Reviewerly/exHarmony huggingface dataset:
PYP Journey at KNUES

Education in our era calls for global learners who reflect on themselves with an open minded and inquiring attitude and lead positive changes in the world in preparation for the Fourth Industrial Revolution and the transition to a hyper-connected society. Therefore, our school is working hard on researching and implementing innovation in elementary education by running a program that incorporates the characteristics of the International Baccalaureate Primary Years Program (IB PYP) for the first time in public schools. Kyungpook National University Elementary School (KNUES) has been conducting the Primary Years Program (PYP) since September 1, 2019, after being certified as an IB candidate school. The IB PYP has only been implemented at a few foreign private schools in Korea, and this is the first time that the curriculum has been introduced at a non-private elementary school. As a school leading the national curriculum, the foremost purpose of our school in implementing the IB PYP is to seek practical methodologies to enhance the effectiveness of the Korean National Curriculum. Therefore, based on the Korean National Curriculum, the IB program naturally incorporates the characteristics of the education system and continues to review the optimization of the Korean elementary education program. This is the major difference between IB education and main education at foreign schools in Korea. Our students are growing into self-directed lifelong learners by asking themselves questions and cooperating with friends through concept-based inquiry classes that transcend the subject. Through implementing the IB PYP education program, our school will improve the student-centered classroom culture and the curriculum-based teaching methods.
QCS-CNN: A Neural Network Optimization Algorithm in Resource-Constrained Environment

Due to the complex models and large amount of calculation, deep neural networks cannot be deployed on resource-constrained devices. In order to apply deep neural networks to resource-constrained devices, this paper proposes a QCS-CNN neural network optimization algorithm. It can compress the input image, while reducing the number of parameters, it can quantize the precision of all parameters in the network from 32 bits to 8 bits. The experiments on MNIST data set show that QCS-CNN neural network optimization algorithm can effectively reduce the amount of calculation and accelerate the inference speed of the model.
Fuzzy Integral Sliding Mode Observer-Based Structure and Motion Estimation of Multiple Objects Using a Monocular Camera

This article proposes a method to estimate the structure and motion (SaM) of multiple objects using a monocular camera. Most of the existing methods for SaM estimation are restricted to a single object by constraining the camera and object motions. In this study, SaM estimation is extended for the case of multiple (or simply two) objects, and the restrictions on the camera and objects motions and the necessity for the information related to the camera velocity are relaxed by using the relative information of the two objects. The relative motion dynamics of partially measurable states of multiple objects are first introduced based on which the ranges (i.e., depths) of the two objects with respect to the camera, relative velocity between the objects, relative velocity between each object and the camera, and velocities of both the camera and objects are then estimated by using the proposed fuzzy integral sliding mode observer. The following cases for the camera and objects—that have static (stationary), dynamic (moving), or general (i.e., either static or dynamic) motions—are studied: 1) Two objects are at the same range with respect to the camera and 2) two objects are at different ranges with respect to the camera. Several situations—in which the ranges of the two objects with respect to the monocular camera, relative velocity between the two objects, and velocities of the camera and objects can be estimated—are described in detail. Simulations and experimental results demonstrate the validity of the proposed method.
Face Detection Using Improved Multi-task Cascaded Convolutional Networks

Compared with traditional face detection methods, deep learning methods can better deal with face detection in unconstrained environment. In this paper, based on the original Multi-Task Cascaded Convolutional Networks (MTCNN), we make corresponding improvements on it. On the premise of ensuring that the accuracy of the improved algorithm for face detection is similar to the original, we improve the detection speed. First of all, the original network structure is optimized. In order to improve the detection speed, the amount of computation should be reduced as much as possible. Therefore, it is proposed to use Depthwise convolution in MobileNet instead of conventional convolution; in addition, median filter is used before image detection to reduce some noise and improve the performance of the algorithm. After training on the Wider Face and Celeba data set, the test images were tested. Experimental results show that the performance of the improved MTCNN is not significantly reduced, and the detection speed is significantly improved.
A Cascading Incremental Training Approach for Large-Scale Distributed Data Based on Support Vector Machine

In this paper, we present a cascading incremental training approach for large-scale and unevenly distributed data based on Support Vector Machine. Instead of using training data directly, data's distribution and scale are concerned, so Synthetic Minority Oversampling Technique and Generative Adversarial Networks are both used to synthesize new samples and add them to the data set to achieve a balanced sample number. Then, we propose a cascading model and divide samples into groups and apply the multi-layer convolutional neural network for pre-training to collect the embedding vectors of training samples. Finally, features extracted from the pre-training will be used for incremental training on Support Vector Machine. A multi-layer convolutional network and cluster are applied to maintain some key information and train features for classification. Additionally, uncertainty strategy is used to obtain potential features. In the comparative experiments with the above dataset, our proposed method performs the state-of-the-art competitors.
A Parallel Implementation of Q-learning

