Reviewerly/exHarmony huggingface dataset:
How Distributed Leadership Facilitates Technology Integration: A Case Study of “Pilot Teachers”

Background/Context Schools and teachers are under immense pressure to adopt technology as a mechanism of educational equity. As such, it is important to understand what school-level practices can support more meaningful technology integration in classrooms. This is especially critical in a time (during the COVID-19 pandemic) when digital learning has been forcibly implemented nationwide, and scholars are voicing concerns that educational organizations’ choices about technology now may lead to lasting issues of power and control, new forms of student inequity, and other unexpected effects. Purpose/Objective/Research Question/Focus of Study This study examines a blended personalized learning school—a school designed to offer a combination of computer-based learning experiences and face-to-face instruction—to demonstrate how leaders can help teachers integrate technology into their classrooms in a meaningful and sustainable way. The research question is: What school practices support teachers to successfully incorporate technology into the classroom? Setting The research site is Binary High School, a personalized learning charter high school in a large urban area that primarily serves historically disadvantaged students. Participants: The participants include the content teachers in Grades 9–11, as well as the school founder, the principal and assistant principal, the student services coordinator, data analysis coordinator, and the IT director. Research Design This research stems from a three-year qualitative case study of a high-tech personalized learning charter high school. I conducted 37 interviews with teachers, students, staff, and administrators and observed dozens of classes, several parent nights, and many professional development meetings and staff meetings. I also collected hundreds of physical and digital documents. Findings/Results The pilot teacher program supported technology integration and showed how distributed leadership practices—specifically, providing opportunities and building capacity for a more collaborative, horizontal leadership structure, supporting teacher professionalization, and sharing the responsibilities for leadership across stakeholders at multiple levels—can support technology-driven educational initiatives. Conclusions/Recommendations For schools interested in technology-based instructional models, a pilot teacher program similar to the one described in this article may be worth exploring. More generally, adopting a distributed perspective of leadership and drawing on practices that exemplify that perspective can help to engage teachers in schoolwide technology integration and classroom reform.
Role of Critical Success Factors in Offshore Quality Requirement Change Management Using SLR

In software engineering field, requirement change management is a challenging job.Ignoring incoming changes results in customer displeasure.It may also result in late product transportation.Managing requirement changes in poor way is the main cause of product failure.It has more diverse effect in global software outsourcing.In software quality requirement change management, it is necessary to address success factors in order to accomplish the requirements of the customers.In this paper, systematic literature review approach is used for documentation and scrutinization of success factors.Total sixteen success factors were recognized having great impact on quality software requirement change management.Our identified success factors like 'Proper Requirement Change Management', 'Rapid Delivery', 'Quality Software Product, Access to Market', 'Project Management', 'Skills and Methodologies', 'Low Cost/Effort Estimation', 'Clear Plan and Road Map', 'Agile Processes', 'Low Labor Cost', 'User Satisfaction', 'Communication/Close Coordination', 'Proper Scheduling and Time Constraints', 'Frequent Technological Changes', 'Robust Model', 'Geographical juncture/Cultural differences' are the crucial factors that affect software quality requirement change.Company size and different database have been used for the analysis of success factors.The databases/search engine used are Google scholar, Science Direct, IEEE Explore and Springer for the exploration of success factors.Companies are analyzed on the basis of their size such as small, medium and large.
Efficient Computation of Expectations under Spanning Tree Distributions

Abstract We give a general framework for inference in spanning tree models. We propose unified algorithms for the important cases of first-order expectations and second-order expectations in edge-factored, non-projective spanning-tree models. Our algorithms exploit a fundamental connection between gradients and expectations, which allows us to derive efficient algorithms. These algorithms are easy to implement with or without automatic differentiation software. We motivate the development of our framework with several cautionary tales of previous research, which has developed numerous inefficient algorithms for computing expectations and their gradients. We demonstrate how our framework efficiently computes several quantities with known algorithms, including the expected attachment score, entropy, and generalized expectation criteria. As a bonus, we give algorithms for quantities that are missing in the literature, including the KL divergence. In all cases, our approach matches the efficiency of existing algorithms and, in several cases, reduces the runtime complexity by a factor of the sentence length. We validate the implementation of our framework through runtime experiments. We find our algorithms are up to 15 and 9 times faster than previous algorithms for computing the Shannon entropy and the gradient of the generalized expectation objective, respectively.
Attention-Based 3D Human Pose Sequence Refinement Network

