midas” problem. This article describes the smooth pursuit properties of continuous variable tasks under Euclid’s algorithm. Two experiments were conducted to investigate the effects of slider speed, initial position of “plus,” and variable value position on smooth pursuit interactions. The initial position and slider speed are combined in 12 combination forms, and the variable value positions are divided into 7 cases. Then, the optimal combination forms and variable value position case are derived. Experiment 1 divided the initial position into four levels of up, down, left and right and divided the slider speed into three levels of 1 deg/s, 2 deg/s, and 3 deg/s, concluding that the optimal combination of initial position and slider speed is that the speed is 2 deg/s and initial position is up. Experiment 2 was conducted on the basis of the conclusion obtained in experiment 1. The position of the variable value was divided into seven cases according to whether the left, center, and right of the smooth pursuit track the variable value appeared. The result showed that the position of the variable value in the center had the greatest influence on the pursuit efficiency. This research has an important value for the parameter set of smooth pursuit in the case of the Euclidean algorithm, it has great guiding significance for building smooth pursuit interaction in the context of the eye-control system.
Active Colorization for Cartoon Line Drawings

In the animation industry, the colorization of raw sketch images is a vitally important but very time-consuming task. This article focuses on providing a novel solution that semiautomatically colorizes a set of images using a single colorized reference image. Our method is able to provide coherent colors for regions that have similar semantics to those in the reference image. An active-learning-based framework is used to match local regions, followed by mixed-integer quadratic programming (MIQP) which considers the spatial contexts to further refine the matching results. We efficiently utilize user interactions to achieve high accuracy in the final colorized images. Experiments show that our method outperforms the current state-of-the-art deep learning based colorization method in terms of color coherency with the reference image. The region matching framework could potentially be applied to other applications, such as color transfer.
Multiscale Recursive Feedback Network for Image Super-Resolution

Deep learning-based networks have achieved great success in the field of image superresolution.However, many networks do not fully combine high-level and low-level information, and fuse local and global information.A multiscale recursive feedback network (MSRFN) for image super-resolution is proposed.First, multiscale convolution is integrated into the feedback network to propose multiscale projection units that adaptively capture image features of different scales by driving a multipath information flow.Next, recursive learning is applied to multiscale projection groups composed of up-and downmultiscale projection units to construct a feedback module that exploits high-level information to correct the low-level representation and refines the features in the early layers.Then, global residual learning and local residual feedback were combined to provide more contextual information for the final reconstruction.Experimental results demonstrate that MSRFN can predict more high-frequency details and alleviate the ringing effect and checkerboard artifacts inherently in CNN-based models.Even when the training datasets are relatively small, MSRFN is still superior to most state-of-the-art methods, especially for large scaling factors (×8).
Development and Accuracy Assessment of a High-Precision Dual-Axis Pre-Commercial Solar Tracker for Concentrating Photovoltaic Modules

In recent decades, advances in the development of solar tracking systems (STSs) have led to concentrating solar technologies to increase their energy conversion efficiency. These systems, however, still have areas of opportunity or improving their performance and reducing their manufacturing costs. This paper presents the design, construction and evaluation of a high-precision dual-axis solar tracking system with a technology readiness level of 7–8. The system is controlled by a low-cost Arduino board in a closed-loop control using a micro-electromechanical solar sensor. Real-time tracking experiments were performed under a clear sky as well as during partly and mostly cloudy days. Solar tracking accuracy was evaluated in an operational environment using test procedures adapted from the International Electrotechnical Commission (IEC) 62817 standard. The total mean instantaneous solar tracking error on a clear day measured with a calibrated digital solar sensor was 0.37° and 0.52° with a developed pinhole projection system. Similarly, the total mean reported solar tracking accuracy achieved was 0.390° on a sunny day and 0.536° on a partially cloudy day. An annual power generation analysis considering a conventional photovoltaic (PV) panel system and a typical concentrator photovoltaic (CPV) module as payloads was also presented. Simulations showed an increase in the generation of up to 37.5% for a flat panel with dual-axis tracking versus a fixed panel. In the case of the CPV system, first, a ray tracing study was implemented to determine the misalignment coefficient, then the annual power generation was estimated. The developed STS allowed the CPV modules to reach at least 90% of their nominal energy conversion efficiency.
ImageExplorer: Multi-Layered Touch Exploration to Encourage Skepticism Towards Imperfect AI-Generated Image Captions

Blind users rely on alternative text (alt-text) to understand an image; however, alt-text is often missing. AI-generated captions are a more scalable alternative, but they often miss crucial details or are completely incorrect, which users may still falsely trust. In this work, we sought to determine how additional information could help users better judge the correctness of AI-generated captions. We developed ImageExplorer, a touch-based multi-layered image exploration system that allows users to explore the spatial layout and information hierarchies of images, and compared it with popular text-based (Facebook) and touch-based (Seeing AI) image exploration systems in a study with 12 blind participants. We found that exploration was generally successful in encouraging skepticism towards imperfect captions. Moreover, many participants preferred ImageExplorer for its multi-layered and spatial information presentation, and Facebook for its summary and ease of use. Finally, we identify design improvements for effective and explainable image exploration systems for blind users.
UTMOS: UTokyo-SaruLab System for VoiceMOS Challenge 2022