Q-learning has been one of the most used algorithms in reinforcement learning. There have been a lot of improvements in the speed of convergence of algorithm and many other improvements for various types of environments. Parallelizing the algorithm is one of the very useful improvements for Q-learning. Parallelizing the algorithm can mean the algorithm can run at multiple times the speed on capable systems. In this paper a technique to parallelize the algorithm is proposed. Experimental results are presented with the improvements achieved and a theoretical reasoning of the algorithm's merit in comparison with other works done in the field are also written.
Providing On-Demand Feedback for Improved Learning of Logical Reasoning in Computer Science and Software Engineering

For teaching introductory courses on logics and discrete mathematics for computer science students, there have been several attempts to use support by existing established “professional” proof assistants. In contrast, we have developed a proof checker the feature set of which is motivated by the educational application. Students access our system via a web-app, which they use to write proofs in the rigorous but mostly-conventional mathematical language of the textbook, and obtain fine-grained on-demand feedback on the correctness of their proof attempts.
Qualitative Prediction and Recognition of ongoing Human Action Sequences Using Deep Neural Networks

Human action detection (HAD) is an important research area in the recent decades that can be applied in many applications such as security, gaming, virtual reality interfaces, surveillance systems, etc. The authors have used object detection algorithms with deep neural network-based classifiers in this experiment. Further, the proposed research work aims to explore the solutions for object detection in HAD. The model has been provided with a set of images, wherein each image, a person will be performing an activity such as standing, sitting, bending, waving, or sleeping. The label of an image will be the activity that is being performed in those images. The model will learn this relationship, and then it can predict the label of an input that it has never seen. The model is trained and tested with a dataset consisting of random images downloaded from the internet.
Shoeclean a case study of shoe laundry services startup in developing mobile application

Shoes and sneaker have become a culture for its owners, the number of type and model of shoes continuously growing. As the shoe industry evolves, the shoe cleaning business has gained more prospects, but the problem is technology has not been widely applied to the industry. The purpose of this research is to develop shoe laundry business with its own system and mobile application that can enhance the business process and ease its customers in ordering the services. The data are from descriptive research using questionnaire and business plan. All the activities during the system analysis and design is based on Six-Day system development theory by Satiznger, Jackson and Burd. The output of this project is the formation of information system application that can help a shoe laundry business.
Icelandic Gigaword Corpus 2 (IGC2) - version 2 (2018)

The Icelandic Gigaword corpus (IGC) is a tagged and lemmatized corpus. Version 2 (2018) consists of approximately 1,400 million running words of text (punctation marks not included). Each running word is accompanied by a morphosyntactic tag and lemma and each text is accompanied by bibliographic information. The corpus is intended for linguistic research and for use in Language Technology projects. The latest texts in this version are from 2018. To enable the use of the corpus in Language Technology projects it is considered important to secure copyright clearance for the texts to be used. Originally the idea was to secure permission from copyright owners to give access to the texts with Creative Commons licenses. All copyright holders could not agree to those terms. The corpus is therefore divided into two parts, IGC1 and IGC2. IGC1 contains texts that can be used with a special license developed for the Tagged Icelandic Corpus (MIM). IGC2 contains official texts and texts that can be used with a CC BY license. All copyright holders have agreed that their material may be used free of licensing charges. Copyright owners that did not accept the CC BY license signed a special declaration developed for the Tagged Icelandic Corpus with necessary amendments for the IGC1. Size of IGC1 is: 799,180,517 (Custom license). Size of IGC2 is: 595,251,597 (CC BY 4.0). ------ Risamalheildin er morkuð og lemmuð malheild. I utgafu 2 (2018) eru um 1.400 milljonir lesmalsorða (greinarmerki ekki meðtalin) i textum sem eru geymdir i stoðluðu sniði i rafraenu formi. Orð i textunum eru greind malfraeðilega og hverjum texta fylgja bokfraeðilegar upplýsingar um verkið sem textinn er ur. Malheildin er aetluð fyrir malrannsoknir og til notkunar i maltaekniverkefnum. Nýjustu textarnir i þessari utgafu eru fra arinu 2018. Til þess að unnt se að nota malheildina i maltaekniverkefnum er logð ahersla a að afla leyfa fra retthofum fyrir notkun textanna. Upphaflega var miðað við að afla leyfa fra retthofum til þess að veita aðgang að textunum með leyfi sem samtokin Creative Commons hafa skilgreint. Ekki gatu allir retthafar samþykkt þa skilmala. Textum malheildarinnar er þvi skipt i tvo hluta, RMH1 og RMH2. I RMH1 eru textar sem veittur er aðgangur að með serstoku leyfi sem byggist a leyfi sem var utbuið fyrir texta Markaðrar islenskrar malheildar (MIM). I RMH2 eru opinberir textar og textar sem leyfður er aðgangur að með CC BY leyfi . Allir retthafar hafa samþykkt að veita afnotarett af textum sinum an þess að greiðsla komi fyrir. Þegar Morkuð islensk malheild var gerð var utbuin serstok samþykkisyfirlýsing með aðstoð logfraeðings sem var notuð með nauðsynlegum breytingum við gerð RMH og retthafar sem samþykktu ekki CC BY leyfið undirrituðu. Staerð RMH 1: 799,180,517 (MIM leyfi) Staerð RMH 2: 595.251.597 (CC BY 4.0).
Research and Application of Visual Monitoring Technology of Protective Equipment Based on Virtual Model