Three-dimensional human mesh reconstruction from a single video has made much progress in recent years due to the advances in deep learning. However, previous methods still often reconstruct temporally noisy pose and mesh sequences given in-the-wild video data. To address this problem, we propose a human pose refinement network (HPR-Net) based on a non-local attention mechanism. The pipeline of the proposed framework consists of a weight-regression module, a weighted-averaging module, and a skinned multi-person linear (SMPL) module. First, the weight-regression module creates pose affinity weights from a 3D human pose sequence represented in a unit quaternion form. Next, the weighted-averaging module generates a refined 3D pose sequence by performing temporal weighted averaging using the generated affinity weights. Finally, the refined pose sequence is converted into a human mesh sequence using the SMPL module. HPR-Net is a simple but effective post-processing network that can substantially improve the accuracy and temporal smoothness of 3D human mesh sequences obtained from an input video by existing human mesh reconstruction methods. Our experiments show that the noisy results of the existing methods are consistently improved using the proposed method on various real datasets. Notably, our proposed method reduces the pose and acceleration errors of VIBE, the existing state-of-the-art human mesh reconstruction method, by 1.4% and 66.5%, respectively, on the 3DPW dataset.
Metrics and methods for robustness evaluation of neural networks with generative models

Recent studies have shown that modern deep neural network classifiers are easy to fool, assuming that an adversary is able to slightly modify their inputs. Many papers have proposed adversarial attacks, defenses and methods to measure robustness to such adversarial perturbations. However, most commonly considered adversarial examples are based on perturbations in the input space of the neural network that are unlikely to arise naturally. Recently, especially in computer vision, researchers discovered "natural" perturbations, such as rotations, changes of brightness, or more high-level changes, but these perturbations have not yet been systematically used to measure the performance of classifiers. In this paper, we propose several metrics to measure robustness of classifiers to natural adversarial examples, and methods to evaluate them. These metrics, called latent space performance metrics, are based on the ability of generative models to capture probability distributions. On four image classification case studies, we evaluate the proposed metrics for several classifiers, including ones trained in conventional and robust ways. We find that the latent counterparts of adversarial robustness are associated with the accuracy of the classifier rather than its conventional adversarial robustness, but the latter is still reflected on the properties of found latent perturbations. In addition, our novel method of finding latent adversarial perturbations demonstrates that these perturbations are often perceptually small.
A Contactless Solution for Monitoring Social Distancing: A Stereo Vision Enabled Real-Time Human Distance Measuring System

Covid-19 has significantly impacted the entire world since late 2019. Keeping a safe social distance while staying in enclosed public spaces has become an important aspect for controlling the spread of the virus. Hence, it is crucial to monitor and ensure that people are keeping a safe distance in densely populated areas. Current human distance measuring systems rely on sensors to achieving such a task. However, the sensors that are handed out might act as a medium for the virus to spread. In this paper, a stereo vision enabled contactless human distance measuring system is presented. The developed system uses a stereo camera to detect people and measure the distance between each person. The system is capable of measuring the distance between multiple people with a precision of around 10cm in real-time (15FPS). Depending on the social distancing rules, the system can send out warnings or alerts if the distance between two people is smaller than the threshold value.
Towards a Class-Aware Information Granulation for Graph Embedding and Classification

Pattern recognition in the graphs domain gained a lot of attention in the last two decades, since graphs are able to describe relationships (edges) between atomic entities (nodes) which can further be equipped with attributes encoding meaningful information. In this work, we investigate a novel graph embedding procedure based on the Granular Computing paradigm. Conversely to recently-developed techniques, we propose a stratified procedure for extracting suitable information granules (namely, frequent and/or meaningful subgraphs) in a class-aware fashion; that is, each class for the classification problem at hand is represented by the set of its own pivotal information granules. Computational results on several open-access datasets show performance improvements when considering also the ground-truth class labels in the information granulation procedure. Furthermore, since the granulation procedure is based on random walks, it is also very appealing in Big Data scenarios.
Containers Resource Allocation in Dynamic Cloud Environments

