Reviewerly/exHarmony huggingface dataset:
THE DEVELOPMENT OF TEACHERS PEDAGOGICAL COMPETENCE IN THE CONDITIONS OF PROFESSIONAL EDUCATIONAL ORGANIZATION

Purpose: The ideas of integrity, unity, systematic organization of teacher’s work allow presenting the work of the teacher – a complex pedagogical reality – in the form of the unity of three interrelated spaces: the teacher’s personality, pedagogical activity, and pedagogical communication. Methodology: The analysis of the works of national and foreign experts devoted to the study of competences and competencies shows convincingly that, first of all, this phenomenon is associated with professional activity. A natural continuation of the study of competence problems in the late twentieth – early XXI century is the scientific and practical interest in the possibility of forming and developing competence in a specially organized environment. Result: For the authors of the article it is important not only how the teacher develops his pedagogical competence in the process of professional activity, but also how it increases the effectiveness of the educational activity of the student. Discussion. In our opinion, the basic competencies of a teacher of a professional educational institution should reflect the culture of the teacher and are divided into professional-pedagogical, social-motivational, information, communicative, creative. Special competencies integrate the knowledge of the teaching subject and methods of teaching, are divided into objective, content, design, reflexive, monitoring. Applications: This research can be used for the universities, teachers, and students. Novelty/Originality: In this research, the model of the development of teacher's pedagogical competence in the conditions of professional educational organization is presented in a comprehensive and complete manner.
Residual Feature Pyramid Architecture for Monocular Depth Estimation

This paper investigates Visualization of Image Depth Information architecture of fully convolutional residual networks, based on it, residual feature pyramid network architecture for monocular image depth estimation is proposed. Firstly, the input monocular RGB image is preprocessed. Secondly, the feature pyramid structures are introduced into the fully convolutional residual networks, which realizes multi-scale feature extraction and reused, at the same time, both the number of parameters in networks and the computational complexity are greatly reduced. Finally, Experiments are done on the commonly used NYU official dataset. Experimental results show that the proposed method has advantages over many recent advanced methods. And the object outlines in our inferred depth maps are clearer and exquisite which look qualitatively better. In addition, this paper uses a simpler networks structure to realize a lower system error.
Bi-directional Capsule Network Model for Chinese Biomedical Community Question Answering

With the rapid development of the Internet, community question answering (CQA) platforms have attracted increasing attention over recent years, particularly in the biomedical field. On biomedical CQA platforms, patients share information about diseases, drugs and symptoms by communicating with each other. Therefore, the biomedical CQA platforms become particularly valuable resources for information and knowledge acquisition of patients. To accurately acquire relevant information, question answering techniques have been introduced in biomedical CQA. However, existing approaches cannot achieve the ideal performance due to the domain-specific characteristics. For example, biomedical CQA involves more complex interactive information between askers and answerers, while CQA techniques designed for the general field can only deal with single interactions between questions and candidate answers within a similar topic. To address the problem, we propose a novel neural network model for biomedical CQA. Our model adopts the bidirectional capsule network to focus on different aspects of biomedical questions and candidate answers, and merges high-level vector representations of questions and answers to capture abundant semantic information. Furthermore, to capture the meaning of Chinese characters, we incorporate the radical of Chinese characters embedding as auxiliary information to improve the performance of Chinese biomedical CQA. We conduct extensive experiments, and demonstrate that our model achieves significant improvement on the performance of answer selection in the Chinese biomedical CQA task.
Improving the Quality of Digital Images Using the Median Filter Technique to Reduce Noise

The combination of point, line, shape and color elements combined to create a physical imitation of an object is called an image. The arrangement of the box elements in the image forms pixels or matrices. each image experiences degradation or loss of quality called noise. The effect of gaussian noise is the number of colored dots that are equal to the percentage of noise. This study raises the topic of improving the quality of digital images using median filter techniques to reduce noise. In this study using color image data (Red Green Blue) as test data and then converted into grayscale images to determine the gray degree of the image. The grayscale image is stored in the database. Then noise is generated by using random numbers. Noise in the form of impulse can be positive or negative in the form of adding pixel values to the original image, or it can reduce the value of the original image. The noise type used is salt &amp; pepper. Gray degrees 0-255 spread. Can be calculated through image histograms. To reduce noise the median filter technique is used. Image histogram as a measure of the spread of numbers from the median filter. The result is a median filter can reduce noise salt and pepper by using a matrix kernel.
Gated Task Interaction Framework for Multi-task Sequence Tagging

