Reviewerly/exHarmony huggingface dataset:
Architecture and Implementation of Focus Crawler for Big Data Collection in Functional Sports Training

research-article Share on Architecture and Implementation of Focus Crawler for Big Data Collection in Functional Sports Training Author: Weiwei Wang Nanchang Institute of Science & Technology, China Nanchang Institute of Science & Technology, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2778–2782https://doi.org/10.1145/3482632.3487513Online:22 November 2021Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Infrared dataset generation for people detection through superimposition of different camera sensors

Infra-red (IR) cameras have found widespread use in many different fields. The most common ones are generally related to industrial applications, particularly maintenance and inspections activities. In the domain of surveillance, instead, they are mostly used for threat detection and security purposes. Pushed by cost reduction and the availability of compact sensors, intelligent IR cameras are gaining popularity in the field of Internet-of-Things, in light of the valuable information made available by the acquired data. Unfortunately, the achievable overall quality is not always satisfactory. For example, low-resolution devices, noise, or harsh environmental conditions, like high temperatures on sunny days, can degrade the quality of the thermal images. This paper presents the development of a portable, low-cost, and low-power thermal scanner prototype consisting of a thermal sensor assisted by a grayscale camera. The prototype is completely made using COTS components and provides 80 × 60 IR and 160 × 120 grayscale images, mostly used to collect and validate the IR-based data. Our application focuses on people detection, for which we present a suitable learning framework together with the corresponding IR dataset, collected and annotated via the paired grayscale images.
A robust correlation filter tracking method based on adaptive model updating

Correlation filter-based target tracking is a simple and efficient method for visual tracking. However, the correlation filter tracker updates the target model with a fixed learning rate which cannot adapt to the change of target appearance for a real-time tracking, resulting in model drift and tracking failure. To deal with these issues, we develop an adaptive model updating method based on the peak-to-sidelobe ratio (PSR) of response map and the mean value of the frame difference. We adjust the learning rate according to different scenarios to improve the target tracking performance. Extensive experimental results conducted on OTB2013 and OTB2015 prove that the proposed FSAMF tracker superiority over others state-of-the-art trackers.
Machine Learning use case in manufacturing – an evaluation of the model’s reliability from an IT security perspective

The use of Machine Learning (ML) solutions for decision automation in manufacturing environments is critical if operators trust ML-predictions without critically questioning them. The vulnerability of ML-applications to data manipulation, data-poisoning and adversarial examples raise concerns about its reliability and security. This paper evaluates an on-edge predictive maintenance solution through an IT security perspective, showing how the model’s forecasting can be affected by intentional data manipulation and thus identifying the system’s vulnerabilities for this particular use case. It concludes with suggestions on how to mitigate threats and manage risks.
Visual positioning method based on line laser 3D measurement system

To real time obtain the pose of a line laser sensor in a manipulator with a high accuracy, in this paper, a line laser three-dimensional(3D) measurement system is designed based on visual positioning method. First, the position and posture of the target in the camera coordinate system is obtained by the coplanar PNP model under isometric constraint and the absolute orientation problem. Second, the pose change matrices of the target relative to the initial position at different times are calculated by the pose of the target in the camera coordinate system to achieve the visual positioning. Third, the relative position and posture relationship matrix between the target and the line laser sensor is solved by the standard small ball method to achieve hand-eye calibration. Fourth, the measurement data of the line laser sensor are unified into the world coordinate system by the pose change matrices and the hand-eye calibration matrix to achieve the point cloud splicing. The experimental result shows that the displacement accuracy of the visual positioning is 0.039 mm, and the rotation accuracy is 4.2×10<sup>-3</sup> rad. The contour measurement accuracy of the line laser 3D measurement system based on the vision positioning technology is 0.55 mm. It can be seen that the system meets the general industrial measurement requirements.
Adaptive Noise Reduction Method of Sensor Signal for Health Monitoring of Civil Engineering Structure

At present, the adaptive noise reduction method of sensor signal can not accurately reduce the noise points, and the ability to identify the change of azimuth of sensor signal is poor, resulting in poor noise reduction ability. In order to solve the above problems, a new adaptive noise reduction method for the health monitoring of civil engineering structure is studied. The mathematical model is established, the expression of noise function is analyzed, the noise function is determined, the software model is used for simulation design, the noise coefficient is extracted, the coupling system is established, the sketch map of the edges of different noisy signals is determined, the absolute coordinates of sensor signals are determined in the coordinate system, and the adaptive noise reduction of sensor signals is carried out according to image threshold and communication mode. The experimental results show that the adaptive denoising method for the health monitoring of civil engineering structure can reduce the noise point by half, and improve the ability to understand the change of the sensor signal azimuth.
The resilience of electricity distribution

