Reviewerly/exHarmony huggingface dataset:
High-Speed Large-Scale Imaging Using Frame Decomposition From Intrinsic Multiplexing of Motion

High-speed imaging plays a vital role in the observation of transient dynamics in scientific research and daily life. However, due to the bandwidth limitation, the existing imaging systems suffer from the trade-off between spatial resolution and temporal resolution. This paper reports a high-speed large-scale imaging technique using frame decomposition, maintaining its high spatial resolution and effectively boosting its frame rate. The proposed method decomposes a single motion-blurred image into latent sharp video frames. It takes the advantage of the intrinsic multiplexing nature of motion, without using any additional modulators for coded compression. Specifically, under the premise of rigid motion, a high-speed video clip can be modeled as a series of affine transformations of a sharp reference frame, and the captured blurred image is the temporal average of these multiple shape frames. Such an affine blur model effectively reduces the variable space by orders of magnitude. To solve the affine model, the differentiable affine operators are introduced to realize gradient-descent optimization. Further, we introduce the distributed optimization framework combining the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l0$</tex-math></inline-formula> -norm total variation regularization and the denoising network to attenuate the ringing artifacts. The optimization follows a coarse-to-fine strategy to further reduce artifacts. As a result, both the sharp reference image and the affine parameters are retrieved. They are finally input into stepwise affine transformation to recover the latent sharp video frames. The stepwise retrieval maintains the nature to bypass the frame order ambiguity. Experiments on public datasets (Kotera <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2019), (Kotera <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2020) validate the reported technique’s sota performance, with average NIQE=4.344 and BLIINDS-II=23.21. Both qualitative and quantitative evaluations on various datasets (Kotera <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2019), (Kotera <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2020), (Zheng <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2013), (Nah <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2017), and real captured data indicate that the proposed method outperforms the existing blur-to-video technique (Jin <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2018), especially in the cases of non-planar motion of either the camera or objects.
Hilbert Transformation Deep Learning Network for Single-Shot Moiré Profilometry

Phase demodulation from a single moiré fringe pattern is an ill-posed inverse problem, which limits the applications of moiré profilometry in dynamic 3D measurement. In this paper, a deep learning-based high-precision technique is used to tackle this problem arose from highly under sampled inputs. Our novel approach, to the best of our knowledge, termed 2D Hilbert transformation network, uses two Res U-Net networks paired with a dichotomous network to generate the desiredπ∕2 phase-shifting fringe pattern referred to the input. This process can be viewed as 2D Hilbert transformation of a fringe pattern. With this network, the wrapped phase can be extracted easily if the sampled fringes pattern is filtered and normalized in advance. Trained using simulated data, experimental results show that the proposed Hilbert transformation network provides a simple but robust solution for phase extraction from a single fringe pattern with phase error less than 0.02rad and, therefore, make it allow for paving a new way to reliable and practical learning-based single-shot Moiré profilometry.
Research on the Application of Virtual VR Technology in Physical Education——Taking College Basketball Teaching as an Example

With the continuous improvement of science and technology in China, virtual VR technology is gradually rising and gradually infiltrating into various industries. In the field of sports, virtual VR technology has developed rapidly and achieved good results, effectively improving the level of intelligent sports technology, and plays a very important role in promoting the current sports performance. However, the application of VR technology in college physical education has not achieved substantial development. This paper mainly analyzes the big data of virtual VR technology in college basketball teaching and training, basketball robot assisted training and specific implementation measures of basketball teaching and training, in order to speed up the application of virtual VR technology in the field of college basketball sports Learning effective reference basis provides new ideas and reference methods for college physical education reform.
Research on Web Front-end Performance Optimization Based on XML

As for the optimization of Web sites, the optimization technology in the background is mature, but the research and attention on the performance optimization of Web front-end is far from enough. Based on XML, this paper first analyzes the research status from the research background of Web front-end performance optimization. Then, the principles of request response mechanism, HTTP protocol analysis, browser response mechanism and the particularity of mobile Web front-end are analyzed. According to the research and analysis of front-end related theories and the experience of engineering practice, a series of optimization schemes are put forward from the aspects of page composition, picture and Web cache. Finally, a comprehensive experimental test is carried out by combining theory with practice, which verifies the positive influence of the optimization scheme on the performance optimization of Web front-end.
Real-Time Face Mask Detection Using Deep Learning Algorithm Over Images During Covid 19 Pandemic

