Reviewerly/exHarmony huggingface dataset:
A Multivariate Evaluation Model of Physical Education Teaching Quality with Random Matrix Optimization Neural Network

The status of physical education courses in education is increasing, there are many problems in teaching traditional physical education courses, and information technology and information-based education are developing continuously, providing enough space for the research of the topic. The online teaching of public physical education courses in colleges and universities is very complex, and its quality evaluation faces many difficulties. Considering the problem that most traditional PE teaching quality evaluation methods are affected by the accuracy of the established model in the process of positioning, a PE teaching quality evaluation model based on random matrix theory is established by using the advantage that random matrix theory does not rely on the simplification and assumptions of the system model and can extract effective information from a large amount of data. Firstly, the influencing factor data and physical education state data are constructed as a state augmentation matrix. Then, the average spectral radius of the characteristic statistic is used to construct indicators to analyze the size of the correlation between each influencing factor and physical education state to achieve the fundamental positioning of physical education quality. The main influencing factors of online teaching of public physical education classes in colleges and universities mainly include teacher quality, teaching process, course resources, and course effect, and the construction of an evaluation index system should be implemented based on adhering to the scientificity, accessibility, independence, and generality from these four aspects to construct the evaluation index system and evaluation criteria of online teaching quality of public physical education class in colleges and universities, which provides the final realization of the goal of physical education class in colleges and universities.
<i>Cushion</i> : A proactive resource provisioning method to mitigate SLO violations for containerized microservices

Deploying microservices in container-based cloud environments increases the agility of resource scaling. However, the delay in autoscaling for microservices caused by container cold start results in response time service-level objectives (SLO) violations under burst workloads. This paper proposes Cushion, a proactive resource provisioning method for containerized microservices to mitigate SLO violations caused by burst workloads, which promptly schedules workloads to reserved container instances when workloads suddenly increase and meanwhile steadily scales the container instances according to the SLOs of the microservices. Cushion was evaluated through prototype-based experiments in a containerized testbed using a benchmark microservice and four web workload traces by comparing it against existing methods. The experimental results show 9.12x times lower SLO violations, 23.75% higher throughput with 24.35% more CPU usage, and 4.85% more memory usage compared to the baseline methods.
Media Promotional for Art in Tangerang City with Audio Visual Adobe Creative

Historical buildings contain elements of art in the city of Tangerang, which have good potential to become a tourist destination, but they must be appropriately managed to generate a source of income for the region. The diversity of ethnicities, races, and religions is an important supporting factor in influencing the diversity of artistic values. One of them is the Lenggang Cisadane Dance which is typical art of Tangerang City. And many historical buildings such as the Thousand Doors Mosque, Fortress Museum, Kalipasir Mosque, Al-Adzhom Mosque, etc. Every historical asset needs to be introduced to the public, and this is the authority of the Communication and Information Technology Office (DisKomInfo). However, the information media used is still very minimal. Therefore, a study was conducted using the KPM method, which outputs video using the Adobe Creative Suite. The media needed to maximize promotion and information is the Video Art, Tourism, and Historic Buildings, with an informative and practical display. The beauty of Tangerang City holds history and high artistic value, so it is hoped that this research can increase public interest in studying history and doing tourism.
A process approach to quality management doubles<scp>NEON</scp>sensor data quality

Abstract A quality management system is critical for ensuring that the data and services provided by an organization meet the needs of its mission. With a mission to collect long‐term open‐access ecological data to better understand how US ecosystems are changing, the National Ecological Observatory Network (NEON) is a highly standardized measurement network distributed across the United States and Puerto Rico collecting data on the biosphere and its interfaces with the pedosphere, hydrosphere and atmosphere. In order to achieve high‐quality, comparable data across the network, a quality management system was developed by applying the seven ISO 9001:2015 principles of quality management: customer focus , leadership, engagement of people, process approach, improvement, evidence‐based decision making and relationship management . The resultant system is integrated throughout NEON's organizational structure with an approach that connects people and operational processes throughout the data life cycle ( process approach ). We describe the system with respect to sensor data (automated measurements), demonstrating its effectiveness through examples, lessons learned and a continuous history of improvement towards quality goals, including a doubling of data quality in NEON's meteorological and soil datasets since 2015 and substantial gains in other sensor datasets. Owing to a focus on quality management principles and particularly the interconnectedness of human and information systems, NEON's quality management system can serve as a model for networks with a variety of organizational structures and sizes.
Automatic random variate generation in Python

