Reviewerly/exHarmony huggingface dataset:
Instantaneous wavefront measurement based on deflectometry

The deflectometry enables high-precision wavefront measurement with large dynamic range. Traditional multi-step phase-shifting fringe-illumination deflectometric methods involve at least three sinusoidal phase-shifting fringe patterns and require a sequential projection, making it not feasible for the instantaneous measurement. In this paper, a colorcoded method with frequency-carrier patterns is proposed to achieve the instantaneous wavefront measurement based on deflectometry. With the color extraction from different channels, composite patterns in x and y directions can be well separated with a single shot. Then, the phase-shifting patterns encoded in different frequencies can be demodulated with the designed filters, by which the local wavefront slopes can be obtained simultaneously to reconstruct the wavefront under test. Both the numerical simulation and experiments are performed to validate the feasibility of proposed method. The proposed method provides a feasible way for the real-time and instantaneous measurement with large dynamic range based on deflectometry.
Uniformity of Adaptive Control of Socio-Economic and Cyber-Physical Changes Using the Lingua-Combinatorial Model of Complex Systems

This paper presents the conceptual framework for revealing the subject-object interactions based on the advances in the theory of complex systems, methodology of the system engineering, artificial intelligence, decentralized platforms, applied in the expert systems for supporting management decisions. This research considers the impact of the global socio-cultural cycle on the development of society, the sustaining a stable state of infrastructure, the dynamism of homeokinetic equilibrium when managing innovations during the development of the master strategic plan, maintaining the system in a zone of the adaptation maximum in a flow of change. Additionally, this paper introduces the risks and threats framework for the anticipated problematic situations, which are identified by applying the cognitive approach and propositions of the adaptive control theory. The guidelines for the integrated integrity of systems and the cyber-physical interactivity in technological ecosystems are developed with application of lingua-combinatorial model and distributed registry technology in the context of maintaining the social and economic security.
Hybrid Non-Local Network for Abstractive Summarization

Abstract In the field of abstractive automatic summarization, the Seq2Seq model constructed by the RNN network is widely used and has achieved perfect results. However, due to the Seq2Seq model and the internal structure of the RNN network, only short-term and local information can be referenced in the process of generating summary, resulting the generated summary contains less information, and may even happen that the generated summary does not match the corresponding document. There have been many previous attempts to improve the model by using the attention mechanism, but only to use long-term information within a certain range. In our paper, we present a abstractive summarization model that enhances the global feature, which called Hybrid Non-Local Network model for single document summarization task, consisting of a non-local block and a traditional Seq2Seq module. Our model uses the non-local network block to transform the Seq2Seq model to better capture global feature. For example, the relationship between words and words, sentences and sentences in a large span of the original document, making the generated summary of higher quality, more informative, more accurate and rich representation of the original document. The experiments in our paper are performed on the CNN/DailyMail corpus. The proposed model was improved by 1.37, 0.47, and 1.16 onR-1 score, R-2 score, andR-L score, separately, compared to the baseline model.
Machine Learning Methods for Classification Textual Information

A method for classifying textual information based on the apparatus of convolutional neural networks is considered. The text preprocessing algorithm is presented. Text preprocessing consists of: lemmatizing words, removing stop words, processing text characters, etc. The word-by-word conversion of the text into dense vectors is performed. Testing is carried out on the basis of the text data of "The 20 Newsgroups". This sample contains a collection of approximately 20,000 news stories in English, which is divided (approximately) evenly between 20 different categories. The accuracy of the best convolutional neural network used in this work on the test set was ~ 74%. The topology of the best neural network is given. The accuracy of voting of neural networks by the Bagging algorithm was ~ 81.5%. Based on a review of similar solutions, a comparison is made with the following text classification algorithms: the support vector method (SVM, 82.84%), the naive Bayes classifier (81%), the k nearest neighbors algorithm (75.93%), and the word bag.
Estimation of sample size influence to lengthened objects detection algorithm

This paper is devoted to the important topic of choosing the sample size for the designed algorithm, which allows us to detect lengthened objects of the underlying terrain by the signal of the pulse radar altimeter. At first, here is given the short description of the algorithm and its distinctions form other existing algorithms. Then the abilities of the given algorithm are discussed. After all, the principle of choosing the number of samples in the train is performed. At last, the recommendations about choosing the lengthened objects, which could be detected, are presented.
Cue-Phrase-Based Text Segmentation and Optimal Segment Concatenation for the NTCIR-14 QA Lab-PoliInfo Task

