Reviewerly/exHarmony huggingface dataset:
Motion Detection and Object Detection: Yolo (You Only Look Once)

Recently, the field of artificial intelligence has seen many advances thanks to deep learning and image processing. It is now possible to recognize images or even find objects inside an image with a standard GPU. Image processing is a recent science that aims to provide specialists from different areas, as to the general public, tools for manipulating these digital data from the real world. The detection of moving objects is a crucial step for systems based on image processing. The movements detected by the classic algorithms are not necessarily interesting for a thorough information search, and the need to distinguish the coherent movements of parasitic movements exists in most cases. In this paper we are going to use a simply webcam and YOLO algorithm for this implementation. The YOLOv3 (Version 3) model makes predictions with a single network evaluation, making this method extremely fast, running in real time with a capable GPU. From there we&#x0027;ll use OpenCV, Python, and deep learning to apply the YOLOv3 object to images and apply YOLOv3 to video streams.
Elimination method of overexposed areas in speckle pattern interferometry

In the practical application of speckle pattern interference, it is often necessary to measure the deformation or defects of some specimens placed in the box. Transparent glass observation windows are usually used on one side of these boxes. In the measurement of laser speckle pattern interference, glass windows often cause overexposure in a certain area of the field of view, which has a great influence on the measurement. In this paper, a method of recognizing and eliminating high reflective region based on polaroid is proposed. By adjusting the polaroid to filter the stray and chaotic obtrusive light, the overexposed area in the field of view is greatly reduced, and then the exposure area caused by glass windows in the field of view is identified and extracted by feature extraction technology. Finally, the overexposed area is interpolated and filled according to the surrounding region information to obtain the measurement map without overexposure. Experimental results show that the proposed method can effectively extract and eliminate the overexposed area, and good measurement results are obtained.
Research on the Construction of Computer Courses in higher Vocational Colleges based on Information Technology

In 2020, China was also severely affected by the global spread of the new coronavirus (2019-nCoV). In the midst of the epidemic, colleges and universities are turning to online teaching. With the phased success of the epidemic, the spirit of "epidemic power" is an important part of the curriculum construction in colleges and universities. Computer-related majors have the identity of social service and are consistent in social practice. In higher vocational colleges, the curriculum construction of computer-related majors is also an important content, to improve students'study enthusiasm, to strengthen the construction of teachers'team, to improve teachers'professional ability, to give full play to their professional advantages, to put curriculum construction in the first task of teaching.
Application of Mobile Phone App to Assist Vocal Music Teaching In Universities under the Background of Big Data

With the continuous promotion and popularization of mobile clients such as smart phones and iPads, a new Learning mode – M-Learning (Mobile Learning) arises at the right moment. Mobile App is used to assist the Application and practice of vocal music teaching in colleges and universities, so that students can make use of their spare time for mobile learning and supplement the deficiencies of traditional classroom teaching. This paper explores the teaching mode, teaching method and teaching effect of efficient vocal music teaching assisted by mobile phone App under the background of big data, as well as the seamless connection between classroom teaching and after-school mobile learning.
Inductive Machine Learning and Feature Selection for Knowledge Extraction from Medical Data: Detection of Breast Lesions in MRI

This paper presents an approach to the problem of breast cancer diagnosis through the data analysis of magnetic mammography observations (MRi Data), developing corresponding hybrid classification models of patient cases into specific classes (e.g. Benign and Malignant). The aim of this work is the contribution of machine learning to the diagnostic process of breast cancer, offering a supportive intelligent tool that can be used by expert doctors as a medical decision-making aiding tool. Data were collected in collaboration with expert doctors and consist of 77 patient cases. The development of the presented classification models is a combination of inductive decision trees, clustering and feature selection techniques. Specifically, nine (9) different classification models were developed and evaluated by using statistical criteria, medical expert knowledge and where possible, using the Chi-Square statistical test. The performance achieved is considered encouraging for application in real-world practice, while further research is underway for associating MR imaging data with data from invasive examinations (biopsies).
How Intelligent Technology Affects Education--Case-based Research