Recent studies have shown that neural models can achieve high performance on several sequence labelling/tagging problems without the explicit use of linguistic features such as part-of-speech (POS) tags. These models are trained only using the character-level and the word embedding vectors as inputs. Others have shown that linguistic features can improve the performance of neural models on tasks such as chunking and named entity recognition (NER). However, the change in performance depends on the degree of semantic relatedness between the linguistic features and the target task; in some instances, linguistic features can have a negative impact on performance. This paper presents an approach to jointly learn these linguistic features along with the target sequence labelling tasks with a new multi-task learning (MTL) framework called Gated Tasks Interaction (GTI) network for solving multiple sequence tagging tasks. The GTI network exploits the relations between the multiple tasks via neural gate modules. These gate modules control the flow of information between the different tasks. Experiments on benchmark datasets for chunking and NER show that our framework outperforms other competitive baselines trained with and without external training resources.
Sense-Through-Foliage Target Detection Based on UWB Radar Sensor Networks

In this chapter, we study sense-through-foliage target detection using ultra-wideband (UWB) radar. We propose a discrete cosine transform (DCT)-based approach for sense-through-foliage target detection when the echo signal quality is good, and a radar sensor network (RSN) and DCT-based approach when the echo signal quality is poor. A RAKE structure which can combine the echoes from different cluster members is proposed for clusterhead in the RSN. We compared our approach with the ideal case when both echoes are available, i.e., echoes with target and without target. We also compared our approach against the scheme in which 2-D image was created via adding voltages with the appropriate time offset as well as the matched filter-based approach. We observed that the matched filter-based could not work well because the UWB channel has memory. Simulation results show that our DCT-based scheme works much better than the existing approaches, and our RSN- and DCT-based approach can be used for target detection successfully while even the ideal case fails to do it. We also apply human-inspired mechanism to sense-through-foliage target detection. One of the great mysteries of the brain is cognitive control. How can the interactions between millions of neurons result in behavior that is coordinated and appears willful and voluntary? There is consensus that it depends on the prefrontal cortex (PFC). Many PFC areas receive converging inputs from at least two sensory modalities. Inspired by human’s innate ability to process and integrate information from disparate, network-based sources, we apply human-inspired information integration mechanisms to target detection in cognitive radar sensor network. Humans’ information integration mechanisms have been modeled using maximum-likelihood estimation (MLE) or soft-max approaches. In this chapter, we apply these two algorithms to cognitive radar sensor networks target detection. DCT is used to process the integrated data from MLE or soft-max. We apply fuzzy logic system (FLS) to automatic target detection based on the AC power values from DCT. Simulation results show that our MLE–DCT–FLS and soft-max–DCT–FLS approaches perform very well in the radar sensor network target detection.
Recent Advances Delivered in Mobile Cloud Computing's Security and Management Challenges

Mobile cloud computing provides an opportunity to restrict the usage of huge hardware infrastructure and to provide access to data, applications, and computational power from every place and in any time with the use of a mobile device. Furthermore, MCC offers a number of possibilities but additionally creates several challenges and issues that need to be addressed as well. Through this work, the authors try to define the most important issues and challenges in the field of MCC technology by illustrating the most significant works related to MCC during recent years. Regarding the huge benefits offered by the MCC technology, the authors try to achieve a more safe and trusted environment for MCC users in order to operate the functions and transfer, edit, and manage data and applications, proposing a new method based on the existing AES encryption algorithm, which is, according to the study, the most relevant encryption algorithm to a cloud environment. Concluding, the authors suggest as a future plan to focus on finding new ways to achieve a better integration MCC with other technologies.
Adaptive Particle Swarm Optimization with Rotational Invariance

Robustness and adaptability are necessary for metaheuristics because they are applied in various environment such as black-box optimization. In this paper, adding a parameter adjustment rule to Particle Swarm Optimization with rotational invariance using correlativity (CRI-PSO), we develop an adaptive CRI-PSO to improve the adaptability and maintain the robustness. First, using the swarm activity as an index evaluating the search state, we analyze a parameter of CRI-PSO based on intensification and diversification. Second, the parameter adjustment rule is based on evaluation and control of the search state. The rule controls the search state to realize intensification and diversification by adaptively adjusting the parameter. Also, the rule is designed so as to the adaptive CRI-PSO satisfies several transformation invariances of the solution space PSO not having. The performances (robustness and adaptability) of the adaptive CRI-PSO are verified through numerical experiments for typical benchmark functions comparing the adaptive CRI-PSO with three types of conventional PSOs.
On components of the Kerdock codes and the dual of the BCH code <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e21" altimg="si7.svg"><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msub></mml:math>

