Reviewerly/exHarmony huggingface dataset:
A Lightweight Smart Camera Solution for Covid-19

With the growth of IoT devices and edge computing technologies, a challenge is to deal with the network capacity and response latency in real-time applications. Such an approach to the solutions is to deploy the Artificial Intelligence (AI) and Internet of Thing (IoT) applications, namely AIoT and Internet of Things (IoT) applications at the edge. In this paper, we introduce a smart AIoT solution to support the control of covid-19 epidemic with two main features: social distance application to control social distance violators in different areas and facemask detection to identify violators who didn't wear a mask. The proposed design architecture comprises three layers: the first one is the Centralized Cloud, which implements the backend, a web server connects to a secure database. The web dashboard helps the administrators manage the streams of surveillance cameras, visualize the number of social distance violators in different areas, and show recorded facemask violators in table for convenient; the second layer is the Edge, social distance and facemask services are packaged into Docker container and deployed in a lightweight Kubernetes (K3s) cluster which have GPU. The Cluster should be deployed on both Jetson Nano and Raspberry Pi 3 devices. This design intends to increase high availability, scalability, self-healing, resource utilization, stability, and automation deployment for the edge services; and the final layer - the End Devices, which includes multiple cameras connected directly to the AI services at Edge. These cameras help us collect and send data to the edge servers for analyzing purpose. The results show that the proposed system works correctly, edge services run at an acceptable framerate.
A Generalized Voronoi Diagram based Robot Exploration Method for Mobile Robots

The Rapidly-exploring Random Tree (RRT) based method has been widely used in the field of autonomous exploration of mobile robots. During autonomous exploration, the robot relies on the RRT tree growth to obtain exploration frontiers. However, due to the trap space problem, such as narrow corridors and mazes, the RRT tree cannot grow to these regions in a limited time, resulting in the slow extraction of frontiers. Moreover, limited to the randomness of the growth of the RRT tree, the frontiers extracted from the RRT treetop have a lot of redundancy. In this paper, we propose a Generalized Voronoi Diagram (GVD) based exploration method to guide robots for efficient exploration. First, a lightweight feature extraction is proposed to extract the GVD information and represent the topological structure of the environment. Second, the GVD features are utilized to quickly generate heuristic frontiers. In order to reduce the redundancy of frontiers, a GVD frontiers fusion and extraction algorithm is proposed. Finally, the GVD nodes are fused and we obtain a feature node set. By using the collision-check module, we get the feature matrix. The path cost is quickly calculated by using the feature matrix. The experimental results show that by comparing with the RRT-Exploration algorithm, our method has better performance in quickly extracting exploration frontiers and reducing backtracking.
The Advance of Generative Model and Variational Autoencoder

Machine learning is a complicated course that contains many subjects which is known as Probability and Mathematical Statistic, approximation theory, convex analysis and the complexity of the algorithm. It concentrates on the way people’s learning behavior is simulated or achieved by the computers, so that they can get new knowledges and skills and reform the now available knowledge and improve their functions by learning this. So, at present, there are still many problems in machine learning research, one of which is the improvement of the generative model. Nowadays, discriminant models are mainly used. This paper introduces the concept of Variational autoencoder models. Besides, this paper discusses Some properties of Variational autoencoder models, provides a reference for improving generative models, and explains the principle of Variational autoencoder. It is hoped that in the future, generative models can also be developed as perfect as discriminant models to achieve significant progress in the field of artificial intelligence.
SunFA - An open-source application for behavior analysis in online video-conferencing

Video-conferencing applications are becoming increasingly popular, especially with the remote working trend after COVID-19. The benefits of meeting online cannot be denied; however, this is still quite limited. In particular, it is essential to monitor and analyze participants' behavior. In this paper, we proposed SunFA - an open-source participants analysis tool for video-conferencing based on face analysis and virtual camera technology. The advantage of our system is that it is compatible with almost available video conferencing applications, such as Google Meet, Skype, Microsoft Teams, Zoom, Slack, etc. Furthermore, we packaged this software as a desktop application for Windows operating system to make it easy to install. The memory usage and execution time evaluation ensure the real-time and resource-saving of a video-conferencing application. We open-source our entire source code and solutions at https://github.com/sun-asterisk-research/sun-fa
Mask Detection for Covid-19 under Extreme Conditions