Abstract Aiming at the problem of heavy workload and tight time for daily operation and maintenance of smart substations, a visual monitoring technology for smart substation protection equipment based on virtual models is proposed, and a visual monitoring system for protection equipment is established. The article establishes the virtual model of the protection equipment panel, configures the association relationship between the graphic elements in the virtual model and the data set entries of the protection device and the optical fiber link, and establishes the logical operation model of the graphic elements.The visual monitoring system for protection equipment collects the data set entries of the protection equipment and the status values of the optical fiber link data set entries, simultaneously display the status of the primitives in the virtual model of the protection device panel according to the status value of the data set entry of the protection device. The visual monitoring system of intelligent substation protection equipment is applied on-site to verify the effectiveness of the technology.
The Energy Blockchain Establishing Policy Exploration Based on Microgird Routing Network

Abstract in order to enhancing the operation efficiency of energy internet, and develop the function and value space, it proposes an energy Blockchain of router network by applying the Blockchain to the energy internet. Concretely, a complex is a router’s storage of energy internet with a distributed storage of Blockchain, it establishes the physical bases of energy Blockchain under the structure of router network. Furthermore, by divided the operation information of energy internet into technical data and business data, it gives the operation process, and realize the generation and management of alliance linkage. Finally, Applying the energy Blockchain to energy management, comparatively analyzed these positive change and application value by Blockchain technology.
Library Publishing: A Catalyst for Change — Russian State Library participated in IFLA Webinar

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A Mixed Target Tracking Algorithm with Type Probability Using the GLMB filter

This paper focus on the state estimation, number of targets estimation and target type estimation of mixed targets and propose a mixed target tracking algorithm with type probability using the GLMB filter. The mixed targets include point target, extended target and group target (indistinguishable). The tracking algorithm mainly includes three aspects: mixed target dynamic modeling, type analysis and tracking estimation. Firstly, measurement model of mixed target is established with the generalized label multi-Bernoulli filter. Secondly, the probability of targets type are calculated. Finally, the parameters of finite mixture models (FMM) are derived by using Gibbs sampling and BIC criterion to track the mixed target. Then the equivalent measurement method is used to replace the measurement of extended target and group target. The shape of the mixed target is estimated by the ellipse approximation modeling method. Simulation experiments are provided to show the effectively of track mixed targets.
An Image Watermarking Algorithm Resistant to Geometric Distortion Matched Based on SIFT Feature Points

We propose a robust watermarking algorithm based on scale invariant feature transformation (SIFT) and pseudo-Zernike moment against geometric attacks. First, the SIFT algorithm is used to extract stable feature points from the carrier image. Then, the size and direction of each local feature region are adaptively determined according to the feature scale and direction. Finally, the feature region with large feature scale non-overlapping is selected from it, and the watermark is embedded into each local feature region by means of quantization modulation pseudo-Zernike moment amplitude method. The simulation results show that the proposed algorithm not only has good transparency, but also has a strong ability to resist conventional signal processing and geometric attacks.
Unified analytic forms for Convolutional Neural Networks and Wavelet Filter Banks

This paper provides a unified analytic framework integrating expressions of several variants of convolutional neural networks and wavelet filter banks. The expressions are derived recursively, from dowstream to upstream layers, for the sequences of features returned at the nodes of a general form of network architecture. The inspiring framework for the derivation of these expressions is that of the so-called M-band convolution filter banks. In addition with the inter-layer inter-node expressions, activation sequences of convolutional neural networks also are mathematically described by suitable algebraic path representations.
Two-Stage Training for Improved Classification of Poorly Localized Object Images