The segmentation subtask of the NTCIR-14 QA Lab-PoliInfo task is finding a segment of text in assembly minutes that corresponds to a summary sentence. We divided the segmentation subtask into two steps, segmentation and search. Cue phrases were effectively used to detect segment boundaries. We compared five methods for detecting segment boundaries: a rule-based method, three supervised learning methods, and a novel semi-supervised learning method. The supervised models were trained using minutes data (in Japanese) we had segmented. In the search step, contiguous segments were concatenated to form larger segments, and the segment that maximized the value of a formula was selected as the answer. We compared the proposed formula with the conventional BM25 formula. We achieved the highest F-measure during the NTCIR-14 formal run despite our method’s simplicity.
Privacy Preserving Data Retrieval on Data Clouds with Fully Homomorphic Encryption

Increasing of the use of data clouds brings some issues. The one of the most important issues is privacy. Users want to protect their own identity information when they searching and they want to know their data stores on the clouds securely. The research brings the solution to the problem. In this research, Fully Homomorphic Encryption (FHE) was used for providing privacy of data. Thanks to the property of the FHE, the values of the processes on the data are do not known by anybody. Besides, with the authentication process, the system protects the identity of the user. A simulation program was prepared to realize the theoretically defined information and the results were evaluated.
Evaluation of different embedding methods for JPEG authentication watermarking

This paper considers the applicability of different data embedding methods for semifragile watermarking systems used for JPEG image authentication. The methods include Least Significant Bit watermarking and various versions of Quantization Index Modulation. In our investigations, we tested the semi-fragility property against JPEG and compared the visual quality of the watermarked images. We also checked the watermark fragility to unacceptable modifications like median filtering, blurring, and adding Gaussian noise. Finally, we analyzed the provided tampering localization error.
Research and system design and implementation of log - based computer forensics technology

Abstract This thesis is in the “large scale research of Internet information security system” of the background, studied the computer log network forensics technology, focuses on the log forensics analysis and integrity protection and authentication technology, designed and implemented based on computer network system log user behavior forensic analysis system. This paper summarizes the relevant concepts of computer forensics technology, electronic evidence and system log, demonstrates the feasibility of log as electronic evidence, and proposes the realization model of computer network forensics based on system log, and analyzes the problems that should be paid attention to in computer log forensics. This paper studies the key issues in the process of log forensics, including the collection, transmission, preservation, analysis and submission of logs, in which the analysis of log forensics and integrity protection and verification are the focus of the research. Forensic analysis of logs is to mine log records related to intrusion events by analyzing massive log data, which is divided into statistical analysis and correlation analysis. Paper presents the overall structure of the system is designed, and the subsystem module function and working process of description, implements a user behavior based on log computer network forensics analysis system, and from the safety of the system, the log acquisition performance, the integrity of the data transmission performance, the log data, forensic analysis and so on five aspects of the performance of the system were tested and analyzed, the analysis results show that the system has the basic function of the network forensics.
Implementation of Unmanned Control of Wheeled Robots

This paper presents an approach to the unmanned control of a wheeled robot, which includes recognition of road infrastructure objects, recognition of continuous and intermittent road markings, generation of control signals. Recognition of road infrastructure objects is carried out using a neural network that generates a segmented image. After that, the segmented image is identified with the found objects, including the roadway, which is used by the road marking recognition subsystem searching for continuous and intermittent lines using the computer vision library. On the basis of the information received from the considered subsystems control commands are generated indicating the direction of movement and speed. The algorithm was developed on a 1:18 scale model of the city infrastructure, where a wheeled robot simulated as a car.
A Demonstration on Initial Segmentation Step on Closed Digital Planar Curve

Polygonal approximation technique is used to represent boundary of a digital image, where the boundary is approximated using piece line segments to present the shape of the original boundary of a digital image. This paper uses the value of the turn angle between two line segments multiplied with the length of those line segments as a measure to detect good curvature points. The boundary acquisition technique produces boundary with many duplicate points. To expedite the process of obtaining final polygon, an initial segmentation step is mandatory. This paper demonstrates the contribution of initial segmentation by freeman chain code on the digital planar curves. The experimental results too support the same.
Massive earthquake countermeasures by establish muster point and migration path using network analysis in Matraman District, Jakarta, Indonesia

