Reviewerly/exHarmony huggingface dataset:
Vehicle detection base on Road Segmentation for surveillance satellites video

Abstract With the development of data acquisition ability, the LEO satellites can work with a surveillance camera on board by focusing a particular area for dozens of seconds, so it drives us to develop some applications based on this data. In this paper, a data preparation method named DERS (Density Estimation based Road Segmentation) is proposed to divide the road from background to reduce the worse impact from relative-motion and then improve the precession and recall for vehicles for surveillance satellite video sequence. DERS has three main steps: frame difference, morphology, pyramid iteration, segmentation. Our experiments with the satellite videos from SkySat-1, and compared between Gaussian Mixture Model (GMM) and GMM with Ders shows that the precision rate and recall has improved of the traditional method with Ders, the precision of GMM with Ders has been improved from 64.42% to 88.71, the recall of GMM with Ders is from 76.21% to 74.16%, and the F-Score has been improved from 69.82% to 80.79%.
A Lightweight Object Detection Framework

Previous generic object detection networks depend on complex structures and huge computational costs, which are not conducive to deployment and application in resource-constrained scenarios, especially in mobile devices. Accordingly, exploring lightweight network has become a focus issue, attracting the widespread attention of industry and academia. In this paper, we proposed a lightweight two-stage object detection framework based convolutional neural network, which includes two components: the feature extractor and the detection components. For the feature extractor component, two architecture modules were proposed: residual cross-branch feature extraction module (RCM) and multi-scale feature fusion module (MFM). The RCM module adopted residual structure and the mechanism of cross-branch multi-channel to increase the receptive field and expression capability for low-level features. The MFM module integrated feature maps with multiple scales to enhance global context information. Moreover, for further optimizing the distribution of features and paying more attention to the detection-related regions, a separate channel self-attention module was proposed. Extensive experiments on the public datasets PASCAL VOC illustrated the superiority of the proposed model.
Distributed Self-Adjusting Tree Networks

The performance of many data-centric cloud applications critically depends on the performance of the underlying datacenter network. Reconfigurable optical technologies have recently introduced a novel opportunity to improve datacenter network performance, by allowing to dynamically adjust the network topology according to the demand. However, the vision of self-adjusting networks raises the fundamental question how such networks can be efficiently operated in a scalable and distributed manner. This article presents <inline-formula><tex-math notation="LaTeX">$DiSplayNet$</tex-math></inline-formula> , the first fully distributed self-adjusting network. <inline-formula><tex-math notation="LaTeX">$DiSplayNet$</tex-math></inline-formula> relies on algorithms that perform decentralized and concurrent topological adjustments to account for changes in the demand. We propose two natural metrics to evaluate the performance of distributed self-adjusting networks, the <i>amortized work</i> (the cost of routing on and adjusting the network) and the <i>makespan</i> (the time it takes to serve a set of communication requests). We present a rigorous formal analysis of the work and makespan of <inline-formula><tex-math notation="LaTeX">$DiSplayNet$</tex-math></inline-formula> , which can be seen as an interesting generalization of analyses known from sequential self-adjusting datastructures. We complement our theoretical contribution with an extensive trace-driven simulation study, shedding light on the opportunities and limitations of leveraging spatial and temporal locality and concurrency in self-adjusting networks.
The Role of Mathematics in the Formation of Pupils’ Creative Activity

The article is devoted to the problem of the development of the creative activity of students in grades 5–6 in the process of teaching mathematics. It is noted that the fulfillment of a creative task requires from students not a simple reproduction of information, but creativity, since the tasks contain a greater or lesser element of obscurity. A creative task is the content, the basis of any interactive method. A creative task (especially practical and close to the student's life) gives meaning to learning, motivates pupils. To develop the creative activity of students, you can use specially developed various software tools (Learning Apps, Mentimeter, Quzizz, etc.) in the lesson. With the help of a huge number of online services, you can create a whole collection of interactive tasks of the following nature: study an interactive lecture and answer the questions; answer questions of the test, quiz (with one or many correct answers); build a timeline and others.
Graphgen-redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation

