Reviewerly/exHarmony huggingface dataset:
Center TextSpotter: A Novel Text Spotter for Autonomous Unmanned Vehicles

Automatic text spotting can help unmanned vehicles read human texts, thus improving the safety and reliability of autonomous driving. Some existing text spotting models use inefficient region proposal networks or modules based on the recurrent neural network. Region proposal networks produce redundant anchors. Recurrent neural networks cannot perform parallel operations well. The existence of these inefficient modules prevents these models from applying in autonomous unmanned vehicles. In this paper, we propose an end-to-end two-stage text spotting model named Center TextSpotter, which is a convolution model that does not involve region proposal networks and recurrent neural network. Moreover, we develop a weakly supervised training method and a feature fusion module. The weakly supervised training method adjusts the loss by weighting the predicted labels to improve the performance of text recognition. The feature fusion module fuses the features of the proposal with the features of the proposal-context to enhance the overall performance. Our model follows the modular design principle. So it can be easily extended and modified. This paper presents an extension scheme based on graph neural network. By adding the graph neural network before the fully connected layer, the model learns better features. Experiments demonstrate that Center TextSpotter can complete the task of text spotting for autonomous unmanned vehicles commendably.
ECO-CPP: Energy constrained online coverage path planning

Some robotic platforms are expected to perform coverage path planning (CPP) on a daily basis. However, at times they may be expected to perform CPP in large unknown spaces, which are not fully coverable with the onboard limited energy supply. In these cases the platforms need to revisit the charging station multiple times, increasing mission times and the total energy needed, reducing the availability of the robots. This paper proposes a novel solution to this energy constrained online coverage path planning problem, showing effectiveness of this solution in various settings. The proposed solution is based on contour following and outperforms existing work in the literature. Additionally, this work presents a new lower bound for the minimum number of charges and energy consumed for both, offline and online, energy constrained coverage path planning problems.
A Cross-Attention BERT-Based Framework for Continuous Sign Language Recognition

Continuous sign language recognition&#x00A0;(CSLR) is a challenging task involving various signal processing techniques to infer the sequences of glosses performed by signers. Existing approaches in CSLR typically use multiple input modalities such as the raw video data and the extracted hand images to improve their recognition accuracy. However, the large modality differences make it difficult to define an integrative framework to effectively exchange and combine the knowledge obtained from different modalities such that they can complement each other for improving the framework&#x0027;s robustness against the gesture variations and background noises in CSLR. To address this issue, we propose a novel cross-attention deep learning framework named the CA-SignBERT. This framework utilizes multiple Bidirectional Encoder Representations from Transformers&#x00A0;(BERT) models to analyze the information from different modalities. Among these BERT models, we introduce a special cross-attention mechanism to ensure an efficient inter-modality knowledge exchange. Besides, an innovative weight control module is proposed to dynamically hybridize their outputs. Experimental results reveal that the CA-SignBERT framework attains state-of-the-art performance in four benchmark CSLR datasets.
Influence of blended instruction on students’ learning effectiveness: the role of Flow

The rapid development and extensive application of information and communication technologies has facilitated blended instruction, which is regarded as the "new normal" in the field of modern education and has become the focus of academic research. This study thus explored the influencing mechanism of blended instruction on students' learning effectiveness from the perspective of complementarity and conflict with the support of flow. This study collected 349 survey data from universities in Southwest China that adopted a blended instruction mode and analyzed them using the structural equation model. The results demonstrated that complementary advantages and practical conflicts in blended instruction influenced students' flow experience during the learning process. Flow experience plays an important role in blended instruction and influences positively students' cognitive, emotional, and behavioral engagement. Learning engagement impacted positively students' learning effectiveness. In addition, self-efficacy positively moderated the relationship between students' learning engagement and learning effectiveness in blended instruction. These findings contribute to related research on blended instruction. The implications and limitations of this study are discussed.
Blockchain-based secure data transmission for internet of underwater things