Mask detection has become a hot topic since the COVID-19 pandemic began in recent years. However, most scholars only focus on the speed and accuracy of detection, and fail to pay attention to the fact that mask detection is not suitable for people living under extreme conditions due to the degraded image quality. In this work, a denoising convolutional auto-encoder, a multitask cascaded convolutional networks (MTCNN) and a MobileNet were used to solve the problem of mask detection for COVID-19 under extreme environments. First of all, a network based on AlexNet is designed for the auto-encoder. This study found that the two-layer max pooling layers in AlexNet could not accurately extract image features but damage the quality of restored image. Therefore, they were deleted, and other parameters such as channel number were also modified to fit the new net, and finally trained using cosine distance. In addition, for MTCNN, this study changed the output condition of ONet from thresholding to maximum return, and lowered the thresholds of PNet and RNet to solve the problem that faces might not be found in low-quality images with mask and other covers. Furthermore, MobileNet was trained using categorical cross entropy loss function with adam optimizer. In the end, the accuracy of system for the photos captured under extreme conditions enhance from 50 % to 85% in test images.
Localisation-Safe Reinforcement Learning for Mapless Navigation

Most reinforcement learning (RL)-based works for mapless point goal navigation tasks assume the availability of the robot ground-truth poses, which is unrealistic for real world applications. In this work, we remove such an assumption and deploy observation-based localisation algorithms, such as Lidar-based or visual odometry, for robot self-pose estimation. These algorithms, despite having widely achieved promising performance and being robust to various harsh environments, may fail to track robot locations under many scenarios, where observations perceived along robot trajectories are insufficient or ambiguous. Hence, using such localisation algorithms will introduce new unstudied problems for mapless navigation tasks. This work will propose a new RL-based algorithm, with which robots learn to navigate in a way that prevents localisation failures or getting trapped in local minimum regions. This ability can be learned by deploying two techniques suggested in this work: a reward metric to decide punishment on behaviours resulting in localisation failures; and a reconfigured state representation that consists of current observation and history trajectory information to transfer the problem from a partially observable Markov decision process (POMDP) to a Markov Decision Process (MDP) model to avoid local minimum.
Research on Internet Corpus Collection Method

With the popularity of websites and the emergence of a large number of text data, the Internet has become an important channel for people to obtain information resources. In today’s society, Internet corpus has become a necessary corpus for linguistic research due to its rich resources, large scale, rich language types and low acquisition cost. It is a common and effective method to obtain corpus on the Internet by using crawler technology. This paper systematically introduces the principle of Internet data transmission, and crawlers are used to crawl the Internet corpus. Finally, some common anti-climbing mechanisms are introduced, which can be circumvented to better crawl corpus.
Confrontation with Noise in Selective Classification

Selective prediction is a powerful tool which enables machine learning models to make predictions with more confidence. In this paper, a selective classifier based on deep neural network (DNN) is applied for image classification tasks with training label noise or Out-of-distribution (OOD) data are implemented. Furthermore, the deep abstaining classifier (DAC) is introduced as a training data cleanser with the DNN model, and the performance of training a DNN after using DAC for cleansing, which is called post-DAC DNN, is compared with that of the original DNN classifier. It is shown that the post-DAC DNN significantly outperforms the baseline model either when the training data is uniformly corrupted or has randomized single-class label noise.
Securing Autonomous Robot Systems

The previous work is two versatile 3D methodologies for independent horticultural UAVs to find impact-free ideal ways in time with the interaction costs in various conditions, containing different obstructions. These strategies were created, roused by two metaheuristic calculations. This concentrate likewise involved an instrument for snagging the board. It observes the best way between the beginning and target stations of each UAV. In way arranging techniques, as a rule, either the robots haphazardly move, or expensive cycles are attempted in tracking down an advanced way. This interaction is performed steadily between the two stations. The UAV attempts to track down a method between two places. It enhances haphazardly made ways to observe the ideal direction. On account of this technique, robots can likewise effectively keep away from impediments. The amount of every one of the tuples' expenses is determined, and the expense of the way is acquired. The suggestion implements some security measures to make the system better.
A Method for Style Transfer from Artistic Images Based on Depth Extraction Generative Adversarial Network

