Reviewerly/exHarmony huggingface dataset:
Cross-cultural training of students by means of foreign literature in formation process of valuable orientations

The article is devoted to the problem of the formation of value orientations by means of foreign literature in the cross-cultural training of students. It is emphasized that foreign literature has an important advantage as it is associated with various texts belonging to different types of cultures. Communication with masterpieces of foreign literature promotes the development of new aesthetic experiences among students, prompts to comprehend the uniqueness of another culture. The main determinants of value orientations are determined. These are the conditions of life, the level of general culture, the ability of man, beliefs, moral principles, the system of values of the individual. The content of the value orientation of the individual is its focus on certain values of the material and spiritual culture of society. It is emphasized that the main goal of the process of formation of value orientations is the study of artistic works based on the awareness of the incarnated values, the comprehension of the moral and aesthetic positions of literary characters, the mastering of the value potential of the work by students, and the understanding of the relation of values expressed in artistic works, in connection with an important system of values. The unity of value orientations, literary knowledge and students’ activity as the leading condition of the effectiveness of the formation of value orientations is revealed. Much attention is paid to the formation of students’ readiness for cross-cultural interaction in practical classes. The main goal of the cross training of students is defined – it is overcoming the difficulties associated with the differences in values, traditions, languages. The peculiarities of students’ educational activity at various stages of the analysis of works are considered, which is structured on awareness of values embodied in artistic works. The article focuses on various types of tasks, performing students to understand the psychological content of actions and personality of literary heroes come to perceive the value position of the author, his aesthetic ideal.
Performance Analysis of Quality Measurement for Biomedical Images

Abstract With the rise used of biomedical images for diagnosis of diseases of a patient at various hospitals, the quality measurement of images is necessary for better diagnosis. The need for biomedical images quality measurement due to the transmission of images from one place to another place within hospitals or remote health centers. The lots of distortions may affect the quality of biomedical images during transmission. So, in this paper, the various image quality assessment parameters discussed for biomedical images. The paper gives information on various full reference-based objective assessment (OA) image quality measurement (FR-IQM) standards. The paper also gives performance analysis of FR-IQM standards for biomedical images.
Clustering Ensemble Approach Based on Incremental Learning

The clustering ensemble aims to combine multiple clustering results into a probably better and more robust consensus clustering. This technique has shown its efficiency in finding bizarre clusters, dealing with noise, and integrating clustering solutions from multiple distributed sources. Consensus clustering methods based on voting mechanism are widely used in literature. The idea behind majority voting is that the judgement of a group is superior to those of individuals. However, Voting-based consensus methods suffer from the problem of assigning the appropriate cluster label to data objects without majority vote. To deal with this ambiguity as well as clustering when datasets are too large or when new information can arrive dynamically at any time, we have proposed a new clustering approach based on two stage clustering technique where in the first stage a clustering ensemble method based on relabeling and voting process is used to cluster the data objects. Therefore, a new set of disjoint sub-clusters is generated based on majority vote, where each data object vote for the cluster in which it belongs and for its corresponding cluster in each other clustering results. data objects without majority vote are collected in new dataset. In the second stage, the new database as well as the set of previously obtained sub-clusters are processed using an incremental clustering algorithm. The used incremental clustering algorithm is initialized using the obtained sub-clusters and operate on the new dataset elements. The main advantage of incremental clustering methods is that the system can updates its assumptions based on recently available learning data without re-examining old data. The proposed approach have been evaluated using different datasets, where the experimental results have demonstrated the effectiveness and robustness of the proposed method.
Parameter Estimation for a VTOL UAV Using Mutant Pigeon Inspired Optimization Algorithm with Dynamic OBL Strategy

Inertial physical parameter estimation for a VTOL Unmanned Aerial Vehicle (UAV) is helpful to the control and calibration. A mutant pigeon-inspired optimization algorithm (MPIO) is proposed in this paper, which is based on dynamic opposition-based Learning (OBL) strategy. The mutant operator with nonlinear characteristic is employed to enhance the global searching ability and accelerate the convergence speed of the globally optimal pigeon. Meanwhile, the dynamic opposition-based learning strategy is introduced to facilitate the central pigeon of the swarm to explore the potential better region. The proposed algorithm is applied to the UAV system with the data from model test. The experimental results indicate that the proposed algorithm shows better performance in converging speed and overall search ability.
Engaging consumers through effective label designs

