Reviewerly/exHarmony huggingface dataset:
Translating Arabic Named Entities into English and Spanish

This study focuses on the translation consistency of Arabic Named Entities (national corporate entities and regulation names) at the UN. Its objective is to promote international understanding in the field of human rights. A statistically significant data sample was extracted from a trilingual corpus of national reports. The quantitative analysis of the data showed a high translation inconsistency that increased when the temporal gap between documents was greater. The qualitative analysis revealed that this inconsistency stemmed from linguistic phenomena as well as the translation techniques used. The results obtained highlight the difficulty of consistently rendering Arabic Named Entities in a complex international setting. They also underline the negative impact of relay translations and the high pressure on human resources. The conclusion is that translator needs should be addressed by expanding UN terminology databases and improving computer-assisted translation tools for Arabic.
Research on the System Structure of IPV9 Based on TCP/IP/M

Abstract Network system structure is the basis of network communication. The design of network model can change the network structure from the root, solve the deficiency of the original network system, and meet the new demand of the future network. TCP/IP as the core network technology is successful, it has shortcomings but is a reasonable existence, will continue to play a role. Considering the compatibility with the original network, the new network model needs to be compatible with the existing TCP/IP four-layer model, at the same time; it can provide a better technical system to implement the future network. Based on the Internet three-layer/four-layer hybrid architecture TCP/IP/M and ISO/IEC next-generation Internet standard solutions, this paper proposes the IPV9 system architecture, which can directly transmit audio and video data with three layers on the premise of not affecting the existing four-layer network transmission. The hybrid structure is a new transmission theory, which requires the establishment of a link before data transmission and the withdrawal of the link after the transmission is completed. It solves the problem of high-quality real-time media communication caused by the integration of three networks (communication network, broadcasting network and Internet) from the underlying structure of the network, realizes the long-distance and large-traffic data transmission of the future network, and lays a solid foundation for the digital currency and virtual currency of the Internet. The system framework is verified by practical application. It have been deployed to verify the compatibility and reliable transmission between IPV9 network and the existing network, under the independent, reliable, secure and controllable network architecture, a new generation of master root server and 13 root domain name servers.
An Efficient Algorithm for Reconstruction Images Corrupted by Some Multiplicative Noises

Abstract In this paper, we propose a novel hybrid model for restoration of images corrupted by multiplicative noise. Using a MAP estimator, we can derive a functional whose minimizer corresponds to the denoised image we want to recover. The energies studied here are inspired by image restoration with non linear variable exponent [1, 2], and it is a combination of fast growth with respect to low gradient and slow growth when the gradient is large. We study a mathematical framework to prove the well posedness of the minimizer problem and we introduce the associated evolution problem, for which we derive numerical approaches. At last, compared experimental results distinctly demonstrate the superiority of the proposed model, in term of removing some muliplicative noise while preserving the edges and reducing the staircase effect.
HAAR Characteristics-Based Traffic Volume Method Measurement for Street Intersections

Traffic volume is an important measurement to design mobility strategies in cities such as traffic light configuration, civil engineering works, and others. This variable can be determined through different manual and automatic strategies. However, some street intersections, such as traffic circles, are difficult to determine their traffic volume and origin-destination matrices. In the case of manual strategies, it is difficult to count every single car in a mid to large-size traffic circle. On the other hand, automatic strategies can be difficult to develop because it is necessary to detect, track, and count vehicles that change position inside an intersection. This chapter presents a vehicle counting method to determine traffic volume and origin-destination matrix for traffic circle intersections using two main algorithms, Viola-Jones for detection and on-line boosting for tracking. The method is validated with an implementation applied to a top view video of a large-size traffic circle. The video is processed manually, and a comparison is presented.
Enhanced EAST: Improving Network's Feature Extraction Ability and Text Complete Shape Perception

EAST [1] is a popular end to end text detector, however, it performs deficiently with long or large texts, because of limited network's feature extraction capacity and narrow receptive fields. Based on EAST, we propose an approach named Enhanced EAST. Firstly, we offer low-level feature layers more semantic information by introducing information from high levels to low levels, which reduces the information gap between different layers. Meanwhile, we utilize a two-stream large kernel convolution to increase receptive fields with reasonable computational cost, therefore, improving the network's features detection and fusion ability. In addition, we also optimize the label generation of training data and design a weighted mask for each text, which can guide the training process to enhance the network's complete shape perception of texts, thus impelling the predicted text boxes locate more accurately. In the end, we perform data equalization and augmentation in the experiments and experiment results on ICDAR 2015, MSRA-TD500 and ICDAR 2017 MLT datasets demonstrate the proposed algorithm achieves a state-of-art performance in multi-oriented scene text detection.
Modeling of Automated Information System Components for Management of Teaching Materials of Discipline

