Reviewerly/exHarmony huggingface dataset:
FedSA: A staleness-aware asynchronous Federated Learning algorithm with non-IID data

This paper presents new asynchronous methods to the Federated Learning (FL), one of the next-generation paradigms for Artificial Intelligence (AI) systems. We consider the two-fold challenges lay ahead. First, non-IID (non-Independent and Identically Distributed) data across devices cause unstable performance. Second, unreliable and slow environments not only slow the convergence but also cause staleness issues. To address these challenges, this study uses a bottom-up approach for analysis and algorithm design. We first reformulate FL by unifying both synchronous and asynchronous updating schemes with an asynchrony-related parameter. We theoretically analyze this new form and find practical strategies for optimization. The key findings include: 1) a two-stage training strategy to accelerate training and reduce communication overhead; 2) strategies of choosing key hyperparameters optimally for these stages to maintain efficiency and robustness. With these theoretical guarantees, we propose FedSA (Federated Staleness-Aware), a novel asynchronous federated learning algorithm. We validate FedSA on different tasks with non-IID/IID and staleness settings. Our results indicate that, given a large proportion of stale devices, the proposed algorithm presents state-of-the-art performance by outperforming existing methods on both non-IID and IID cases.
Underwater Image Restoration and Enhancement Based on a Fusion Algorithm With Color Balance, Contrast Optimization, and Histogram Stretching

A fusion algorithm is proposed for the restoration and enhancement of underwater images. Color balance, contrast optimization and histogram stretching are carried out. To alleviate the effect of color shift in an underwater image, the scalar values of R, G, B channels are renewed so that the distributions of the three channels in histogram are similar. Instead of refining the transmittance in dark channel prior based restoration, an optimized contrast algorithm is employed by which the optimal transmittance is determined. To further improve the brightness and contrast of underwater images, a histogram stretching algorithm based on the red channel is given. To verify the effectiveness of the proposed fusion algorithm, experimental underwater images are treated. Results show that the quality of underwater images is improved significantly, both in term of subjective visual effect and objective evaluation. The proposed underwater image processing strategy is also compared with some popular techniques. Comparison results indicate the advantage of the proposed strategy over others.
Research on College Physical Education and Sports Training Based on Virtual Reality Technology

With the continuous development of society and the rapid development of science and technology, virtual reality technology is also developing rapidly. It has been widely used in all walks of life and is playing an irreplaceable role. Modern education has also begun to integrate with science. Sports are also constantly deepening the application of information technology. In order to make students have a new understanding of college physical education and sports training and improve the technical level and training quality of college sports athletes, this paper studies the application of virtual reality technology in physical education and sports training. In this paper, the semisupervised framework is used to implement motion input and interactive virtual scene algorithms. First, based on Q statistics, virtual simulation and differential selection algorithms are used to select athletic students with strong autonomous learning ability and then use the classifier’s neighbor confidence. The formula selects the student with the highest learning level and marks it. Experimental results show that this method can effectively assist physical education activities and improve students’ learning efficiency. Students’ efficiency in sports has increased by 30%. At the same time, 2/3 of people believe that their interest in sports training has increased 80% and another 90% of college coaches believe that the use of virtual reality technology in physical education is very necessary, which can improve the technical level and training quality of college sports athletes and contribute to the reserve of Chinese competitive sports talents.
Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning

Learning adaptable policies is crucial for robots to operate autonomously in our complex and quickly changing world. In this work, we present a new meta-learning method that allows robots to quickly adapt to changes in dynamics. In contrast to gradient-based meta-learning algorithms that rely on second-order gradient estimation, we introduce a more noise-tolerant Batch Hill-Climbing adaptation operator and combine it with meta-learning based on evolutionary strategies. Our method significantly improves adaptation to changes in dynamics in high noise settings, which are common in robotics applications. We validate our approach on a quadruped robot that learns to walk while subject to changes in dynamics. We observe that our method significantly outperforms prior gradient-based approaches, enabling the robot to adapt its policy to changes based on less than 3 minutes of real data.
ProxEmo: Gait-based Emotion Learning and Multi-view Proxemic Fusion for Socially-Aware Robot Navigation

We present ProxEmo, a novel end-to-end emotion prediction algorithm for socially aware robot navigation among pedestrians. Our approach predicts the perceived emotions of a pedestrian from walking gaits, which is then used for emotion-guided navigation taking into account social and proxemic constraints. To classify emotions, we propose a multi-view skeleton graph convolution-based model that works on a commodity camera mounted onto a moving robot. Our emotion recognition is integrated into a mapless navigation scheme and makes no assumptions about the environment of pedestrian motion. It achieves a mean average emotion prediction precision of 82.47% on the Emotion-Gait benchmark dataset. We outperform current state-of-art algorithms for emotion recognition from 3D gaits. We highlight its benefits in terms of navigation in indoor scenes using a Clearpath Jackal robot.
Edge Intelligence (EI)-Enabled HTTP Anomaly Detection Framework for the Internet of Things (IoT)