The problem of labeled graph generation is gaining attention in the Deep Learning community. The task is challenging due to the sparse and discrete nature of graph spaces. Several approaches have been proposed in the literature, most of which require to transform the graphs into sequences that encode their structure and labels and to learn the distribution of such sequences through an auto-regressive generative model. Among this family of approaches, we focus on the Graphgen model. The preprocessing phase of Graphgen transforms graphs into unique edge sequences called Depth-First Search (DFS) codes, such that two isomorphic graphs are assigned the same DFS code. Each element of a DFS code is associated with a graph edge: specifically, it is a quintuple comprising one node identifier for each of the two endpoints, their node labels, and the edge label. Graphgen learns to generate such sequences auto-regressively and models the probability of each component of the quintuple independently. While effective, the independence assumption made by the model is too loose to capture the complex label dependencies of real-world graphs precisely. By introducing a novel graph preprocessing approach, we are able to process the labeling information of both nodes and edges jointly. The corresponding model, which we term Graphgen-redux, improves upon the generative performances of Graphgen in a wide range of datasets of chemical and social graphs. In addition, it uses approximately 78% fewer parameters than the vanilla variant and requires 50% fewer epochs of training on average.
Construction of Materialized Views in Non-Binary Data Space

Materialized views are heavily used to speed up the query response time of any data centric application. In literature, the construction and dynamic maintenance of materialized views are carried out in a Binary Data Space where all attributes are given the same weight. Considering different weights may be particularly significant when similar queries are posed by multiple users, as taking into account the number of accesses to the different attribute values may reflect into the ability of tuning the materialized views accordingly. The methodology to construct weighted materialized view introduced in this paper is based on the association mining techniques, by applying it in a Non-Binary Data Space. The proposed algorithm has been verified by simulation experiments with two benchmark datasets using practical transactional queries. The experimental results prove the superiority of our proposal in terms of query Hit-Miss ratio and flexibility of view size extendibility according to the requirement of practical applications.
Node classification problem based on potential information and representation learning between nodes

Representation learning of social network nodes refers to the feature expression of nodes in low-dimensional vector space. Most of the current methods are based on the network topology to achieve node feature learning, but the potential information of the network topology is not fully utilized. To solve this problem, we propose a graph embedding algorithm based on the similarity between nodes. This method obtains a new network topology through the second-order similarity between nodes. This network topology can enhance the correlation between nodes, thereby improving the effect of node classification in the network. We use Macro-F1 and Micro-F1 evaluation indicators to evaluate the benchmark algorithm and the SNE algorithm proposed in this paper on four real data sets. The experimental results show that the SNE algorithm can improve the effect of node classification.
Incremental multi‐view correlated feature learning based on non‐negative matrix factorisation

In real-world applications, large amounts of data from multiple sources come in the form of streams. This makes multi-view feature learning cost much time when new instances rise incrementally. Dealing with these growing multi-view data becomes a challenging problem. Some single-view methods focus on processing the data dynamically, but they are not suitable for multi-view data. Some online multi-view methods are proposed to tackle it, but they ignore the influence of uncorrelated items in each view. Therefore, in this study, the authors propose a new algorithm, called Incremental Multi-view Correlated Feature Learning (IMCFL) based on non-negative matrix factorisation, to learn the common feature across views. By separating uncorrelated items of new instances and constructing incremental joint learning of correlated and uncorrelated features, the proposed IMCFL can eliminate the influence of uncorrelated information in the individual view and improve the effectiveness of incremental multi-view common feature learning. Extensive experiments on real-world datasets confirm its superiority by comparing it with other state-of-the-art incremental and non-incremental methods.
DEVELOPING A PROGRAM TO IMPROVE READING INSTRUCTION AND ACHIEVEMENT AT J. GLENN EDWARDS ELEMENTARY SCHOOL

ABSTRACTNatalie P. Kelly, DEVELOPING A PROGRAM TO IMPROVE READING INSTRUCTION AND ACHIEVEMENT AT J. GLENN EDWARDS ELEMENTARY SCHOOL (Under thedirection of Dr. William A. Rouse). Department of Educational Leadership, March 2019.The reading deficiencies of 21st century students in todayâ€™s classrooms are challenging our public school system and resulting in a national epidemic for the improvement of reading instruction and learning. Specifically at J. Glenn Edwards Elementary School (JGEES) in LeeCounty, NC the reading problem could not be ignored as the school was performing below the state and district averages. Therefore, this study was conducted to improve reading instruction and achievement at JGEES through the utilization of Improvement Science and the Plan, Do, Study, Act (PDSA) Cycle. The literature research and the stakeholderâ€™s input led to the development and implementation of the E-Trifecta: Engaging, Encouraging, and Empowering, Reading Program at JGEES for the purpose of improving reading. The program consisted of high engagement techniques with technology integration and high yield exemplary best practice instructional strategies. The full implementation of the E-Trifecta Reading Program in grades three through five at JGEES resulted in improved reading achievement scores as evidenced by the 1.3% increase in third grade, the 0.2% increase in fourth grade, and the 2.7% increase in fifth grade, from October 2018, to January 2019. Furthermore, a comparison of the prior year assessment scores from January 2018, to the current year assessment scores of January 2019, revealed a greater increase in reading achievement with third grade improving 9.9%, fourth grade improving 3.6%, and fifth grade improving 4.5%. The study results indicated that the implementation of a reading program centralized on engagement, encouragement, and empowerment with the use of technology and high yield exemplary best practices will result in improved reading instruction and reading achievement scores.
Understanding Xacro Misunderstandings