Internet of Things (IoTs) is an integrated network collection of heterogeneous objects which enable seamless integration between systems, humans, devices, and various other things, to support pervasive computing for smart systems. IoT-driven systems and sensors continuously ingest data resulting in an increased volume and velocity of information which can lead to critical concerns such as security of the data and scalability of the system. The Internet of Underwater Things (IoUTs) is a specific genre of IoTs in which data related to oceanic ecosystems is continuously sensed through underwater sensors. IoUT has emerged as an innovative paradigm to support smart oceans. However, there are several critical challenges which IoUT system designers must consider such as (1) scalability of the system to handle large volumes of oceanic data and (2) security of data that is transmitted from IoT sensors deployed underwater. Blockchain as a newly emerged technology and an enabling platform allows decentralized and secure transmission of data among a wide group of untrustworthy parties. This research aims to exploit blockchain technology to secure IoUT data transmission by exploiting Interplanetary File System (IPFS) method. Additionally, this study also addresses the system's scalability in two aspects, (1) scalability, and (2) security. We used a case study-based approach and performed experiments to evaluate the proposed solution's usability and efficiency in terms of query response (i.e., performance), and algorithmic execution (i.e., efficiency). The proposed solution unifies blockchain technologies to secure IoT-driven systems and provides guidelines to engineer and develop next-generation of robust and secure blockchain-aided distributed IoT systems.
Aircraft detection in satellite imagery using deep learning-based object detectors

Over the recent years, object detection in satellite imagery has become a crucial task in remote sensing applications. Specifically, the detection of aircraft is critical for military scenarios. Numerous computer vision techniques have been applied for this problem, however, robust and efficient detection of aircraft in satellite imagery poses several challenges, such as, variance of color, size, aspect ratio and orientation of aircraft and complex backgrounds. In this paper, we provide an in-depth review of deep learning methods for object detection applied to aircraft detection including both conventional and state-of-the-art techniques. We also provide a comprehensive experimental and comparative analysis of deep learning-based object detectors including Region-based CNN (RCNN), Fast RCNN, Faster RCNN and You Only Look Once (YOLO) encapsulating the most cited feature extraction networks including Alexnet, VGG-16, Resnet-18, Resnet-50, Resnet-101 and Inception-v3. The detailed quantitative comparison allows for selection of the most suitable set of object detector, backbone feature extraction network and hyperparameters for aircraft detection in satellite imagery with trade-off options between precision and detection time. The evaluation is based on mean average precision (mAP), precision versus recall, time complexity and computational complexity. Finally, we draw our conclusions and identify promising future work.
A Local–Global Estimator Based on Large Kernel CNN and Transformer for Human Pose Estimation and Running Pose Measurement

Running pose in the crowd can serve as an early warning of most abnormal events (e.g., chasing, fleeing and robbing), which can be achieved by human behavior analysis based on human pose measurement. Although deep convolutional neural networks (CNNs) have achieved impressive progress on human pose estimation, how to further improve the trade-off between estimation accuracy and speed remains an open issue. In this work, we first propose an efficient local-global estimator for human pose estimation (called LGPose). Then based on the keypoints estimated by our LGPose, a simple regression model is defined by using the geometry of the joints to achieve fast and accurate running pose measurement. To model the relationships between the human keypoints, visual transformer (ViT) encoder is adopted to learn the long-range interdependencies between them at the pixel level. However, the operation of transformer encoder is based on sequence processing that linearly projects 2D image patches to 1D tokens. It loses the important local information. Yet, locality is crucial since it has relevance to lines, edges and shapes. To learn the locality, we design effective CNN modules, rather than the original fully-connected network, into the feedforward module of ViT. Experiments on MPII and COCO Keypoint val2017 dataset show that the proposed LGPose achieves the best trade-off among the compared state-of-the-art methods. Moreover, we build a lightweight running movement dataset to verify the effectiveness of our LGPose. Based on the human pose estimated by our LGPose, we propose a regression model to measure running pose with an accuracy of 86.4% without training any other classifier. Our source codes and running dataset will be made publicly available.
Journal quality criteria: Measurement and significance

The landscape of scientific communication imposes obvious requirements on its actors, which are not always unambiguously interpreted by the latter. The basic criterion of scientific communication is its quality. Since scientific journals serve as the main channel for rational debates based on facts, their quality level seems to be the most significant for the development of scientific knowledge. Today, scientific journals are undergoing a massive transformation: from changing the format of content presentation to becoming a body of collective communication. These changes cannot but affect the quality characteristics of journals. Since the changes are multilateral in nature, the scientific, editorial, and publishing communities have not formed an unambiguous opinion about which criteria of the journal characterize its quality aspects, and which can be attributed to “formal”, “technical”. In this article, the authors, based on Russian and international expert opinion, analyze the quality criteria for journals. Based on the data of the questionnaire developed by the authors, which considers the categories and criteria of the Scopus expert system, a survey of editors (n = 130) of Russian scientific journals was conducted. The questionnaire was completed anonymously, and the study participants were made aware of its objectives. The data obtained made it possible to analyze the editors’ ideas about the qualitative characteristics of a scientific journal and their influence on its promotion. A sufficiently deep understanding by the editors of the essence and specifics of the analyzed characteristics was recorded. At the same time, certain aspects require targeted work on their optimization and development. There is also an obvious need for further research on the topic, considering the subject field of journals traditions.
Layer-Wise Fast Adaptation for End-to-End Multi-Accent Speech Recognition

