Reviewerly/exHarmony huggingface dataset:
A Multiresolution Vector Data Compression Algorithm Based on Space Division

Vector data compression can significantly improve efficiency of geospatial data management, visualization and data transmission over internet. Existing compression methods are either based on information theory for lossless compression mainly or based on map generalization methods for lossy compression. Coordinate values of vector spatial data are mostly represented using floating-point type in which data redundancy is small and compression ratio using lossy algorithms is generally better than that of lossless compression algorithms. The purpose of paper is to implement a new algorithm for efficient compression of vector data. The algorithm, named space division based compression (SDC), employs the basic idea of linear Morton and Geohash encoding to convert floating-point type values to strings of binary chain with flexible accuracy level. Morton encoding performs multiresolution regular spatial division to geographic space. Each level of regular grid splits space horizontally and vertically. Row and column numbers in binary forms are bit interleaved to generate one integer representing the location of each grid cell. The integer values of adjacent grid cells are proximal to each other on one dimension. The algorithm can set the number of divisions according to accuracy requirements. Higher accuracy can be achieved with more levels of divisions. In this way, multiresolution vector data compression can be achieved accordingly. The compression efficiency is further improved by grid filtering and binary offset for linear and point geometries. The vector spatial data compression takes visual lossless distance on screen display as accuracy requirement. Experiments and comparisons with available algorithms show that this algorithm produces a higher data rate saving and is more adaptable to different application scenarios.
Deep Learning and Neural Networks Overview

Since you are reading this book, it is safe to assume that you know how deep learning has gained popularity in recent years. There is a very good reason for deep learning’s increasing popularity: its uncanny accuracy performance. Especially when there are abundant data and available processing power, deep learning is the choice of machine learning experts. The performance comparison between deep learning and traditional machine learning algorithms is shown in Figure 3-1.
Integration of Interactive CFD Simulations with AR and VR for Educational Use in CRE

Engineering simulations have since long been considered an imperative design and analysis tool for chemical reaction engineering (CRE). Notably, reactor and process design are domains in which multiphysics computational fluid dynamics (CFD) simulations are applied to understand phenomena that are difficult, time-consuming or expensive to be explored with experiments. On the other hand, advanced post-processing methods and virtual reality can enable high-quality educational content with engineering simulations. Nevertheless, the utilization of these tools in education is still underdeveloped. This study explores the potential of integration of multiphysics CFD simulations with AR/VR and its educational use. By iterating on this potential we present a generic system architecture with inter-disciplinary consideration of two building stones: (1) Recent advancements in CFD & multiphysics simulations, and (2) Emerging immersive technologies. A case study is demonstrated applying the generic methodology in order to illustrate the integration. The study promotes a rich and engaging environment with engineering simulations where interactivity can be empowered to entertain learners with AR/VR. This will primarily assist enthusiasts to develop feasible complementary environments with simulations.
Recurrent Neural Networks for Computing the Moore‐Penrose Inverse with Momentum Learning

We are concerned with a kind of iterative method for computing the Moore-Penrose inverse, which can be considered as a discrete-time form of recurrent neural networks. We study the momentum learning scheme of the method and discuss its semi-convergence when computing the Moore-Penrose inverse of a rankdeficient matrix. We prove the semi-convergence for our new acceleration algorithm and obtain the optimal momentum factor which makes the fastest semi-convergence. Numerical tests demonstrate the effectiveness of our new acceleration algorithm.
ALGORITHM FOR THE FORMATION OF A PERSONAL LEARNING ENVIRONMENT BY MEANS OF STRUCTURING THE EDUCATIONAL SPACE OF THE UNIVERSITY BASED ON OFFICE365 AND MS TEAMS SERVICES