The Xacro XML macro language can be used to augment the Universal Robot Description Format (URDF) and is part of a critical toolchain from geometric representations to simulation, visualization, and system execution. However, mem-bers of the robotics community, especially newcomers, struggle to troubleshoot and understand the interplay between systems and the Xacro preprocessing pipeline. To better understand how system developers struggle with Xacros, we manually examine 712 Xacro-related questions from the question and answer site answers.ros.org and find Xacro misunderstandings fit into eight key categories using a systematic, qualitative approach called Open Coding. By examining the 'tags' applied to questions, we further find that Xacro problems manifest in a befuddlingly broad set of contexts. This hinders onboarding and complicates system developers' understanding of representations and tools in the Robot Operating System. We aim to provide an empirical grounding that identifies and prioritizes impediments to users of open robotics systems, so that tool designers, teachers, and robotics practitioners can devise ways of improving robot software tooling and education.
Image Classification Using TensorFlow GPU

There are several image classification and a complicated methods that are been overlooked with many articles. This article reviews the latest practices, issues, and options for billing classification. Emphasis is placed on synthesizing important advanced category strategies and targeting strategies that can be used to improve ranking accuracy. Billing sorting is a classic problem in image processing, computer vision, and machine learning. In this article, we study deep learning-based image classification using the TensorFlow GPU. Because the datasets were bridges; CIFAR-10 and MNIST FASHION for the classification module. The results show the efficiency and accuracy of deep learning-based image classification using the TensorFlow GPU. Additionally, some critical issues are mentioned that affect overall performance. However, simple research is needed to identify and reduce uncertainties in the image processing chain to improve classification accuracy.
Performance study on mounting system for displacement transducer in mechanical tests of timber samples using photogrammetry method

This paper presents a unique study to reveal the effect of the mounting system of the linear variable differential transformer (LVDT) on the mechanical property tests of timber materials. This effect has been overlooked in the past but proven to be significant enough that will distort the measurements completely if the displacement is relatively small. The reason for causing this error is because the LVDT is not directly measuring targeting points on a sample, instead, the LVDT is measuring the distance between one point on the stop-end (where the tip rested on) and the holder. The wrong common sense hopes that this point on the stop-end and holder reliably following the movement of the sample points. But this study reveals that it is not always the case with the aid from the photogrammetry method. The messages of this paper are simple but alertly useful and, important: (1) the mounting system and the method of installation of the LVDTs have a significant impact on the actual displacement measurement in a mechanical properties test of timber materials. It should be carefully designed and validated before the actual test; (2) the displacement distribution is not uniform across the timber sample. This should be taken into account when selecting the mounting points of the LVDTs. Testing standards should consider this effect when recommending displacement transducers to be used in a timber-related test.
Rule-based Morphological Inflection Improves Neural Terminology Translation

Current approaches to incorporating terminology constraints in machine translation (MT) typically assume that the constraint terms are provided in their correct morphological forms.This limits their application to real-world scenarios where constraint terms are provided as lemmas.In this paper, we introduce a modular framework for incorporating lemma constraints in neural MT (NMT) in which linguistic knowledge and diverse types of NMT models can be flexibly applied.It is based on a novel cross-lingual inflection module that inflects the target lemma constraints based on the source context.We explore linguistically motivated rule-based and data-driven neuralbased inflection modules and design English-German health and English-Lithuanian news test suites to evaluate them in domain adaptation and low-resource MT settings.Results show that our rule-based inflection module helps NMT models incorporate lemma constraints more accurately than a neural module and outperforms the existing end-to-end approach with lower training costs. 1
Implementation of Information Technology in the Construction of Physical Education Platform

As well-rounded education has been extensively promoted, a growing attention has been devoted to the cultivation of students’ qualities. Since physical constitution plays a fundamental role in the cultivation of students’ qualities, the functions of physical education should be highlighted. In the information age, physical education (PE) instructors should keep abreast of the times. Therefore, PE instructors should master the information technology-based physical education model, so as to cultivate students’ interest in sports and improve the effects of physical education teaching. In view of this, this study investigates the optimization strategies for informatization construction of physical education platform through the description of the significance of introducing information technology into physical education and the analysis of the status quo of implementation of information technology in physical education platform.
Research on Screening Methods for People Susceptible to Motion Sickness and Fatigue After Long Voyage