The design of product labels is of high importance for it ensures that the label communicates the necessary information accurately and efficiently to the consumers. This project follows the design development of one such label used primarily for anti-counterfeiting. In this project, usability studies and 5-Seconds Tests(5STs) are designed and conducted to understand how consumers would verify such a label and also to gauge the usability of the label's information. Through the tests, we understand consumer behavior and their perceptions of the label. Thus, aiding to identify factors such as the label's prominence, placement of the text and clarity of the label's purpose that affects the degree to which a consumer engages with it. Ultimately, design recommendations are made to assist in overcoming the identified bottlenecks. Moreover, the project highlights the importance of user research and how comprehending consumers can guide designers to build information formats with a prominent outcome.
Research on Wine Analysis Based on Data Preprocessing

In the times of data increasing explosively, data preprocessing technology is particularly important for extracting information from massive data. In this paper, data preprocessing technology was implemented by building models including missing data imputation, duplicate values removal, outlier detections, data standardization and data statute based on the wine data in the UCI data set. Then the preprocessed data was compared with raw data with K-means algorithm, linear regression model and decision tree classification algorithm. The experimental results showed that after data preprocessing, the clustering error was significantly reduced, the fitness of the linear regression model increased and the classification accuracy of decision tree was higher, which showed the importance of data preprocessing and may have some referenced value to optimize data processing.
Twenty years of pre-competitive geoscience data in the Capricorn Orogen: the link between mineral systems and crustal evolution

SummaryState- and federal-generated pre-competitive geoscience data is critical for exploration success. These relatively low-cost but high-quality regional-scale datasets ultimately reduce the financial risk to explorers by reducing the search space and allowing a more targeted use of exploration expenditure.In Western Australia, over 20 years of geological mapping and associated research in the Capricorn Orogen has led to a robust understanding of the orogen architecture and its temporal and thermal evolution. Recent in situ geochronology work in the northern part of the orogen, has bridged the gap between prospect-scale ‘exploration’ geoscience data with regional- and province-scale data, ultimately leading to a better understanding of the regional-scale drivers and pathways for gold mineralization in this part of the orogen. This information is critical for exploration models as it opens up older parts of the northern Capricorn basins that were traditionally considered unprospective, and refines and focusses exploration strategies to target the major crustal structures and their ancillary structures.
Improving a Startup Learning Framework Through an Expert Panel

It is not easy to provide real-world experience to students in an academic setting. When it comes to the development of a software startup, the connection with real users and customers is a must; otherwise, it becomes just a technical challenge. Furthermore, universities and the academic environment are paying more attention to this topic in the past years. One interesting work developed in this regard is the Challenge Based Startup Learning. In this sense, this paper aims at exploring, studying, and extending the Challenge Based Startup Learning Framework by running an expert panel. The idea behind this approach is to collect useful feedback in order to improve the framework to provide student with a better software startup development experience.
Efficient Subspace Clustering Based on Enhancing Local Structure and Global Structure

Subspace clustering (SC) achieves excellent clustering result via learning an affinity matrix and applying the spectral clustering on the matrix in order to divide data points into several different subspaces. One of the challenges faced by various SC methods is to construct a fitting affinity matrix from the given data. Sparse subspace clustering (SSC) learns the affinity matrix by minimizing l1 norm. Log-determinant approximation to rank (CLAR) learns the affinity matrix by employing a special function, called Log-determinant (LogDet), to take the place of rank function. In this paper, we propose an improved SSC algorithm by using LogDet function and Frobenius norm. The improved algorithm could effectively improve the grouping effect and obtain block diagonal solution, especially when the database size is large. Experiments on the Synthetic database, the ‘Hopkins155’ database and ‘Extended Yale B’ database, are demonstrations of the proposed approach could get a better clustering result.
A class of constacyclic codes containing formally self-dual and isodual codes

In this paper, we investigate a class of constacyclic codes which contains isodual codes and formally self-dual codes. Further, we introduce a recursive approach to obtain the explicit factorization of $x^{2^m\ell^n}-\mu_k\in\mathbb{F}_q[x]$, where $n, m$ are positive integers and $\mu_k$ is an element of order $\ell^k$ in $\mathbb{F}_q$. Moreover, we give many examples of interesting isodual and formally self-dual constacyclic codes.
Gradient-Based Adaptive Image Super Resolution