The generation of random variates is an important tool that is required in many applications. Various software programs or packages contain generators for standard distributions like the normal, exponential or Gamma, e.g., the programming language R and the packages SciPy and NumPy in Python. However, it is not uncommon that sampling from new/non-standard distributions is required. Instead of deriving specific generators in such situations, so-called automatic or black-box methods have been developed. These allow the user to generate random variates from fairly large classes of distributions by only specifying some properties of the distributions (e.g. the density and/or cumulative distribution function). In this note, we describe the implementation of such methods from the C library UNU.RAN in the Python package SciPy and provide a brief overview of the functionality.
Artificial intelligence and its applications in digital hematopathology

The advent of whole-slide imaging, faster image data generation, and cheaper forms of data storage have made it easier for pathologists to manipulate digital slide images and interpret more detailed biological processes in conjunction with clinical samples. In parallel, with continuous breakthroughs in object detection, image feature extraction, image classification and image segmentation, artificial intelligence (AI) is becoming the most beneficial technology for high-throughput analysis of image data in various biomedical imaging disciplines. Integrating digital images into biological workflows, advanced algorithms, and computer vision techniques expands the biologist's horizons beyond the microscope slide. Here, we introduce recent developments in AI applied to microscopy in hematopathology. We give an overview of its concepts and present its applications in normal or abnormal hematopoietic cells identification. We discuss how AI shows great potential to push the limits of microscopy and enhance the resolution, signal and information content of acquired data. Its shortcomings are discussed, as well as future directions for the field.
General Surgery Clinical Information Management System Based on Intelligent Data Management

In order to improve the effect of surgical clinical information management, this paper combines the intelligent data management technology to construct the general surgery clinical information management system. Based on the design and development of general surgery clinical medical case information management system, this paper studies the mutual conversion between XML data and relational data, and designs a clinical medical case information management system based on the design idea of layered architecture. The clinical medical case information management system mainly adopts the modular design idea and adopts a digital microscopic image storage mode based on HDFS architecture. After building the system function structure, the system effect is verified by simulation test. From the experimental statistical results, it can be seen that the general surgery clinical information management system based on intelligent data management proposed in this paper has a good effect.
Methodology for image retrieval based on binary spacepartitioning and perceptual image hashing

The paperfocuses on the content-based image retrieval systems building. The main challengesin the construction of such sys-tems are considered, the components of such systems are reviewed, and a brief overview of the main methods and techniques that have been used in this area to implement the main components of image search systems is given.As one of the options for solving such a problem, an image retrievemethodology based on the binary space partitioning method and the perceptual hashing method is proposed. Space binary partition trees are a data structuresobtained as follows: the space is partitioned by a hyperplane into two half-spaces, and theneach half-space is recursively partitioned until each node contains only a trivial part of the input features. Perceptual hashing algorithms make it possible to represent an image as a 64-bit hash value, with similar images represented by similar hash values. As a metric for determining the distance between hash values, the Hamming distance is used, this counts the number of dis-tinct bits.To organize the base of hash values, a vp-tree is used, which is an implementation of the binary space partitioning struc-ture.For the experimental study of the methodology, the Caltech-256 data set was used, which contains 30607 images divided into 256 categories, the Difference Hash, P-Hash and Wavelet Hash algorithms were used as perceptual hashing algorithms, the study was carried out in the Google Colab environment.As part of an experimental study, the robustnessof hashing algorithms to modification, compression, blurring, noise, and image rotation was examined. In addition, a study was made of the process of building a vp-tree and the process of searching for images in the tree. As a result of experiments, it was found that each of the hashing algorithms has its own advantages and disadvantages. So, the hashing algorithm based on the difference inadjacentpixel values in the image turned out to be the fastest, but it turned out to be not very robustto modification and image rotation. The P-Hash algorithm, based on the use of the discrete cosine transform, showed better resistance to image blurring, but turned out to be sensitive to image compression. The W-Hash algorithm based on the Haar wavelet transform made it possible to construct the most efficient tree structure and provedto be resistant to image modification and compression.The proposed technique is not recommended for use in general-purpose image retrieval systems; however, it can be useful in searching for images in specialized databases. As ways to improve the methodology, one can note the improvement of the vp-tree structure, as well as the search for a more efficient method of image representation, in addition to perceptual hashing.
Event-Related Potentials and Selective Attention

