Reviewerly/exHarmony huggingface dataset:
Realization of Music-Assisted Interactive Teaching System Based on Virtual Reality Technology

Virtual reality technology has attracted researchers' attention because it can provide users with a virtual interactive learning environment. Based on the theory of virtual reality technology, this paper proposes the system model design and architecture of virtual interactive music-assisted interactive teaching and realizes key technologies such as modeling, music-assisted interactive teaching scene interaction, and database access. In the simulation process, based on the VRML/X3D bottom interactive system template, after comprehensive application research, comparative analysis of various modeling methods, the system verified the use of digital cameras combined with the modeling technology based on music elements to collaboratively establish VRML virtual model connections. For inline node function, we combined it with Outline3D to realize VRML integration and then use VizX3D, X3D-Edit to build X3D model and realize the conversion from VRML to X3D, which solves the system completeness problem of music-assisted interactive teaching. The experimental results show that, according to the statistical analysis of the data after the experiment, when the position changes in the virtual 3D music-assisted interactive teaching scene, it will be displayed in the plane layer, and the real-time coordinates of the virtual music-assisted interactive teaching scene displayed in HTML have case. By analyzing the scenes and dynamic effects in the works, the effects of the virtual world can be better displayed through the performance of details. The better accuracy and delay error reached 89.7% and 3.11%, respectively, which effectively improved the effect and feasibility of applying virtual reality technology to music-assisted interactive teaching.
Crypto-Coins and Credit Risk: Modelling and Forecasting Their Probability of Death

This paper examined a set of over two thousand crypto-coins observed between 2015 and 2020 to estimate their credit risk by computing their probability of death. We employed different definitions of dead coins, ranging from academic literature to professional practice; alternative forecasting models, ranging from credit scoring models to machine learning and time-series-based models; and different forecasting horizons. We found that the choice of the coin-death definition affected the set of the best forecasting models to compute the probability of death. However, this choice was not critical, and the best models turned out to be the same in most cases. In general, we found that the cauchit and the zero-price-probability (ZPP) based on the random walk or the Markov Switching-GARCH(1,1) were the best models for newly established coins, whereas credit-scoring models and machine-learning methods using lagged trading volumes and online searches were better choices for older coins. These results also held after a set of robustness checks that considered different time samples and the coins’ market capitalization.
Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment

The Electronic Medical Record (EMR) provides an opportunity to manage patient care efficiently and accurately. This includes clinical decision support tools for the timely identification of adverse events or acute illnesses preceded by deterioration. This paper presents a machine learning-driven tool developed using real-time EMR data for identifying patients at high risk of reaching critical conditions that may demand immediate interventions. This tool provides a pre-emptive solution that can help busy clinicians to prioritize their efforts while evaluating the individual patient risk of deterioration. The tool also provides visualized explanation of the main contributing factors to its decisions, which can guide the choice of intervention. When applied to a test cohort of 18,648 patient records, the tool achieved 100% sensitivity for prediction windows 2-8 h in advance for patients that were identified at 95%, 85% and 70% risk of deterioration.
Shortest Dubins Paths to Intercept a Target Moving on a Circle

We present a path planning problem for a pursuing unmanned aerial vehicle (UAV) to intercept a target traveling on a circle. The target is cooperative, and its position, heading, and speed are precisely known. The pursuing UAV has nonholonomic motion constraints, and therefore the path traveled must satisfy the minimum turn radius constraints. We consider the class of Dubins paths as candidate solutions, and analyze the characteristics of the six modes of Dubins paths where the final position is restricted to lie on the target circle with heading in the tangential direction of the circle. For each Dubins mode, we derive the feasibility limits, discontinuities, and local extrema. Using this analysis the intercepting paths are found by a systematic bisection search in the feasible regions of each of the Dubins modes. We prove that the algorithm finds the optimal (shortest length) intercepting path if it is a Dubins path. If the shortest intercepting path is not a Dubins path for any given instance, the algorithm finds a tight lower bound and an upper bound to the optimal solution.
Learning Sequential Descriptors for Sequence-Based Visual Place Recognition