The distribution of electricity is a service that is essential to the life of the nation. It is undergoing rapid change due to digitization and the energy transition. The pandemic has revealed the magnitude of the need to improve the resilience and to continue the transformation of the distribution network, which is so important to our sovereignty.
Research of the Stegosignal Propagation through the Acoustic Environment

Abstract The paper considers two main applications of stegosystems: as a digital watermark, for copyright protection, and as a method for secretly transmitting information through an acoustic environment. A stegosystem is studied theoretically and experimentally when information is transmitted over different distances in an acoustic medium in the presence of extraneous noise. Nowadays great attention is paid to the protection of intellectual property. Illegal distribution of media content, including copies of musical compositions, is widespread and reaches unprecedented proportions. Protecting audio files from unauthorized copying and using becomes increasingly important. Digital watermarks are a promising method for solving this problem. The purpose of the article is to develop an effective method for protecting audio files from illegal use by embedding a digital watermark (DW). After detection the illegal distribution of their product, the copyright holder will be able to track the presence of the digital watermark and apply to the court for damages.
Dynamic sample selection method based on classification prediction

Recent research has shown that sample selection in the training phase is crucial for improving the accuracy of object detection. For a long time, we always use manually designed IoU threshold to filter out low-quality candidates, but recently some work shows that dynamic sample selection is better for training an object detector. In our paper, we present a new adaptive dynamic sample selection strategy based on both IoU and Classification prediction. Our experiment applies in PASCAL VOC dataset, without any whistle, achieved 84% mAP. Also, our method doesn’t require any extra computation, which can guarantee high speed in the testing phase.
Water Classification Based on Underwater Monocular Image

The oceans occupy a considerable part of our planet and are underexplored compared to their own significance. The bottom of the sea was reached, but there are several difficulties involved in trying to extract information from that location. In the spectrum of robotics and computer vision, underwater images are particularly challenges due to the vast range of existing aquatic environments, presenting diverse characteristics. A robot working underwater needs to understand the environment to which it is exposed. Thus, the proposed work aims to help the capabilities of its vision system addressing the problem of classifying underwater images according to their water type. The proposed approach takes into account the color channels and uses a classification tool widely accepted in the scientific world as a basis. The method is developed through the recognition of patterns observed in several underwater images of varying depths and types. We achieve good results when compared to state-of-the-art methods opening several opportunities for underwater image processing.
Affective Palettes for Scientific Visualization: Grounding Environmental Data in the Natural World

As computing capacity increases and data grows in both size and complexity, we are capable of understanding our surroundings with increasing nuance. Visualizing this often-multivariate environmental data presents complex visual scenes to be navigated, parsed, analyzed, and communicated. We draw from both the natural world and artistic color theory to present 1) a new color system, designed to establish an affective connection between big environmental data and its original source material, 2) a tool for extracting these workable palettes from natural imagery, and 3) a selection of pre-made linear colormaps and discrete color sets drawn from natural environments.
Fingerprint segmentation of UV induced fluorescence image using fuzzy enhancement

Fingerprints, a kind of reliable and accessible biological characteristic for individual identification, are commonly used on many occasions such as the mobile phone unlocking and criminal investigation. In this paper, a method based on fuzzy enhancement is proposed to segment fluorescence images of invisible fingerprints which are induced by ultraviolet laser on porous paper substrates. With the purpose of achieving the segmentation of latent fingerprints, the fuzzy enhancement and morphological operators are employed to extract the minutia while the ridge structures and valley structures are ruined by intensive background noises. In combination with the directional image, the method proposed could effectively remove the noises and obtain clear visible minutia simultaneously. The effectiveness of the method proposed shows great promise for further analysis of latent fingerprints and even in criminal investigation.
Learning Lightweight Neural Networks via Channel-Split Recurrent Convolution

