Reviewerly/exHarmony huggingface dataset:
SAGCN: Towards Structure-Aware Deep Graph Convolutional Networks on Node Classification

Graph Convolutional Networks (GCNs) have recently achiev-ed impressive performance in different classification tasks. However, over-smoothing remains a fundamental burden to achieve deep GCNs for node classification. This paper proposes Structure-Aware Deep Graph Convolutional Networks (SAGCN), a novel model to overcome this burden. At its core, SAGCN separates the initial node features from propagation and directly maps them to the output at each layer. Furthermore, SAGCN selectively aggregates the information from different propagation layers to generate structure-aware node representations, where the attention mechanism is exploited to adaptively balance the information from local and global neighborhoods for each node. Our experiments verify that the SAGCN model achieves state-of-the-art performance in various semi-supervised and full-supervised node classification tasks. More importantly, it outperforms many other backbone models, by using half the number of layers, or even fewer layers.
Text Entailment Generation with Attention-based Sequence-to-sequence Model

Text entailment needs semantic similarity judgment between two sentences and is a good task to measure text understanding. If we realize entailment generation, we can apply it to summarization that keeps semantics between an original text and a generated text. These days, neural networks are employed to construct modules that encode an original text and generate summarization. In natural language processing, sequence-to-sequence models, which realize sequential learning, are employed to develop machine translation. Moreover, attention mechanism is proposed to improve machine translation considering word alignment between a source language and a target language. In this paper, we applied an attention-based sequence-to-sequence model to an entailment generation task and confirmed the system realized entailment generation. The proposed method can capture important words in the input text and generate a frequent sentence, which is grammatically correct and semantically appropriate. The results mean the proposed system understands a text semantically.
Discussion on the key technologies of Intelligent Teaching System application in the era of artificial intelligence 2.0

In order to explore the problems of the key technology of intelligent teaching system in the era of artificial intelligence 2.0 and promote the development of intelligent teaching system better, we study the status and role of the four key technologies in intelligent teaching system. This paper takes four modules of intelligent teaching system as the objects, and discusses four key technologies supporting the application of intelligent teaching system, namely natural language processing, question answering system, expression recognition, educational data mining, etc. This paper discusses the status and the role of key technologies in the intelligent teaching system in the era of artificial intelligence 2.0, and the problems, causes and the necessity of solving in the application and development process of intelligent teaching system. The research results of this paper can provide a theoretical basis for the application of intelligent teaching system in the era of artificial intelligence 2.0, and standardize the orderly development of intelligent teaching system.
A Clinical Reasoning Learning Groupware: An exploratory study of factors affecting the adoption behavior

Mobile-Based Collaborative Learning (MBCL) has become a crucial technology for improving learning services in terms of quality, availability and efficiency at an acceptable cost. Although widespread utilization of smart-phones as well as the acknowledged potential of exploiting them to ameliorate medical learning services notably clinical reasoning learning, the study of the adoption and acceptance intention of such technology by the medicine learners remains a subject open to investigation. Nevertheless, little investigation has been done to draw any systematic study of the medicine learners' intention to adopt and accept Groupware technology, particularly in mobile-based clinical reasoning collaborative learning context. The purpose of the present research work was to propose a theoretic UTAUT-based model (UTAUT: Unified Theory of Acceptance and Use of Technology) and then investigate it through empirical observation in order to conclude the key elements impacting medicine learners' intention to accept our proposed Groupware system called: Clinical Reasoning Collaborative Learning (CRCL) for clinical reasoning mobile learning.
COVID-19 Dynamics Prediction by Improved Multi-Polynomial Regression Model

For months since December in 2019, Corona Virus Disease 2019 (COVID-19) epidemic has spread worldwide. The growing severity of this disease in the U.S indicates that predicting its evolutionary dynamics is attached more importance for a better countermeasure towards COVID-19. Considering predicting cumulative positive cases in the United States (U.S) from the very beginning, a Multi-Polynomial Regression model in a simpler and more efficient manner is proposed in this study. By combining both characteristic features (CFs) and temporal features (TFs) after dataset categorization and feature selection depending on correlation analysis, cross-validation and parameter optimization to tune are applied and the model to discover the close-to-optimal structure is reshaped. Results reveal that this model performs well in both accuracy and efficiency, and its R2 scores of New Mexico, North Dakota, New Hampshire, and Oregon are all more than 0.90 and the R2 score of U.S Virgin Islands can be improved from 0.53 to nearly 1.00 by importing more CFs. This model is efficient in inference with an average training time of 2.45s and an average running time of 5.1×10-3s. In particular, the model can be manually set to adjust to different state conditions by changing the number of selected CFs.
Chaos Synchronization and Its Application to Secure Communications