The variety and complexity of accents pose a huge challenge to robust Automatic Speech Recognition&#x00A0;(ASR). Some previous work has attempted to address such problems, however most of the current approaches either require prior knowledge about the target accent, or cannot handle unseen accents and accent-unspecific standard speech. In this work, we aim to improve multi-accent speech recognition in the end-to-end&#x00A0;(E2E) framework with a novel layer-wise adaptation architecture. Firstly, we propose a robust deep accent representation learning architecture to obtain accurate accent embedding, and some advanced schemes are designed to further boost the quality of accent embeddings, including phone posteriorgram&#x00A0;(PPG) feature, TTS based data augmentation in the training stage, test-time augmentation and multi-embedding fusion in the testing stage. Then, the layer-wise adaptation with accent embeddings is developed for fast accent adaptation in ASR, and two types of adapter layers are designed, including the gated adapter layer and multi-basis adapter layer. Compared to the usual two-pass adaptation, these adapter layers are injected between the ASR encoder layers to encode the accent information in ASR flexibly, and perform fast adaption on the corresponding speech accent. The experiments on Accent AESRC corpus show that the proposed deep accent representation learning can capture accurate accent knowledge, and get high performance on accent classification. The new layer-wise adaptation architecture with the accurate accent embedding outperforms the other traditional methods, and obtains consistent <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>15&#x0025; relative word error rate&#x00A0;(WER) reduction on all kinds of testing scenarios, including seen accents, unseen accents and accent-unspecific standard speech.
Application of Virtual Reality Technology in Adolescent Mental Health Science Education

The 21st century is a time of rapid advances in science and technology, and scientific knowledge and literacy are highly relevant to the development of the country. Science education is the main means of learning scientific and cultural knowledge, and its teaching quality determines the formation of national scientific literacy. In addition, information technology teaching assistants are one of the main research areas in the field of educational technology, and they have long attracted the attention of researchers in the field of education. Based on these facts, this research introduces virtual reality technology into science education to make a favorable influence on the psychological well-being of young people, through color feature extraction, gray-level cooccurrence matrix feature extraction, Marching Cubes algorithm, and image synthesis technology to be scientifically and effectively combined with science education, and the popularization rate of students receiving science education has increased by 26.9%. As a result, the mental health of students was improved, and the number of students with subhealth mentality decreased by 16.9%. Adolescence is a transitional period for adolescents from immaturity to maturity. Adolescents are prone to fall into depression, anxiety, rebellion, autism, and other emotions, which will have a damaging impact on the growth of adolescents’ mental health. The use of virtual reality technology in science education has opened up a new field of virtual teaching and learning. This pattern of education will make a huge influence on the future growth of education and the creation of teaching concepts.
Monkeypox Diagnostic-Aid System with Skin Images Using Convolutional Neural Networks

Background: Monkeypox is similar to classical smallpox and, until now, isolated cases have been detected in Africa, but in 2022 it has spread around the world and was declared an international health emergency by the WHO at the end of July.Objective: In this work, we collect monkeypox images from official government websites and public datasets (as well as images of healthy people and people with other skin diseases) and create the MonkeypoxSkin dataset (publicly available), which contains close-up images of the skin where you can see the posthulas formed by the disease. With these images, we design, implement, and evaluate several diagnostic aid systems for monkeypox disease.Methods: The classifiers developed and evaluated are based on Convolutional Neural Networks models and some ensembles composed of a combination of those models, obtaining automatic classification results between healthy, monkeypox and other skin damages, given a close skin tissue image.Results: The results show a system accuracy greater than 93% when using a unique CNN model (VGG-19 and ResNet50), and greater than 98% when using a CNN ensemble formed by ResNet50, EfficientNet-B0 and MobileNet-V2.Conclusions: In contrast to the work found to date, this classifier focusses on disease classification based on a low-resolution image obtained from the skin surface, so it can be integrated into a mobile application for mass screening. To do this, it has been necessary to design a proprietary dataset that is publicly available for use. Furthermore, the results obtained improve the accuracy of the previous works.
Underwater image enhancement based on noise residual and color correction aggregation network

