Reviewerly/exHarmony huggingface dataset:
An Epipolar HS-NCC Flow Algorithm for DSM Generation Using GaoFen-3 Stereo SAR Images

Radargrammetry is a widely used methodology to generate the large-scale Digital Surface Model (DSM). Stereo matching is the most challenging step in radargrammetry due to the significant geometric differences and the inherent speckle noise. The speckle noise results in significant grayscale differences of the same feature points, which makes the traditional Horn–Schunck (HS) flow or multi-window zero-mean normalized cross-correlation (ZNCC) methods degrade. Therefore, this paper proposes an algorithm named Epipolar HS-NCC Flow (EHNF) for dense stereo matching, which is an improved HS flow method with normalized cross-correction constraint based on epipolar stereo images. First, the epipolar geometry is applied to resample the image to realize the coarse stereo matching. Subsequently, the EHNF method forms a global energy function to achieve fine stereo matching. The EHNF method constructs a local normalized cross-correlation constraint term to compensate for the grayscale invariance constraint, especially for the SAR stereo images. Additionally, two assessment methods are proposed to calculate the optimal cross-correlation parameter and smoothness parameter according to the refined matched point pairs. Two GaoFen-3 (GF-3) image pairs from ascending and descending orbits and the open Light Detection and Ranging (LiDAR) data are utilized to fully evaluate the proposed method. The results demonstrate that the EHNF algorithm improves the DSM elevation accuracy by 9.6% and 27.0% compared with the HS flow and multi-window ZNCC methods, respectively.
Improved Method for Oriented Waste Detection

Waste detection is one of the main problems preventing the realization of automated waste classification, which is a basic function for robotic arms. In addition to object identification in general image analysis, a waste-sorting robotic arm not only needs to identify a target object but also needs to accurately judge its placement angle so that it can determine an appropriate angle for grasping. In order to solve the problem of low-accuracy image detection caused by irregular placement angles, in this work, we propose an improved oriented waste detection method based on YOLOv5. By optimizing the detection head of the YOLOv5 model, this method can generate an oriented detection box for a waste object that is placed at any angle. Based on the proposed scheme, we further improved three aspects of the performance of YOLOv5 in the detection of waste objects: the angular loss function was derived based on dynamic smoothing to enhance the model’s angular prediction ability, the backbone network was optimized with enhanced shallow features and attention mechanisms, and the feature aggregation network was improved to enhance the effects of feature multi-scale fusion. The experimental results showed that the detection performance of the proposed method for waste targets was better than other deep learning methods. Its average accuracy and recall were 93.9% and 94.8%, respectively, which were 11.6% and 7.6% higher than those of the original network, respectively.
Fractional Order Model Identification of a Person with Parkinson’s Disease for Wheelchair Control

The paper focuses on the design of an intelligent interface that compensates for the incapacity of a person with Parkinson’s disease to drive a wheelchair. The fractional order model that defines a person with Parkinson’s disease is investigated. An identification technique based on the analysis of the frequency behavior of the movement of a wheelchair driven by a with Parkinson’s disease person on the test trajectory is proposed and a delay time crossover model with fractional order exponent β=1.5 is inferred. The fractional dynamic model of the “disabled man-wheelchair” system is discussed and a control system is proposed to compensate for the inability of the wheelchair driver. The conditions that ensure the stability of the closed loop control system are inferred. An experimental technique for analyzing movement performance is developed and a quality index is proposed to evaluate these experiments. The values of this index on the tests performed on Parkinson’s patients are analyzed and discussed.
Organic molecule single-photon sources

Abstract Since the onset of the 1990s, the development of single-molecule spectroscopy has led to the discovery of various quantum optical signatures in organic single molecules. The single-photon nature of an organic chromophore is of particular importance because of its potential as a nonclassical photon source at room temperature for quantum information science and optical quantum computing. This condensed review focuses on the introduction of fundamental knowledge and basic experimental methods for single-photon sources and organic molecular photophysics and summarizes the research developments in this field, providing an understanding of quantum emission from organic single molecules.
Robust fusion of GM-PHD filters based on geometric average

The Geometric Average (GA) fusion has recently been widely used in multi-sensor multi-target tracking. However, the classical GA-based fusion method performs poorly in the distributed Gaussian Mixture Probability Hypothesis Density (GM-PHD) filters due to the inconsistency of the fused cardinality distribution. Moreover, the performance of the GA fusion method deteriorates further in the presence of missed detections. To this end, this paper presents a Robust Consistency-Balanced GA (RCB-GA) fusion algorithm for the distributed GM-PHD filters. First, we introduce a consistency compensation factor in the fused GM-PHD to dynamically adjust the consistency level of the cardinality distribution according to the spatial proximity of the Gaussian components contained in the local PHDs. Secondly, a three-step fusion strategy separating the fusion of the detection and prediction components is proposed. The robustness of the fusion algorithm is improved by reasonably utilizing the missed detection target information contained in the prediction components of the local PHD. Finally, simulation experiments evaluate the effectiveness and robustness of the proposed algorithm in multi-sensor multi-target tracking scenarios.
Jigsaw-ViT: Learning jigsaw puzzles in vision transformer