Since the beginning of the 90ies, chaos has been viewed as a powerful tool for several applications. The complexity of the chaotic pattern is promising for the generation of true random numbers. Because of its unpredictability, chaos also shed the light on secure communications, especially in the optoelectronics field. Several methods of encryption were studied with a particular focus on the chaos being the carrier among which the message is hidden. This method relies on synchronization (or anti-synchronization) of chaos and implies one emitter and one receiver. This technique is well mastered when it comes to fiber optics, with the latest efforts achieving high data rates and low bit-error rates (BER). At the opposite, there are only a few theoretical reports for free-space chaos synchronization and communications, and experiments are never mentioned, though they could be very relevant.
Analysis and visualization in graph database management systems

Abstract The following questions of visual interpretation of the data in the graph models of data are considered in the work with graph database management system (DBMS) with DBMS Neo4j as an example; convenience of data visualization in the form of graphic images as compared with the electronic worksheets was confirmed; a high connectivity of data was revealed for DBMS NEO4j. To do all these algorithms from DBMS NEO4j library are employed. Clusterization is applied for the estimation of the quality of affinity for the predetermined data sets. Here method of silhouette assessment is used. Basing on the performed experimental studies a conclusion was made concerning the quality of revealing the similarity between the predetermined data sets with the use of graph DBMS NEO4j.
UAV Modular Control Architecture

This paper presents a novel control prototype for autonomous unmanned aerial vehicle (UAVs) that can operate in dynamic environments. This prototype is called “UAV Modular Control Architecture” or UMCA. It consists of specific components that are organized into two layers: mission planning and mission Supervision. The mission planning layer generates an appropriate flight plan to meet the given mission, and the mission supervision layer interprets the plan and evaluates its execution. The UMCA leads to large advantages such as adaptability, flexibility, robustness, and extensibility.
Road Sign Segmentation for Smart Bicycles

Bicycles are low-carbon emitting and inexpensive means of transportation that are popular worldwide. In densely populated areas, bicycle sharing programs are helping to expand access to these vehicles. As bicycle sharing programs grow and more people use bicycles on the road, there is a need to improve the performance and safety of bicycle operation. One opportunity for improving the safety of bicycle operation is to add automated road sign detection and identification. Once a road sign is detected and identified, this data can be used to improve autonomous navigation of the vehicle, to share data between bicycles on the road for better traffic control, and to remind or prompt the operator about current road status. The Mask-RCNN deep learning architecture is trained to detect, identify, and segment several common Vietnamese road signs. Training images were captured from a camera mounted on a bicycle traveling on the road. Results show that the trained neural net successfully recognizes signs, with varying success rates across trials correlating heavily with the number and quality of training annotations.
Characters Recognition of Korean Historical Document Base on Data Augmentation

Character recognition of historical document is one of the most important basic tasks in the digitization of historical document. This paper is aimed at the few-shot learning problem of Korean ancient character recognition, a data enhancement method that combines traditional data and a conditional deep convolution generation confrontation network is proposed to obtain expanded samples. Then analyzed the performance of the Lenet@5 and Lenet@8 network models in Korean ancient character recognition. The experimental results show that the expanded sample significantly enriches the experimental data, and the improved Lenet@8 network model is better than Lenet@5, which can better acquire image features and greatly improve the classification accuracy. The proposed method can solve the problem of Recognition of Text-Image Characters of Korean Historical Document. Keywords-Character recognition of Korean historical document; Few-shot learning; Data augmentation; Generative Adversarial Networks; Convolutional neural network.
Identification of Papillary Patterns in Human Fingers and Toes on the Basis of General Characteristics

The article gives a brief overview of the types of papillary ridge patterns of the skin, their location on the digits of fingers and toes. In this study, the authors of the article aimed to determine whether the papillary ridge patterns of a person’s fingers and toes on the hands and feet of the same side are identical in terms of general characteristics. The methods used were the printing ink imprints on dactyloscopic maps and Microsil® impressions, and the comparison of the obtained impressions.
Sequence-Level Self-Teaching Regularization