The rapid development of information technology, computer technology, and communication technology has not only changed the way people live and work, but also has a huge impact on the way of learning and the development of education. The rapid development of the Internet has promoted online learning to become one of the main forms of people's learning; the development of cloud computing and 5G technology has promoted the convenience and equalization of the acquisition of educational resources; the application of virtual reality and augmented reality technology has solved the security issue in education scenarios Technology is no longer used in education in a single form. The integration of intelligent technologies such as “big data + AI” has become a new driving force and way of thinking in the current society. After all, intelligent technology will exist in all aspects of educational activities like multimedia technology, and how it is applied to educational scenarios, how to influence and change education are topics that need to be continuously explored. Through case analysis, the article attempts to explore the ways and methods of intelligent technology and its core algorithm applied in education and teaching activities, as well as the impact of intelligent technology on education and teaching activities.
Real-time Online College Writing Teaching Methods to Promote Interaction Between Class Members －Based on the results of analyzing the needs of college students

The purpose of this study is to construct and propose real-time online college writing teaching methods to promote interaction between class members (instructor-learner, learner-learner).&lt;br/&gt;Through surveys and interviews with learners who hadn’t experienced real-time, online college writing education, we confirmed that the main perceptions and demands of learners for real-time online classes were as follows: First, the learners’ reluctance to show oneself in such a class and the demand for interaction with minimal online exposure. Second, the burden of writing in an isolated environment and the demand for detailed assistance of instructor’s in the writing process. Third, the reluctance to show the learner’s written text and the demand for receiving sufficient feedback anonymously.&lt;br/&gt;Therefore, this study proposed the following three teaching methods: First, a method to ensure learner anonymity to allow learners to participate in classes using a “sub-character” rather than their real names. Second, to conduct a short [lecture-practice-feedback] method, instead of a one-time [lecture-practice] method, was implemented several times in one class. Thirdly, to simultaneously share the learners' practice results through chat windows and to provide multiple types of feedback.&lt;br/&gt;This study confirmed that the proposed methods promote interaction between members in an online writing education environment and generate positive teaching and learning effects by reviewing specific class application cases and the learners' responses to these classes.
-NONUNIFORM MULTIRESOLUTION ANALYSIS

Abstract Gabardo and Nashed [‘Nonuniform multiresolution analyses and spectral pairs’, J. Funct. Anal. 158 (1) (1998), 209–241] have introduced the concept of nonuniform multiresolution analysis (NUMRA), based on the theory of spectral pairs, in which the associated translated set $\Lambda =\{0,{r}/{N}\}+2\mathbb Z$ is not necessarily a discrete subgroup of $\mathbb{R}$ , and the translation factor is $2\textrm{N}$ . Here r is an odd integer with $1\leq r\leq 2N-1$ such that r and N are relatively prime. The nonuniform wavelets associated with NUMRA can be used in signal processing, sampling theory, speech recognition and various other areas, where instead of integer shifts nonuniform shifts are needed. In order to further generalize this useful NUMRA, we consider the set $\widetilde {\Lambda }=\{0,{r_1}/{N},{r_2}/{N},\ldots ,{r_q}/{N}\}+s\mathbb Z$ , where s is an even integer, $q\in \mathbb {N}$ , $r_i$ is an integer such that $1\leq r_i\leq sN-1,\,(r_i,N)=1$ for all i and $N\geq 2$ . In this paper, we prove that the concept of NUMRA with the translation set $\widetilde {\Lambda }$ is possible only if $\widetilde {\Lambda }$ is of the form $\{0,{r}/{N}\}+s\mathbb Z$ . Next we introduce $\Lambda _s$ -nonuniform multiresolution analysis ( $\Lambda _s$ -NUMRA) for which the translation set is $\Lambda _s=\{0,{r}/{N}\}+s\mathbb Z$ and the dilation factor is $sN$ , where s is an even integer. Also, we characterize the scaling functions associated with $\Lambda _s$ -NUMRA and we give necessary and sufficient conditions for wavelet filters associated with $\Lambda _s$ -NUMRA.
Underwater Descattering from Light Field

Traditional imaging methods and computer vision algorithms are often ineffective when images are acquired in scattering media, such as underwater, fog, or biological tissue. Compared with the atmospheric scattering environment, densely underwater scattering modeling is more difficult. In this chapter, we introduce an underwater scattering image restoration and depth estimation algorithm based on light field imaging. First, we present a new single-image restoration algorithm which removes backscatter and attenuation from images better than existing methods, and we apply it to each view in the light field. Second, we combine a novel transmission-based depth cue with existing correspondence and defocus cues to improve light field depth estimation. In densely scattering media, our transmission depth cue is critical for depth estimation since the images have low signal-to-noise ratios which significantly degrades the performance of the correspondence and defocus cues. Finally, we propose shearing and refocusing multiple views of the light field to recover a single image of higher quality than what is possible from a single view. We demonstrate the benefits of our method through extensive experimental results in a water tank.
Segmentation and Extraction of GBM Tumor in Brain MRI Medical Images: Comparative Study