In recent years, with the rapid development of the Internet of Things (IoT), various applications based on IoT have become more and more popular in industrial and living sectors. However, the hypertext transfer protocol (HTTP) as a popular application protocol used in various IoT applications faces a variety of security vulnerabilities. This article proposes a novel HTTP anomaly detection framework based on edge intelligence (EI) for IoT. In this framework, both clustering and classification methods are used to quickly and accurately detect anomalies in the HTTP traffic for IoT. Unlike the existing works relying on a centralized server to perform anomaly detection, with the recent advances in EI, the proposed framework distributes the entire detection process to different nodes. Moreover, a data processing method is proposed to divide the detection fields of HTTP data, which can eliminate redundant data and extract features from the fields of an HTTP header. Simulation results show that the proposed framework can significantly improve the speed and accuracy of HTTP anomaly detection, especially for unknown anomalies.
The Impact of Immersive Virtual Reality on EFL Learners’ Listening Comprehension

Virtual reality via mobile-rendered head-mounted displays (MVR) has emerged as a valuable language learning tool. However, research has yet to fully access its effects on English as a Foreign Language (EFL) listening. Therefore, this study investigated the impact of MVR on EFL learners’ listening comprehension. Seventy-two seventh graders in Taiwan were recruited and randomly assigned to an experimental (MVR player) or control (video watcher) group. The MVR players played the language learning VR app Mondly, using a mobile-rendered head-mounted display. The video watchers watched a walkthrough video of Mondly on a PC screen. Listening comprehension tests, a presence questionnaire and interviews were used to evaluate the participants’ listening comprehension, sense of presence, and perception of MVR-assisted EFL listening. The results showed the MVR players’ listening comprehension and retention was significantly higher than the video watchers’. The interviews revealed that the majority of the MVR players found MVR-assisted EFL listening engaging and beneficial. MVR offered learners access to simulated, interactive, and immersive virtual environments to perform authentic learning activities, helping them activate prior knowledge and make appropriate inferences. Furthermore, virtual presence in MVR brought learner involvement from the fringe to the center, prevented cognitive overload, reduced anxiety, and thus aided comprehension.
Multi-Source Adversarial Sample Attack on Autonomous Vehicles

Deep learning has an impressive performance of object detection and classification for autonomous vehicles. Nevertheless, the essential vulnerability of deep learning models to adversarial samples makes the autonomous vehicles suffer severe security and safety issues. Although a number of works have been proposed to study adversarial samples, only a few of them are designated for the scenario of autonomous vehicles. Moreover, the state-of-the-art attack models only focus on a single data source without considering the correlation among multiple data sources. To fill this blank, we propose two multi-source adversarial sample attack models, including the parallel attack model and the fusion attack model to simultaneously attack the image and LiDAR perception systems in the autonomous vehicles. In the parallel attack model, adversarial samples are generated from the original image and LiDAR data separately. In the fusion attack model, the adversarial samples of image and LiDAR can be generated from a low-dimension vector at the same time by fully exploring data correlation for data fusion and adversarial sample generation. Through comprehensive real-data experiments, we validate that our proposed models are more powerful and efficient to break down the perception systems of autonomous vehicles compared with the state-of-the-art. Furthermore, we simulate possible attack scenarios in Vehicular Ad hoc Networks (VANETs) to evaluate the attack performance of our proposed methods.
Scope of Medical Bots in Clinical Domain

There is no question that Medical Science has come very far over the past 5 years but still when any human treats any patient there is always a chance of human error. This lead to the use of artificial intelligence combined with medical robots to make them smarter and error-free to reduce the mishaps caused by the doctors. Now not only getting health care has become safer but also has solved the problem of hospitals being understaffed. The chapter consists of a basic introduction of medical bots about why they came into existence and their features. It also gives a vivid description about what is artificial intelligence and how it is being used together with machines to make them perform better. The chapter then explains the types of medical bots available out there in the world and we have made a broad classification of the types of medical bots. Discussion of the types with examples and their need is thoroughly explained in the later part of the chapter. We then discuss the ongoing projects and current development going on in this field with examples. We further discuss the key technologies used or present in the world which are helping to make better bots for this industry. Lastly we discussed the major challenges faced by the scientists in this challenging field which makes it even difficult to achieve.
SPinPong - Virtual Reality Table Tennis Skill Acquisition using Visual, Haptic and Temporal Cues