The algorithm of formation of a personal learning environment (PLE) and learning group environment for the organization of electronic distance learning in medical higher education institutions is offered. It was noted that the introduction of the COVID-19 quarantine raised the issue of reorganization of the learning space and accelerated the process of introducing the distance learning model. The experience gained by the authors during quarantine made it possible to form the stages of deploying the learning environment in the medical higher education institution and to define the educational space of the group as the main element of this environment. The main technological steps of creating subsystems of the educational space of the university for using cloud services MSO365 were considered using the example of Zaporizhzhia State Medical University. The conditions that allow creating a hierarchy of the learning environment were defined, namely: a project of the structure of the learning environment with the coordination of departments; registration of all participants of the learning process and creation of their accounts in Active Directory of the university; automated procedure of transferring group members to AD MSO365. The paper provides an example of a hierarchy of names, which is a reflection of the structure of university departments in terms of staffing and determines the rights of access to the usage of services that ensure the implementation of the corresponding functions. The authors found that the user account is the first integrating service that allows the user to form a personal learning environment using OneDrive, web-site MS SharePoint, MS Forms and other office applications. It was noted that the user account is the lowest level of the hierarchy of the learning environment, the usage of which provides a learning space for groups. According to the authors, it is reasonable to use MS Groups to form academic groups. Using MS Teams, a teacher gets a flexible tool to form a learning environment for various organizational forms, namely: lectures, seminars, practical classes, independent work, etc. The authors proposed rules for forming group names in MS Groups and MS Teams. They presented an algorithm for deploying the department's learning environment and proposed a suggestion of structuring the subject's learning environment using MS Teams channels for a small number of groups in the department.
The Use of Immersive Virtual Reality for Vocabulary Acquisition: a Systematic Literature Review

Many teachers consider vocabulary acquisition a low intellectual activity. However, substantial time is needed for teachers and learners, in this subject, since there are vast numbers of words to be learned. In this context, it is expected that vocabulary acquisition assisted by Immersive Virtual Reality (IVR) can be a powerful learning method. Therefore, this paper aims to summarize the state-of-the-art concerning the use of IVR in vocabulary acquisition through a systematic literature review. Results pointed out that IVR enables autonomous learning with an increase in the speed of the educational process, and its use has shown higher effectiveness, positive attitudes, engagement, and motivation on the part of students.
Adaptive trainable non‐linear reaction diffusion for Rician noise removal

Rician noise reduction is an essential issue in magnetic resonance imaging (MRI). Recently, learning-based methods have achieved great success in dealing with image restoration problems, which provide fast inference and good performance. One limitation of these methods, however, is that the training procedure is usually noise-level dependent, i.e. the trained models are bound to a specific noise level and lack the ability to automatically adapt to different noise levels. In this study, the authors propose a variational model for Rician noise removal by integrating a noise adaption function into the field of experts image prior, which can adapt to different noise levels. Instead of directly solving the energy minimisation problem, the authors unroll the gradient descent step of the energy functional for several iterations, the time-dependent parameters of which can be learned through a supervised training process. The authors call this methodology as the noise adaptive trainable non-linear reaction–diffusion model. The proposed methodology is robustness against noise level changing and noise distributions. Experimental results over -, - and PD-weighted MRI data set demonstrate that the proposed model can achieve superior performance compared with other methods in terms of both the peak signal-to-noise ratio and the structural similarity index.
Political, technical and pedagogical effects of the COVID-19 Pandemic in Mathematics Education: an overview of Brazil, Chile and Spain

This article discusses the problems and questions that are raised in Education and specifically in Mathematics Education from the problematization of the effects of the pandemic of COVID-19. For this, it was brought together three researchers in Mathematics Education from Brazil, Chile and Spain, who started to meet virtually. Based on these discussions, the group provided the context for each of the three countries in terms of how they have been dealing with the pandemic and selected three tales they lived related to the pandemic, that were used as material elements for a joint discussion in a non localized way. With this path, as results, it is shown an overview of the effects of COVID-19 in different countries related to education, that were categorized in Technical, Political and Pedagogical dimensions. Besides that, questions are presented to Mathematics Education produced from the problematization of the effects of the pandemic.
Research on the algorithm of urban waste classification and recycling based on deep learning technology

In view of the rapid growth of municipal solid waste (MSW), a large number of MSW are transported to the outside of the city for landfill or incineration, only part of the MSW is treated innocuously, the speed of MSW treatment is slow, and the level of garbage classification intelligence is low, this paper proposes an algorithm of MSW classification and recycling based on deep learning technology, and uses convolution neural network to build garbage intelligence simultaneous interpreting and classification algorithm, which improves the accuracy and speed of garbage image recognition. The algorithm is compared with the traditional BP neural network algorithm. The simulation results show that the algorithm is 30% faster than the traditional algorithm. The classification algorithm has faster response speed, higher accuracy and stronger robustness.
The Analysis of the Characteristics and Evolution of the Collaboration Network in Blockchain Domain

