Reviewerly/exHarmony huggingface dataset:
Possibilities of the method of educational projects in the process of studying History for forming general competences of college students

The article considers the possibilities of the project method for the formation of general competencies of students in the course of studying history in college. The importance of the formation of common competencies is briefly presented, the advantages of the project method for the formation of common competencies are revealed, and based on the experience of teaching history, the competencies formed by various types of projects are indicated. The possibilities of the project method for assessing the level of general competencies are shown. The option is proposed for evaluating the elements of general competencies manifested as a result of work on the project.
DNS Tunneling: A Deep Learning based Lexicographical Detection Approach

Domain Name Service is a trusted protocol made for name resolution, but during past years some approaches have been developed to use it for data transfer. DNS Tunneling is a method where data is encoded inside DNS queries, allowing information exchange through the DNS. This characteristic is attractive to hackers who exploit DNS Tunneling method to establish bidirectional communication with machines infected with malware with the objective of exfiltrating data or sending instructions in an obfuscated way. To detect these threats fast and accurately, the present work proposes a detection approach based on a Convolutional Neural Network (CNN) with a minimal architecture complexity. Due to the lack of quality datasets for evaluating DNS Tunneling connections, we also present a detailed construction and description of a novel dataset that contains DNS Tunneling domains generated with five well-known DNS tools. Despite its simple architecture, the resulting CNN model correctly detected more than 92% of total Tunneling domains with a false positive rate close to 0.8%.
TECHNOLOGY AND LANGUAGE LEARNING: VOCATIONAL HIGH SCHOOL GRADUATE STUDENTS’ PERSPECTIVES ON TOEIC TEST

Purpose of the study: This study aims to investigate graduate vocational high school students' perspectives on the use of technology in language learning (e.g., iPad, Personal Data Assistant, computer tablet, and smartphone) for international language testing. Based on the test-takers' data from 2017-2018, the results of the TOEIC score showed continuous descending or unstable performances.&#x0D; Methodology: It is mixed-method research as the design by involving twenty-five graduate students of vocational high schools situated in Surakarta city. The convenience sampling was taken to cope with the different English proficiency. Open-ended questions and questionnaires become the primary instruments applied to get comprehensive data from them.&#x0D; Main Findings: The result of this study shows that a self- study and fun condition are achieved by using mobile learning technology to enhance TOEIC language learning. Conducive learning is created, as stated by 92% of graduate students, to project mobile learning to be able to facilitate them in learning TOEIC language proficiency test.&#x0D; Applications of this study: These findings from this research bring benefits for the vocational high schools, both state and private, as the reference for a further policy of conducting the instructional process in TOEIC language proficiency test to gain projected score required by related workings industry especially in Surakarta city and Indonesia.&#x0D; Novelty/Originality of this study: As most of the instructional process of TOEIC learning implemented by speech and teacher-centered in the class, the inclusion of technology brings new perspective and paradigm not only effective and efficient way of achieved passing grade score but also allow them to be independent, confident and gain more their problem-solving skill.
The role of the Scientific Library of the State University of Management in organizing the University’s information educational space

The availability of an informational and educational space is considered as a necessary requirement to the functioning of a modern university. The importance of the university’s library in creating such informational and educational space is described. The qualitative shift in libraries activities is emphasized by a library’s ability to communicate with other objects in an informational and educational space of a university. The integrative function of the university library, which provides the informational and educational space organizational process, is pointed out to be understudied. The objects of the informational and educational space in the State University of Management are defined. The brief information about the features of the university library collection is given. The activities of the scientific library of the State University of Management in organizing the informational and educational space of the university and providing it with informational and educational materials are characterized. The specificity of the library work is illustrated by different examples, such as interaction with e-library systems, the development of scientific and educational activities by using both traditional and e-learning resources, the creating of various bibliographic reference services, web indexing of publications of the professors and other university workers in scientometrical systems. The integrity of the scientific library of the State University of Management into different stages of creating educational resources in the informational and educational space of the university. It is emphasized that the implementation of the library integrative function contributes to the development of the unified informational and educational space of the university. The article can be relevant to university libraries specialists and the researchers of this type of libraries.
Cognitive Technologies for Geology Exploration