Every country in this world has been severely affected by the Coronavirus. It has grown into a global pandemic. Millions of people all over the world have been infected with the coronavirus and it has caused numerous deaths all over the planet. Due to the prevailing health crisis, the entire world was put in lockdown. All economical activities were put on hold to break the chain of the transmission of the coronavirus. This led to a global economic crisis. After a certain degree of hold on the transmission of the coronavirus, economic operations were allowed to help the plummeting economy, but the health crisis due to the coronavirus is still at large. Unless a significant percentage of the population in a country is vaccinated, the only way to fight the pandemic is to adopt preventive measures to break the transmission, and wearing a mask properly is essential in this regard. But unfortunately, people often disregard such advisory. Therefore, the authorities want to keep a watch on the crowd through video surveillance and use technology to identify the violators. Image processing is highly useful in this regard. But conventional image processing algorithms cannot handle a large set of unstructured data generated by CCTV footages. Image processing augmented with deep learning techniques are used in such cases. This chapter elucidates the use of deep learning methods in image processing of video surveillance footage to detect whether a person in the footage wears a mask properly or not.
Online Collaborative Text Editor Using Socket.IO

One of difficulties when we work in group is to team up in a proficient manner. Additionally, working from distance with a group is a bad dream particularly when wemake a report and conceptualizing thoughts. From this point of view, making a constant shared word processor was conceived. In this position paper we contend for working from a training- based viewpoint when going up against issues of shared altering. In this paper, we present Fresh Edit, an online, open- source, community text comment instrument using Socket.IO.
Intelligent Love Letter Generator Based on GPT-2 Model

The confession wall is one of the most popular virtual communities in colleges and universities. It has the functions of expressing love, information release, academic exchange and thought guidance, among which the most important is expressing love. In this paper, an intelligent love letter generator is invented using GPT-2 mode in NLP (Natural Language Processing) based on the words collected from confession wall of college. In addition, emojis can be automatically selected which could match the love letter generated by the model. This smart love letter generator fits the fashion trend of contemporary young people, with a wide audience and good practical significance.
The Power of Augmented Reality for Smart Environments: An Explorative Analysis of the Business Process Management

The chapter proposes an explorative analysis of the use of augmented reality for smart environments highlighting the twofold capability of augmented reality in improving business processes and business environments. Thus, the research question is: “how does augmented reality forge smart environments by improving the business process?” We report evidence from various fields of application such as military, medicine, architecture, automotive, and retail. Indeed, the results show improvements and influences of augmented reality in terms of information management, planning and control, change process, knowledge and performance management, people and customer management, providing a cross-analysis between four dimensions of an organization: resources, process, people, and customer. These improvements enhance business workplaces and connected activities such as learning rooms, online and offline stores, operational rooms, and manufacturing and maintenance departments. The evidence from this study suggests that companies should analyze changes brought by augmented reality about the business process and redesign process to ensure a beneficial impact on the whole business.
Research on Pedestrian Re-identification Method Based on Visual Attention Mechanism

Based on the method of attention mechanism, this paper researches on pedestrian re-identification. First, ResNet50 is used as the backbone network, and several model preprocessing methods are added as the baseline network. Then the channel attention mechanism module SENet is added to form the SE-ResNet50 network, and it can learn the importance of different dimensions of feature vectors, and focus attention on the corresponding dimensions. After the improvement, the model’s rank-1 on the Market-1501 data set increased by 1.5%, MAP increased by 2.0%, and the model’s rank-1 increased by 0.1% on the DukeMTMC-reID data set, MAP increased by 0.8%. In addition, this article also conducts a study on the importance of loss function, and the model obtains the best improvement effect when the ratio of ID loss to TriHard loss is 1 to 1.5. Rank-1 on Market-1501 increases by 0.2%, mAP increases by 0.2%, and on the DukeMTMC-reID data set, rank-1 increased by 1.1% and mAP increased by 2.1%. Finally, the actual video collection of campus scenes is carried out, and the trained model is applied to the pedestrian re-recognition in the actual scene. The results show that the model has an outstanding ability to recognize pedestrians in the context of a more complex environment.
Dynamic sampling RRT for improved performance in large environments