Blockchain is a decentralized database, which can protect the safety of trade and avoid double payment.Due to the widespread attention of researchers, the studies of this field have increased sharply in recent years.It is meaningful to reveal the development level and trends based on this literature.This paper adopts bibliometric methods to study the collaboration characteristics from the levels of author, institution and country.Furthermore, several kinds of collaboration networks and their centrality analysis are also presented, which not only display the development level and collaboration degree but also the evolution of author collaboration modes in different phases.
Improved Multi-domain Convolutional Neural Networks Method for Vehicle Tracking

In the field of intelligent transportation, background complexity, lighting changes, occlusion, and scale transformation affect the tracking results of moving vehicles in the video. We propose an improved vehicle object tracking algorithm based on Multi-Domain Convolutional Neural Networks (MDNet), combining the instance segmentation method with the MDNet algorithm, adding two attention mechanisms to the algorithm. The module extracts better features, ensures that the vehicle object adapts to changes in appearance, and greatly improves tracking performance. Our improved algorithm has a tracking precision rate of 91.8% and a success rate of 67.8%. The Vehicle Tracking algorithm is evaluated on the Object Tracking Benchmark (OTB) data set. The tracking results are compared with eight mainstream object tracking algorithms, and the results show that our improved algorithm has excellent performance. The object tracking precision rate and tracking success rate of this algorithm have achieved excellent results in many cases.
Analysis of spammers’ behavior on a live streaming chat

&lt;span id="docs-internal-guid-f908fd2e-7fff-1849-4fda-c2cf9baed97e"&gt;&lt;span&gt;Live streaming is becoming a popular channel for advertising and marketing. An advertising company can use this feature to broadcast and reach a large number of customers. YouTube is one of the streaming media with an extreme growth rate and a large number of viewers. Thus, it has become a primary target of spammers and attackers. Understanding the behavior of users on live chat may reduce the moderator’s time in identifying and preventing spammers from disturbing other users. In this paper, we analyzed YouTube live streaming comments in order to understand spammers’ behavior. Seven user’s behavior features and message characteristic features were comprehensively analyzed. According to our findings, features that performed best in terms of run time and classification efficiency is the relevant score together with the time spent in live chat and the number of messages per user. The accuracy is as high as 66.22 percent. In addition, the most suitable technique for real-time classification is a decision tree.&lt;/span&gt;&lt;/span&gt;
Implementation of decision tree algorithm on FPGA devices

&lt;span id="docs-internal-guid-01e673b1-7fff-8dc3-6b99-14ed17cd6b49"&gt;&lt;span&gt;Machine learning techniques are rapidly emerging in large number of fields from robotics to computer vision to finance and biology. One important step of machine learning is classification which is the process of finding out to which category a new encountered observation belongs based on predefined categories. There are various existing solutions to classification and one of them is decision tree classification (DTC) which can achieve high accuracy while handling the large datasets. But DTC is computationally intensive algorithm and as the size of the dataset increases its running time also increases which could be from some hours to days even. But thanks to field programmable gate arrays (FPGA) which could be used for large datasets to achieve high performance implementation with low energy consumption. Along with FPGA’s, python is used for accelerating the application development and python is leveraged by using python productivity for zynq (PYNQ), a python development environment for application development. This paper provides the literature review of an implementation of DTC for FPGA devices along with future work that can be done.&lt;/span&gt;&lt;/span&gt;
A Learning-Based Mathematical Programming Formulation for the Automatic Configuration of Optimization Solvers

We propose a methodology, based on machine learning and optimization, for selecting a solver configuration for a given instance. First, we employ a set of solved instances and configurations in order to learn a performance function of the solver. Secondly, we formulate a mixed-integer nonlinear program where the objective/constraints explicitly encode the learnt information, and which we solve, upon the arrival of an unknown instance, to find the best solver configuration for that instance, based on the performance function. The main novelty of our approach lies in the fact that the configuration set search problem is formulated as a mathematical program, which allows us to a) enforce hard dependence and compatibility constraints on the configurations, and b) solve it efficiently with off-the-shelf optimization tools.
Binocular multi-line structured light matching

In this paper, the problem of binocular multiline structured light matching is studied. In this paper, the problem of binocular multi-line structured light matching method is studied. In binocular multi-line structured light matching, the laser stripe center point of two images is matched by using the position relation between binocular phase machine and the light plane. In the case of multiple matching points, the method of reverse reprojection is used to screen out the unique matching points. At the same time, the effects of using different matching window strategies in reprojection matching and reverse reprojection screening are analyzed. On the other hand, it improves the screening efficiency of multiple matching points. The experiment shows that the method can not only make full use of the laser fringe center point, but also improve the unique matching rate and the measuring efficiency.
Constrained Optimization Based on Ensemble Differential Evolution and Two-Level-Based Epsilon Method