In this study, a comparison between two image segmentation methods has been discussed; the first method is based on normal brain's tissue recognition then tumor extraction using Thresholding method. The second method is classification based on fuzzy EM segmentation, which is used for both brain recognition and tumor extraction. Medical image examinations often use more information that are acquired from multiple imaging modalities, this use is increasing due to the complementary information that can be obtained from data fusion. The later improves the quality of the diagnosis; for instance, the image fusion can be developed for data from different modalities or different individuals, it may also concern fusion of image data with an external model, this can express prior knowledge about the problem at hand. The image data used in this comparative study belong to Algerian patients with Glioblastoma multiform. Since the goal of these methods is to detect, segment, extract, classify and measure properties of the brain normal and abnormal (tumor) tissues, the results of comparative study provided a guide tool of which method is more accurate in term of GBM volume estimation for the studied samples of the patients.
Sports training simulation system based on video processing technology

With the development of information technology and sports science related technology, sports training simulation technology based on sports video processing is increasingly developed and mature. Through the application of video processing and tracking technology to track and analyze the athletes in the sports video, the simulation training of athletes can be realized, and the scientific and reasonable formulation of sports plan can be realized. Based on this, this paper proposes a multi motion recognition algorithm based on cloud monitoring, which monitors through the cloud server, and then realizes the corresponding communication and data transmission with the motion recognition terminal, and finally realizes the monitoring of athletes' various movements and scientific and reasonable sports training. Based on this, this paper designs the relevant system platform based on the algorithm and carries out experiments. The experimental results show that the algorithm has certain practicability and innovation.
How to Tweak a Cryptographic Permutation by Direct Manipulation

Cryptographic primitives with internal state and bijective state-transition function can be improved by modifying a small number of transitions, achieving larger periodicity without increase of state space. We show how to tweak such a permutation best for two different targets: minimum cycle length, and average cycle length for random start points.
Cross-modal Retrieval based on Big Transfer and Regional Maximum Activation of Convolutions with Generalized Attention

Image-text retrieval is a challenge topic since image features are still not good enough to represent the high-level semantic information, though the representation ability is improved thanks to advances in deep learning. This paper proposes a cross-modal image-text retrieval framework (BiTGRMAC) based on big transfer and region maximum activation convolution with generalized attention, where big transfer (BiT) trained with large amount data is utilized to extract image features and fine-tuned on the cross-modal image datasets. At the same time, a new generalized attention region maximum activation convolution (GRMAC) descriptor is introduced into BiT, which can generate image features through attention mechanism, then reduce the influence of background clustering and highlight the target. For texts, the widely used Sentence CNN is adopted to extract text features. The parameters of image and text deep models are learned by minimizing a cross-modal loss function in an end-to-end framework. Experimental results show that this method can effectively improve the accuracy of retrieval on three widely used datasets.
Estimating the Efficiency of the Energy Service Market Functioning in Ukraine

Energy service sector provides services aimed at improving energy efficiency by introducing energy-efficient appliances, advanced energy-saving technologies and modern energy-efficient equipment. To estimate the efficiency of the energy service market functioning, it is necessary to identify the efficiency indicators. An approach to estimating the efficiency of the energy service market functioning, based on the analysis of external and internal factors of influence on the final energy consumption, is proposed in this paper. We propose to use the amount of natural gas consumed by the households, condominiums and communal heating supply companies as the energy efficiency indicator. Reduction in consumption with a constant number of users would indicate the efficiency of energy-saving technologies and other services. The mathematical model of dependence of the index of the energy service market efficiency, that is, the amount of natural gas consumed by the population, on the factors of influence is built on the basis of the stochastic approach. A short-term forecast of the efficiency of the energy service market functioning is made according to this model.
Study on Hierarchical Evaluation of Students' Short-term Achievement Fluctuation in Online Piano Teaching Mode

Compared with other disciplines, piano discipline has more requirements for students' subjective thinking ability. This paper analyzes the fluctuation characteristics of students' short-term performance in online piano teaching mode, establishes a constraint parameter model of students' short-term performance management in online piano teaching mode by taking the gain effect of teaching mode and students' understanding level of online teaching as constraint variables, and carries out quantitative evaluation and decision-making of students' short-term performance management in online piano teaching mode through statistical regression analysis of panel data. The key feature distribution data of students' short-term performance management in online piano teaching mode is constructed. The fuzzy prediction and correlation analysis of students' short-term performance management in online piano teaching mode are carried out by elastic regression analysis of teaching effect. The blockchain distribution model of students' short-term performance fluctuation in online piano teaching mode is established. Blockchain fusion technology is adopted to realize hierarchical evaluation of students' short-term performance fluctuation in online piano teaching mode. The results of empirical analysis show that the designed short-term performance management model of online piano teaching mode has higher reliability and better evaluation accuracy, which improves students' short-term performance management ability of online piano teaching mode.
Knee Lift Detection using Convolutional Neural Network Method with FPGA Hardware Design