The annual renewal of the set of teaching materials - is the basis of methodological work at the University. In addition to the creative component, this process includes a large amount of routine work. Currently, the automation of teaching activities of academics is reduced to the use of office programs for creating and editing text files in most cases. Moreover, there is no software that could fully automate the process in the free access. This problem indicates that this area needs automation tools to reduce the complexity of component preparation for the teaching materials of the discipline. In this direction functional simulation of an automated information system using object-oriented and structural approach to the analysis of activity of the business process participants is performed. The authors propose to automate the development of methodological documents on the basis of a single reference database of standard text fragments, a universal conceptual model of which is presented in the work. Software realization options of automated information system are considered in relation to development “from scratch” to allow convenient processing of electronic documents in the form of office applications using modern technologies and programming languages, as well as adjusting the existing specialized software designed for processing text documents. The results of experimental studies allow us to conclude that the approach to the automated process of compiling an interrelated documents set on the basis of a single reference database allows to reduce significantly time costs and to improve the readiness level of the set of documents for further modification.
MaTRU‐KE revisited: CCA2‐secure key establishment protocol based on MaTRU

Summary Quantum attack–resistant cryptosystems are required for secure communication since there is a big demand to have quantum computers. Lattice‐based cryptography is one of the quantum‐secure families due to its key/ciphertext sizes and performance. NTRU‐based cryptosystems, a member of lattice‐based cryptosystems, have received much more attention. MaTRU, a noncommutative version of NTRU with some matrix properties, is used to obtain a key exchange protocol in 2018. In this paper, we focus on MaTRU‐based key exchange protocols having CCA2 properties. We propose CCA2‐secure versions of MaTRU‐KE and then provide a security analysis of CCA2‐secure key establishment protocols. We also provide a comparison with the previous ones.
Unsupervised Qualitative Scoring for Binary Item Features

The qualitative score of tags is widely used to describe which product is better in terms of the given property. For example, in a restaurant-navigation site, properties such as food, location, and mood are given in the form of numerical values, representing the goodness of each aspect. In this paper, we propose a novel approach to estimate the qualitative score from the binary features of products. Based on a natural assumption that an item with a better property is more popular among users who prefer that property, in short, "experts know best", we introduce one discriminative and two generative models with which user preferences and item-qualitative scores are inferred from user-item interactions. Our approach contributes to resolving the following difficulties: (1) no supervised data for the score estimation, (2) implicit user purpose, and (3) irrelevant tag contamination. We evaluate our models by using two artificial datasets and two real-world datasets of movie and book ratings and observe that our models outperform baseline models.
Game Traffic Classification Based on DNS Domain Name Resolution

The accurate classification of network game traffic is the technical basis for the campus network resources and the refined management of student learning behavior. This article first uses DNS reverse resolution to capture the domain name characteristics of the captured network game traffic, uses the domain name feature to mark the game traffic, and builds the network game feature data set. Then, the network game data set is used to train the decision tree CART to optimize the classification model parameters. Finally, the optimized classification model is tested with Moore standard data set and this data set, respectively. The results show that the accuracy of network game traffic classification based on DNS domain name resolution can reach 94%, and its classification performance is better than Moore data set.
Multilingual Knowledge Graph Embeddings with Neural Networks

Multilingual knowledge graphs constructed by cross-lingual knowledge alignment have attracted increasing attentions in knowledge-driven cross-lingual research fields. Although many existing knowledge alignment methods such as MTransE based on linear transformations perform well on cross-lingual knowledge alignment, we note that neural networks with stronger nonlinear capacity of capturing alignment features. This paper proposes a knowledge alignment neural network named KANN for multilingual knowledge graphs. KANN combines a monolingual neural network for encoding the knowledge graph of each language into a separated embedding space, and a alignment neural network for providing transitions between cross-lingual embedding spaces. We empirically evaluate our KANN model on cross-lingual entity alignment task. Experimental results show that our method achieves significant and consistent performance, and outperforms the current state-of-the-art models.
Qualitative studies on human-data interaction and data analysis collaboration in Latin America