Depth extraction generative adversarial network (DE-GAN) is designed for artistic work style transfer. Traditional style transfer models focus on extracting texture features and color features from style images through an autoencoding network by mixing texture features and color features using high-dimensional coding. In the aesthetics of artworks, the color, texture, shape, and spatial features of the artistic object together constitute the artistic style of the work. In this paper, we propose a multi-feature extractor to extract color features, texture features, depth features, and shape masks from style images with U-net, multi-factor extractor, fast Fourier transform, and MiDas depth estimation network. At the same time, a self-encoder structure is used as the content extraction network core to generate a network that shares style parameters with the feature extraction network and finally realizes the generation of artwork images in three-dimensional artistic styles. The experimental analysis shows that compared with other advanced methods, DE-GAN-generated images have higher subjective image quality, and the generated style pictures are more consistent with the aesthetic characteristics of real works of art. The quantitative data analysis shows that images generated using the DE-GAN method have better performance in terms of structural features, image distortion, image clarity, and texture details.
Alternative Approaches to Chinese Imagery: Audiovisual aesthetics in <i>A Reflection in the Brook</i>

The importance that Chinese composers attach to their nation’s cultural traditions in their electronic music compositions has become a dominant trend in Chinese electronic music. This has generally led to a ‘Chinese imagery’ in Chinese electronic music compositions. Among China-inspired electroacoustic music, the interactive multimedia work A Reflection in the Brook (小青, 2013) shows a unique expression. The author explores how the composer recreated a controversial female figure in Chinese history through a completely real-time audiovisual language: Feng Xiaoqing, thereby presenting Chinese imagery in electroacoustic music through an alternative approach. The audiovisual relationship in A Reflection in the Brook will be analysed through the lens of Michel Chion’s audiovisual theory and the perspective of musical composition techniques, further presenting the audiovisual aesthetics of multimedia electronic music.
Mining Association Rules from a Single Large Graph

Knowledge mining from single graph plays an important role in decision support systems on single graphs such as social networks, bioinformatics, etc. In recent years, the problem of Frequent Subgraph Mining (FSM) from a single graph have been developed and attracted several studies. However, the problem of mining association rules or links from frequent subgraphs has not had many contributions. In this article, we state the problem of direct mining association rules from frequent subgraphs. Existing approaches on this topic perform the task in two phases. First, they traverse the search space to directly discover parent-child relationships from the discovered frequent subgraphs, then association rules are generated. We propose a one-phase algorithm, named So-GPARs, to generate rules as soon as frequent supergraphs are constructed from already existing frequent subgraphs. Our experiments on three single graph datasets show that the one-phase algorithm is more efficient than the two-phase algorithm in terms runtime of the rules generating phase.
To Train your Machine, Reward it

Can you train a robot in the same way you would train a pet? The answer may surprise you…
Pilot study on the effectiveness of the "Relax 2" board-game in improving vocabulary and sentence formation sills in Romanian

"Board-games constitute a tool that simultaneously develops children in many ways and motivates them to learn. The aim of this experiment is to examine the effectiveness of using boardgames in Romanian lessons and how the attitudes of Hungarian children change towards the Romanian language. To this end, the author created a board-game for Hungarian speaking children. The aim of the game is to bring the children closer to the Romanian language, to make it possible for them to playfully master the key words in the topics present in the elementary classes, to increase their existing vocabulary, and to develop their sentence-forming skills. 21 children participated in the research. The children learned 14 topics during the experiments, of which 7 topics were learned using traditional methods and also 7 topics were learned using the board-game. To measure the level of knowledge, the children wrote a test at the beginning and at the end of each lesson. The results show that children were able to learn more words using board-games than using traditional methods. It also became clear that when using board games, more than half of the children (56.46%) managed to form a correct sentence, while when using traditional methods, far less than half (35.37%) were able to form a correct sentence. By the end of the experiment, the children liked the Romanian lessons and their learning motivation increased significantly."
Identification of Distorted RF Components via Deep Multi-Task Learning