Abstract The issue of earthquake with magnitude 8 SR that threatens Jakarta has recently become a major concern due to the chance of catastrophic. This is closely related to the existence of a seismic gap located in Southern Java, Sunda Strait Megathrust. The seismic gap is a segment of an active fault known to produce a significant earthquake that shows no earthquake activities for an unusually long time. This situation implies that the moving crust is reserving potential energy which going to be release and resulting massive earthquake. The potential earthquake source located around 200 km from Jakarta which has densely populated and frequent annual disasters such as floods and fires. Matraman district is one of the densely populated areas in Jakarta with a population density of 319,291 per km2. Through GIS, this research uses network analysis methods to create an evacuation route in the event of a disaster. This method works with a cluster system to obtain the most effective paths in pre-determined zones. The evacuation route resulted from this analysis used 21 zones located in Matraman sub-district. Each zone has various evacuation time. The fastest time range between 1 to 3 minutes and the longest about 4 to 12 minutes. This evacuation route is also usable for SAR team and firefighter to reach the area of disaster.
Comparative Analysis of Educational Methodical Documentation and Didactic Possibilities of Implementing Individualized Learning for Graduate Students in Russia and China

In the article, the author defines the concept of "individualized learning", analyzes the advantages and disadvantages of individualized learning. Comparing the Federal State Educational Standards of Higher Education, Educational Standard of Moscow State University and the corresponding educational methodological documentation of China, the author analyzes the didactic possibilities of implementing individualized learning for graduate students from the aspects of training forms, learning programs of graduate students and auxiliary training requirements. The results show that both countries paid attention to the implementation of individualized learning, and these educational methodological documents provide a guarantee for universities to carry out individualized learning. Analyzing the methods of individualized learning for cultivating graduate students, proposed by Russian and Chinese scholars, the author summarizes the common features of the implementation of individualized learning in universities of the two countries.
Constrainted Subspace Low-Rank Representation with Spatial-Spectral Total Variation for Hyperspectral Image Restoration

The restoration of hyperspectral Images (HSIs) corrupted by mixed noise is an important preprocessing step. In an HSI cube, the spectral vectors can be separated into different classification based on the land-covers, which means the spectral space can be regarded as an union of several low-rank subspaces. Subspace low-rank representation (SLRR) is a powerful tool in exploring the inner low-rank structure of spectral space and has been applied for HSI restoration. However, the traditional SLRR framework only seek for the rank-minimum representation under a given dictionary, which may treat the structured sparse noise as inherent low-rank components. In addition, the SLRR framework cannot make full use of the spatial information. In this paper, a framework named constrainted subspace low-rank representation with spatial-spectral total variation (CSLRR-SSTV) is proposed for HSI restoration. In which, an artificial rank constraint is involved into the SLRR framework to control the rank of the representation result, which can improve the removal of the structured sparse noise and exploit the intrinsic structure of spectral space more effectively, and the SSTV regularization is applied to enhance the spatial and spectral smoothness. Several experiments conducted in simulated and real HSI datasets demonstrate that the proposed method can achieve a state-of-the-art performance both in visual quality and quantitative assessments.
Online Performance of Swahili Orature: The Need for a New Category?

This paper discusses the concept of the online performance of oral literature. The central argument advanced in this discussion arises out of the contemporary categorisation of literature, notably the distinction between oral and written literature based on their forms of presentation — that is oral versus written presentation. The invention of the Internet and the introduction of such technology into online enabled devices such as mobile phones and tablets, as well as the integration of various communication programmes, has enabled instantaneous written communications as well as non-instantaneous communication. This type of communication has instigated the emergence of a unique language whose features have no discernible cognates with either spoken or written language. Furthermore, besides the emergence of this unique language, some genres of oral literature, are regularly performed online in a unique way using the above-mentioned technologies as well as the unique linguistic features mentioned. The background research undertaken, and which underpins the discussion in this paper, was predicated on the theory of performance without necessarily denigrating the role and significance of the text in works of literature. The data on online riddle performance was collected and analysed and the results presented in this paper. By using online Swahili riddle performance; I propose the formation a new genre of literature that espouses the features of this type of communication.
Improving Bilingual Lexicon Induction on Distant Language Pairs