Summary Analysis and interpretation of geological data for decision making at the exploration stage is one of the most difficult and important tasks for the O&amp;G industry. At this stage, experts aim at maximizing the understanding of the value of an asset, while dealing with high levels of uncertainty. IT solutions based on classical mathematical algorithms are not able to support such complex operations, for they require well-defined and structured data-process models. The new generation of technological systems capable of continuous analysis and cognition of the surrounding world are better fit to cope with such tasks. A Joint research project between Gazprom-Neft and IBM Research has shown evidence that cognitive technologies have the potential for application in geology exploration. The global goal is to support all professionals involved in the process of geological data analysis so that they can make more efficient and informed decisions by automating the routine tasks and reducing the number of unnecessary cycles, iterations. The results at the current stage corroborate the value of introducing cognitive technologies not only for solving linear problems of processing and interpreting geological data, but they will form the basis for the transformation of standard approaches throughout the geological exploration cycle.
Graphics and Virtual Environments for Serious Games

The articles in this special section focus on computer graphics and virtual environments for serious games. & SINCE THE SWIFT and dramatic emergence of serious games as a field of study in its own right, numerous challenges have been revealed. Serious games share most of their infrastructure with entertainment games and other interactive virtual and augmented reality (AR) environments and progressive development of games technologies and interactive techniques also improve serious games; however, the transfer of existing and new technologies and solutions to the younger application domain of serious games is not always straightforward, requiring creative solutions. For this Special Issue, we have accepted five articles that demonstrate the breadth of techniques for graphics and virtual environments for serious games. These articles are a mix of original submissions and of extended and revised versions of selected best papers presented at the VSGames 2019 conference.
Performance and Security Evaluation of S-Box Using Current-Pass Optimized Symmetric Pass Gate Adiabatic Logic

Abstract Secure adiabatic logics are identified as the optimal solution for cryptographic modules. We previously proposed an adiabatic logic called Current-Pass Optimized Symmetric Pass Gate Adiabatic Logic (CPO-SPGAL). The proposed CPO-SPGAL realizes a flat current waveform by considering the current path compared with conventional adiabatic logics. In this paper, to confirm more details about countermeasure against power analysis attacks, we compare S-box circuits based on the conventional and proposed adiabatic logics which are implemented using 0.18 $$\upmu $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>μ</mml:mi> </mml:math> m standard CMOS. From the SPICE simulation for correlation power analysis (CPA), 409,600 power consumption traces are obtained, and the hamming distance/weight are calculated. The simulation results show that the proposed S-box is more resistant to CPA attacks than the existing adiabatic S-boxes.
On the Effectiveness of Neural Text Generation Based Data Augmentation for Recognition of Morphologically Rich Speech

Advanced neural network models have penetrated Automatic Speech Recognition (ASR) in recent years, however, in language modeling many systems still rely on traditional Back-off N-gram Language Models (BNLM) partly or entirely. The reason for this are the high cost and complexity of training and using neural language models, mostly possible by adding a second decoding pass (rescoring). In our recent work we have significantly improved the online performance of a conversational speech transcription system by transferring knowledge from a Recurrent Neural Network Language Model (RNNLM) to the single pass BNLM with text generation based data augmentation. In the present paper we analyze the amount of transferable knowledge and demonstrate that the neural augmented LM (RNN-BNLM) can help to capture almost 50% of the knowledge of the RNNLM yet by dropping the second decoding pass and making the system real-time capable. We also systematically compare word and subword LMs and show that subword-based neural text augmentation can be especially beneficial in under-resourced conditions. In addition, we show that using the RNN-BNLM in the first pass followed by a neural second pass, offline ASR results can be even significantly improved.
A Novel Approach of Texture Description using DWT and GLCM on Digital Images

Texture is a predominant characteristic in the images and very useful for identification of the objects. In the present work the focus is on the analysis of textural features in digital images. A dataset containing 8 images has been used in which the size of all images is 640×640 pixels. The Gray Level Co-occurrence Matrix (GLCM) with Discrete Wavelet Transform (DWT) is used to analyze the texture. Five textural measures, viz., contrast, correlation, energy, and entropy of scaled image are computed on four different offset values 0 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> , 45 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> , 90 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> and 135 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> separately. It is observed that the value of entropy, correlation, energy and homogeneity decrease with the increasing levels of DWT whereas the value of contrast increases with corresponding offset value.
On Two Measures of Distance Between Fully-Labelled Trees