Learning an advanced skill in sports requires a huge amount of practice and players also have to overcome both physical difficulties and the dullness of repetitive training. Returning a fast spin shot in table tennis could be taken as an example, as athletes need to judge the spin type and decide the racket pose within a second, which is difficult for beginners. Therefore, in this paper, we show how to design an intuitive training system to acquire this specific skill using different cues in Virtual Reality (VR). Using VR, we can easily provide visual information, attach haptic devices, and distort the speed of time, however, it is difficult to decide which types of information could benefit the training. In an initial study, by comparing real world training with VR training, we showed the effect of VR training and obtained some insights about augmentation for training spin shots. The training system was then improved by adding three new conditions using different visualizations and temporal distortions, as well as a haptic racket for creating realistic feedback. Finally, we performed a detailed experiment, which suggest a significant improvement of skill for each condition compared to the baseline, while a qualitative evaluation indicates that both users' motivation and their understanding of spin are increased by using our system.
STDnet-ST: Spatio-temporal ConvNet for small object detection

Object detection through convolutional neural networks is reaching unprecedented levels of precision. However, a detailed analysis of the results shows that the accuracy in the detection of small objects is still far from being satisfactory. A recent trend that will likely improve the overall object detection success is to use the spatial information operating alongside temporal video information. This paper introduces STDnet-ST, an end-to-end spatio-temporal convolutional neural network for small object detection in video. We define small as those objects under 16×16 px, where the features become less distinctive. STDnet-ST is an architecture that detects small objects over time and correlates pairs of the top-ranked regions with the highest likelihood of containing those small objects. This permits to link the small objects across the time as tubelets. Furthermore, we propose a procedure to dismiss unprofitable object links in order to provide high quality tubelets, increasing the accuracy. STDnet-ST is evaluated on the publicly accessible USC-GRAD-STDdb, UAVDT and VisDrone2019-VID video datasets, where it achieves state-of-the-art results for small objects.
Affordances and Core Functions of Smart Learning Environments: A Systematic Literature Review

Smart learning environments (SLEs) have gained considerable momentum in the last 20 years. The term SLE has emerged to encompass a set of recent trends in the field of educational technology, heavily influenced by the growing impact of technologies, such as cloud services, mobile devices, and interconnected objects. However, the term SLE has been used inconsistently by the technology-enhanced learning (TEL) community since different research works employ the adjective “smart” to refer to different aspects of novel learning environments. Previous surveys on SLEs are narrowly focused on specific technologies or remain at a theoretical level that does not discuss practical implications found in empirical studies. To address this inconsistency and also to contribute to a common understanding of the SLE concept, this article presents a systematic literature review of papers published between 2000 and 2019 discussing SLEs in empirical studies. Sixty-eight papers out of an initial list of 1341 papers were analyzed to identify the following: 1) what affordances make a learning environment smart; 2) which technologies are used in SLEs; and 3) in what pedagogical contexts are SLEs used. Considering the limitations of previous surveys, and the inconsistent use of the SLE concept in the TEL community, this article presents a comprehensive characterization to describe SLEs through their affordances, the technologies used, and pedagogical approaches considered in the selected papers. As a result, specific core functions of SLEs are identified and explained. This work aims at ensuring a relevant knowledge base and reference toward the implementation of future SLEs.
SOSNet: A Graph Convolutional Network Approach to Fine-Grained Cyberbullying Detection

Amidst the COVID-19 pandemic, cyberbullying has become an even more serious threat. Our work aims to investigate the viability of an automatic multiclass cyberbullying detection model that is able to classify whether a cyberbully is targeting a victim's age, ethnicity, gender, religion, or other quality. Previous literature has not yet explored making fine-grained cyberbullying classifications o f s uch m agnitude, a nd existing cyberbullying datasets suffer from quite severe class imbalances. To combat these challenges, we establish a framework for the automatic generation of balanced data by using a semi-supervised online Dynamic Query Expansion (DQE) process to extract more natural data points of a specific class from Twitter. W e also propose a Graph Convolutional Network (GCN) classifier, using a graph constructed from the thresholded cosine similarities between tweet embeddings. With our DQE-augmented dataset, which we have made publicly available, we compare our GCN model using eight different tweet embedding methods and six other classification models over two sizes of datasets. Our results show that our proposed GCN model matches or exceeds the performance of the baseline models, as indicated by McNemar statistical tests.
Enlighten-GAN for Super Resolution Reconstruction in Mid-Resolution Remote Sensing Images