Professionals from different areas deal with data in various formats and do not always know how to extract information and knowledge from those datasets. Our studies focused on getting the user (data analyst) to reflect and project interactive narrative visualizations based on data. We used a model to help users to build knowledge about computing resources and techniques they may appropriate and employ in a data analysis context. Our results indicate pertinent ways of representing relevant narrative aspects in data visualizations. The purpose of this work is to investigate people's needs in relation to data analysis, and define strategies and solutions to bridge the gap between data-analysis professionals and computational solutions. Additionally, given the importance of culture in HDI research, we believe we need to direct our efforts to develop human-data interaction in LatAm context.
Research on Multi-sub-Graph Parallel Analysis Method for Power Grid Graph Data

In recent years, with the expansion of power grid scale and in-depth information construction, the data generated by the operation of power grid information system has increased rapidly, and the power big data environment has gradually formed. The power grid graph data has gradually become an important data storage and analysis solution due to its good support for power grid topology analysis, which is used as the basis for analysis of power grid business systems. In this paper, a multi-sub-graph parallel analysis method based on region partitioning is proposed. Firstly, the power grid graph data is divided into multiple sub-graphs based on region attributes, and then a parallel analysis algorithm for multiple sub-graphs is proposed. Related experiments show that the parallel analysis scheme of power grid graph data is effective and can meet the actual needs of power grid graph.
Feature learning using Stacked Autoencoder for Multimodal Fusion, Shared and Cross Learning on Medical Images

The analysis of medical images and to find meaningful patterns in it is a cumbersome task, even with the use of techniques of Computer Vision when the dataset is very large. In such a situation deep learning is a handy tool, because of its ability to learn and extract meaningful patterns and features from the images. The use of multiple modalities of training data to train system has been in practice for conventional machine learning algorithms. Here, in this paper, we are going to present a Deep Learning based architecture for extraction of features from large training set of medical images. The deep learning model is tested against conventional techniques by performing Multimodal fusion, Shared Learning and Cross Learning on it. It was found out that Deep Learning model performs superior than the conventional techniques in multimodal fusion and shared learning settings.
3D Reconstruction by Single Camera Omnidirectional Multi-Stereo System

Omnidirectional catadioptric systems are popular in robotic applications thanks to their large field of view. For 3D scene reconstruction in a single shot, usually two different catadioptric cameras are needed. More cameras may contribute to better reconstruction while larger mounting space and higher power cost are required. In this paper, a single camera multi-stereo catadioptric system with vertical and horizontal baseline structure is proposed. It features achieving multi-pair of central or non-central omnidirectional stereos in a compact manner. To make the 3D reconstruction process general and adaptive to various types of system configurations, a flexible calibration and reconstruction algorithm pipeline is presented. The algorithm features approximating the system into multiple central sub-cameras and carrying out the stereo matching in a spherical representation. In addition, an effective 3D point cloud fusion algorithm is proposed to optimize the reconstruction results from multiple stereo pairs. The experiment carried out with synthetic and real data verified the feasibility and effectiveness of our system.
Image Enhancement Method in Decompression Based on F-shift Transformation

In order to process a compressed image, such as a JPG image, a common way is to decompress the image first to get each pixel, and then process it. In this paper, we propose a method for image enhancement in the process of decompression. The image is compressed by using two-dimensional F-shift (TDFS) and two dimensional Haar wavelet transform. To enhance the image during decompression, firstly we adjust the brightness of the whole image by modifying the approximation coefficient and enhance the decompressed low frequency component part using the contrast limited adaptive histogram equalization (CLAHE) method. Finally, we decompose the remaining data and do the last step of image enhancement. Contrast with CLAHE and the state-of-art method, our method can not only combination merits of the spatial domain method and the transform domain method, but also can reduce the process complexity and maintain the compressibility of the original image.
Face Swapping Using Modified Dlib Image Morphology