In our previous research, we proposed a frame-level self-teaching network to regularize the deep neural network during training. In this paper, we extend the previous approach and propose a sequence self-teaching network to regularize the sequence-level information in speech recognition. The idea is to generate the sequence-level soft supervision labels from the top layer of the network to supervise the training of lower layer parameters. The network is trained with an auxiliary criterion in order to reduce the sequence-level Kullback-Leibler (KL) divergence between the top layer and lower layers, where the posterior probabilities in the KL-divergence term is computed from a lattice at the sequence-level. We evaluated the sequence-level self-teaching regularization approach with bidirectional long short-term memory models on LibriSpeech task, and show consistent improvements over the discriminative sequence maximum mutual information trained baseline.
Research on Transfer Learning of Multi-layer Neural Network against Network Data

In the confrontation training of generative confrontation network, insufficient target sample training set will cause the model to fail to accurately learn the corresponding features, but it is difficult to obtain the target sample training set that needs to be manually made and marked. In response to this problem, the paper proposes a two-layer generative confrontation network model based on multi-layer neural network migration learning. In the first layer of the network, the model learns the approximate distribution of the target sample in the structural space through the pseudo-target sample. The learning ideas are transferred to the model and adjusted according to a small number of target samples in the second layer network. In the experiment, the paper verified the improvement of the model in Chinese font generation and picture frame conversion, and effectively trained a better model in a small number of target sample training sets.
Stability Metrics for Continuous Integration of Service-Oriented Systems

One of the key principles of the service orientation is the standardised service contract. However, the assumption that the service contract is kept unmodified during the whole life-cycle of a system is not always held. Evolution changes on the service APIs have an impact on the maintainability of their programming clients within the system making difficult the continuous integration of the services. The metrics that have currently been applied for the service maintainability assess the service coupling, cohesion, complexity, and granularity. Software stability can further contribute in assessing the maintainability of systems. However, it is challenging to measure the stability of service APIs without having evolved their programming clients, because it should be measured by considering the types of the evolution changes in APIs that have direct impact on the programming clients. To address this challenge, we define a set of mappings between evolved service APIs based on which the stability changes can be determined. We further specify a generic algorithm that recognises the evolution changes required on the programming clients of the evolved APIs. We finally define an initial version of a suite of metrics that estimate the stability of a service system without assuming the existence of the evolved programming clients.
Feature Pyramid Hierarchies for Multi-scale Temporal Action Detection

Temporal action detection is a challenging but promising task in video content analysis. It is in great demand in the field of public safety. The main difficulty of the task is precisely localizing activities in the video especially those short duration activities. And most of the existing methods can not achieve a satisfactory detection result. Our method addresses a key point to improve detection accuracy, which is to use multiscale feature maps for regression and classification. In this paper, we introduce a novel network based on classification following proposal framework. In our network, a 3D feature pyramid hierarchies is built to enhance the ability of detecting short duration activities. The input RGB/Flow frames are first encoded by a 3D feature pyramid hierarchies, and this subnet produces multi-level feature maps. Then temporal proposal subnet uses these features to pick out proposals which might contain activity segments. Finally a pyramid region of interest (RoI) pooling pipeline and two fully connected layers reuse muti-level feature maps to refine the temporal boundaries of proposals and classify them. We use late feature fusion scheme to combine RGB and Flow information. The network is trained end-to-end and we evaluate it in THUMOS'14 dataset. Our network achieves a good result among typical methods. A further ablation test demonstrate that pyramid hierarchies is effective to improve detecting short duration activity segments.
Part-of-Speech Tagging Using Long Short Term Memory (LSTM): Amazigh Text Written in Tifinaghe Characters

Long short term memory (LSTM) networks have been gaining popularity in modeling sequential data such as phoneme recognition, speech translation, language modeling, speech synthesis, chatbot-like dialog systems, and others. This paper investigates the attention-based encoder-decoder LSTM networks in TIFINAGH part-of-speech (POS) tagging when it is compared to Conditional Random Fields (CRF) and Decision Tree. The attractiveness of LSTM networks is its strength in modeling long-distance dependencies. The experiment results show that Long short-term memory (LSTM) networks perform better than CRF and Decision Tree that have a near performance.
Multiscale Sensor Fusion for Display-Centered Head Tracking

Emerging display usage scenarios require head tracking both at short (<; 1m) and modest (<; 3m) ranges. Yet it is difficult to find low-cost, unobtrusive tracking solutions that remain accurate across this range. By combining multiple head tracking solutions, we can mitigate the weaknesses of one solution with the strengths of another and improve head tracking overall. We built such a combination of two widely available and low-cost trackers, a Tobii Eye Tracker and a Kinect. The resulting system is more effective than Kinect at short range, and than the Tobii at a more distant range. In this paper, we discuss how we accomplish this sensor fusion and compare our combined system to an existing mechanical tracker to evaluate its accuracy across its combined range.
Figlearn: Filter and Graph Learning Using Optimal Transport