Previously, generative adversarial networks (GAN) have been widely applied on super resolution reconstruction (SRR) methods, which turn low-resolution (LR) images into high-resolution (HR) ones. However, as these methods recover high frequency information with what they observed from the other images, they tend to produce artifacts when processing unfamiliar images. Optical satellite remote sensing images are of a far more complicated scene than natural images. Therefore, applying the previous networks on remote sensing images, especially mid-resolution ones, leads to unstable convergence and thus unpleasing artifacts. In this paper, we propose Enlighten-GAN for SRR tasks on large-size optical mid-resolution remote sensing images. Specifically, we design the enlighten blocks to induce network converging to a reliable point, and bring the Self-Supervised Hierarchical Perceptual Loss to attain performance improvement overpassing the other loss functions. Furthermore, limited by memory, large-scale images need to be cropped into patches to get through the network separately. To merge the reconstructed patches into a whole, we employ the internal inconsistency loss and cropping-and-clipping strategy, to avoid the seam line. Experiment results certify that Enlighten-GAN outperforms the state-of-the-art methods in terms of gradient similarity metric (GSM) on mid-resolution Sentinel-2 remote sensing images.
Development of a Histopathology Informatics Pipeline for Classification and Prediction of Clinical Outcomes in Subtypes of Renal Cell Carcinoma

Histopathology evaluation is the gold standard for diagnosing clear cell (ccRCC), papillary, and chromophobe renal cell carcinoma (RCC). However, interrater variability has been reported, and the whole-slide histopathology images likely contain underutilized biological signals predictive of genomic profiles.To address this knowledge gap, we obtained whole-slide histopathology images and demographic, genomic, and clinical data from The Cancer Genome Atlas, the Clinical Proteomic Tumor Analysis Consortium, and Brigham and Women's Hospital (Boston, MA) to develop computational methods for integrating data analyses. Leveraging these large and diverse datasets, we developed fully automated convolutional neural networks to diagnose renal cancers and connect quantitative pathology patterns with patients' genomic profiles and prognoses.Our deep convolutional neural networks successfully detected malignancy (AUC in the independent validation cohort: 0.964-0.985), diagnosed RCC histologic subtypes (independent validation AUCs of the best models: 0.953-0.993), and predicted stage I ccRCC patients' survival outcomes (log-rank test P = 0.02). Our machine learning approaches further identified histopathology image features indicative of copy-number alterations (AUC > 0.7 in multiple genes in patients with ccRCC) and tumor mutation burden.Our results suggest that convolutional neural networks can extract histologic signals predictive of patients' diagnoses, prognoses, and genomic variations of clinical importance. Our approaches can systematically identify previously unknown relations among diverse data modalities.
Research on landscape design system based on 3D virtual reality and image processing technology

In order to ensure the rationality of landscape design, it is of great practical significance to simulate the planning effect. Aiming at the problems of two traditional landscape planning effect simulation systems, a new landscape planning effect simulation system was designed based on virtual reality technology. The overall framework of the system is composed of user layer, application layer and display layer. According to the system frame structure, hardware such as acquisition equipment, main control equipment, output and display equipment are selected; according to the system framework structure, the system software is designed to run the main program. In addition, this paper also based on 3D image processing technology landscape design system. In the design process, 3D landscape image can be preprocessed to remove noise information and redundant information, and 3D landscape image feature enhancement method is used to realize 3D landscape image feature enhancement and improve the clarity of landscape design image. Combined with the example of garden green space, according to the parametric plant modeling method, the morphological structure parameters of green plants are obtained, and the three-dimensional models of various plant modeling are established; the rules of parametric description of plant spatial layout are adopted, based on the open scene graph rendering engine, and the oculus rift virtual reality equipment is integrated to realize the virtual construction and display of three-dimensional garden vegetation landscape. The results show that the design effect of the system is very clear and of good quality. It has a high score in the design of courtyard landscape and garden landscape, and the system has low application cost, low energy consumption and high operation efficiency.
Human detection based on deep learning YOLO-v2 for real-time UAV applications

Recent advancements in the field of Artificial Intelligence (AI) have provided an opportunity to create autonomous devices, robots, and machines characterised particularly with the ability to make decisions and perform tasks without human mediation. One of these devices, Unmanned Aerial Vehicles (UAVs) or drones are widely used to perform tasks like surveillance, search and rescue, object detection and target tracking, and many more. Efficient real-time object detection in aerial videos is an urgent need, especially with the increasing use of UAV in various fields. The sensitivity in performing said tasks demands that drones must be efficient and reliable. This paper presents our research progress in the development of applications for the identification and detection of person using the convolutional neural networks (CNN) YOLO-v2 based on the camera of drone. The position and state of the person are determined with deep-learning-based computer vision. The person detection results show that YOLO-v2 detects and classifies object with a high level of accuracy. For real-time tracking, the tracking algorithm responds faster than conventionally used approaches, efficiently tracking the detected person without losing it from sight.