Reviewerly/exHarmony huggingface dataset:
MCO Scenario Ontologies

In the Modern Conflict Ontology (MCO), there are parts that don’t depend on the scenario or situation. These situation-independent parts apply regardless of the situation. For example, the genericAnimal class is available for use in every scenario, even if it is never instantiated. This chapter deals with the parts of the MCO that are situation-dependent, those that depend on and describe the scenario.
MODIFIED ADVANCED ENCRYPTION STANDARD FOR MORE SECURE COMMUNICATION

The Advanced Encryption Standard (AES) algorithm has become the default choice for various security services in copious applications. Internet of things (IoT), internetworking of smart devices, embedded with sensors, software, electronics and network connectivity that enables to communicate with each other to exchange and collect data through an uncertain wireless medium. Recently IoT devices are dominating the world by providing it's versatile functionality and real-time data communication. Apart from versatile functionality of IoT devices, they are very low battery powered, small and sophisticated, and experience lots of challenges due to unsafe communication medium. But due to recent emerge of IoT devices, the main concern are shifting to moderate security and less energy consumption rate. In existing Modification for the AES Algorithm (MAES) a new 1-dimensional Substitution Box is implemented by formulating a novel equation for constructing a square matrix in affine transformation phase of MAES where the area hardware is more. Affine transformation has scaling, translation and rotation while receiving data from satellites these operations are performed. In proposed method the mix column is replaced by random permutation. In this area is considered based on look up tales. The random permutation stage performs the XoR operation between the Linear-Feedback Shift Register (LFSR) and state matrix from shift rows. The main concern is shifting to moderate security and less area consumption. The proposed method has mainly four steps. This will improve the security level and also the area and delay will be reduced when compare to existing method.
Innovation and Exploration on Mode of Biology Graduate Students in Application-oriented Universities

Based on the methods of postgraduate training at home and abroad, this paper analyzes the practical problems in the training of biology graduate students in application-oriented universities in China, such as the lack of hardware and software facilities and the lack of close connection between the training system and the market.Fusion development by building the open cultivation path, advancing with The Times to update the concept, precision lock training target system of thinking, put forward the classification of the training and precisely because of material teaching, strengthening teacher management, improve DaiTu actual effect and integration of academic resources, strengthening the construction of the platform, pay attention to the problems, propose solutions of biology graduate student training mode innovation, strive for applied undergraduate college biology graduate training quality, formation of operational stronger biology graduate cultivation model.
Dynamic Curriculum Learning for Low-Resource Neural Machine Translation

Large amounts of data has made neural machine translation (NMT) a big success in recent years. But it is still a challenge if we train these models on small-scale corpora. In this case, the way of using data appears to be more important. Here, we investigate the effective use of training data for low-resource NMT. In particular, we propose a dynamic curriculum learning (DCL) method to reorder training samples in training. Unlike previous work, we do not use a static scoring function for reordering. Instead, the order of training samples is dynamically determined in two ways - loss decline and model competence. This eases training by highlighting easy samples that the current model has enough competence to learn. We test our DCL method in a Transformer-based system. Experimental results show that DCL outperforms several strong baselines on three low-resource machine translation benchmarks and different sized data of WMT' 16 En-De.
The Design of Experimental Performance Analysis and Visualization System

The analysis of experimental performance is an essential task to any experiment. With the increasing demand on experimental data mining and utilization. methods of experimental data analysis abound, including visualization, multi-dimensional performance evaluation, experimental process modeling, performance prediction, to name but a few. We design and develop an experimental performance analysis and visualization system, consisting of data source configuration component, algorithm management component, and data visualization component. It provides us feasibilities such as experimental data extraction and transformation, algorithm flexible configuration and validation, and multi-views presentation of experimental performance. It will bring great convenience and improvement for the analysis and verification of experimental performance.
Development of marketing interaction between the university and students through the use of digital marketing tools and technologies