Morphing is an image processing technique used for the metamorphosis from one image to another. Apart from its application in entertainment industry, image morphing is also used in computer based trainings, electronic book illustrations, presentations, education purposes etc. The idea is to get the transition from source image to target image with maximum matching. To accomplish this, Image Morphing has gained attention from multimedia users and entertainment seekers in order to obtain fancier transitions and animations. The proposed Face Swapping technique is used to transform the source image to target image and vice-versa. The results are compared to the available pre-trained Dlib model for landmarks and the results are most encouraging. The landmarks highlight the important facial attributes.
Logistics of People and Information in Employers-Based Virtual Test for University Students

Article discusses logistics around multifunctional and widely reproducible model for testing university student’s practical knowledge and skills, matching them to the labor market needs, contributing to a university business cooperation, providing international outreach, increasing student’s employability, providing quality assurance for curriculum content and more. Authors argue that all these things can be accomplished by a trivial tool – virtual test that is voluntary, cannot be retaken, is highly applicable to the engineering sciences and does not require capital investments. Article describes logistics of people and information within the proposed solution, including virtual and physical conditions, feedback loops, input/output sections and more. Article consists of literature review, expert discussions, model developed by the authors and analysis of previous initiatives in this area.
RADICALLY NEW FEATURES OF THE POSITIVE KEYBOARD

The article highlights a new alternative to traditional keyboards - a positive keyboard. The positive keyboard sharply reduces the complexity of the keyboard, namely the number of keys from 30 to 9 by increasing the length of a codeword from 1 to 2. This is made possible by graphical support, which is laid out in the geometry of the 9-key layout. One or two of these keys generate a signal, and the rest, as it turns out, can be quite close in their shape to the entered characters. That's why this keyboard is positive: all of its 9 keys are useful at any time, unlike traditional keyboards, which have a performance of 1/30 (approximately 3%). Therefore, a positive keyboard does not require marking, which is also a source of errors. Thanks to this, a positive keyboard frees up the creative abilities of a person.
Simulation and Experimental Verification on Vibration and Noise of Current Transformer

Vibration and noise in power equipment is an important factor affecting its normal operation. Through simulation by multi-physics field, the flux density in the core, the magneto-strictive force on the core and the noise caused by the magneto-strictive effect of the core are studied. Through numerical analysis and measurement experiments, the main causes of noise produced by the current transformer are found out.
A Hierarchical Update Buffer Management for 360-Degree Video Streaming

Virtual reality can provide users with immersive experience. However, because of its high requirement of bandwidth, it is still challenge to transmit 360-degree video. To overcome this issue, tile-based VR transmission strategy was proposed to reduce the data by dividing the 360-degree video into several tiles and streaming corresponding tiles according to user's viewport. While the prediction accuracy is only guaranteed in a short time and it may cause frequent rebuffering when the network fluctuates, two-tier buffer system was proposed to tackle this problem. Nevertheless the two-tier method doesn't consider the switching frequency of rate in user's viewport. In this paper, we propose a novel hierarchical update buffer management method by jointly considering the probability of rebuffering and the switching frequency of rate in FoV to improve user's QoE. The simulation results show that our proposed algorithm outperforms other methods in proving the smoothness of the viewing experience.
Analysis of the methods for projection of spaces for good manufacturing practice

BACKGROUND: In this Paper we will present the evolution of the best practice for biobanks, the technical and medical standards for collecting, processing and storing, as well as the socio-economic standards for biobank management.&#x0D; AIM: The aim of this Thesis is to analyse and to present the methods for projection of spaces for good manufacturing practice.&#x0D; METHODS: The European Union has adopted guidelines for good manufacturing practice which define the requirements for manufacturing of sterile products. In the following text we will describe the details for determining microbiological cleanliness and cleanliness of the particles in the air, on the surfaces, etc.&#x0D; RESULTS: The length of time between the collecting of blood or tissues could affect the final result. The preparation of sterile products requires special conditions, in order the risk of microbiological contamination and certain pyrogenic contaminations to be minimized. We make difference between four levels of guidelines for good manufacturing practice, which in this Paper are taken from the practice of the European Union. Level A is local zone with high-risk procedures, e.g. filling, closing of bottles, opening of ampoules and bottles and making septic connections. Level B is aseptic preparation and filling of the samples. Level C and D are the clean spaces for less critical procedures for preparation of sterile products.&#x0D; CONCLUSION: Technical standards, medical standards, socio-economic standards for biobank management, informatics practices for biobanks, economic recommendations for biobanks have been established, as well as a quality of biobanks has been provided.