Reviewerly/exHarmony huggingface dataset:
Visual Information Computing and Processing Model Based on Artificial Neural Network

This paper analyzes the parallel and serial information processing structure of visual system and proposes a visual information processing model with three layers: visual receptor layer, visual information conduction and relay layer, and information processing layer of visual information computing and processing area. Based on the analysis, abstraction, and simplification of the biological prototype of each layer in the visual system, a framework model of an artificial neural system corresponding to the visual system is proposed. An artificial neural network model is proposed to simulate the mechanism of visual attention. A network model is formed by introducing the saliency mask map as additional information on the benchmark network, and the selective enhancement operation is performed on the extracted features in different regions according to the mask map. The experimental results show that the visual computing processing network model can effectively improve the classification performance of the network when the appropriate saliency mask is used. The visual information computing and processing model network can work effectively for different data sets and different structures of the benchmark network, which is a universal network model. The complexity of visual information computing and processing model network is very small, and the improvement of network performance is not at the cost of increasing model complexity, but in the way of improving network efficiency. The performance of artificial neural network visual information computation and processing model is directly related to the performance of saliency map used as mask map.
Investigating Adaptive Robot Tutoring in a Long-Term Interaction in Higher Education

Learning in universities challenges students to engage in self-directed learning, which requires a high degree of self-motivation while individual support by teachers is limited. Research on social robots has already demonstrated their potential to support students in their learning process. In this paper, we focus on the benefits of adaptivity of a robotic tutor in a higher education scenario. To this end, we conducted a field study over three sessions over the course of a semester and implemented two conditions (adaptive and non-adaptive) of a robotic tutor to support students with exam preparation. After participant learned with both version in random order in the first two sessions, their preferred condition was used in the third session. Our results show that significantly more students preferred to learn with the adaptive robotic tutor. Additionally, participation resulted in significantly better exam performance compared to the average of the course. However, there was no significant difference in the learning experience such as motivation or need satisfaction between conditions.
Reimagining RViz: Multidimensional Augmented Reality Robot Signal Design

From RViz to augmented reality (AR), a wide variety of robot signal visualizations exist for conveying robot capabilities. Many of the visualizations designed for AR, however, have not isolated multiple salient Virtual Design Elements (VDEs) for a given signal and comparatively evaluated combinations of those VDEs. To address this, we identify multiple VDEs for AR signaling of the following core robot capabilities: navigation, light detection and ranging (LiDAR), camera, face detection, audio localization, and natural language processing. We evaluated each signal's VDE combinations with an Amazon Mechanical Turk study (n=150) where participants watched 4 videos for each signal (consisting of 2 independent VDE choices) and rated the clarity and visual appeal of each signal. The results define a set of the most clear and visually appealing signal visualization designs and inform about interaction effects among VDEs. The resulting VDEs offer design insights and a baseline for continued research into AR robot capability signalling.
Effects of Artificial Intelligence and Virtual Reality in Martial Arts Sports on Students’ Physical and Mental Health

As a treasure left over from Chinese traditional culture, martial arts have a long history and lasts for a long time. With the country’s promotion and support of martial arts culture, martial arts have also attracted more and more people’s attention. In order to carry forward the martial arts culture, the combination of martial arts and sports has formed a set of martial arts sports suitable for today’s students, such as martial arts aerobics. It is used as a class exercise for students, and basically, every student in the school has learned it and practiced it often. However, with the development of the times, it is difficult for today’s students to arouse students’ enthusiasm for martial arts sports for traditional martial arts sports. Artificial intelligence technology and virtual reality, as products of today’s digital age, attract the attention and use of students with their unique features. This also means that the reform of martial arts sports based on artificial intelligence and virtual reality technology is imperative in order to make more students regain their enthusiasm for martial arts sports. However, the specific impact of artificial intelligence and virtual reality on the physical and mental development of students in martial arts sports is a problem to be confirmed by research. Through in-depth research on artificial intelligence and virtual reality technology, this paper shows that artificial intelligence and virtual reality martial arts sports can increase the lung capacity of boys by about 14% and girls by 16%; the effect on physical fitness can increase the lower body strength of boys by 3% and the flexibility of girls by 30%; as for the impact on mental health, it can be concluded that the emotional and cognitive dimensions of boys and girls have increased by about 19% compared with the previous values.
Target-Cognisant Siamese Network for Robust Visual Object Tracking