Containers technology has become very popular in recent years, since it allows users to focus on designing their applications in a modular way and abstracting away the environments in which they actually run. Cloud providers such as AWS (Amazon Web Services) and GCP (Google Cloud Platform) offer their users managed containers platforms that orchestrate, schedule and execute multiple containers over a multi-tenant cloud infrastructure. As these services gain popularity, it is becoming more and more challenging to manage them in a way that effectively utilized the existing resources. The latter has a significant economical impact on cloud providers when it comes to their compute infrastructure investment costs and the price they can offer to their customers. In this paper, we approach this challenge by developing multidimensional container resource allocation algorithms designed to be deployed in dynamic cloud environments with different types of applications under varying loads scenarios. Our algorithms allocate for each container an available engine to execute it, in a way that maximizes the overall revenue. We design our algorithms and provide a constant worst-case approximation bound using the Local Ratio technique. Our evaluation, based on real-world scenarios, indicates that the performance of our algorithms is up to a factor of two better than the performance of existing scheduling algorithms, when the available resources are scarce.
Partial 3D object retrieval using local binary QUICCI descriptors and dissimilarity tree indexing

A complete pipeline is presented for accurate and efficient partial 3D object retrieval based on Quick Intersection Count Change Image (QUICCI) binary local descriptors and a novel indexing tree. It is shown how a modification to the QUICCI query descriptor makes it ideal for partial retrieval. An indexing structure called Dissimilarity Tree is proposed which can significantly accelerate searching the large space of local descriptors; this is applicable to QUICCI and other binary descriptors. The index exploits the distribution of bits within descriptors for efficient retrieval. The retrieval pipeline is tested on the artificial part of SHREC'16 dataset with near-ideal retrieval results.
Dynamic Probabilistic Graph Convolution for Facial Action Unit Intensity Estimation

Deep learning methods have been widely applied to automatic facial action unit (AU) intensity estimation and achieved the state-of-the-art performance. These methods, however, are mostly appearance-based and fail to exploit the underlying structural information among AUs. In this paper, we propose a novel dynamic probabilistic graph convolution (DPG) model to simultaneously exploit AU appearances, AU dynamics, and their semantic structural dependencies for AU intensity estimation. Firstly, we propose to use Bayesian Network to capture the inherent dependencies among AUs. Secondly, we introduce probabilistic graph convolution that allows to perform graph convolution on the distribution of Bayesian Network structure to extract AU structural features. Finally, we introduce a dynamic deep model based on LSTM to simultaneously combine AU appearance features, AU dynamic features, and AU structural features for AU intensity estimation. In experiments, our method achieves comparable and even better performance with the state-of-the-art methods on two benchmark facial AU intensity estimation databases, i.e., FERA 2015 and DISFA.
Improving Accuracy of Respiratory Rate Estimation by Restoring High Resolution Features with Transformers and Recursive Convolutional Models

Non-contact evaluation of vital signs has been becoming increasingly important, especially in light of the COVID-19 pandemic, which is causing the whole world to examine people’s interactions in public places at a scale never seen before. However, evaluating one’s vital signs can be a relatively complex procedure, which requires both time and physical contact between examiner and examinee. These requirements limit the number of people who can be efficiently checked, either due to the medical station throughput, patients’ remote locations or the need for social distancing. This study is a first step to increasing the accuracy of computer vision-based respiratory rate estimation by transferring texture information from images acquired in different domains. Experiments conducted with two deep neural network topologies, a recursive convolutional model and transformers, proved their robustness in the analyzed scenario by reducing estimation error by 50% compared to low resolution sequences. All resources used in this research, including links to the dataset and code, have been made publicly available.
Application of computer simulation and high-precision visual matching technology in green city garden landscape design

Graphic images of computer three-dimensional simulation technology are widely used in the construction industry. Technical applications mainly include 3D simulation software and some 3D modeling tools. In the application field, there are simulation simulations of industrial architecture and interior design, urban landscape design, and demonstration simulations during construction. Based on the computer simulation technology, this thesis made a 3D simulation model with a sense of reality. Based on the calculation amount of the computer system model surface and the calculation amount of basic map texture resources, the production of the model was completed. 3D simulated points, lines, surfaces and various levels of polygonal forms. As a result, the accuracy of the spatial element layout optimization model becomes lower and the convergence speed becomes slower. A reasonable layout simulation method for landscape space elements is proposed. Selected patch area, landscape percentage, patch number, maximum patch index and average patch area as the browsing index. Use Ahp to calculate the weight of the above landscape index, and use the matching test to change and adjust the judgment result. The results show that the proposed 3D simulation model has excellent visual effects, can clearly reflect all design elements of landscape design, and its high precision and high convergence rate can be effectively applied to urban landscape design.
Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers

Human activity recognition (HAR) by wearable sensor devices embedded in the Internet of things (IOT) can play a significant role in remote health monitoring and emergency notification to provide healthcare of higher standards. The purpose of this study is to investigate a human activity recognition method of accrued decision accuracy and speed of execution to be applicable in healthcare. This method classifies wearable sensor acceleration time series data of human movement using an efficient classifier combination of feature engineering-based and feature learning-based data representation. Leave-one-subject-out cross-validation of the method with data acquired from 44 subjects wearing a single waist-worn accelerometer on a smart textile, and engaged in a variety of 10 activities, yielded an average recognition rate of 90%, performing significantly better than individual classifiers. The method easily accommodates functional and computational parallelization to bring execution time significantly down.
Free-Text Keystroke Dynamics for User Authentication

In this research, we consider the problem of verifying user identity based on keystroke dynamics obtained from free-text. We employ a novel feature engineering method that generates image-like transition matrices. For this image-like feature, a convolution neural network (CNN) with cutout achieves the best results. A hybrid model consisting of a CNN and a recurrent neural network (RNN) is also shown to outperform previous research in this field.
Accelerating Kinodynamic RRT* Through Dimensionality Reduction

Sampling-based motion planning algorithms such as RRT* are well-known for their ability to quickly find an initial solution and then converge to the optimal solution asymptotically as the number of samples tends to infinity. However, the convergence rate can be slow for high-dimensional planning problems, particularly for dynamical systems where the sampling space is not just the configuration space but the full state space. In this paper, we introduce the idea of using a partial-final-state-free (PFF) optimal controller in kinodynamic RRT* [1] to reduce the dimensionality of the sampling space. Instead of sampling the full state space, the proposed accelerated kinodynamic RRT*, called Kino-RRT*, only samples part of the state space, while the rest of the states are selected by the PFF optimal controller. We also propose a delayed and intermittent update of the optimal arrival time of all the edges in the RRT* tree to decrease the computation complexity. We tested the proposed algorithm using 4-D and 10-D state-space linear systems and showed that Kino-RRT* converges much faster than the kinodynamic RRT* algorithm.
An Empirical Performance Comparison between MySQL and MongoDB on Analytical Queries in the COMEX Database

The NoSQL database is an alternative to manage big data. It is essential to change from a relational database to a NoSQL database to evaluate the new scenario's performance. In this context, our paper presents an empirical performance comparison between MySQL and MongoDB on analytical queries in the COMEX Database. The COMEX Database is responsible for all Brazilian foreign trade statistics and many agencies need to constantly make big analytical queries on it to analyze business-related queries. The main results indicate that non-relational databases can improve the queries performance.
Generative adversarial network

Generative Adversarial Network (GAN) has shown remarkable results in many computer vision tasks such as image generation, text generation, text-to-image generation, image-to-image translation, and so on. This chapter discusses the structure and principle of GAN. Next, it introduces and explains Wasserstein distance and the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), which is an improved version of the GAN. Finally, the chapter presents an example program of building, training, and testing the WGAN-GP model on the large-scale CelebFaces Attributes (CelebA) Dataset.
Altered mineral mapping based on ground-airborne hyperspectral data and wavelet spectral angle mapper tri-training model: Case studies from Dehua-Youxi-Yongtai Ore District, Central Fujian, China

Mineral mapping is an important procedure for the utilization of mineral resources, and it is also significant to the analysis of mineralization zone especially for altered minerals. The emergence of remote sensing, especially hyperspectral data has become a new approach for mineral mapping on a wide scale. In addition, spectral angle mapping (SAM) is a commonly used classifier to distinguish the minerals, but the discrimination ability is weak on a mapping scale, and the identification accuracy is poor for a series of minerals with similar spectral curves when a single classifier is applied. In this work, altered minerals are identified at Dehua-Youxi-Yongtai Ore District uniting ground and airborne hyperspectral data, and wavelet SAM (WSAM) tri-training model is constructed to discriminate the category of 9 altered minerals. Experimental results demonstrate that the proposed technique provides the identification accuracy of 82% and 70% for virtual and XRD verifications, and the mapping result is believable compared with measured sampling.