In this chapter, the author shares the optimistic outlook that the event-related electric fields we record on the scalp are related to important aspects of information processing in the brain. ERP measures may also permit inferences about the allocation of processing resources to different stimuli and tasks. Another useful function of ERPs is to help create a proper taxonomy of psychological processes. According to many theories, expectancies are established from moment to moment about the words that are likely to come next in a sentence, based on the ongoing semantic and syntactic contexts; that is, people process sentences in terms of a continual updating of the semantic context or schema of the message being received.
Spontaneous gaze interaction based on smooth pursuit eye movement using difference gaze pattern method

Human gaze is a promising input modality for being able to be used as natural user interface in touchless technology during Covid-19 pandemic. Spontaneous gaze interaction is required to allow participants to directly interact with an application without any prior eye tracking calibration. Smooth pursuit eye movement is commonly used in this kind of spontaneous gaze-based interaction. Many studies have been focused on various object selection techniques in smooth pursuit-based gaze interaction; however, challenges in spatial accuracy and implementation complexity have not been resolved yet. To address these problems, we then proposed an approach using difference patterns between gaze and dynamic objects' trajectories for object selection named Difference Gaze Pattern method (DGP). Based on the experimental results, our proposed method yielded the best object selection accuracy of and success time of ms. The experimental results also showed the robustness of object selection using difference patterns to spatial accuracy and it was relatively simpler to be implemented. The results also suggested that our proposed method can contribute to spontaneous gaze interaction.
The Effect of Points Dispersion on the <i>k</i>-nn Search in Random Projection Forests

Partitioning trees are efficient data structures for <i>k</i>-nearest neighbor search. Machine learning libraries commonly use a special type of partitioning trees called <i>k</i>d-trees to perform <i>k</i>-nn search. Unfortunately, <i>k</i>d-trees can be ineffective in high dimensions because they need more tree levels to decrease the vector quantization (VQ) error. Random projection trees rpTrees solve this scalability problem by using random directions to split the data. A collection of rpTrees is called rpForest. <i>k</i>-nn search in an rpForest is influenced by two factors: 1) the dispersion of points along the random direction and 2) the number of rpTrees in the rpForest. In this study, we investigate how these two factors affect the <i>k</i>-nn search with varying <i>k</i> values and different datasets. We found that with larger number of trees, the dispersion of points has a very limited effect on the <i>k</i>-nn search. One should use the original rpTree algorithm by picking a random direction regardless of the dispersion of points.
Real-Time Path Planning for Fully Actuated Autonomous Surface Vehicles

This paper presents a method for real-time path planning for fully actuated autonomous surface vehicles in confined waters. The goal is to continuously generate a collision-free path from a given initial pose to a given final pose. Both the own vehicle and static obstacles are represented as convex polygons. As soon as the environment changes, or other initial or final poses are specified, a warm start is performed, in which the results of previous solutions are reused. An optimal sampling-based approach is used to explore the search space. In a cost function, the length of the path is weighted together with the distance to all obstacles. Some parts of the cost function are calculated in advance and stored in look-up tables to reduce the computation time. The result is an optimal path from an initial pose to a final pose that avoids collisions of the vehicle with static obstacles. The proposed warm start procedure is tested by real-time experiments using different scenarios.
Risk Sensitivity and Entropy Regularization in Prototype-based Learning

Prototype-based learning methods have been extensively studied as fast, recursive, data-driven, interpretable, and robust learning algorithms. We study the effect of entropy regularization in prototype-based learning regarding (i) robustness with respect to the dataset and the initial conditions, and (ii) the generalization properties of the learned representation. A duality relationship, with respect to a Legendre-type transform, between free energy and Kulback-Leibler divergence measures, is used to show that entropy-regularized prototype-based learning is connected to exponential objectives associated with risk-sensitive learning. We use these results to incentivize the development of entropy-regularized prototype-based learning algorithms by highlighting its properties, including (i) memory and computational efficiency, (ii) gradient-free training rules, and (iii) the ability to simulate an annealing optimization process that results in progressively growing competitive-learning neural network architectures.
Sentiment Analysis using Term based Method for Customers’ Reviews in Amazon Product