High-quality radio frequency (RF) components are imperative for efficient wireless communication. However, these components can degrade over time and need to be identified so that either they can be replaced or their effects can be compensated. The identification of these components can be done through observation and analysis of constellation diagrams. However, in the presence of multiple distortions, it is very challenging to isolate and identify the RF components responsible for the degradation. This paper highlights the difficulties of distorted RF components’ identification and their importance. Furthermore, a deep multi-task learning algorithm is proposed to identify the distorted components in the challenging scenario. Extensive simulations show that the proposed algorithm can automatically detect multiple distorted RF components with high accuracy in different scenarios.
Security of Railway Control Systems: A Survey, Research Issues and Challenges

With the rapid development of railway transporta- tion, higher requirements for capacity are increasing and amount of communication, computer and control technologies are applied in train control systems which is the popular method for train control. The application of 3 C brings the enhancement of railway transportation performance. However, the number of internal and external cyber security threats is rising, which could lead to some railway accidents. On the other hand, safety is the core of the design process for railway but security is rarely considered due to the closure of traditional railway systems. As cyber security is threatening the normal operation of industrial control systems and the critical infrastructure, security issues of train control systems should be paid more attention as railway transportation play an important role for society and economy. However, due to the safety-critical characteristics, safety assessment and analysis works have been performed for a long time, but cyber security related works are rarely considered. In the paper, we demonstrate the security situation of train control systems based on the inherent features and the experience of other industrial control systems, where the technical defects and potential threats are summarized. According to the practical engineering experience, the current security protection strategies are illustrated, which shows the popular security protection methods are limited and cannot realize the defense-in-depth. Finally, some research challenges of security issues of train control systems are identified.
A pivot-based simulated annealing algorithm to determine oblique splits for decision tree induction

Abstract We describe a new simulated annealing algorithm to compute near-optimal oblique splits in the context of decision tree induction. The algorithm can be interpreted as a walk on the cells of a hyperplane arrangement defined by the observations in the training set. The cells of this hyperplane arrangement correspond to subsets of oblique splits that divide the feature space in the same manner and the vertices of this arrangement reveal multiple neighboring solutions. We use a pivoting strategy to iterate over the vertices and to explore this neighborhood. Embedding this neighborhood search in a simulated annealing framework allows to escape local minima and increases the probability of finding global optimal solutions. To overcome the problems related to degeneracy, we rely on a lexicographic pivoting scheme. Our experimental results indicate that our approach is well-suited for inducing small and accurate decision trees and capable of outperforming existing univariate and oblique decision tree induction algorithms. Furthermore, oblique decision trees obtained with this method are competitive with other popular prediction models.
Geochemical multifractal modeling of soil and stream sediment data applied to gold prospectivity mapping of the Pitangui Greenstone Belt, northwest of Quadrilátero Ferrífero, Brazil

Geochemical mapping has become essential to prospective assessments in both brownfield and greenfield exploration targeting. During the last two decades, several techniques have been applied to the identification of mineralization's geochemical signature. Among these techniques, Hierarchical Clustering (HC) and Principal Component Analysis (PCA) have been widely used. Other techniques, like Multifractal Spectrum (MS) and Local Singularity Mapping (LSM), are efficient in the generation of models suitable to determine the type of database statistical distribution and mapping zones of geochemical enrichment and depletion. We combined these techniques for soil and stream sediment geochemistry and generated two prospective geochemical models for the gold deposits from the Pitangui Greenstone Belt (PGB), located at northwest of Quadrilátero Ferrífero – Brazil. The results of PCA and HC showed a geochemical association of As-Bi-Mo-Sb with gold mineralization in the PGB. According with descriptive statistical analysis, these elements do not display normal or log-normal statistical distribution, which allowed the application of multifractal models on the geochemical mapping. MS technique revealed that all elements associated with gold have a multifractal distribution. In addition, geochemistry data shows that high contents of elements measured in the stream sediment analysis displays greater dispersion when compared to elements of soil geochemistry data, due to their mobility in environmental conditions. We used LSM technique to map geochemical enrichment of the elements associated with gold. The maps were evaluated using the Ground Truth Analysis (GTA) and the results showed a good performance in the identification of prospective zones. The anomalies identified in both geochemical maps showed a strong correlation between geochemical enrichment zones and geological factors that favors gold mineralization. These results bring a new perspective on the distribution of geochemical anomalies using, for the first time, the multifractal geochemical modeling on the PGB. The geochemical maps point to new targets for mineral exploration and contribute significantly for the prospective models of the region.