The article considers the possibilities of developing marketing interaction of higher education organizations with stakeholders using digital marketing tools and technologies. The study presents an overview of the University stakeholder groups in Russia and their classification according to the quadrants of influence. The author provides the tools and means of digital marketing communications in the context of stakeholder groups and the prospects for their use for organizing marketing communicative interaction of the University. The paper studies in detail the possibilities of developing marketing interaction between the university and students on the basis of modeling the student’s experience on the path of his movement from the applicant to the graduate. The presented student experience includes six main stages, including the search for a University for admission; submission of documents and enrollment; educational and extracurricular activities; graduation and graduation; employment; career development. The author highlights within each stage of the student’s experience the main points of contact with the University. The paper presents opportunities for the development of marketing interaction between the University and students through the introduction of digital tools and marketing technologies, as well as their impact on the student experience model. The article considers the possibility of developing software services as promising digital tools and technologies for use in higher education organizations, including the functional requirements for such services at the level of user requirements, including the applicant and student, as well asthe possibilities of digital marketing communications with students, including both online and offline communications. Based on the study, the author makes a conclusion about the main advantages of the development of digital tools and marketing technologies in organizing marketing interaction between the University and students, as one of the key groups of stakeholders.
DIDACTIC POSSIBILITIES OF INTERDISCIPLINARY TASK SITUATIONS IN THE PROCESS OF ACHIEVING SUBJECT RESULTS OF LEARNING AT THE INTEGRATED RUSSIAN LANGUAGE LESSONS

The article considers the didactic possibilities of inter-subject task situations in the development of subject competencies of students of Years 5-7 at the Integrated Russian Lessons. The study was conducted in line with an integrative approach to learning, and its novelty is to identify the specifics of an inter-subject target situation as a methodological technique and component of an integrated lesson at the level of inter-subject relationships. The theoretical significance of the results of the study, selectively reflected in the article, consists in the development and theoretical justification of the components of the methodology for the development of subject competencies of schoolchildren through the application of the inter-disciplinary task situation on integrated lessons of the Russian language in Years 5-7; their practical significance is determined by a set of inter-disciplinary task situations developed for different stages of the lesson, taking into account the solution of didactic problems.
PREDICTIVE DEMAND SERVICE FOR PUBLIC TRANSIT USING CNN/ ATENDIMENTO PREDITIVO DE DEMANDA DO TRANSPORTE PÚBLICO COLETIVO USANDO CNN

Several cities in Brazil undergo a territorial expansion and inhabitants constantly, this process is called urbanization.An uncontrolled urbanization generates many difficulties, highlighting the mobility of public transport, since many citizens depend on this mobility, we have, for example, public transport in Goiânia, which directly affects the living conditions of passengers.For your foreknowledge, a model capable of mirroring the performance of your demand is essential, providing that the system meets users in an acceptable way.A two-dimensional CNN is a CNN model that has a hidden convolutional layer that operates on a 1D sequence, it is a convenient mechanism to simulate a univariate forecast of time series of the predictive service of Goiânia's public transport.The method is equivalent to an analysis of the focal parts that make up the public transport system and how to represent it in the 1D convolutional
Iron, Manganese, Chromium, Titanium and Vanadium Deposits

In Mongolia, about 111 iron ore deposits and occurrences are known to exist in the country; however, only five deposits are of marginal economic interest. Twenty deposits have small reserve base, and the remaining 86 are iron occurrences and mineral showings. Detailed mineral exploration for iron ore deposits has so far been limited, and only reserves of the Tumur Tolgoi and Bayan Gol deposits have been estimated as of industrial category. Most of the iron ore deposits are of contact-metasomatic skarn type followed by hematite-magnetite-limonite deposits of sedimentary type originating from ferruginous sedimentary beds by metamorphism and metasomatism. Most iron deposits in Mongolia are closely associated with manganese mineralization.
Negative Sampling in Knowledge Representation Learning: A Mini-Review

Knowledge representation learning (KRL) aims at encoding components of a knowledge graph (KG) into a low-dimensional continuous space, which has brought considerable successes in applying deep learning to graph embedding. Most famous KGs contain only positive instances for space efficiency. Typical KRL techniques, especially translational distance-based models, are trained through discriminating positive and negative samples. Thus, negative sampling is unquestionably a non-trivial step in KG embedding. The quality of generated negative samples can directly influence the performance of final knowledge representations in downstream tasks, such as link prediction and triple classification. This review summarizes current negative sampling methods in KRL and we categorize them into three sorts, fixed distribution-based, generative adversarial net (GAN)-based and cluster sampling. Based on this categorization we discuss the most prevalent existing approaches and their characteristics.
The Construction and Practice of an Interactive Informative Platform for Experiment Education in the School with Overseas Chinese Students

Combining the highly internationalized character in Jinan University caring for overseas Chinese, we integrate the technologies and platform of multimedia, QR code and WeChat to build an information education platform including management and safety of laboratories, and experimental courses.The interactive platform provides bilingual contents that meets the requirement of domestic and international students and highly arouse the study interest and stimulate the initiative of students, as a result to improve their comprehensive quality and innovative ability.
Enhancing Visualization Applications Using Open Data Sources