The success of Vision Transformer (ViT) in various computer vision tasks has promoted the ever-increasing prevalence of this convolution-free network. The fact that ViT works on image patches makes it potentially relevant to the problem of jigsaw puzzle solving, which is a classical self-supervised task aiming at reordering shuffled sequential image patches back to their original form. Solving jigsaw puzzle has been demonstrated to be helpful for diverse tasks using Convolutional Neural Networks (CNNs), such as feature representation learning, domain generalization and fine-grained classification. In this paper, we explore solving jigsaw puzzle as a self-supervised auxiliary loss in ViT for image classification, named Jigsaw-ViT. We show two modifications that can make Jigsaw-ViT superior to standard ViT: discarding positional embeddings and masking patches randomly. Yet simple, we find that the proposed Jigsaw-ViT is able to improve on both generalization and robustness over the standard ViT, which is usually rather a trade-off. Numerical experiments verify that adding the jigsaw puzzle branch provides better generalization to ViT on large-scale image classification on ImageNet. Moreover, such auxiliary loss also improves robustness against noisy labels on Animal-10N, Food-101N, and Clothing1M, as well as adversarial examples. Our implementation is available at https://yingyichen-cyy.github.io/Jigsaw-ViT.
Temporal action detection with dynamic weights based on curriculum learning

To enable temporal action localization, the computer needs to recognize the locations and classes of action instances in a video. The main challenge to temporal action detection is that the videos are often long and untrimmed, consisting of varying action content. Existing temporal action detection frameworks exhibit a gap between the training and testing phases, which is detrimental to model performance. Specifically, all positive samples are trained identically in the training phase. By contrast, in the testing phase, the positive samples with the best classification and localization scores are selected, while all others are suppressed. To mitigate this issue, we build an auxiliary branch to unify the training and testing procedures. In the construction of the auxiliary branch, we design a dynamic weighting strategy based on curriculum learning, where the weights of training samples are a combination of their classification and localization scores. Motivated by the speculation of curriculum learning, we emphasize the importance of classification and localization scores in different training stages. The classification score accounts for a higher proportion of the combined score in the early stages of the training process. As the epoch increases, the localization score gradually increases in proportion as well. The experimental results demonstrate that our methodology of curriculum-based learning enhances the performance of current action localization techniques. On THUMOS14, our technique outperforms the existing state-of-the-art technique (57.6% vs 55.5%). And the performance on ActivityNet v1.3 ([email protected]) reaches 35.4%.
The identical distribution hypothesis is equivalent to the parameter discrepancy hypothesis: Adversarial attacks on graph neural networks

Graph neural networks are vulnerable to adversarial attacks. Previous studies have mainly focused on the describing attack methodology, but few attempted to refine model hypotheses. This paper summarizes current attack theories as contradictory data hypotheses, highlighting the lacking vital precondition: distribution consistency between training and test sets. We propose an identical distribution hypothesis to address this issue and consequently establish a consistent attack model. Then we analyze the distinctive problem for a graph poisoning attack, derive the influence of perturbations in the training set on the test set, and delimit the attack feasible region. The attack model’s closed form solution is proposed and proven on the attack gradient, and we show that the proposed method based on the identical distribution hypothesis is equivalent to inducing significant changes for training parameters before and after an attack, establishing the equivalence between the hypotheses of identical distribution and parameter discrepancy. Numerical examples validate the attack gradient correctness and equivalence theorem, and experimental results verify the reasonableness of the identical distribution hypothesis and the effectiveness of the proposed method. The proposed attacks outperform current state-of-the-art attacks.
Attention Mechanism Architecture for Arabic Sentiment Analysis

This article tackles the problem of sentiment analysis in the Arabic language where a new deep learning model has been put forward. The proposed model uses a hybrid bidirectional gated recurrent unit (BiGRU) and bidirectional long short-term memory (BiLSTM) additive-attention model where the Bidirectional GRU/LSTM reads the individual sentence input from left to right and vice versa, enabling the capture of the contextual information. However, the model is trained on two types of embeddings: FastText and local learnable embeddings. The BiLSTM and BiGRU architectures are put into competition to identify the best hyperparameter set for the model. The developed model has been tested on three large-scale commonly employed Arabic sentiment dataset: large-scale Arabic Book Reviews Dataset (ABRD), Hotel Arabic-Reviews Dataset (HARD), and Books Reviews in the Arabic Dataset (BRAD). The testing results demonstrate that our model outperforms both the baseline models and the state-of-the-art models reported in the original references of these datasets, achieving accuracy scores of 98.6%, 96.19%, 95.65% for LARB, HARD, and BRAD, respectively. Furthermore, to demonstrate the generalization capabilities of our model, the performances of the model have been evaluated on three other natural language processing tasks: news categorization, offensive speech detection, and Russian sentiment analysis. The results demonstrated the developed model is language- and task-independent, which offers new perspectives for the application of the developed models in several other natural language processing challenges.
Neural Variational Gaussian Mixture Topic Model