Customers' review in Amazon platform plays an important role for making online purchase decision making, however the reviews are snowballing in E-commerce day by day.The active sharing of customers' experience and feedback helps to predict the products and retailers' quality by using natural language processing.This paper will focus on experimental discussion on Amazon products reviews analysis coupled with sentiment analysis using term-based method and N-gram to achieve best findings.The investigation of sentiment analysis on amazon product gain more valuable information on related text to solve problem related services, products information and quality.The analysis begins with data pre-processing of Amazon products reviews then feature extraction with POS tagging and term-based concept.e-Commerce customer's reviews normally classify different experience into positive, negative and neutral to judge human behavior and emotion towards the purchase products.The major findings discussed in this journal will be using four different classifier and N-grams methods by computing accuracy, precision, recall and F1-Score.TF-IDF method with N-gram shows unigram with Support Vector Machine learning with highest accuracy results for Amazon product customers' reviews.The score reveals that Support Vector Machine for unigram achieved 82.27% for accuracy, 82% precision, 80% Re-call and 72% F1-Score.
Development of typical meteorological year for massive renewable energy deployment in Togo

Renewable energy (RE) penetration assessment and the development typical meteorological year (TMY) for five cities are considered together in this study. Thus, an integrated method is utilised encompassing RE status assessment and the Sandia method to generate the typical meteorological months of TMY. TMY and long-term data (LT) are then compared as well as a PV system of 3kWc output using TMY and LT under statistical errors. Until 2020 only 11.27% out of 360.02 MW capacity in power demand was RE (hydro and solar). LT and TMY are close for all the towns with a better closeness for Sokode. The latter predicts PV system performance within 2% of the LT in all the sites. More investment has to be put in RE sector because of its potential: 5.27 kWh/m2/day of mean solar radiation, 1238.21 mm of average annual precipitation and 7 m/s of mean wind speed at 50 m above the ground.
On the Distribution of Kurepa’s Function

Kurepa’s function and his hypothesis have been investigated by means of numerical simulation. Particular emphasis has been given to the conjecture on its distribution, that should be one of a random uniform distribution, which has been verified for large numbers. A convergence function for the two has been found.
Attentive Generative Adversarial Network with Dual Encoder-Decoder for Shadow Removal

Shadow removal is a fundamental task that aims at restoring dark areas in an image where the light source is blocked by an opaque object, to improve the visibility of shadowed areas. Existing shadow removal methods have developed for decades and yielded many promising results, but most of them are poor at maintaining consistency between shadowed regions and shadow-free regions, resulting in obvious artifacts in restored areas. In this paper, we propose a two-stage (i.e., shadow detection and shadow removal) method based on the Generative Adversarial Network (GAN) to remove shadows. In the shadow detection stage, a Recurrent Neural Network (RNN) is trained to obtain the attention map of shadowed areas. Then the attention map is injected into both generator and discriminator to guide the shadow removal stage. The generator is a dual encoder-decoder that processes the shadowed regions and shadow-free regions separately to reduce inconsistency. The whole network is trained with a spatial variant reconstruction loss along with the GAN loss to make the recovered images more natural. In addition, a novel feature-level perceptual loss is proposed to ensure enhanced images more similar to ground truths. Quantitative metrics like PSNR and SSIM on the ISTD dataset demonstrate that our method outperforms other compared methods. In the meantime, the qualitative comparison shows our approach can effectively avoid artifacts in the restored shadowed areas while keeping structural consistency between shadowed regions and shadow-free regions.
3D Human Motion Posture Tracking Method Using Multilabel Transfer Learning

To overcome the high position and posture angle tracking error, long tracking loss time and posture tracking update response time, and low fitness problem of traditional human motion posture tracking methods, in this paper, a three-dimensional (3D) human motion posture tracking method using multilabel transfer learning is proposed. According to the human structure composition and degree of freedom constraints, the 3D human joint skeleton model is constructed to generate the 3D human pose image and perform the noise reduction operation. The background difference is used to detect the 3D human moving target. Using multilabel transfer learning, human motion posture features are extracted from joint position and joint angle, and the estimation results of 3D human motion posture are obtained. The tracking error of human motion posture is corrected by three-step search, and the visual 3D human motion posture tracking results are output. The results show that, compared with the traditional human motion posture tracking method, the position and posture angle tracking errors of the proposed method are 2.18 mm and 0.178 deg, respectively. The tracking loss time and posture tracking update response time are shorter, which proves that the proposed method has more advantages in tracking accuracy and higher adaptability.