Lightweight neural networks refer to deep networks with small numbers of parameters, which can be deployed in resource-limited hardware such as embedded systems. To learn such lightweight networks effectively and efficiently, in this paper we propose a novel convolutional layer, namely Channel-Split Recurrent Convolution (CSR-Conv), where we split the output channels to generate data sequences with length T as the input to the recurrent layers with shared weights. As a consequence, we can construct lightweight convolutional networks by simply replacing (some) linear convolutional layers with CSR-Conv layers. We prove that under mild conditions the model size decreases with the rate of $O\left( {\frac{1}{{{T^2}}}} \right)$. Empirically we demonstrate the state-of-the-art performance using VGG-16, ResNet-50, ResNet-56, ResNet-110, DenseNet-40, MobileNet, and EfficientNet as backbone networks on CIFAR-10 and ImageNet. Codes can be found on https://github.com/tuaxon/CSR_Conv.
Markerless Mobile Augmented Reality (MAR) Development of Wayang Krucil Figures as an Effort to Increase Knowledge and Character Learning

Cultural development, especially in the field of education, can be realized through various media and concepts. The purpose of this study is to introduce the characters in the wayang “Wayang Krucil” which will have an impact on the development of the character of the nation’s successor to the Indonesian people, especially children and adolescents. This learning application can visualize the character of the “Wayang Krucil” 3D object through augmented reality without markers. This study follows the Borg and Gall development research method. The use of augmented reality has several objectives, namely (1) to provide cultural education related to the character of wayang krucil; (2) provide education on visualization of the form and movement of wayang kulit. 3D characters are designed using a low poly subdivision surface technique, with a minimum number of polys but still maintaining character details. The markerless Augmented reality method works by scanning the surrounding environment and no trigger image is required to capture augmented reality content. Based on the results of data analysis, the following important points are obtained (a) the information displayed is history, developmental figures, 3D characters of “Wayang Krucil” and descriptions; (b) The implementation of augmented reality without markers is also equipped with rotate and positioning functions. The result of this design is a Mobile Augmented Reality application which is a learning medium for the wayang krucil character. Based on lab test results, 87% of applications can be run starting from the installation stage, UI display, description display and AR display
An Edge Device for Personal Big Data Tracking

In this paper, we present our design of a smart wristband, iFit, which runs our customized operating system to monitor the wearer's daily activities. The smart wristband consists of a 3-axis gyroscope and a 3-axis accelerometer which records the user's motion data as six-axis time series data. We train a pattern model for different activities, such as walking, running and playing badminton. So iFit can automatically detect the types of ongoing sports and record a precise calorie consumption based on user's personal profile (e.g., weight and height).
Research on Extraction Method of Navigation Line of Substation Wheeled Inspection Robot Based on Machine Vision

Aiming at the problem of different roads for intelligent inspection robots in substations, a navigation line extraction method suitable for three types of road conditions is proposed. First, extract the region of interest from the acquired image, perform grayscale processing and median filter denoising. Use Otsu method to binarize the denoised image, and morphological processing is used to reduce the noise of the image twice. In order to effectively filter out false edges and extract edge features better, an improved Canny edge detection algorithm is proposed. The algorithm uses the horizontal, vertical, 45° and 135° gradient templates to calculate the image gradient; At the same time, the interpolation method in non-maximum suppression is improved, and Otsu method is used to determine the high and low thresholds of the image. The algorithm optimizes the Canny edge extraction effect, and finally uses a line fitting algorithm to extract the navigation line.
Single image dehazing via combining the prior knowledge and CNNs

Aiming at the existing single image haze removal algorithms, which are based on prior knowledge and assumptions, subject to many limitations in practical applications, and could suffer from noise and halo amplification. An end-to-end system is proposed in this paper to reduce defects by combining the prior knowledge and deep learning method. The haze image is decomposed into the base layer and detail layers through a weighted guided image filter (WGIF) firstly, and the airlight is estimated from the base layer. Then, the base layer image is passed to the efficient deep convolutional network for estimating the transmission map. To restore object close to the camera completely without amplifying noise in sky or heavily hazy scene, an adaptive strategy is proposed based on the value of the transmission map. If the transmission map of a pixel is small, the base layer of the haze image is used to recover a haze-free image via atmospheric scattering model, finally. Otherwise, the haze image is used. Experiments show that the proposed method achieves superior performance over existing methods.
PredNet: a simple Human Motion Prediction Network for Human-Robot Interaction