Neural variational inference-based topic modeling has gained great success in mining abstract topics from documents. However, these topic models usually mainly focus on optimizing the topic proportions for documents, while the quality and the internal construction of topics are usually neglected. Specifically, these models lack the guarantee that semantically related words are supposed to be assigned to the same topic and are difficult to ensure the interpretability of topics. Moreover, many topical words recur frequently in the top words of different topics, which makes the learned topics semantically redundant and similar, and of little significance for further study. To solve the above problems, we propose a novel neural topic model called Neural Variational Gaussian Mixture Topic Model (NVGMTM). We use Gaussian distribution to depict the semantic relevance between words in the topics. Each topic in NVGMTM is considered as a multivariate Gaussian distribution over words in the word-embedding space. Thus, semantically related words share similar probabilities in each topic, which makes the topics more coherent and interpretable. Experimental results on two public corpora show the proposed model outperforms the state-of-the-art baselines.
Adoption of personal service robots in India

Adopting a new product depends on consumers’ product perceptions, psychographics, and cultural contexts. This study combines stimulus-organism-response (S-O-R) and two-factor models to develop a comprehensive model for consumers’ intention to adopt artificial intelligence-powered personal service robots. Data were collected from 400 consumers in India, using existing validated scales from the literature. Structural equation modelling and path analysis were used to test the model fit. Findings suggest that consumers’ intention to adopt a personal service robot is based on perceived usefulness and risk, mediated by resistance bias. Contributions to the extant academic knowledge and marketing applications are discussed.
Dilated high-resolution network driven RGB-T multi-modal crowd counting

Crowd counting aims to estimate the number of pedestrians in a scene. However, the problems of insufficient illumination and large-scale variation affect the accuracy of crowd counting. In this paper, a dilated high-resolution network (DHRNet) driven RGB-T multi-modal crowd counting model is proposed to address the above problems. In terms of the importance of RGB and thermal modalities, a thermal-main and RGB-auxiliary strategy is chosen instead of treating them equally as in previous works. In terms of the fusion of RGB and thermal modalities, a cross-modal fusion module is designed and embedded in the input feature level of DHRNet. In terms of DHRNet output feature utilization, a multilayer perceptron regression head is proposed to predict high-quality density maps. The experimental results on public datasets show that our proposed network significantly outperforms state-of-the-art methods and reaches a new level of performance. The ablation studies verify the effectiveness of the thermal-main and RGB-auxiliary strategy and the proposed modules.
Underwater image restoration for seafloor targets with hybrid attention mechanisms and conditional generative adversarial network

Underwater image restoration is of great significance in underwater vision research. Affected by the underwater scene, the original underwater image usually has problems such as the color deviation, underexposure, noise, blur and less effective information. To address the above problems, we present an underwater image restoration method for seafloor targets with a hybrid attention mechanism-based conditional generative adversarial network. Firstly, we design a U-net generator network for encoding and decoding the input image. In the skip connections of U-net, we develop a hybrid attention mechanism module that includes spatial and channel attentions to enhance the depth of the network. Then, a multi-modal loss function is designed in the generator network, which takes into account the global content, color, local texture, image gradient, and style information. Qualitative and quantitative evaluations on the reconstructed images are conducted by comparing the proposed method with many previously published works. Experimental results demonstrate that the proposed method generates more visually appealing images and provides higher objective evaluation index scores. Furthermore, ablation experiments are implemented to verify the effectiveness of the proposed hybrid attention module. Noise robustness experiments show that the proposed method also has good denoising ability. Application testing experiments prove that the proposed method has a good application prospect in the seafloor target detection area.
Algebraic coherent confluence and higher globular Kleene algebras

We extend the formalisation of confluence results in Kleene algebras to a formalisation of coherent confluence proofs. For this, we introduce the structure of higher globular Kleene algebra, a higher-dimensional generalisation of modal and concurrent Kleene algebra. We calculate a coherent Church-Rosser theorem and a coherent Newman's lemma in higher Kleene algebras by equational reasoning. We instantiate these results in the context of higher rewriting systems modelled by polygraphs.
Immersive Learning, Immersive Scenarios, and Immersive Technologies