We present the UTokyo-SaruLab mean opinion score (MOS) prediction system submitted to VoiceMOS Challenge 2022. The challenge is to predict the MOS values of speech samples collected from previous Blizzard Challenges and Voice Conversion Challenges for two tracks: a main track for in-domain prediction and an out-of-domain (OOD) track for which there is less labeled data from different listening tests. Our system is based on ensemble learning of strong and weak learners. Strong learners incorporate several improvements to the previous fine-tuning models of self-supervised learning (SSL) models, while weak learners use basic machine-learning methods to predict scores from SSL features. In the Challenge, our system had the highest score on several metrics for both the main and OOD tracks. In addition, we conducted ablation studies to investigate the effectiveness of our proposed methods.
Using Extended Reality in Flight Simulators: A Literature Review

Extended reality has long been utilized in the games industry and is emergent for pilot training in the military and commercial airline sectors. This paper follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to present a systematic quantitative literature review (SQLR) on the use of extended reality in flight simulators. It also encompasses recent studies of teaching and learning in immersive, virtual environments in non-aviation disciplines. The review identified 39 papers spanning all areas of the virtuality continuum across academic, commercial, and military aviation sectors, as well as engineering and medicine. The SQLR found that extended reality in flight simulators is being introduced in the commercial and military aviation sectors. However, within academia, hardware constraints have hindered the ability to provide positive empirical evidence of simulator effectiveness. While virtual reality may not replace traditional flight simulators in the near future, the technology is available to supplement classroom training activities and some aspects of simulator procedure training with promising cognitive learning outcomes. However, its usefulness as a mechanism of skills transfer to the real world has not been evaluated, highlighting numerous research opportunities.
Conditioned and composed image retrieval combining and partially fine-tuning CLIP-based features

In this paper, we present an approach for conditioned and composed image retrieval based on CLIP features. In this extension of content-based image retrieval (CBIR) an image is combined with a text that provides information regarding user intentions, and is relevant for application domains like e-commerce. The proposed method is based on an initial training stage where a simple combination of visual and textual features is used, to fine-tune the CLIP text encoder. Then in a second training stage we learn a more complex combiner network that merges visual and textual features. Contrastive learning is used in both stages. The proposed approach obtains state-of-the-art performance for conditioned CBIR on the FashionIQ dataset and for composed CBIR on the more recent CIRR dataset.
A Novel Multiscale Adaptive Binning Phase Congruency Feature for SAR and Optical Image Registration

Automatic registration of synthetic aperture radar (SAR) and optical images is still a challenging problem because of the potential differences in geometric and intensity. In this work, we propose a robust and efficient method for improving the registration performance of SAR and optical images. Our work consists mainly of two steps, including the feature point detection stage and the feature description stage. In the first stage, we present a new feature extraction method, named nonlinear diffusion-based Harris-Laplace (NDHL) detector, which incorporates the nonlinear diffusion and a spatial consistency strategy into the Harris-Laplace detector, which can weaken the modality variations and enhance the structural features of the multimodal images, and can detect many more similar and highly repeatable feature points. In the second stage, we design a novel structural descriptor, named multiscale adaptive binning phase congruency (MABPC). The proposed MABPC descriptor encodes multiscale phase congruency features with an adaptive binning spatial structure, which brings an improvement of the robustness against geometric and nonlinear intensity discrepancies. Experimental results on both simulated and real image pairs show that the proposed registration method achieves encouraging performance improvements over other state-of-the-art methods. In comparison with the ROS-PC and RIFT methods, the proposed method obtains an average 54.6% improvement for the number of correct matches and has an average registration precision of 1.38 pixels.
Incremental Learning Based on Anchored Class Centers for SAR Automatic Target Recognition

Although deep learning methods have achieved great success in synthetic aperture radar automatic target recognition (SAR ATR), their accuracies decline sharply as new classes are learned, which is known as catastrophic forgetting. The overlapping or confusion between the representations of new and old classes in the feature space is the main cause of catastrophic forgetting. In this paper, the Incremental Class Anchor Clustering (ICAC) is proposed to address this issue. ICAC solves this problem from three perspectives: first, how to learn the new classes; second, how to enable the model to recognize and classify the old classes; third, how to solve the imbalance between old classes and new classes. To learn the new classes, ICAC adaptively adds new anchored class centers for new classes, and the features of each new class will be clustered around the corresponding anchored class center. To enable the model to recognize and classify the old classes, ICAC stores some exemplars for the old classes to ensure the classification ability of the old classes without losing the old class centers in the feature space. At the same time, ICAC adopts knowledge distillation to further alleviate catastrophic forgetting. To solve the imbalance between old classes and new classes, ICAC proposes a learning strategy named Separable Learning (SL), which computes the losses of the old and new exemplars separately and then adds the two losses to make a gradient descent. Experiments on the MSTAR dataset and OpenSARShip dataset demonstrate the effectiveness of this method in SAR automatic targets recognition.
ECTRIMS 2022 – ePoster