Objective: This study intends to select a simple and easy method for screening people who are prone to seasickness and seasickness by observing the physiological parameters, seasickness, and navigation fatigue of the crew after a long voyage. Method: Data (including data, weight, Body Mass Index (BMI), their susceptibility to motion sickness, motion sickness symptoms, fatigue degree, etc.,) of 49 subjects who participated in the 200-day long voyage were collected data and the relevance between the above factors and motion sickness symptoms and fatigue symptoms was analyzed. Results: After the voyage, a total of 16 subjects had symptoms of motion sickness, and 41 subjects had symptoms of fatigue. 16 subjects had both seasickness and fatigue symptoms. BMI was positively correlated with motion sickness and fatigue scores. Conclusion: People with a higher BMI index may be more susceptible to motion sickness and fatigue during a long voyage.
Relation of Web Service Orchestration, Abstract Process, Web Service and Choreography

We refine the relation of Web service orchestration, abstract process, Web service, and Web service choreography in Web service composition, under the situation of cross-organizational corporation. We also introduce the formal verification process of this relation through an example.
ASSESSMENT OF THE SAFETY OF PASSENGER CARS IN CASE OF AN EMERGENCY ROLLOVER ON THE RAILROAD TRACKS

The purpose of the work is to assess the safety of passenger cars in case of an emergency rollover on the body of railroad tracks. The paper introduces a method for predicting injury of railway transport passengers as a result of swinging over the wagon on the body of railroad tracks. The method of research is mathematical modeling of scenarios of swinging over the wagon on a flat bottom or earth tramp of the railway track. A model of a passenger compartment has been developed, which is supplemented with models of a roomette, hand luggage and an anthropometric dummy. The originality of the work is the use of mannequin models for an accident with the rollover of a compartment car on the body of the railroad tracks and obtaining data on the interaction of fit models and a compartment car. The result of the study is the reported values of possible injury to passengers during an emergency rollover of a passenger car. Namely, the values of the head injury criterion, cervical vertebrae, breast and hips of the crash test dummy have been obtained. In comparison of the two considered scenarios of swinging over the wagon, the value of the head injury criterion for overturning the car on an inclined surface is 15% higher, the neck injury criterion is 30% higher, and the hip and chest injury criterion is 23% higher for mannequins on the upper shelves of the compartment due to their interaction with hand luggage. The obtained values do not exceed critical ones. The most dangerous positions of the mannequin model in the compartment of the car are revealed. Conclusions concerning the sufficient safety of the passenger car are formed and recommendations for the development of additional technical solutions to improve the safety of passenger cars are given.
Data Classification for Neural Network Training

This study proposes models that solve the pattern recognition problem on the example of x-ray images of bone fractures. These models are based on neural network programming. The training efficiency for these models exceeds the teacher network performance by almost 5 times with insignificant network training quality reduction. Using the proposed technique, the neural network was trained in several ways, including the conventional method. Additionally, the network's binary classification was changed. The deviations of the obtained network values from the predicted responses are minimal, which has formed the base for the introduced concept of the model's indecisiveness and uncertainty. Furthermore, various experiments with the network were made to test these models, which lead to the idea of using data classification for neural network training.
The Influence of Corporate Universities on the Formation of the Competitive Advantages of Higher Education in the Russian Federation

Competitive advantages predetermine the success of the implementation of the tasks assigned to higher educational institutions that have developed under the influence of the globalization of the world economic space. Using the experience of corporate universities in the work of state and non-state educational institutions of higher education will improve the quality of educational processes, as well as the integration of theory into the practice of producing socio-economic systems. This process will increase the economic security of the sovereign territory, as well as the success of higher education adaptation to new challenges that have arisen as a result of the socio-economic crisis that arose as a consequence of the pandemic. The introduction of new educational standards in the field of higher education creates new tasks for the entire system of higher education in Russia. The experience of corporate education in foreign countries is invaluable in planning an effective strategy for the development of educational processes in our territory.
Applying technology 4.0 to a social design responding to the COVID-19 pandemic

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Phuong Nguyen Bao Cuc; Applying technology 4.0 to a social design responding to the COVID-19 pandemic. AIP Conf. Proc. 20 September 2021; 2406 (1): 020041. https://doi.org/10.1063/5.0066497 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search