The basic rapidly exploring random tree (RRT) path-planning algorithm uses a uniform random sampling over the entire free space of the environment in the process of producing new points for the RRT tree. That approach achieves good results in environments with simple and distant obstacles but becomes far more challenging in large and complex environments with narrow passages between obstacles. This paper presents an improvement of the sampling method used in the RRT search and path-planning algorithm to increase its ability to explore in large, complex, and dense environments. The main idea is to make the sampling over a dynamic space that grows with the RRT tree, which increases the ability to explore and finding narrow passages in large and complicated environments. To illustrate the benefits of dynamic sampling, a theoretical analysis of the fundamental principle with results from a simulated experiment is presented as well.
A General Text Mining Method to Extract Echocardiography Measurement Results from Echocardiography Documents

BackgroundIn everyday medical practice, the results of cardiac ultrasound examinations are generally recorded in unstructured text, from which extracting relevant information is an important and challenging task. This paper presents a generally applicable language and corpus-independent text mining method for extracting and structuring numerical measurement results and their descriptions from echocardiography reports.MethodThe developed method is based on generally applicable text mining preprocessing activities, it automatically identifies and standardizes the descriptions of the cardiac ultrasound measures, and it stores the extracted and standardized measurement descriptions with their measurement results in a structured form for later usage. The method does not contain any regular expression-based search and does not rely on information about the structure of the document.ResultsThe method has been tested on a document set containing more than 20,000 echocardiographic reports by examining the efficiency of extracting 12 echocardiography parameters considered important by experts. The method extracted and structured the echocardiography parameters under the study with good specificity (lowest value: 0.813, highest value: 1.0) and excellent specificity (for all cases 1.0). The F1-score ranged between 0.873 and 1.0.ConclusionsThe presented case study has shown that the proposed method can extract measurement results from echocardiography documents with high confidence without performing a direct search or having any a priori knowledge about the data recording habits. Furthermore, it effectively handles spelling errors, abbreviations and the highly varied terminology used in descriptions. As it does not rely on any information related to the structure or the language of the documents, or recording habits, it can be applied for processing any free-text written medical texts.
The Use of Creative Works of Younger Schoolchildren to Improve the Success of Their Educational Activities

The article proposes for consideration the problem of using creative works as an effective way to implement an individually differentiated approach in teaching successful younger schoolchildren. The author relies on the idea that difficulties in the learning process can occur in children who demonstrate different levels of educational activity, in particular, also in excellent schoolchildren and "good schoolchildren", therefore, providing them with different levels of creative tasks allows for the successful prospective intellectual and creative development of such schoolchildren. Examples of the use of differentiated creative tasks in the lessons of the Russian language and literary reading are given.
Speech Emotion Recognition Based on Henan Dialect

In this paper, according to international standards, referring to CASIA Chinese phonetic library, recording the phonetic library based on Henan dialect. And use computer software to reduce noise. Use Matlab to preprocess the speech signal such as framing and windowing, and extract the short-term energy, formant frequency, pitch frequency, Mel cepstrum coefficient, and other characteristic parameters contained in the speech signal. Use KNN, BP neural network, PNN neural network, and LVQ neural network to recognize and process recorded dialect. It can be seen from the recognition rate of the LVQ neural network is the highest. For the five emotions, the recognition results of various algorithms are different.
Identification of Hydrothermal Alteration and Weathering Process in Western Jahajpur Belt

The distinct varieties of alteration and weathered minerals in several litho-stratigraphic sequences imply the distinct process and condition of deformation and crystallization. The study areas are structurally controlled and have different grades of hydrothermally altered and weathered ores minerals such as Talc, Clay, Hematite, Goethite and Limonite. These altered and weathered ores belongs to different rock strata of various formations. The variation in chemical composition implies different geochemistry. To discriminate different grades of iron ores, the study adopted several techniques such as identification of morphology, microstructures and measurement of grain size analysis of minerals. The petrographic analysis shows the leaching (alteration) and metamorphism in the iron ore minerals. The pattern, order and sequence of leaching of iron oxides and alteration of clay minerals including structural deformation define the applicability of several techniques.
SSQoE: Measuring Video QoE from the Server-Side at a Global Multi-tenant CDN