Siamese trackers have become the mainstream framework for visual object tracking in recent years.However, the extraction of the template and search space features is disjoint for a Siamese tracker, resulting in a limited interaction between its classification and regression branches.This degrades the model capacity accurately to estimate the target, especially when it exhibits severe appearance variations.To address this problem, this paper presents a target-cognisant Siamese network for robust visual tracking.First, we introduce a new target-cognisant attention block that computes spatial cross-attention between the template and search branches to convey the relevant appearance information before correlation.Second, we advocate two mechanisms to promote the precision of obtained bounding boxes under complex tracking scenarios.Last, we propose a max filtering module to utilise the guidance of the regression branch to filter out potential interfering predictions in the classification map.The experimental results obtained on challenging benchmarks demonstrate the competitive performance of the proposed method. Research Highlights (Required)It should be short collection of bullet points that convey the core findings of the article.It should include 3 to 5 bullet points (maximum 85 characters, including spaces, per bullet point.)• A new Target-Cognisant Siamese-based anchor-free tracker.• The proposed method computes cross-spatial attention for refining the measurement of spatial similarity.• Two tracking mechanisms are used to promote the precision of bounding box prediction.• A max filtering module is proposed to filter out similar distractors.• Our method achieves competitive performance on several tracking datasets.
Segmentation of Breast Tubules in H&amp;E Images Based on a DKS-DoubleU-Net Model

The formation of breast tubules plays an important role in the pathological grading of breast cancer. Breast tubules surrounded by a large number of epithelial cells are located in the subcutaneous tissue of the chest. The shapes of breast tubules are various, including tubular, round, and oval, which makes the process of breast tubule segmentation a difficult task. Deep learning technology, capable of learning complex data structures via efficient representation, could help pathologists accurately detect breast tubules in hematoxylin and eosin (H&amp;E) stained images. In this paper, we propose a deep learning model named DKS-DoubleU-Net to accurately segment breast tubules with complex appearances in H&amp;E images. The proposed DKS-DoubleU-Net model suggests using a DenseNet module as the encoder of the second subnetwork of DoubleU-Net, which utilizes dense features between layers and strengthens the propagation of features extracted in all previous layers, in order to better discover the intrinsic characteristics of breast tubules with complex structures and diverse shapes. Moreover, a feature fusing module called Kernel Selecting Module (KSM) is inserted before each output layer of the two U-Net branches of the DoubleU-Net, to implement a multiscale feature fusion via a self-adaptive kernel selecting for the sake of accurate segmentation of breast tubules in different sizes. The experiments on the public BRACS dataset and a private clinical dataset have shown that our model achieves better segmentation performance, compared to the state-of-art models of U-Net, DoubleU-Net, ResUnet++, HRNet, and DeepLabV3+. Specifically, on the public BRACS dataset, our method produced an F1-Score of 92.98%, which outperforms the F1-Score of U-Net, DoubleU-Net, and HRNet by 4.24%, 0.37%, and 1.68%, respectively, and is much better than performances of DeepLabV3+ and ResUnet++ by 7.83% and 23.84%, respectively. On the private clinic dataset, the proposed model achieved an F1-Score of 73.13%, which has shown an improvement of 10.31%, 1.89%, 4.88%, 15.47%, and 31.1% to the performances of the U-Net, DoubleU-Net, HRNet, DeepLabV3+, and ResUnet++, respectively. Superior performance could also be observed when comparing the proposed DKS-DoubleU-Net with the others using the metrics of Dice and mIou.
Time-Multiplexed Coded Aperture and Coded Focal Stack -Comparative Study on Snapshot Compressive Light Field Imaging

A light field (LF), which is represented as a set of dense, multi-view images, has been used in various 3D applications. To make LF acquisition more efficient, researchers have investigated compressive sensing methods by incorporating certain coding functionalities into a camera. In this paper, we focus on a challenging case called snapshot compressive LF imaging, in which an entire LF is reconstructed from only a single acquired image. To embed a large amount of LF information in a single image, we consider two promising methods based on rapid optical control during a single exposure: time-multiplexed coded aperture (TMCA) and coded focal stack (CFS), which were proposed individually in previous works. Both TMCA and CFS can be interpreted in a unified manner as extensions of the coded aperture (CA) and focal stack (FS) methods, respectively. By developing a unified algorithm pipeline for TMCA and CFS, based on deep neural networks, we evaluated their performance with respect to other possible imaging methods. We found that both TMCA and CFS can achieve better reconstruction quality than the other snapshot methods, and they also perform reasonably well compared to methods using multiple acquired images. To our knowledge, we are the first to present an overall discussion of TMCA and CFS and to compare and validate their effectiveness in the context of compressive LF imaging.
Early in mission Landsat 9 geometric performance