In this paper we propose a convolutional neural network (CNN) model to detect whether the action of knee lift is standard or not. We use HRNet as the previous part of system which can localize human anatomical keypoints to generate 2D keypoints as the input of our model. We can more easily distinguish whether the knee lift action has reached the required position or not. The simulation is based on the 2D skeleton points as the keypoints. Besides, we also implement this CNN model in FPGA hardware to reduce inference time.
Fall Detection Algorithm of the Elderly Based on BP Neural Network

With the rapid development of population aging, falling is a very serious problem for the elderly. Real-time detection of whether the elderly has fallen can minimize the damage caused by falling. Therefore, this paper proposes a fall detection method based on BP neural network. The algorithm uses a three-layer BP reverse neural network to collect human motion data by wearing a three-axis acceleration sensor (MMA7660FC). After feature extraction of the data, network training is carried out, and the neuron weight and learning rate are adjusted to the training process so that it can realize the function of fall detection. Experimental results show that the algorithm can identify falls well, and its accuracy rate can reach 99.44%.
Immersive Virtual Reality in K-12 and Outcomes: A Meta-analysis

research-article Share on Immersive Virtual Reality in K-12 and Outcomes: A Meta-analysis Authors: Huifen Guo Chongqing Normal University, China Chongqing Normal University, ChinaView Profile , Yan Ma Chongqing Normal University, China Chongqing Normal University, ChinaView Profile , Aizhu Wang Chongqing Normal University, China Chongqing Normal University, ChinaView Profile , Xiang Zhu Chongqing Normal University, China Chongqing Normal University, ChinaView Profile , Lijiao Deng Chongqing Normal University, China Chongqing Normal University, ChinaView Profile , Wenxiang Fan Hangzhou Normal University, China Hangzhou Normal University, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 2928–2934https://doi.org/10.1145/3482632.3487542Online:22 November 2021Publication History 0citation45DownloadsMetricsTotal Citations0Total Downloads45Last 12 Months45Last 6 weeks6 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
Tight lower bound for average number of terms in optimal double-base number system using information-theoretic tools

We propose to use information-theoretic tools for analyzing the number of terms in the optimal double-base number system (DBNS). Most previous works analyze the largest number we obtain from the optimal DBNS for 0≤m<2n. They show that the largest number would be in Θ(n/log⁡n). On the other hands, when one analyze the performance of a cryptographic algorithm, it is more common to consider the average number of terms. One believes that we may be able have smaller asymptotic number for the average number. Unfortunately, using tools in information theory, we show in this paper that the average number is also in Θ(n/log⁡n).
Fishtank Sandbox: A Software Framework for Collaborative Usability Testing of Fish Tank Virtual Reality Interaction Techniques

Human-computer interaction researchers have been studying how we can interact with virtual objects in a virtual environment efficiently. Many usability experiments do not have the same control parameters. The lack of consistency makes comparing different interaction techniques difficult. In this article, we present a software framework for usability study in FTVR interaction techniques. The software framework provides fixed control parameters (e.g., task, graphic settings, and measuring parameters), the ability for other researchers to incorporate their interaction techniques as an add-on, and enabling individuals to participate in the experiment over the internet. The article explores a new way for VR/AR researchers to approach usability experiments using the framework and discuss the challenges that it brings.
Power Line Recognition and Foreign Objects Detection Based on Image Processing

In order to improve the efficiency of power line inspection, this paper uses the power line image collected, through the method of image processing, to identify power lines and detect foreign bodies. First, the Canny operator is improved by using Otsu adaptive acquisition of double thresholds for edge extraction of power line images. Then an improved Hough transform is used to detect straight lines in edge images for improving the accuracy of power line detection. Furthermore, a local contour detection method without considering the relationship between power lines and foreign objects is proposed to extract foreign objects on power lines, which can realize decoupling of power line recognition and fault detection. Through the simulation test of the collected power lines, the accuracy of the proposed method for power line extraction and foreign objects detection in the image is verified.