Over the past decade, video streaming on the Internet has become the primary source of our media consumption. Billions of users stream online video on multiple devices with an increasing expectation that video will be delivered at high quality without any rebuffering or other events that affect their Quality of Experience (QoE). Video streaming platforms leverage Content Delivery Networks (CDNs) to achieve this at scale. However, there is a gap in how the quality of video streams is monitored. Current solutions rely on client-side beacons that are issued actively by video players. While such approaches may be feasible for streaming platforms that deploy their own CDN, they are less applicable for third-party CDNs with multiple tenants and diverse video players. In this paper, we present a characterization of video workload from a global multi-tenant CDN and develop SSQoE: a methodology deployed on the server side which estimates rebuffering experienced by video clients using passive measurements. Using this approach, we calculate a QoE score which represents the health of a video stream across multiple consumers. We present our findings using this QoE score for various scenarios and compare it to traditional server and network monitoring metrics. We also demonstrate the QoE score’s efficacy during large streaming events such as the 2020 Superbowl LIV. We show that this server-side QoE estimation methodology is able to track video performance at an AS or user agent level and can easily pinpoint regional issues at the CDN, making it an attractive solution to be explored by researchers and other CDNs.
Disentanglement Enables Cross-Domain Hippocampus Segmentation

Limited amount of labelled training data are a common problem in medical imaging. This makes it difficult to train a well-generalised model and therefore often leads to failure in unknown domains. Hippocampus segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis and treatment of neuropsychatric disorders. Domain differences in contrast or shape can significantly affect segmentation. We address this issue by disentangling a T1-weighted MRI image into its content and domain. This separation enables us to perform a domain transfer and thus convert data from new sources into the training domain. This step thus simplifies the segmentation problem, resulting in higher quality segmentations. We achieve the disentanglement with the proposed novel methodology ’Content Domain Disentanglement GAN’, and we propose to retrain the UNet on the transformed outputs to deal with GAN-specific arte-facts. With these changes, we are able to improve performance on unseen domains by 6-13% and outperform state-of-the-art domain transfer methods.
Policy Gradient for Continuing Tasks in Discounted Markov Decision Processes

Reinforcement learning aims to find policies that maximize an expected cumulative reward in Markov decision processes with unknown transition probabilities. Policy gradient (PG)-algorithms use stochastic gradients of the value function to update the policy. A major drawback of PG-algorithms is that they are limited to episodic tasks (multiple finite-horizon trajectories) unless stringent stationarity assumptions are imposed on the trajectories. Hence, they need restarts and cannot be fully implemented online, which is critical for systems need to adapt to new tasks and/or environments in deployment. Moreover, the standard stationary formulation ignores transient behaviors. This motivates our study of discounted MDPs of infinite horizon without restarts. However, it is unknown if in this case following stochastic PG-type estimates would improve the policy. The main result of this work is to establish that when policies belong to a reproducing kernel Hilbert space (RKHS), and the kernel is selected properly, then these PG-estimates are ascent directions for the value function conditioned to any arbitrary initial point. This allows us to prove convergence of our online algorithm to the local optima. A numerical example shows that an agent running our online algorithm learns to navigate and succeeds in a surveillance task that requires looping between two goal locations. This example corroborates our theoretical findings about the ascent directions of subsequent stochastic gradients. It also shows how our online algorithm guides the agent through a continuing cyclic trajectory that does not comply with the standard stationarity assumptions in the literature for non-episodic training.
Application of Big Data Information Processing Technology in the Improvement of Practical English Teaching Mode

Under the background of strengthening quality-oriented education, the English teaching mode in many schools is still the traditional knowledge-based teaching mode. This kind of teaching mode is far from practical application for language learning with strong application. This article analyzes the existing problems of traditional English teaching, and proposes the basic ways and methods to use big data technology to improve the practical English teaching mode.
Investigation of numerical solutions for chaotic Lorenz system using Mathcad software

In this paper, we investigate the accuracy of numerical solution for chaotic Lorenz system. Mathcad software is used as case-study, built-in algorithms used are Runge-Kutta of fourth order (RK4), Adams backward differential formula (AdamsBDF) and developed series method. The numerical integration that are also checked involves Gauss-quadrature and Simpson’s quadrature rules. All the graphical results are showed for all the different numerical methods.