Landsat 9 (L9) was launched on September 27, 2021, from Vandenberg Space Force Base in California. The U. S. Geological Survey (USGS) released Level-1 data, geometrically orthorectified and radiometrically calibrated imagery in digital numbers that can be scaled to Top-of-Atmosphere reflectance, and Level-2 data, geometrically orthorectified and radiometrically calibrated surface reflectance imagery, to the public on February 10, 2022. From September 27, 2021 to early January of 2022, the satellite and its two instruments, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS), were in their commissioning phase, updating key radiometric and geometric calibration parameters for both the spacecraft and the instruments. The data acquired during the commissioning phase of the spacecraft and instruments were reprocessed with the newly determined post-launch calibration parameters prior to the releasing of the data to the public. After the public release of the data, the calibration parameters of the sensors and the spacecraft continue to be monitored to ensure the data released to the public is of the same high quality as previous Landsat data products. This paper discusses three key geometric performance aspects of the L9 spacecraft and its instruments during its early mission time frame (September 27, 2021 to June 27, 2022) including geodetic accuracy, geometric accuracy, and within band registration accuracy of the L9 products generated.
Sociological Factors of the Influence of the Educational Environment on the Professional Burnout of University Teachers

The article presents the results of the author's research conducted in the universities of the city of Novosibirsk. The author proceeds from the fact that the modernisation of education has an impact not only on the educational process as part of the optimisation of teaching, conditions and quality of education, but also on its participants, including teachers.&#x0D; The implementation of the national project &amp;amp;quot;Education&amp;amp;quot;, the transition to digital technologies in education, modern socio-political and economic realities lead to fundamental changes in the conditions for the functioning of higher educational institutions, an increase in the requirements for the quality of professional training and the emergence of new functions and responsibilities of teachers. Practice shows that innovations have both a positive and a negative impact on the educational environment and teachers themselves as key subjects in the implementation of educational activities. Based on the results of the analysis of the data collected during the field study, the author comes to the conclusion that professional burnout of a teacher as a response syndrome, manifested in exhaustion, reduced productivity, and depersonalisation, is a consequence of a system of factors. Based on the results of the study, the author considers a wide range of professional risks, highlighting the causes and managerial variables that contribute to the manifestation of professional burnout among teachers. The research tools allowed to determine the causes of professional burnout, the presence of symptoms and signs, the level of exhaustion, depersonalisation and reduction. The analysis of the data obtained led to the conclusion that the complication of the conditions of professional activity due to the action of a system of sociological factors leads to an increase in the number of teachers with a pronounced degree of professional burnout, regardless of the level of the position held, while the dependence of the degree of burnout on the experience of teaching was established. The areas of professional activity that are most vulnerable to deformations and possible measures for the prevention and overcoming of the professional burnout syndrome are also identified. It has been established that the professional burnout of teachers negatively affects the quality of the educational process, leads to problems in the staffing of universities and requires the development of managerial decisions to optimise the educational environment in order to minimise professional risks and reduce the negative impact of factors on the health and activities of university teachers.
Comparison of image registration methods for combining laparoscopic video and spectral image data

Laparoscopic procedures can be assisted by intraoperative modalities, such as quantitative perfusion imaging based on fluorescence or hyperspectral data. If these modalities are not available at video frame rate, fast image registration is needed for the visualization in augmented reality. Three feature-based algorithms and one pre-trained deep homography neural network (DH-NN) were tested for single and multi-homography estimation. Fine-tuning was used to bridge the domain gap of the DH-NN for non-rigid registration of laparoscopic images. The methods were validated on two datasets: an open-source record of 750 manually annotated laparoscopic images, presented in this work, and in-vivo data from a novel laparoscopic hyperspectral imaging system. All feature-based single homography methods outperformed the fine-tuned DH-NN in terms of reprojection error, Structural Similarity Index Measure, and processing time. The feature detector and descriptor ORB1000 enabled video-rate registration of laparoscopic images on standard hardware with submillimeter accuracy.
Towards an automatic generation of natural gestures for a storyteller robot