Human-Robot Interaction (HRI) is becoming increasingly viable for flexible and resilient manufacturing, combining the intelligence and dexterity of humans with the precision and strength of robots. However, HRI incurs the breakage of well-established safety procedures and requires robots to be aware of their environment, especially their human co-workers. This calls for human motion prediction, which can improve the performance in HRI scenarios and contribute towards safer HRI. In this regard, we propose PredNet, a simple recurrent neural network architecture designed to predict human motion in a prediction window of 1 second. To address the lack of production-related HRI scenarios for training and validating PredNet, we develop simple HRI scenarios in a simulation environment, consisting of the following human actions: walking, lifting boxes and wiping. For a real world validation, we use Mogaze dataset. Furthermore, we propose a novel metric, namely, Volumetric Occupancy Error (VOE) towards measuring the safety performance of motion prediction architectures aimed to be applied in industrial settings. On both HRI scenarios and Mogaze datasets, PredNet performs better than baseline RED architecture.
Applications and Future Work

In this chapter, we will show the applications of some illumination processing methods introduced in the previous chapters in robot vision, including UAV target tracking under complex illumination conditions, road and navigation line extraction in rural scenes, passable area recognition, tracking in rainy scenes, and object detection in highlight. The results show the effectiveness and value of our models and preprocessing algorithms. Finally, the future research in this field is prospected.
Super-resolution of wide-field infrared and low light level images using convolutional networks

SRCNN firstly applies convolutional neural network to image super-resolution reconstruction, which is the most representative method of super-resolution reconstruction algorithm based on deep learning. In this paper, SRCNN was taken as an example to discuss the application of deep learning method in the super-resolution reconstruction of Wide-Field Infrared and Low Light Level Images. The main work was to compare the effects of training data sets on reconstruction results. Two kinds of data sets were used to train SRCNN. Model 1 used 91 ordinary natural images as training data set, and model 2 used 29 ordinary natural images and 62 Wide-Field images as training data sets. Two groups of Wide-Field Infrared and Low Light Level Images were tested by using the models trained from the two datasets, and the PNSR and SSIM parameters of the test results were compared.
A Low Cost IoT Based Remote Monitoring and Feedback Collection Mechanism for Exhibits

Remote monitoring and feedback collection is an important task for various service industries. Monitoring helps to assure quality of service while Collection of unbiased feedback is always on the top priority for any service industry like museums. Museum being a visitor centric institute heavily works on this field. This work is mainly focused on assuring quality of the Mobile science Exhibition program run by National Council of Science Museums. This Mobile Science Exhibition (MSE) is a unique and prestigious and largest outreach program run by the National Council of Science Museums. The first bus was launched in the year 1965[3] by Birla Industrial and technological Museums (BITM). Since the first day it has been one of the most successful programs run by NCSM. Hence authority has always had a dedicated focus on this program and making it more successful. With the recent decision of expending the volume of the program to a greater extent has created some operational challenges like assuring the maximum popularization of this program while operating these MSE with the help of external agencies. In this paper we have proposed the use of advanced Internet of things (IoT) based technology to solve the corresponding challenges. In addition to solving the operational challenges, our proposal will provide very useful indirect feedback data that can be used for different kinds of analysis to improve the quality of the program. The cost and complexity of the proposed system is designed by considering its feasibility to adoption by a developing country.
Exploration on the Training Mode of Computer Professionals under the Background of Artificial Intelligence

This paper analyzes the key issues in the training of computer professionals under the new situation, creatively thinks about computer professional education, and puts forward the concept of diversified talent training. Using engineering education professional certification as a starting point, it discusses the construction of computer majors under the background of artificial intelligence. This paper provides new ideas for talent training, and designs a talent training program to better cultivate innovative talents in computer science that can adapt to the development of the times and face the needs of applications.
THE IMPLEMENTATION OF DIARY WRITING ACTIVITY TO IMPROVE STUDENTS’ WRITING SKILL IN RECOUNT TEXT AT SMAN 1 NATAR

The aim of this study are to find out whether there is significant improvement in students writing skill and to investigate what language features produced by students after being taught by using diary writing. The sample of this research was X IPS-4 at SMAN 1 Natar. The writing test was administered as the instrument of this research. The data were analyzed by using repeated measure t-test in which the significance was determined by p&lt;0,005. The result showed that the students’ mean score in pretest 60.82 while in the posttest mean score was 74.12 in which t-value&gt;t-table (11.882&gt;2.452) or sig p&lt;0.05 (0.000&lt;0.05). Furthermore, specific time and passive voice are the new language features that produced by students. It can be concluded that diary writing activity is effective to improve students’ writing skill.