In many applications, a dataset can be considered as a set of observed signals that live on an unknown underlying graph structure. Some of these signals may be seen as white noise that has been filtered on the graph topology by a graph filter. Hence, the knowledge of the filter and the graph provides valuable information about the underlying data generation process and the complex interactions that arise in the dataset. We hence introduce a novel graph signal processing framework for jointly learning the graph and its generating filter from signal observations. We cast a new optimisation problem that minimises the Wasserstein distance between the distribution of the signal observations and the filtered signal distribution model. Our proposed method outperforms state- of-the-art graph learning frameworks on synthetic data. We then apply our method to a temperature anomaly dataset, and further show how this framework can be used to infer missing values if only very little information is available.
Class Conditional Alignment for Partial Domain Adaptation

Adversarial adaptation models have demonstrated significant progress towards transferring knowledge from a labeled source dataset to an unlabeled target dataset. Partial domain adaptation (PDA) investigates the scenarios in which the source domain is large and diverse, and the target label space is a subset of the source label space. The main purpose of PDA is to identify the shared classes between the domains and promote learning transferable knowledge from these classes. In this paper, we propose a multi-class adversarial architecture for PDA. The proposed approach jointly aligns the marginal and class-conditional distributions in the shared label space by minimaxing a novel multi-class adversarial loss function. Furthermore, we incorporate effective regularization terms to encourage selecting the most relevant subset of source domain classes. In the absence of target labels, the proposed approach is able to effectively learn domain-invariant feature representations, which in turn can enhance the classification performance in the target domain. Comprehensive experiments on three benchmark datasets Office-31, Office-Home, and Caltech-Office corroborate the effectiveness of the proposed approach in addressing different partial transfer learning tasks.
MEDIA LITERACY IN THE STRUCTURE OF PROFESSIONAL COMPETENCE OF FUTURE TEACHERS

Today in Ukraine the issue of media literacy is becoming increasingly important, and the need of the population for critical thinking is becoming more pronounced.Media literacy of the young generation is a leading factor in its socialization, learning, becomes a mean of distance and a source of nonformal education.In 2016 the introduction of a new version of the Concept of Media Education in Ukraine, approved by the Presidium of the National Academy of Pedagogical Sciences, is also important for understanding media educational processes in Ukraine.
Generative Image Inpainting for Large-Scale Edge Area

In recent years, applying deep learning to computer vision is a very popular research direction, and a number of models with amazing effects have appeared. Deep learning-based approaches for end-to-end image inpainting have shown promise results. Recent research has made great progress in repairing rectangular and free-form areas but there are still many problems and room for improvement. For example, artifacts, blur and color missing still exist among the completion results of the large-scale border area. In this paper, we propose an end-to-end GAN-based image inpainting method, which has a better effect on the large boundary area. Our model is a two-stage adversarial network. The first stage completes the corresponding edge image, and the second stage uses the edge image generated in the first stage as a prior to complete the color image. We added parallel residual blocks to the edge completion network, and for the image completion network we replace the original residual blocks with multi-scale dilated convolution fusion blocks. Besides, a content loss based on DenseNet is added to the second stage. Experiments on multiple publicly available datasets show that our results have better effects on larger edge areas and can increase the average PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index).
PrivTech for Data Risk &amp;amp; Value: It's Time to Cross the Valley of Death

Despite clear advancement by the R&D community in attenuating the privacy-utility dichotomy, as well as a public and private market that is increasingly demanding both privacy protection and continual innovation, the transition and adoption of PrivTech capabilities to operational deployment is slow-going at best and moribund at worst. Why have repeated demonstrations that disprove the zero-sum conclusion of privacy vs. utility continued to fall on deaf ears, struggling to mature from proof-of-concept into pilot programs and onward toward general adoption? This article offers a progressive but pragmatic way to think about PrivTech (privacy- preserving computation) against the sociopolitical backdrop of stated needs and capability gaps facing government and industry. Our intent is to help bridge this "Valley-of-Death" between government-funded PrivTech and the enormous demand for the sharing of sensitive data for legitimate purposes, by dispelling misperceptions and lack of awareness that impede the trust needed to propel PrivTech from the lab and into the business processes of government and industry.