Constrained optimization problems (COPs) are common in many fields, and the search algorithm and constraint handling technique play important roles in the constrained evolution algorithms. In this article, we propose a new optimization algorithm named CETDE based on ensemble differential evolution (DE) and a two-level epsilon-constrained method. In the ensemble DE variant, some promising parameters and mutation strategies constitute the candidate pool, and each element in the pool coexists throughout the search process and competes to generate new solutions. The two-level epsilon method is proposed by incorporating a generation and a population comparison level to retain more promising solutions without degrading the solution quality. Moreover, a diversity promotion scheme is developed to improve the population distribution when the search becomes trapped in a small region. The superior performance of CETDE is validated by comparison with some state-of-the-art COEAs over two sets of artificial benchmarks and five real-world problems. The competitive results show that CETDE is an effective method for solving COPs.
Meta-Strategy Based on Multi-Armed Bandit Approach for Multi-Time Negotiation

Multi-time negotiation which repeats negotiations many times under the same conditions is an important class of automated negotiation. We propose a meta-strategy that selects an agent's individual negotiation strategy for multi-time negotiation. Because the performance of the negotiating agents depends on situational parameters, such as the negotiation domains and the opponents, a suitable and effective individual strategy should be selected according to the negotiation situation. However, most existing agents negotiate based on only one negotiation policy: one bidding strategy, one acceptance strategy, and one opponent modeling method. Although the existing agents effectively negotiate in most situations, they do not work well in particular situations and their utilities are decreased. The proposed meta-strategy provides an effective negotiation strategy for the situation at the beginning of the negotiation. We model the meta-strategy as a multi-armed bandit problem that regards an individual negotiation strategy as a slot machine and utility of the agent as a reward. We implement the meta-strategy as the negotiating agents that use existing effective agents as the individual strategies. The experimental results demonstrate the effectiveness of our meta-strategy under various negotiation conditions. Additionally, the results indicate that the individual utilities of negotiating agents are influenced by the opponents' strategies, the profiles of the opponent and its own profiles.
A Collaborative Framework Supporting Ontology Development Based on Agile and Scrum Model

Ontology describes concepts and relations in a specific domain-knowledge that are important for knowledge representation and knowledge sharing. In the past few years, several tools have been introduced for ontology modeling and editing. To design and develop an ontology is one of the challenge tasks and its challenges are quite similar to software development as it requires many collaborative activities from many stakeholders (e.g. domain experts, knowledge engineers, application users, etc.) through the development cycle. Most of the existing tools do not provide collaborative feature to support stakeholders to collaborate work more effectively. In addition, there are lacking of standard process adoption for ontology development task. Thus, in this work, we incorporated ontology development process into Scrum process as used for process standard in software engineering. Based on Scrum, we can perform standard agile development of ontology that can reduce the development cycle as well as it can be responding to any changes better and faster. To support this idea, we proposed a Scrum Ontology Development Framework, which is an online collaborative framework for agile ontology design and development. Each ontology development process based on Scrum model will be supported by different services in our framework, aiming to promote collaborative activities among different roles of stakeholders. In addition to services such as ontology visualized modeling and editing, we also provide three more important features such as 1) concept/relation misunderstanding diagnosis, 2) cross-domain concept detection and 3) concept classification. All these features allow stakeholders to share their understanding and collaboratively discuss to improve quality of domain ontologies through a community consensus.
How Safe Is Your Playground? Analyzing Soil in Scottish Schools through a University Outreach Project

School pupils often perceive STEM subjects to be challenging; thus, in this project, an interactive, nationwide STEM investigation was designed to stimulate their interest. The “Soils in Scottish Schools” project provided teachers with a flexible package of resources to allow them to enhance the profile and understanding of soil science of their pupils. This package included presentations, experimental procedures, activities, and monthly newsletters which could be adapted to the particular learning environment of their pupils. In the focal point of the project, teachers were supported to encourage their pupils to collect samples from their school ground which were then analyzed for copper, lead, and zinc levels. This year-long project also afforded teachers with the opportunity to develop the pupils’ investigative skills through engagement with experiments in their own classrooms where they could engage with real data.