Super-resolution (SR) has been used in the realm of remote sensing to improve the resolution of an image and get more detailed spatial information than the original image captured by the sensor on the acquisition device. Several SR methods with different approaches, only focusing on sharpening the edges and forgetting non-edge areas. One of the SR methods that utilize prior gradients, can produce high resolution (HR) images in a short time and produce sharp images for nonhomogeneous areas. But for areas that tend to be homogeneous, a lot of noise appears. This problem will affect the remote sensing process due to the amount of noise that arises. This paper offers to use dynamic weighting on the gradient prior that will reduce the noise on the homogeneous area, while still able to maintains to produce the sharp edges in non-homogeneous areas. An experimental comparison is conducted on both homogeneous and non-homogeneous area using the previous method and the proposed method.
Poster

Deep neural networks (DNNs) gained in popularity as an effective machine learning algorithm, but their high complexity leads to the lack of model interpretability and difficulty in the verification of deep learning. Fuzzing, which is an automated software testing technique, is recently applied to DNNs as an effort to address these problems by following the trend of coverage-based fuzzing. However, new coverage metrics on DNNs may bring out the question of which layer to measure the coverage in DNNs. In this poster, we empirically evaluate the performance of existing coverage metrics. By the comparative analysis of experimental results, we compile the most effective layer for each of coverage metrics and discuss a future direction of DNN fuzzing.
Investigation of Systematic Error Characteristics and Error Elimination Method in Digital Image Correlation

In this study, systematic errors and an error elimination method in digital image correlation are investigated for improving accuracy and spatial resolution of deformation measurements. Characteristics of the systematic error of measured displacements in DIC are investigated using an imaging process simulationSimulation. The speckle size of the random pattern on the target surface, the gap between each micro lens on the imaging sensor, and the relative position between the micro lenses and the imaged pattern on the micro lenses are considered in the simulationSimulation. The error investigation results show that those factors affect the characteristics of the systematic error. Possible error characteristics in actual DIC measurements using random patterns are considered. It is then found that the function of the possible error characteristics shows complex profile and the error becomes zero at integer displacements in pixel. In practical deformation measurement in DIC, the error profile can be obtained using additional measured shift data, if the actual shift amount as the relative translation amount of the target and the camera is known. The function approximation of the error profile and an error elimination method are investigated.
Geophysics and neural networks: learning from computer vision

SummaryThe rapid advancement of computer vision is changing the way many industries approach image analysis. Object detection, image segmentation, textual characterisation and other related processes are helping people extract more meaningful information and relationships from their new and existing imagery. Although more typically applied to photography, this paper shows how computer vision techniques can be meaningfully applied to geophysical and other geoscientific datasets.An introduction to neural networks is followed by an application of computer vision via the ResNet-50 convolutional neural network to the GGMplus regional gravity model of Australia. This results in the quantitative characterisation of geophysical texture and the ability to spatially query areas for morphological similarity.
Information Retrieval in Search Engines Using Pseudo Relevance Feedback Mechanism

Online data generation is increasing rapidly due to technological development. In order to handle the huge volume of data, big data methodology is used widely. Though big data can able to process huge data, it has its own issues including information storage, data management, and retrieving data. In big data, data retrieval is an issue where the method of data retrieval and implementation environment differs. Though data mining has become evolving technology, it requires an optimal retrieval mechanism to extract data from the hugerepository.Therefore, an information retrieval method which in turn should effectively perform data retrieval in terms of both speed and accuracy need to be implemented. Topic relevance mechanism is used to retrieve an appropriate document from the repository, these documents are then ranked based on user preference using pseudo-relevance feedback mechanism. And hence the accuracy of the retrieved document is improved.
Recruitment Data Analysis Using Machine Learning in R Studio

Job classification is a system used to divide all jobs within a company and put them on a different regulated scale based on the overall work, salary level, skills, and commitment associated with a clear cut job. This likewise encourages organizations to look at similar jobs in various organizations inside their industry. Since the knowledge base is huge, it is difficult to physically identify the errors. Therefore, two machine learning algorithms namely Support Vector Machine and Random Forest are used to calculate the error percentage. The algorithm with the least error percentage is implemented to recruit employees. Our analysis overall shows that the random forest is more efficient than SVM.
Leveraging Graph Neighborhoods for Efficient Inference