In robotics, visual place recognition (VPR) is a continuous process that receives as input a video stream to produce a hypothesis of the robot's current position within a map of known places. This work proposes a taxonomy of the architectures used to learn sequential descriptors for VPR, highlighting different mechanisms to fuse the information from the individual images. This categorization is supported by a complete benchmark of experimental results that provides evidence of the strengths and weaknesses of these different architectural choices. The analysis is not limited to existing sequential descriptors, but we extend it further to investigate the viability of Transformers instead of CNN backbones. We further propose a new ad-hoc sequence-level aggregator called SeqVLAD, which outperforms prior state of the art on different datasets. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/vandal-vpr/vg-transformers</uri> .
UHD Low-light image enhancement via interpretable bilateral learning

Convolutional neural networks (CNNs) have achieved unparalleled success in the single Low-light Image Enhancement (LIE) task. Existing CNN-based LIE models over-focus on pixel-level reconstruction effects, hence ignoring the theoretical guidance for sustainable optimization, which hinders their application to Ultra-High Definition (UHD) images. To address the above problems, we propose a new interpretable network, which capable of performing LIE on UHD images in real time on a single GPU. The proposed network consists of two CNNs: the first part is to use the first-order unfolding Taylor's formula to build an interpretable network, and combine two UNets in the form of first-order Taylor's polynomials. Then we use this constructed network to extract the feature maps of the low-resolution input image, and finally process the feature maps to form a multi-dimensional tensor termed a bilateral grid that acts on the original image to yield an enhanced result. The second part is the image enhancement using the bilateral grid. In addition, we propose a polynomial channel enhancement method to enhance UHD images. Experimental results show that the proposed method significantly outperforms state-of-the-art methods for UHD LIE on a single GPU with 24G RAM (100 fps).
Dynamic DNN model selection and inference off loading for video analytics with edge-cloud collaboration

The edge-cloud collaboration architecture can support Deep Neural Network-based (DNN) video analytics with low inference delays and high accuracy. However, the video analytics pipelines with edge-cloud collaboration are complex, involving the decision-making for many coupled control knobs. We propose a deep reinforcement learning-based approach, named ModelIO, for dynamic DNN <u>Model</u> selection and <u>I</u>nference <u>O</u>ffloading for video analytics with edge-cloud collaboration. We jointly consider the decision-making for video pre-processing, DNN model selection, local inference, and offloading in a video analytics system to maximize performances. Our method can learn the optimal control policy for video analytics with the edge-cloud collaboration without complex system modeling. We implement a real-world testbed to conduct the experiments to evaluate the performances of our method. The results show that our method can significantly improve the system processing capacity, reduce average inference delays, and maximize overall rewards.
Atlas level rate distortion optimization for 6DoF immersive video compression

The Moving Picture Experts Group (MPEG) has started an immersive media standard project to enable multi-view video and depth representation in three-dimensional (3D) scenes. The MPEG immersive video (MIV) standard explores the six degree of freedom (6DoF) technologies of immersive content to support motion parallax. Despite the standard being designed to compress multi-view immersive media, MIV coding has not been investigated from the perspective of bit allocation. This paper presents an efficient bit allocation scheme for atlas level compression. The proposed model establishes a model of view synthesis distortion and analyzes the impact of distortion on complete views and patches. This paper also introduces packing alignment to separate two types of patches and characterize the distortion for each MIV atlas. By considering these characteristics, the proposed model derives a bitrate ratio between texture and geometry for model-based view-rendering optimization. Experimental results showed that the proposed method achieved a more accurate reconstruction of sequences under common test conditions (CTCs).
Revisiting super-resolution for internet video streaming

Recent advancements of neural-enhanced techniques, especially super-resolution (SR), show great potential in revolutionizing the landscape of Internet video delivery. However, there are still quite a few key questions (e.g., how to choose a proper resolution configuration for training samples, how to set the training patch size, how to perform the best patch selection, how to set the update frequency of SR model) that have not been well investigated and understood. In this paper, we perform a dedicated measurement study to revisit super-resolution techniques for Internet video streaming. Our measurements are based on real-world video datasets, and the results provide a number of important insights: (1) It is possible that the SR model trained with low-resolution patches (e.g., (540p, 1080p) pairs) can achieve almost the same performance as that trained with high-resolution patches (e.g., (1080p, 2160p) pairs); (2) Compared to the saliency of training patches, the size of training patches has little impact on the performance of trained SR model; (3) The improvement of video quality brought by more frequent SR model update is not very significant. We also discuss the implications of our findings for system design, and we believe that our work is essential for paving the way for the success of future neural-enhanced video streaming systems.
Optimization of a Reed-Solomon code-based protocol against blockchain data availability attacks