The last decade brought a significant increase in the amount of data and a variety of new inference methods for reconstructing the detailed evolutionary history of various cancers. This brings the need of designing efficient procedures for comparing rooted trees representing the evolution of mutations in tumor phylogenies. Bernardini et al. [CPM 2019] recently introduced a notion of the rearrangement distance for fully-labelled trees motivated by this necessity. This notion originates from two operations: one that permutes the labels of the nodes, the other that affects the topology of the tree. Each operation alone defines a distance that can be computed in polynomial time, while the actual rearrangement distance, that combines the two, was proven to be NP-hard.We answer two open question left unanswered by the previous work. First, what is the complexity of computing the permutation distance? Second, is there a constant-factor approximation algorithm for estimating the rearrangement distance between two arbitrary trees? We answer the first one by showing, via a two-way reduction, that calculating the permutation distance between two trees on n nodes is equivalent, up to polylogarithmic factors, to finding the largest cardinality matching in a sparse bipartite graph. In particular, by plugging in the algorithm of Liu and Sidford [ArXiv 2020], we obtain an 𝒪(n^{4/3+o(1}) time algorithm for computing the permutation distance between two trees on n nodes. Then we answer the second question positively, and design a linear-time constant-factor approximation algorithm that does not need any assumption on the trees.
Nested Subspace Arrangement for Representation of Relational Data

Studies on acquiring appropriate continuous representations of discrete objects, such as graphs and knowledge base data, have been conducted by many researchers in the field of machine learning. In this study, we introduce Nested SubSpace (NSS) arrangement, a comprehensive framework for representation learning. We show that existing embedding techniques can be regarded as special cases of the NSS arrangement. Based on the concept of the NSS arrangement, we implement a Disk-ANChor ARrangement (DANCAR), a representation learning method specialized to reproducing general graphs. Numerical experiments have shown that DANCAR has successfully embedded WordNet in ${\mathbb R}^{20}$ with an F1 score of 0.993 in the reconstruction task. DANCAR is also suitable for visualization in understanding the characteristics of graphs.
Discriminative Robust Head-Pose and Gaze Estimation Using Kernel-DMCCA Features Fusion

There remain outstanding challenges for improving accuracy of multi-feature information for head-pose and gaze estimation. The proposed framework employs discriminative analysis for head-pose and gaze estimation using kernel discriminative multiple canonical correlation analysis (K-DMCCA). The feature extraction component of the framework includes spatial indexing, statistical and geometrical elements. Head-pose and gaze estimation is constructed by feature aggregation and transforming features into a higher dimensional space using K-DMCCA for accurate estimation. The two main contributions are: Enhancing fusion performance through the use of kernel-based DMCCA, and by introducing an improved iris region descriptor based on quadtree. The overall approach is also inclusive of statistical and geometrical indexing that are calibration free (does not require any subsequent adjustment). We validate the robustness of the proposed framework across a wide variety of datasets, which consist of different modalities (RGB and Depth), constraints (wide range of head-poses, not only frontal), quality (accurately labelled for validation), occlusion (due to glasses, hair bang, facial hair) and illumination. Our method achieved an accurate head-pose and gaze estimation of 4.8 ∘ using Cave, 4.6 ∘ using MPII, 5.1 ∘ using ACS, 5.9 ∘ using EYEDIAP, 4.3 ∘ using OSLO and 4.6 ∘ using UULM datasets.
Enterprise IT Operations

Over the past decade or so, for most enterprises, information technology (IT) has shifted from being a support function to be a synonym for business wellness. During the same period, though, the scale and complexity of IT for running business has grown significantly; today, performing any business function requires complex interplay of many, often invisible and dynamically changing, technology components. This is making design resilient and interruption-free IT a significant challenge. This chapter discusses limitations of traditional approaches for managing enterprise IT operations; introduces the concept of cognitive automation, a novel approach that blends intelligence with automation to transform enterprise IT operations; and describes the design of ignio™, a cognitive automation platform for enterprises. The author concludes by highlighting the challenges in driving cognitive transformation of enterprise operations and providing some suggestions for embarking upon this journey.
Consistent MetaReg: Alleviating Intra-task Discrepancy for Better Meta-knowledge

In the few-shot learning scenario, the data-distribution discrepancy between training data and test data in a task usually exists due to the limited data. However, most existing meta-learning approaches seldom consider this intra-task discrepancy in the meta-training phase which might deteriorate the performance. To overcome this limitation, we develop a new consistent meta-regularization method to reduce the intra-task data-distribution discrepancy. Moreover, the proposed meta-regularization method could be readily inserted into existing optimization-based meta-learning models to learn better meta-knowledge. Particularly, we provide the theoretical analysis to prove that using the proposed meta-regularization, the conventional gradient-based meta-learning method can reach the lower regret bound. The extensive experiments also demonstrate the effectiveness of our method, which indeed improves the performances of the state-of-the-art gradient-based meta-learning models in the few-shot classification task.
Floquet formalism for multiphoton dynamics in superconducting quantum circuits