Underwater images are easily affected by absorption and scattering of light, resulting in color cast, blurriness, marine snow and other degradations. However, many existing deep learning methods treat noise removal and essential color preservation as a whole; thus, results often fall short of expectations. To address this issue, we propose a network with two sub-network branches to divide the underwater image enhancement (UIE) into two subtasks: noise removal and color correction. Specifically, we first design a noise residual network that uses dual attention fusion modules to focus on the noise of the input image and use a residual structure to remove it. Simultaneously, we design a color correction network that consists of a reinforced LeWin Transformer block-based encoder and decoder to improve the color cast. Additionally, our proposed block can reinforce the long-range dependencies and local context, and can be incorporated into existing denoising methods to boost their performance. Experimental results on UIE show that our proposed method has excellent noise-removal ability on underwater images while preserving essential colors. In addition, the effectiveness of each component of our network is further validated through ablation studies.
Combination of Deep Cross-Stage Partial Network and Spatial Pyramid Pooling for Automatic Hand Detection

The human hand is involved in many computer vision tasks, such as hand posture estimation, hand movement identification, human activity analysis, and other similar tasks, in which hand detection is an important preprocessing step. It is still difficult to correctly recognize some hands in a cluttered environment because of the complex display variations of agile human hands and the fact that they have a wide range of motion. In this study, we provide a brief assessment of CNN-based object identification algorithms, specifically Densenet Yolo V2, Densenet Yolo V2 CSP, Densenet Yolo V2 CSP SPP, Resnet 50 Yolo V2, Resnet 50 CSP, Resnet 50 CSP SPP, Yolo V4 SPP, Yolo V4 CSP SPP, and Yolo V5. The advantages of CSP and SPP are thoroughly examined and described in detail in each algorithm. We show in our experiments that Yolo V4 CSP SPP provides the best level of precision available. The experimental results show that the CSP and SPP layers help improve the accuracy of CNN model testing performance. Our model leverages the advantages of CSP and SPP. Our proposed method Yolo V4 CSP SPP outperformed previous research results by an average of 8.88%, with an improvement from 87.6% to 96.48%.
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Some of the data collected from practical applications are usually heavily corrupted. In subspace clustering, the common method is to use the specific regularization strategy to correct these corrupted data by virtue of the prior knowledge, which could result in a suboptimal clustering solution. To alleviate the problem, we develop a novel formulation named subspace clustering via joint ℓ1,2 and ℓ2,1 (L12-21) norms (SCJL12-21). Specifically, we identify and exclude the heavily corrupted data points (unimportant data points) from participating in the linear representation of other points by imposing the ℓ1,2 norm on the representation matrix, and improve the robustness to outliers by enforcing the ℓ2,1 constraint on the error matrix. The joint ℓ1,2 and ℓ2,1 minimization leads to a good representation matrix which enhances the clustering performance. Related ℓ1,2 and ℓ2,1 norm constrained optimization problem is solved by utilizing the augmented Lagrange multiplier method. The effectiveness of the proposed method is demonstrated through experiments on the constructed datasets as well as the two practical problems of motion segmentation and face clustering.
3D surface reconstruction scheme for dynamic objects: Principle and implementation

• A new dual-step phase shift method is proposed for wrapped phase calculation in dynamic 3-D measurement. • Dual-frequency dual-step phase shift method is further proposed for unwrapped phase calculation. • The influence of ambient light on the proposed dual-frequency dual-step phase shift method is analyzed. This paper introduces a high-speed, low-cost and easy-to-implement three dimensional (3D) surface measurement approach for dynamic objects. First, we proposed a new dual-frequency dual-step phase shift method to extract unwrapped phase, which has high accuracy and few fringes. We also analyzed the influence of ambient light on the proposed method. Secondly, we compare a variety of dither binarization methods and chose a suitable error diffusion kernel. Finally, we introduce the implementation of the dynamic 3D measurement system. Experiments on 3D measurement of dynamic objects demonstrate the valid of the proposed scheme. Further comparison of the phase accuracy between the proposed method and existing methods confirms the robustness of the proposed dual-frequency dual-step phase shift method.