ASBK is a recent blockchain protocol tackling data availability attacks against light nodes; it employs two-dimensional Reed-Solomon codes to encode the list of transactions and a random sampling phase where adversaries are forced to reveal information. In its original formulation, only codes with rate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1/4$</tex> are considered, and a theoretical analysis requiring computationally demanding formulas is provided. This makes ASBK difficult to optimize in situations of practical interest. In this paper, we introduce a much simpler model for such a protocol, which additionally supports the use of codes with arbitrary rate. This makes blockchains implementing ASBK much easier to design and optimize. Furthermore, disposing of a clearer view of the protocol, some general features and considerations can be derived. As a concrete application, we consider relevant blockchain parameters and find network settings that minimize the amount of data downloaded by light nodes. Our results show that the protocol benefits from the use of codes defined over large finite fields, with code rates that may be even significantly different from the originally proposed one.
COVID-Bot, an Intelligent System for COVID-19 Vaccination Screening: Design and Development

Coronavirus continues to spread worldwide, causing various health and economic disruptions. One of the most important approaches to controlling the spread of this disease is to use an artificial intelligence (AI)-based technological intervention, such as a chatbot system. Chatbots can aid in the fight against the spread of COVID-19.This paper introduces COVID-Bot, an intelligent interactive system that can help screen students and confirm their COVID-19 vaccination status.The design and development of COVID-Bot followed the principles of the design science research (DSR) process, which is a research method for creating a new scientific artifact. COVID-Bot was developed and implemented using the SnatchBot chatbot application programming interface (API) and its predefined tools, which are driven by various natural language processing algorithms.An evaluation was carried out through a survey that involved 106 university students in determining the functionality, compatibility, reliability, and usability of COVID-Bot. The findings indicated that 92 (86.8%) of the participants agreed that the chatbot functions well, 85 (80.2%) agreed that it fits well with their mobile devices and their lifestyle, 86 (81.1%) agreed that it has the potential to produce accurate and consistent responses, and 85 (80.2%) agreed that it is easy to use. The average obtained α was .87, indicating satisfactory reliability.This study demonstrates that incorporating chatbot technology into the educational system can combat the spread of COVID-19 among university students. The intelligent system does this by interacting with students to determine their vaccination status.
Computational Models-Based Detection of Peripheral Malarial Parasites in Blood Smears

The most common human parasite as per the medical experts is the malarial disease, which is caused by a protozoan parasite, and Plasmodium falciparum, a common parasite in humans. A microscopist with expertise in malaria diagnosis must conduct this complex procedure to identify the stages of infection. This epidemic is an ongoing disease in some parts of the world, which is commonly found. A Kaggle repository was used to upload the data collected from the NIH portal. The dataset contains 27558 samples, of which 13779 samples carry parasites and 13779 samples do not. This paper focuses on two of the most common deep transfer learning methods. Unlike other feature extractors, VGG-19's fine-tuning and pretraining made it an ideal feature extractor. Several image classification models, including VGG-19, have been pretrained on larger datasets. Additionally, deep learning strategies based on pretrained models are proposed for detecting malarial parasite cases in the early stages, in addition to an accuracy rating of 98.34 ∗ 0.51%.
Attribute-Based Zero-Shot Learning for Encrypted Traffic Classification

As more and more network applications have adopted encryption for user privacy, it poses a great challenge to identify increasing types of encrypted traffic. Recent methods mainly focus on leveraging machine learning or deep learning to improve the effectiveness of classification, and achieve good results in their experiments. However, most methods are developed for a limited number of traffic types on a close-world dataset, lacking the ability to transfer knowledge learned from available labeled data of known classes to the identification of unknown classes, of which the data is unseen during training. In this paper, we propose a novel attribute-based zero-shot learning (ZSL) framework for encrypted traffic classification, with both fine granularity for general classification and good scalability for identifying unknown classes. The framework is based on our defined attribute semantic space, consisting of two components: i) a feature-attribute embedding model to learn the mapping between flow features and attributes from seen classes. We use Temporal Convolution Network (TCN) for flow feature embedding and Simple Recurrent Units (SRU) for attribute embedding, with attention mechanisms introduced in both models for interpretability. ii) a GAN-based feature generation model FAE-G that leverages the trained FAE model to improve the generalization of the classifier for unseen classes. For generalized ZSL (GZSL) tasks, we introduce gradient-based rejection to classify both seen and unseen classes in a two-step way. The experimental results demonstrate that our method shows excellent performance in fine-grained classification, and also achieves presentable results in the identification of unknown classes.
Investigating Verbs of Confession Through a Syntactic and Semantic Annotation Tool