In online learning the positive effect of using multimedia continues to grow gradually with the use of interactive video and interactive multimedia. Parallel to virtual reality, augmented reality, mixed reality, and extended reality, known as immersive technologies, are becoming popular, and the point of view for online learning has been improved and expanded. This context opened an opportunity to the concepts like immersive learning, immersive learning environments, and immersive scenarios to emerge. What are the model implementations of immersive technology use? What kind of studies have been conducted by using which immersive technologies? In order to answer all of these questions, it is aimed to systematically review the literature in the SCOPUS database between the years 2018-2022 and analyze the applied research. Thus, it will be possible to create an insight directed to the immersive learning scenarios in immersive learning environments and design the learning environments of the future in online learning.
Schoolteachers' Perspectives of Augmented Reality in Classroom Teaching

The advancement of technology has influenced our educational settings and students' learning immensely in recent decades. Augmented reality (AR) is one of them. The focus of this study was to investigate schoolteachers' perspectives of AR in classroom teaching. A qualitative method with a case study design was adopted for the study. Nineteen participants who were schoolteachers in southwest United States were solicited for interviews. The following research questions guided the study: (1) How do schoolteachers perceive the advantages of AR in teaching? (2) How do schoolteachers perceive the disadvantages of AR in teaching? (3) How do schoolteachers plan to integrate AR in classroom teaching? (4) How do schoolteachers foresee the support from the school districts in regards to the use of AR in teaching? (5) What suggestions do schoolteachers have for implementing AR in teaching? The findings showed 50 general themes and 14 significant themes. Suggestions for future research were recommended.
Metaverse Technologies and Applications in the Future of Online Learning

Metaverse, one of the most popular concepts of today, is the fictional universe in which there are many virtual reality environments. Like every emerging technology, this concept has started to be researched rapidly in the academic field. Metaverse contains important opportunities for many fields, such as education. VR technology is of great importance in the emergence of the metaverse. Especially VR headsets play a key role for metaverse in terms of immersive VR experience. In this chapter, leading VR headsets and metaverse applications are presented comparatively and analyzed systematically. It can be said that standalone VR headsets come to the fore in terms of usability and portability, and dominate almost the entire global market. Metaverse applications such as MeetinVR, Engage, Horizon Workrooms, AltspaceVR, etc., have started to gain an important place in the business life and education sector in the metaverse world. Finally, the use of metaverse in the education system of the future is discussed and potential research topics related to the concept of metaverse are presented.
Hybrid Target Selections by ”Hand Gestures + Facial Expression” for a Rehabilitation Robot

In this study we propose a "hand gesture + face expression" human machine interaction technique, and apply this technique to bedridden rehabilitation robot. "Hand gesture + Facial expression" interactive technology combines the input mode of gesture and facial expression perception. It involves seven basic facial expressions that can be used to determine a target selecting task, while hand gestures are used to control a cursor's location. A controlled experiment was designed and conducted to evaluate the effectiveness of the proposed hybrid technology. A series of target selecting tasks with different target widths and layouts were designed to examine the recognition accuracy of hybrid control gestures. An interactive experiment applied to a rehabilitation robot is designed to verify the feasibility of this interactive technology applied to rehabilitation robots. The experimental results show that the "hand + facial expression" interactive gesture has strong robustness, which can provide a novel guideline for designing applications in VR interfaces, and it can be applied to the rehabilitation robots.
Shared-Weight-Based Multi-Dimensional Feature Alignment Network for Oriented Object Detection in Remote Sensing Imagery

Arbitrarily Oriented Object Detection in aerial images is a highly challenging task in computer vision. The mainstream methods are based on the feature pyramid, while for remote-sensing targets, the misalignment of multi-scale features is always a thorny problem. In this article, we address the feature misalignment problem of oriented object detection from three dimensions: spatial, axial, and semantic. First, for the spatial misalignment problem, we design an intra-level alignment network based on leading features that can synchronize the location information of different pyramid features by sparse sampling. For multi-oriented aerial targets, we propose an axially aware convolution to solve the mismatch between the traditional sampling method and the orientation of instances. With the proposed collaborative optimization strategy based on shared weights, the above two modules can achieve coarse-to-fine feature alignment in spatial and axial dimensions. Last but not least, we propose a hierarchical-wise semantic alignment network to address the semantic gap between pyramid features that can cope with remote-sensing targets at varying scales by endowing the feature map with global semantic perception across pyramid levels. Extensive experiments on several challenging aerial benchmarks show state-of-the-art accuracy and appreciable inference speed. Specifically, we achieve a mean Average Precision (mAP) of 78.11% on DOTA, 90.10% on HRSC2016, and 90.29% on UCAS-AOD.