Synopsis High-order nonlinear multiphoton dynamics of closed and open periodically driven quantum systems are studied within the semiclassical Floquet formalism. Multiphoton up/down frequency conversion in a super-conducting circuit is realized and faithfully reproduced with calculations. The absorption spectra are suppressed at sharp avoided crossings of the Floquet states. We include the effect of coupling to cavity by time evolving the master equation. The characteristics of the time-averaged absorption spectra are investigated by probing details near and around the exceptional points where suppression occurs.
Neural voice cloning with a few low-quality samples

In this paper, we explore the possibility of speech synthesis from low quality found data using only limited number of samples of target speaker. We try to extract only the speaker embedding from found data of target speaker unlike previous works which tries to train the entire text-to-speech system on found data. Also, the two speaker mimicking approaches which are adaptation and speaker-encoder-based are applied on newly released LibriTTS dataset and previously released VCTK corpus to examine the impact of speaker variety on clarity and target-speaker-similarity .
ClarQ: A large-scale and diverse dataset for Clarification Question Generation

Question answering and conversational systems are often baffled and need help clarifying certain ambiguities. However, limitations of existing datasets hinder the development of large-scale models capable of generating and utilising clarification questions. In order to overcome these limitations, we devise a novel bootstrapping framework (based on self-supervision) that assists in the creation of a diverse, large-scale dataset of clarification questions based on post-comment tuples extracted from stackexchange. The framework utilises a neural network based architecture for classifying clarification questions. It is a two-step method where the first aims to increase the precision of the classifier and second aims to increase its recall. We quantitatively demonstrate the utility of the newly created dataset by applying it to the downstream task of question-answering. The final dataset, ClarQ, consists of ~2M examples distributed across 173 domains of stackexchange. We release this dataset in order to foster research into the field of clarification question generation with the larger goal of enhancing dialog and question answering systems.
Running in Production

If what you developed is good and not a one-off, you’ll want to put it into production and have it run on a schedule. Valuable results should be delivered on a regular basis, possibly to another piece of software that’ll execute on the findings. This topic is huge, and we’ll only scratch the surface here by focusing on technical parts in Databricks.
D-VPnet: A Network for Real-time Dominant Vanishing Point Detection in Natural Scenes

As an important part of linear perspective, vanishing points (VPs) provide useful clues for mapping objects from 2D photos to 3D space. Existing methods are mainly focused on extracting structural features such as lines or contours and then clustering these features to detect VPs. However, these techniques suffer from ambiguous information due to the large number of line segments and contours detected in outdoor environments. In this paper, we present a new convolutional neural network (CNN) to detect dominant VPs in natural scenes, i.e., the Dominant Vanishing Point detection Network (D-VPnet). The key component of our method is the feature line-segment proposal unit (FLPU), which can be directly utilized to predict the location of the dominant VP. Moreover, the model also uses the two main parallel lines as an assistant to determine the position of the dominant VP. The proposed method was tested using a public dataset and a Parallel Line based Vanishing Point (PLVP) dataset. The experimental results suggest that the detection accuracy of our approach outperforms those of state-of-the-art methods under various conditions in real-time, achieving rates of 115fps.
THE DEEP LEARNING APPROACH FOR PAN SHARPENING ASTER SWIR DATA

Summary Multispectral satellite images are effective for solving a wide range of problems in the various application areas. In particular, ASTER spectroradiometer images recorded in the Short Wave Infra-Red (SWIR) and Thermal Infrared Ranges (TIR) ranges can provide the lithological mapping and mineralogical composition of rocks. The scale of the resulting maps is based on the spatial resolution of the recorded digital images corresponding to the sensor. The ASTER spectroradiometer corresponds to the thematic maps with 1: 100000 - 1: 500000 scales. The goal is to increase the spatial resolution (upsampling) ASTER images in the SWIR and TIR bands with saving their spectral characteristics. The problem is the inability to use most known Pan-Sharpening methods because of ASTER images specification. The stack of imagery does not have the appropriate high spatial resolution panchromatic reference image Using any image (from visible or near-infrared (VNIR) spectral ranges) as a reference is not a good idea because they contain the data captured in the narrow spectral range (only in green, or red, or infrared). Therefore, the applying of standard approaches causing significant distortion of the spectral characteristics of SWIR images after the Pan-Sharpening procedure. In this paper, the main idea is using Convolutional Neural Networks (CNN) and Deep Learning techniques for superresolution resampling Aster multispectral imagery. Accuracy assessment of proposed model shows better RMSE metric values (up to 50%) in the spectral characteristics of transformed images, as compared with standard pan-sharpening technics.