With the development of big data and deep learning, Chinese information processing systems need more comprehensive quantitative research to carry out large-scale automatic analysis. Syntactic and semantic analysis is a hot topic in recent years. The current research on Chinese verbs of confession lacks syntactic and semantic exploration based on large-scale corpora. This paper used a self-developed syntactic and semantic annotation tool based on dependency grammar to check the automatic annotation results. It then further also explored the features of verbs of confession. It found that this type of verbs can mainly act as five kinds of syntactic functions: predicates, subjects, attributives, objects and occurring in coordinate structure. There are mainly six types of semantic roles collocated with them, of which the highly frequent ones are agent, time and content. The difference of this type of verbs is only 招供 zhāogòng acts as fronting-objects; and only 承认 chéngrèn does not act as preposition-objects. This research is helpful for enriching the analysis of the deep knowledge of verbs and providing references for lexicography, Chinese language teaching and natural language processing.
Peer-to-Peer Variational Federated Learning Over Arbitrary Graphs

This paper proposes a federated supervised learning framework over a general peer-to-peer network with agents that act in a variational Bayesian fashion. The proposed framework consists of local agents where each of which keeps a local “posterior probability distribution” over the parameters of a global model; the updating of the posterior over time happens in a local fashion according to two subroutines of: 1) variational model training given (a batch of) local labeled data, and 2) asynchronous communication and model aggregation with the 1-hop neighbors. Inspired by the popular federated learning (model averaging), the framework allows the training data to remain distributed on mobile devices while utilizing a peer-to-peer model aggregation in a social network. The proposed framework is shown to allow for a systematic treatment of model aggregation over any arbitrary connected graph with consistent (in general, non-iid) local labeled data. Specifically, under mild technical conditions, the proposed algorithm allows agents with local data to learn a shared model explaining the global training data in a decentralized fashion over an arbitrary peering/connectivity graph. Furthermore, the rate of convergence is characterized and shown to be a function of each individual agent’s data quality weighted by its eigenvector centrality. Empirically, the proposed methodology is shown to work well with efficient variation Bayesian inference techniques to train Bayesian neural networks in a decentralized manner even when the local data batches are not identically distributed.
RAFM: Recurrent Atrous Feature Modulation for Accurate Monocular Depth Estimating

Current top-performing coarse-to-fine monocular depth estimation systems mainly depend on deeper backbones, such as a full ResNet50. These systems benefit from the powerful multiscale feature representations but suffer from the high computational costs and memory overheads. Conversely, the recurrent depth refinement systems fulfill the lightweight architecture, while the local multiscale context often limits their accuracies. To handle this problem, we propose an atrous spatial (AS) module by utilizing atrous convolutions with different rates, which efficiently captures the multiscale context. We further assemble our AS module to the current recurrent depth refinement system R-MSFM, constructing a powerful monocular depth estimation system called RAFM. To evaluate its effectiveness, we conduct the comprehensive experiments and show that our RAFM outperforms the previous SOTA methods in all metrics on KITTI while keeping the real-time speed. For accuracy, our RAFM achieves a square relative error (Sq Rel) of 0.702 on KITTI, a 12.6&#x0025; error reduction from the previous SOTA method (0.802). For model size and inference speed, our RAFM gets a frame rate of 33fps on a GPU with 7.5&#x00A0;M parameters, which is faster and more lightweight than the recent SOTA method (5fps and 128&#x00A0;M). The code is available at <uri>https://github.com/jsczzzk/RAFM</uri>.
Eduino: A Telegram Learning-Based Platform and Chatbot in Higher Education

This paper presents Eduino, a chatbot that is built on the popular social media platform 'Telegram' and serves as a learning-based platform conversational agent. Following the methodology of Data Science Research, three iterative stages are carried out for the development. Eduino provides a feature set covering functionality for acquiring lecture notes and course schedules, completion of course related quizzes, and contacting course professors through a conversational messaging interface. The chatbot was evaluated using a usability test and semi-structured interviews. The overall perception from students was positive. Students appreciated the efficiency of acquiring course content in a new novel format and found the experience to be immersive and user friendly.