State-of-the-art object classifiers finetuned from a pretrained (e.g. from ImageNet) model on a domain-specific dataset can accurately classify well-localized object images. However, such classifiers often fail on poorly localized images (images with lots of context, heavily occluded/partially visible, and off-centered objects). In this paper, we propose a two-stage training scheme to improve the classification of such noisy detections, often produced by low-compute algorithms such as motion based background removal techniques that run on the edge. The proposed two-stage training pipeline first trains a classifier from scratch with extreme image augmentation, followed by finetuning in the second stage. The first stage incorporates a lot of contextual information around the objects, given access to the corresponding full images. This stage works very well for classification of poorly localized input images, but generates a lot of false positives by classifying non-object images as objects. To reduce the false positives, a second training is done on the tight ground-truth bounding boxes (as done traditionally) by using the trained model in the first stage as the initial model and very slowly adjusting its weights during the training. To demonstrate the efficacy of our approach, we curated a new classification dataset for poorly localized images - noisy PASCAL VOC 2007 test dataset. Using this dataset, we show that the proposed two-stage training scheme can significantly improve the accuracy of the trained classifier on both well-localized and poorly-localized object images.
Start of Epidemy in a City: Short-Term Forecast of Covid-19 with GMDH-Based Algorithms and Official Medical Statistics

The sudden onset and quick development of an unknown epidemic may lead to tragic consequences: panic of population due to victims and unpreparedness of authorities for effectively help to population.These circumstances define extremely high requirements to the tools for short-term operational forecast.Namely, such tools should provide reliable results when model of phenomenon is unknown (factors of disease spreading) and data are limited (time series of observations).GMDH-based algorithms just meet these requirements unlike modern differential or advanced statistical models.In this study we test different algorithms from GMDH Shell platform on the example of Covid-19 epidemic in Moscow during the period March 30-April 12, 2020.The forecast horizon is from 1 to 7 days, the initial information is only the official dynamics of diseased patients.Our model is autoregression with variables of different powers.The results of forecast are compared with the accuracy of popular statistical autoregression using exponential smoothing with trend.We suppose that the proposed approach will be useful for short-term forecast at the start of epidemic due to its simplicity and reliability.
Research on the application of clustering and classification in battlefield damage identification

Abstract This paper explores the solutions to the problem of battlefield damage pattern recognition. The main research results of the two components of clustering and classification are discussed respectively, and the main characteristics and existing problems of the existing methods are analyzed. The advantages, disadvantages and application scope of different methods are described in detail, which provides a basic theoretical guidance for the study of battlefield damage identification.
Distance Modular Advanced Training Course: from Design to Implementation

This article makes an attempt to consider the scientific foundations of the design of modular distance courses for the implementation of additional professional training programs for teaching staff. The authors focus on the difference between the modular distance course and other forms of advanced training, content and functional features, as well as the prospects for its implementation in the system of additional professional education.
Information and analytical support for the development of teacher education in Russia

The relevance of the study is due to the need to implement the Russian national system of teachers’ professional growth, in the context of strategic priorities (reflected in the national projects “Education” and “Science”), as well as in the context of a new pedagogical reality: digitalization, integration into professional pedagogical education of practical knowledge, change of teachers’ generations, etc. The aim of the study was to develop scientific ideas about the structure and principles of analytical support for updating lifelong education (its structure, content and process). The initial materials of the research were as follows: educational standards and programs in pedagogy (levels of secondary and higher professional education); research materials from leading Russian scientific schools in the field of pedagogical education, etc. The results of the study clearly shown the existing significant backlog of scientific-theoretical and scientific-applied research corresponded with the answers to the main challenges which the system of professional pedagogical education faces nowadays. At the same time, the need was revealed to consolidate the efforts of research teams, as well as the generation of scientific ideas in order to solve problems by the compositions of interregional expert groups. To solve the problem of consolidation, the organization of expert and analytical activities in teacher education has been developed, and its following main components have been identified: network management of expert and analytical activities, coordination of experts on problematic issues in teacher education and its key issues, and development of expert infrastructure.
Data Quality Improvements for Internet of Things Using Artificial Neural Networks

The Internet of Things has gained considerable attention due to its potential applications in multiple domains. However, some deployment environments may be hostile and this may affect the quality of data (QoD) and alter its accuracy. In order to ensure a high level of reliability, an IoT system should be able to clean its own sensed data by discarding those instances that are erroneous or incoherent. To achieve the data quality improvements, this paper suggests a new approach based on Artificial Neural Network (ANN). The proposed scheme can prematurely and efficiently detect outliers before forwarding them to a central processing unit. The performance of this proposed solution is validated through simulations, using a real dataset, and compared with other well-known models. Our findings demonstrate that the proposed approach outperforms the compared models in terms of accuracy, f-score, recall and precision metrics.