In the paper we investigate the structure of i-components of two classes of codes: the Kerdock codes and the duals of the linear uniformly packed codes with parameters of the primitive double-error-correcting BCH code. We prove that for any admissible length the punctured Kerdock code consists of two i-components and the duals of the linear uniformly packed codes with parameters of the primitive double-error-correcting BCH code is an i-component for any i. We give an alternative proof for the fact presented by De Caen and van Dam in 1999 that the restrictions of the Hamming scheme to the doubly shortened Kerdock codes are association schemes.
Socio-epistemological factors of contemporary university modernization

The article deals with the outlook analysis of contemporary university modernization as a socio-cultural project. The main groups of factors, which accelerate or slow down the process are considered. Among them are: intellectual trends in interpretation of the phenomenon of the University (mainly as a post-ideological); specifics of the given trends implementation (in accordance to legitimation limitations); modern tendencies of science development (in the context of difference in dynamics between education and science); to-day circumstances of contemporary society (as intensively changing and requiring for rethinking of the epistemological university grounds).
Gated Sequential Recommendation with Dynamic Memory Network

Recommender systems provide users with a ranked list of items based on users' historical behavior. Existing methods usually embed users with their temporal dynamics, which may have lost their long-term, intrinsic preferences. RNN is widely adopted to model users' behaviors for its expressiveness on modeling sequential information. However, RNN-based methods are insufficient to capture users' intrinsic preferences due to short-term dependency issues. In this paper, we aim to make progress from two perspectives: 1. extract users' intrinsic preferences from their historical behaviors more effectively and robustly; 2. explore a fine-tuning way to combine users' intrinsic preferences and external dynamic interests. To do so, we propose a modification of Dynamic Memory Network with gating mechanism for generating a dynamic, precise representation for individual users. We also design an automatic and implicit method, and it utilizes the intrinsic preferences as a trigger signal to search the input sequences and retrieves the temporal dynamics of users in the memory module. As a result, a desirable combination of temporal dynamics and intrinsic preferences is achieved. Extensive experiments on three benchmark datasets have shown our model's superiority over the existing baseline method. Further analysis of the attention weights of each hop demonstrated the effectiveness of the proposed method.
A Practice on Neural Machine Translation from Indonesian to Chinese

Machine translation is used to implement the translation between different languages. Neural machine translation is one of the most popular machine translation methods which have made a great progress in recent years especially on universal languages. However, domestic translation software for non-universal languages is limited and also needs improving. In this paper, we carry out a practice on neural machine translation from Indonesian to Chinese. We first build a bilingual corpus of Indonesian and Chinese. After that, we train translation models using neural machine translation method, with traditional statistical machine translation models as baselines. In the later stages of our practice, we develop a Web software, named as Lore Translator, basing on our so-far best translation model. The performance of our model is comparable to previous work.
Selective Hypothesis Transfer for Lifelong Learning

Selective transfer has been proposed as an alternative for transferring fragments of knowledge. Previous work showed that transferring selectively from a group of hypotheses helps to speed learning on a target task. Similarly, existing hypotheses could benefit by selective backward transfer of recent knowledge. This setting applies to supervised machine learning systems that observe a sequence of related tasks. We propose a novel scheme for bi-directional transfer between hypotheses learned sequentially using Support Vector Machines. Transfer occurs in two directions: forward and backward. During transfer forward, a new binary classification task is to be learned. Existing knowledge is used to reinforce the importance of subspaces on the target training data that are related to source support vectors. While this target task is learned, subspaces of shared knowledge between each source hypothesis and the target hypothesis are identified. Representations of these subspaces are learned and used to refine the sources by transferring backward. Albeit fundamental, the exploration of the problem of hypothesis refinement has been very limited. We define this problem and propose a solution. Our experiments show that a learning system can gain up to 5.5 units in mean classification accuracy of tasks learned sequentially using our scheme, within 26.6% of the number of iterations when these tasks are learned from scratch.
An improved (1+1) evolutionary algorithm for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e47" altimg="si11.svg"><mml:mi>k</mml:mi></mml:math>-median clustering problem with performance guarantee