An increasing number of data visualization tools have started to support the automatic generation of modifications, embellishments, and natural language annotations (data facts) to aid in better understanding the data being visualized. Concurrently, many applications in data science now benefit from the use of an increasingly diverse set of open data sources to augment existing data sets to enhance their value. In this paper we present a framework for using open data-based augmentations to generate embellishments and data facts to enhance existing visualizations. Our approach is based on a semi-automated process intended to involve the user, where possible augmentations are automatically ranked based on the data facts they are capable of generating, allowing users to choose augmentations to effectively enhance existing data visualizations in an explorative manner. We show the benefit of suggesting ranked augmentations from one open data source, Wikidata, by demonstrating that a high number of data facts and embellishments can be produced utilizing the top suggested augmentations. Finally, we describe the architecture of a prototype system implementing the approach.
Scientific Critique and Digital Humanities

The article provides an overview of the problems faced by scientifi c criticism in the era of the development of digital humanities. It signals that the emergence of new tools, methods and research areas related to the digitization process forces a revision of the existing evaluation paradigms. Besides, it focuses, for example, on other aspects, such as citation rates, the impact of research on society, ways of disseminating research, interdisciplinarity, internationalization, and open access. It highlights issues related to the evaluation of research in the fi eld of humanities according to the criteria developed for the sciences. It points to the need of creation indicators in the humanities, the aim of which would be a reliable assessment of all aspects of the specifics of a researcher’s work, not only bibliometric data.
Feature Selection Using Local Interpretable Model-Agnostic Explanations on Metagenomic Data

Metagenomics is one of the emerging concepts in personalized medicine approaches to take care of and improve human health. Numerous studies have revealed that metagenomic data can associate with a vast of human diseases. Recent advancements in machine learning techniques and computation resources enable us to speed up the data processing and also improve the performance in diagnosis accuracy. However, we face difficulties to process metagenomic data due to its complexities and high dimension. This work proposes an approach based on an explanation model to perform feature selection tasks. The proposed approach selects a small set of features from the original features with Interpretable Model-agnostic Explanations that can obtain better performances than feature selection based on importance scores generated from a robust learning machine learning such as Random Forests. We are expected that the approach can be an efficient feature selection method compared to classic feature selection techniques.
Automatic Polyp Recognition from Colonoscopy Images Based on Bag of Visual Words

Colorectal cancer (CRC) is one of the most popular cancer in the world. Adenoma and sessile serrated polyp precursor lesions claim over 95% of CRC. The incidence of CRC is reduced 76-90% through the early diagnosis and removal of colorectal polyps. Colonoscopy is the golden standard for the detection of colorectal polyps but about 25% of polyps were missed during colonoscopy examinations. In this study, we proposed a novel method to recognize polyps from colonoscopy images based on bag-of-visual-words (BoW) with extracted regions of interest. The proposed method generates a histogram of visual word occurrences to represent an image, and uses support vector machine (SVM) with error correcting output codes (ECOC) for the detection of polyps. A dataset composed of 131 cases’ clinical data were used to train and test the proposed method. Validation demonstrates an average specificity of 97.8±1.5%, an average sensitivity of 97.2±1.7%, and an average accuracy of 97.5±1.0%.
Parametric amplifier for Bell measurement in continuous-variable quantum state teleportation

We discuss the parametric amplifier as a replacement for the beam splitter in the Bell measurement in continuous-variable quantum state teleportation. It shows the advantage of loss-tolerance in the process.
Predicting Response Quantity from Linguistic Characteristics of Questions on Academic Social Q&amp;A Sites

Academic social Q&A websites have a lower response quantity than other types of social Q&A. To help academic social Q&A platforms implement mechanisms to improve the quantities of responses to questions that are rarely answered and to predict these quantities, this study uses 93 features representing the linguistic characteristics of academic questions, and compares several methods of prediction to determine the one that delivers the best performance. It also identifies the most useful feature set for such predictions.
Using MCycleGAN to Realize High-Resolution Reconstruction of Solar Speckle Image