Free accessAbstractFirst published online October 12, 2022ECTRIMS 2022 – ePosterVolume 28, Issue 3_supplhttps://doi.org/10.1177/13524585221123682
Learning Pixel Trajectories with Multiscale Contrastive Random Walks

A range of video modeling tasks, from optical flow to multiple object tracking, share the same fundamental challenge: establishing space-time correspondence. Yet, approaches that dominate each space differ. We take a step to-wards bridging this gap by extending the recent contrastive random walk formulation to much denser, pixel-level spacetime graphs. The main contribution is introducing hierarchy into the search problem by computing the transition matrix between two frames in a coarse-to-fine manner, forming a multiscale contrastive random walk when ex-tended in time. This establishes a unified technique for self-supervised learning of optical flow, keypoint tracking, and video object segmentation. Experiments demonstrate that, for each of these tasks, the unified model achieves performance competitive with strong self-supervised approaches specific to that task. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Project page at https://jasonbian97.github.io/flowwalk
StyleGAN-Human: A Data-Centric Odyssey of Human Generation

Unconditional human image generation is an important task in vision and graphics, enabling various applications in the creative industry. Existing studies in this field mainly focus on “network engineering” such as designing new components and objective functions. This work takes a data-centric perspective and investigates multiple critical aspects in “data engineering”, which we believe would complement the current practice. To facilitate a comprehensive study, we collect and annotate a large-scale human image dataset with over 230K samples capturing diverse poses and textures. Equipped with this large dataset, we rigorously investigate three essential factors in data engineering for StyleGAN-based human generation, namely data size, data distribution, and data alignment. Extensive experiments reveal several valuable observations w.r.t. these aspects: 1) Large-scale data, more than 40K images, are needed to train a high-fidelity unconditional human generation model with a vanilla StyleGAN. 2) A balanced training set helps improve the generation quality with rare face poses compared to the long-tailed counterpart, whereas simply balancing the clothing texture distribution does not effectively bring an improvement. 3) Human GAN models that employ body centers for alignment outperform models trained using face centers or pelvis points as alignment anchors. In addition, a model zoo and human editing applications are demonstrated to facilitate future research in the community. Code and models are publicly available (Project page: https://stylegan-human.github.io/ . Code and models: https://github.com/stylegan-human/StyleGAN-Human .)
MonoGround: Detecting Monocular 3D Objects from the Ground

Monocular 3D object detection has attracted great attention for its advantages in simplicity and cost. Due to the ill-posed 2D to 3D mapping essence from the monocular imaging process, monocular 3D object detection suffers from inaccurate depth estimation and thus has poor 3D detection results. To alleviate this problem, we propose to introduce the ground plane as a prior in the monocular 3d object detection. The ground plane prior serves as an additional geometric condition to the ill-posed mapping and an extra source in depth estimation. In this way, we can get a more accurate depth estimation from the ground. Meanwhile, to take full advantage of the ground plane prior, we propose a depth-align training strategy and a precise two-stage depth inference method tailored for the ground plane prior. It is worth noting that the introduced ground plane prior requires no extra data sources like LiDAR, stereo images, and depth information. Extensive experiments on the KITTI benchmark show that our method could achieve state-of-the-art results compared with other methods while maintaining a very fast speed. Our code, models, and training logs are available at https://github.com/cfzd/MonoGround.
When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition

Recently, most handwritten mathematical expression recognition (HMER) methods adopt the encoder-decoder networks, which directly predict the markup sequences from formula images with the attention mechanism. However, such methods may fail to accurately read formulas with complicated structure or generate long markup sequences, as the attention results are often inaccurate due to the large variance of writing styles or spatial layouts. To alleviate this problem, we propose an unconventional network for HMER named Counting-Aware Network (CAN), which jointly optimizes two tasks: HMER and symbol counting. Specifically, we design a weakly-supervised counting module that can predict the number of each symbol class without the symbol-level position annotations, and then plug it into a typical attention-based encoder-decoder model for HMER. Experiments on the benchmark datasets for HMER validate that both joint optimization and counting results are beneficial for correcting the prediction errors of encoder-decoder models, and CAN consistently outperforms the state-of-the-art methods. In particular, compared with an encoder-decoder model for HMER, the extra time cost caused by the proposed counting module is marginal. The source code is available at https://github.com/LBH1024/CAN .