Evolutionary algorithms (EAs) have been widely studied in numerical experiments and successfully applied in practice. However, most existing EAs are designed without a theoretical analysis for their performance guarantee. In this paper, we define a fitness function to guide the well-known (1+1) evolutionary algorithm, called (1+1) EA, to optimize the NP-hard k-median problem. We prove that the (1+1) EA can obtain a performance guarantee of 5 for k-median problem in polynomial expected runtime O(mn2⋅dmax) if all distances between data points and cluster centers are integers, where m and n are respectively the cardinalities of the data set and cluster center set, and dmax denotes the largest distance between data points and cluster centers. To tackle the general case that the distances between data points and cluster centers are real number, we propose an improved (1+1) EA, which employs a distance scaling scheme during the evolutionary process. Our rigorous theoretical analysis shows that the improved (1+1) EA obtains a performance guarantee of 5+ε in expected runtime O(ε−1mn2log(m⋅dmax)), where ε>0 is a constant. This study reveals that an appropriate scaling scheme can help EAs to obtain a constant performance guarantee in polynomial expected runtime and provides insights into design EAs for some NP-hard problems. In addition, we conduct experiment to compare the efficiency of the improved (1+1) EA with three clustering algorithms on optimizing three UCI datasets.
A Multiple Local Model Learning for Nonlinear and Time-Varying Microwave Heating Process

This paper proposes a multiple local model learning approach for nonlinear and nonstationary microwave heating process (MHP). The proposed local learning framework performs model adaption at two levels: (1) adaptation of the local linear model set, which adaptively partitions the process's data into multiple process states, each fitted with a local linear model; (2) online adaptation of model prediction, which selects a subset of candidate local linear models and linearly combines them to produce the model prediction. Adaptive process state partition and fitting a new local linear model to the newly emerging process state is based on statistical hypothesis testing, and the optimal combining coefficients of the selected subset linear models are obtained by minimizing the mean square error with the constraint that the sum of these coefficients is unity. A case study involving a real-world industrial MHP is used to demonstrate the superior performance of the proposed multiple local model learning approach, in terms of online modeling accuracy and computational efficiency.
A fast DFT method for generally k sparse signals recovery

Abstract In this paper, a new method to fast compute DFT of generally sparse signals is presented. Firstly, the original signal is downsampled with different time shifts, and the discrete Fourier Transforms (DFTs) of downsampled signals are calculated by FFT. Then the DFTs are used to determine and measure the K non-zero (significant) freq. grids by combining the moment preserving problem with the BigBand method. The proposed method is hardware-friendly, and simulation results show that the proposed method has better recovery performance compared with other methods.
MPSSD: Multi-Path Fusion Single Shot Detector

Recent prevalent one stage detectors, such as single shot detector (SSD) and RetinaNet, are able to detect objects faster than two stage ones while maintaining comparable accuracy. To further boost the accuracy, many studies focus on enhancing the multi-scale feature pyramid. Most of these current proposals focus on strengthening features on one pyramid, ignoring the rich connection among different scale features. In contrast, we propose a novel multi-path design to fully utilize the localization and semantics information. First, we exploit the original SSD multi-scale features as our base pyramid. Then we fuse these features in different groups to generate multi-path feature pyramids. Finally, we combine these pyramids through a novel and effective aggregation module, to obtain the final informative pyramid for detection. Comparative experiments on benchmark PASCAL VOC and MS COCO datasets have shown that our proposed method outperforms many state-of-the-art detectors. As an illustrative example, for input image with size 512×512, we can achieve a mean Average Precision (mAP) of 81.8% on VOC2007 test and 33.1% mAP on COCO test-dev2015.
Prediction of Poker Moves Using Sequential Model and TensorFlow

We analyze neural network algorithms that can play poker game. Poker is attributed to the group of games with incomplete information, i.e. all game-related information is unknown until the end of the game. Therefore, poker is an interesting case used to check the abilities of machine learning models to make optimal decisions. We created a sequential model of poker move prediction using TensorFlow.js machine learning library and its sequential model. The model was trained on UCI Poker Hand dataset. We tested various combinations of model parameters: number of model layers, activation, loss function, optimization function, and number of epochs. The best model configuration for predicting the moves of the poker game with three layers with Mean Squared Error (MSE) loss function, Adam (0.001) optimizer, relu activation in the first layer had, relu6 activation in the second layer and selu activation in the third layer has achieved an accuracy of 99%.
On-the-Job Training System to Support Gifted School Activities for In-Service Teachers With Online Video Conferencing

In-service teacher training is conducted at schools in Japan to address various educational challenges with the aim of enriching on-the-job training (OJT) at schools through teacher collaboration and off-the-job training (Off-JT) outside of schools. However, in Miyazaki prefecture, OJT does not function well at remote schools or small schools in the mountains, and various conditions make it difficult for teachers to participate in training outside the school premises, even if they would like to. In this chapter, the authors propose a support system that aims to revitalize OJT/in-school training through the effective use of video conferencing technology, and a support system for Off-JT/out-of-school training through the construction of a new training system. This chapter introduces excellent initiatives at small schools in the mountains that make use of the authors' training system. The authors believe this training system can offer many important insights into how teachers are trained in developing countries and numerous countries with various geographic issues.