Several probabilistic extensions of description logic languages have been proposed and thoroughly studied. However, their practical use has been hampered by intractability of various reasoning tasks. While present-day knowledge bases (KBs) contain millions of instances and thousands of axioms, most state-of-the-art reasoners are capable of handling small scale KBs with thousands of instances. Thus, recent research has focused on leveraging the structure of KBs and queries in order to speed up inference runtime. However, these efforts have not been satisfactory in providing reasoners that are suitable for practical use in large scale KBs. In this study, we aim to tackle this challenging problem. In doing so, we use a probabilistic extension of OWL RL (called PRORL) as a modeling language and exploit graph neighborhoods (of undirected graphical models) for efficient approximate probabilistic inference. We show that subgraph extraction based inference is much faster and has comparable accuracy to full graph inference. We perform several experiments, in order to support our claim, over a NELL KB containing millions of instances and thousands of axioms. Furthermore, we propose a novel graph-based algorithm to automatically partition inferences rules based on their structure for efficient parallel inference.
Shedding Light on the Dark: The Impact of Legal Enforcement on Darknet Transactions﻿

Darknet markets have been used increasingly for the transaction of drugs in the last decade. The growth of illicit drug transactions on darknet markets has led enforcement agencies to invest a greater proportion of time and effort to monitor and crack down on criminal activities on darknet websites. While large-scale site shutdown efforts involving policing agencies across various countries can help in slowing down the growth of these markets, such enforcement strategies may not be sustainable in the long run given the cost and time they require to coordinate. Thus, there is a need for alternative cost-efficient strategies to police darknet markets on a regular basis. In response, this study attempts to empirically evaluate and quantify the effectiveness of selectively targeting large drug vendors on darknet sites. Using data from the three largest darknet markets, we employ a difference-in-difference procedure to assess the impact of the arrest of a major drug dealer on subsequent darknet activities. Specifically, we contrast various outcomes from the policed site (Silk Road 2) with those from non-policed sites (Agora and Evolution) and find that enforcement efforts on the policed site reduced subsequent transaction levels and the number of remaining vendors. The enforcement was not only effective in deterring users in the same country as the arrestees, but also had a spillover effect on darknet participants who were beyond the prosecutorial jurisdictions of the arrestees. Test results on heterogeneous effects further suggest that small darknet drug vendors were most deterred by the arrest event and that drug vendors selling dangerous drugs were more deterred relative to those selling less dangerous drugs. Our findings have policy and theoretical implications for law makers, enforcement agencies, and academics.
Development of Client and Server Software for Use in Mass Service Facilities

Over time, the number of people using the Internet in everyday life is increasing. That's why the most popular software development now is the development of Internet applications, which each user accesses from any device. In this work, an application and server software for the Android operating system has been developed that presents the implementation of the Drear project. The main goal of the project is to provide smartphone users with the possibility to search and view mass service malls. It also provides the opportunity to view and reserve places in a convenient and easy way.
Vision models for tone mapping in cinema

Based on recent findings and models from vision science, we present in this chapter effective tone mapping and inverse tone mapping algorithms for production, post-production and exhibition. These methods are automatic and real-time, and they have been both fine-tuned and validated by cinema professionals, with psychophysical tests demonstrating that the proposed algorithms outperform both the academic and industrial state-of-the-art. We believe these methods bring the field closer to having fully automated solutions for important challenges for the cinema industry that are currently solved manually or sub-optimally. As in the case of gamut mapping, we also show that state-of-the-art image quality metrics are not capable of predicting observers' preferences for tone mapping and inverse tone mapping results.
2nd Connected World/Web &amp; Semantic Interoperability Workshop 2019 (IoT-CWSI 2019)

A Connected World/Web of Things is an emerging area that not only requires development of infrastructure but also deployment of new data services capable of supporting multiple, scalable (cloud-based) and interoperable (cross-systems and cross-domain) applications. In the race of designing the Connected World/Web as part of the Future Internet architecture, academia and Information and Communication Technology (ICT) industry communities have realized that a common problem to be tackled is the interoperability of the data and their availability for providing information services. Worldwide there is an increasingly focusing on how to evolve communications technologies to enable a Connected World, thus Semantic-enabled Web of Things systems will need to interact and be interconnected for offering the always-promoted everything-connected interoperability paradigm. The Connected World/Web & Semantic Interoperability workshop provides to developers, scientific researchers, industry experts and general audience interested in the evolution of the Internet of connected things world, the possibility to explore interoperability research challenges and provide a room to expose their research scientific ideas and progress and also explore new trends and opportunities of using semantic web technologies for solving problems and creating new solutions on emerging specialized paradigms like the current Internet of connected Things or simply acknowledge as Internet of Things (IoT).