Natural gesturing is very important for the credibility of social robots. It is even more crucial for storytelling robots since the expression, emotion and emphasis must be highlighted. In this paper we propose a hybrid gesture generation approach for a storytelling robot that combines beats automatically generated by a GAN with a probabilistic semantic related gesture insertion system. Beats are executed according to a probability based on the duration of the sentences and semantic gesture insertions are dependent of the previous occurrences of the gestures associated to the words. The polarity of the text is extracted and affects several features of the motion to arouse emotion. A qualitative evaluation of robot behavior is conducted and confirms the approach as a promising one as storytelling system.
Step-by-Step Task Plan Explanations Beyond Causal Links

Explainable robotics refers to the challenge of designing robots that can make their decisions transparent to humans. Recently, a number of approaches to task plan explanation have been proposed, which enable robots to explain each step in their plan to humans. These approaches have in common that they are based on the causal links in the plan. We discuss problems with using causal links for plan explanation. Particularly, their inability to distinguish enabling actions from requiring actions can lead to counter-intuitive explanations. We propose an extension that allows for making this relevant distinction and demonstrate how it can be applied to create a robot that explains its actions.
Application of Generative Adversarial Networks in Gesture Recognition

Aiming at the problem of insufficient accuracy of gesture recognition under the condition of small samples, a gesture generation method based on Generative Adversarial Networks is proposed to expand the dataset. For the expansion of gesture dataset, the idea of adversarial training in Generative Adversarial Networks is adopted, the discriminator model of deep convolution and the generator model of deep transpose convolution are designed respectively, the training process is optimized by using the way of adaptive learning rate, and the gesture is generated according to the gesture image in the gesture dataset created by the user. Then the accuracy is verified by using the real gesture image and the generated gesture image. With that, based on the complex characteristics of the algorithm for generating gesture image, it is proposed to directly generate the Fourier Descriptors of the image, so that the gesture has translation, scaling and rotation invariance, and their accuracy and training time are tested respectively. The experimental results show that comparing with generating gesture images, the training time of directly generating Fourier Descriptors is shorter and the recognition accuracy is higher.
Immersive Human-Robot Interaction for Dexterous Manipulation in Minimally Invasive Procedures

Interaction between humans and robots is inevitable in robotic surgeries. The purpose of this work is to enhance human-robot interaction for computer-assisted minimally invasive surgeries through immersive technology. Our developed system includes a robotic manipulator designed to perform minimally invasive surgeries and a mixed reality-based interface that facilitates immersive interaction between a human operator and a collaborative robot. The system provides visual and dexterous augmentation through the immersive projection of the endoscopic view in proximity to the surgical site and a virtual remote-center-of-motion (RCM) intuitively specified by the users. Experimental observation demonstrated sub-millimeter and sub-degree accuracy between the spatial representation of the physical robot and its virtual model. Safety design and robot intelligence to enhance the interaction between humans and robots via MR technology will further be developed to prepare the technology for actual clinical use in the long run.
Similarity Mask Mixed Attention for YOLOv5 Small Ship Detection of Optical Remote Sensing Images

Ship detection has always been an important and challenging task. Small ship targets and complex backgrounds in optical remote sensing images can both lead to false alarms and missed alarms in detection. The YOLO series algorithms have been widely used in optical remote sensing ship target detection, which has the advantage of fast detection speed. However, the YOLO series algorithms have poor detection performance when facing small targets. Therefore, we propose a new algorithm, SMMA-YOLOv5, which can improve the model detection accuracy without significantly increasing the model size. We first introduce a self-attention mechanism to replace some of the convolutional layers to capture the global information of the feature map. Second, we integrate an efficient channel attention (ECA) model to the self-attention mechanism to enable information interaction between channels without adding additional computational overhead. Furthermore, we propose a new similarity mask structure to filter out the invalid regions in the feature map based on the elements’ similarity. The experiments on the public MASATI ship dataset indicate that SMMA-YOLOv5 improves Precision by 3.9%, Recall by 5.3%, and AP by 5.4%, and prove the effectiveness of the algorithm while maintaining real-time detection.