High-resolution reconstruction of solar speckle image is one of the important research contents in astronomical image processing.High-resolution image reconstruction based on deep learning can obtain the end-to-end mapping function from low-resolution image to high-resolution image through neural network model learning, which can recover the high-frequency information of the image.However, when used to reconstruct the sun speckle image with single feature, more noise and fuzzy local details, there are some shortcomings such as too smooth edge and easy loss of high-frequency information.In this paper, the structure features of input image and reconstructed image are added to CycleGAN network to get MCycleGAN.High frequency information is obtained from structural features by generator network, and the feature difference is calculated to enhance the ability of net-work to reconstruct high-frequency information.The edge of the reconstructed image is clearer.Compared with the speckle mask method level 1+ used by Yunnan Observatory, the results show that the proposed algorithm has the advantages of small error, fast reconstruction speed and high image clarity.
Genetic Algorithm-Based Optimization of Clustering Data Points by Propagating Probabilities

Dalmia, Shailja Sriram, Aditya Ashwin, T. S.Clustering is among the pivotal elementary operations in the field of data analysis. The efficiency of a clustering algorithm depends on a variety of factors like initialization of cluster centers, shape of clusters, density of the dataset, and complexity of the clustering mechanism. Previous work in clustering has managed to achieve great results but at the expense of a trial and error approach to achieve optimal values for user-defined parameters which have a huge bearing on the quality of the clusters formed. In this work, we propose a solution that optimizes the user-defined parameters for clustering algorithm called Probability Propagation (PP) by harnessing the capabilities of Genetic Algorithm (GA). In order to overcome this sensitivity in PP, a novel optimization technique is applied by obtaining the optimal values of $$\delta $$ and s using GA by maximizing inter-cluster spread and minimizing intra-cluster spread among the clusters being formed. The proposed method was found to retrieve top chromosomes (bandwidth and s) with a similar number of clusters, thus eliminating the sensitivity of user-defined parameters which is optimized by using GA.
Formal Description of Manufacturing Process based on Domain Ontology Construction

In order to solve the problem of process knowledge sharing, integration and reuse in the field of machinery manufacturing due to the complexity, dispersion and diversity of process knowledge. Taking into account the advantages of ontology in knowledge representation, this paper proposes an ontology-based knowledge management framework in the production line. On the basis of inductively analyzing the attributes of the mechanical manufacturing process attributes and intra-process and interprocess relationships, an improved conceptual ontology expression model of the 4-tuple process is proposed.
Automatic Stochastic Dithering Techniques on GPU: Image Quality and Processing Time Improved

Dithering or error diffusion is a technique used to obtain a binary image, suitable for printing, from a grayscale one.At each step, the algorithm computes an allowed value of a pixel from a grayscale one, applying a threshold and, therefore, causing a conversion error.To obtain the optical illusion of a continuous tone, the obtained error is distributed to adjacent pixels.In literature there are many algorithms of this type, to cite some Jarvis, Judice and Ninke (JJN), Stucki, Atkinson, Burkes, Sierra but the most known and used is the Floyd-Steinberg.We compared various types of dithering, which differ from each other for the weights and number of pixels involved in the error diffusion scheme.All these algorithms suffer from two problems: artifacts and slowness.First, we address the artifacts, which are undesired texture patterns generated by the dithering algorithm, leading to a less appealing visual results.To address this problem, we developed a stochastic version of Floyd-Steinberg's algorithm.The Weighted Signal to Noise Ratio (WSNR) is adopted to evaluate the outcome of the procedure, an error measure based on human visual perception that also takes into account artifacts.This measure behaves similarly to a low-pass filter and, in particular, exploits a contrast sensitivity function to compare the algorithm's result and the original image in terms of similarity.We will show that the new stochastic algorithm is better in terms of both WSNR measurement and visual analysis.Secondly, we address the method's inherent computational slowness: we implemented a parallel version of the Floyd-Steinberg algorithm that takes advantage of GPGPU (General Purtose Graphics Processing Unit) computing, drastically reducing the execution time.Specifically, we observed a quadratic time complexity with respect to the input size for the serial case, whereas the computational time required for our parallel implementation increased linearly.We then evaluated both image quality and the performance of the parallel algorithm on a exhaustive image database.Finally, to make the method fully automatic, an empirical technique is presented to choose the best degree of stochasticity.
About the influence of features of fitness-functions on the convergence of the genetic algorithm

The adaptive capabilities of a parallel version of a multipopulation genetic algorithm are considered depending on the characteristics of certain classes of fitness-functions. Ways are proposed to increase the rate of convergence to the optimal solution based on effective control of algorithm parameters and strategies for the exchange of chromosome-solutions between populations. The results of computer experiments with the optimization of fitness-functions with various ratios of insignificant and significant factors are presented. The dependence of the convergence rate of the algorithm in the presence of a random effect on the values of fitness-functions is studied.