Aligning the representation spaces of two languages to induce a bilingual lexicon achieves attractive results on European language pairs. Unfortunately, current solutions perform terribly on distant language pairs. To address this problem, we analyze existing models for the lexicon induction task of distant language pairs, such as English-Chinese. We propose an framework for the task with improved preprocessing, mapping and inference accordingly. Experimental results show that our proposed approach enhances the accuracy of bilingual lexicons substantially on English-Chinese, as well as some other distant language pairs.
Crossing Borders: Forensic Science and the Fourth Industrial Revolution

Journal of Forensic SciencesVolume 65, Issue 1 p. 6-7 President’s Editorial Crossing Borders: Forensic Science and the Fourth Industrial Revolution Zeno J. Geradts Ph.D., Corresponding Author Zeno J. Geradts Ph.D. z.geradts@nfi.minvenj.nl Netherlands Forensic Institute – Digital Technology and Biometrics, Laan van Ypenburg 6, Zuid-holland, The Hague, 2497 GB The NetherlandsSearch for more papers by this author Zeno J. Geradts Ph.D., Corresponding Author Zeno J. Geradts Ph.D. z.geradts@nfi.minvenj.nl Netherlands Forensic Institute – Digital Technology and Biometrics, Laan van Ypenburg 6, Zuid-holland, The Hague, 2497 GB The NetherlandsSearch for more papers by this author First published: 19 November 2019 https://doi.org/10.1111/1556-4029.14236Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume65, Issue1January 2020Pages 6-7 RelatedInformation
Methodology of creative work: transformation structure

The authors consider the phenomenon of transformation of methodological strategies of social cognition, which in theory reflects the grounds of self-preservation and self-replication of social and anthropological reality. The article underlines that at present time social cognition tools are going through a renewal that is dynamic and subject to theoretical and methodological changes. The emphasis is shifting from research of the general social level toward individuality, subjectivity, spontaneity and cultural uniqueness of fractals of social existence. They observe the danger in creation of social reality, if it is not guided by organic unity of knowledge and morality. From our point of view, morality is a systemic factor of social reality, because as high as social barriers in modern world may be, universal values do exist and preserve and they are exactly the things that give human society an opportunity to survive on a global scale.
Graph isomorphism recognition using scheme matrices

The issue of isomorphism graphs recognition is as follows: whether there is a polynomial algorithm that recognizes isomorphism of each pair of graphs. The issue of isomorphism recognition is one of the most important in the graph theory and belongs to the class of NP-complete problems which includes problems for which polynomial algorithms are unknown. Despite numerous attempts, the problem of verifying the isomorphism of graphs belongs to those ones that cannot be classified according to their complexity. The issue of computational complexity of the problem remains open, although for some special cases efficient algorithms with polynomial computational complexity have been constructed. The paper develops and justifies a graph isomorphism recognition method whose computational complexity is polynomial.
Insights of professional activity at the stage of professionalization

Specific character of a dynamic development of economy and society place demands on new highly-qualified personnel of any sphere professional activity, especially the sphere connected with interaction with people who have modern high-tech skills. Demands’ changes on the subject of professional activity brings on a qualitative restructuring of professionalization’s process, that facilitates an integration of educational and professional spheres with a conceptional, technological and theoretical supporting. Successful professional activity depends a lot upon a successful period of early professionalization that is impossible without proper provided insights of a profession. This article shows the results of an empirical study regarding the analysis of peculiarities of professional ideas of pre-service teachers. A specific attention is given to the problem of study of professional activity ideas effect at the stage of early professionalization, which changes during the period of ideas’ transformation concerning professional activity and career building that is caused by the changes in the essence of professionalization from a socio normed event into an individual work of professional aimed at self-determination and self-actualization in profession.
A Method to Improve Speed of Training Algorithm in Artificial Hydrocarbon Networks

Artificial hydrocarbon networks (AHN) is a supervised machine learning method inspired on chemical carbon networks that simulate heuristic chemical rules involved within organic molecules to represent the structure and behavior of data. However, training AHN depends on a relevant number of parameters. In that sense, the original training algorithm presents some issues to find suitable parameters in a reasonable amount of time. Thus, this paper proposes a new training algorithm for AHN based on the concept of extreme learning machines, to update weight parameters related to the molecular functions. To evaluate the effectiveness of the proposed algorithm, binary classification and regression tests are performed over real public datasets from a central data repository specialized in machine learning problems. The results obtained validated that the updating of the weight parameters using the new training algorithm in the molecular structures is efficient and maintains the expected results of model accuracy. In addition, this work increased up to 24.88% the speed of the training phase in contrast to the original algorithm.