Reviewerly/exHarmony huggingface dataset:
Research of a New Algorithm for Acceleration of Fractal Images Compression

Today the subject of fractal compression of images continues to remain perspective. It is connected with the fact that the ideas about self-similitude of the world put in this Benoit Mandelbrot technology, have huge potential. On the other hand the difficult mathematical apparatus which is the cornerstone of this area of knowledge, does not provide concrete computing procedures for the realization. And it provides the huge field for carrying out researches.
Tomographic Image Segmentation Model for Features Extraction of Oilseeds Based on Graph Theory

This paper presents an evaluation model of oilseeds based on both the use of X-ray computed tomography, concepts of computational semantics, and digital image processing techniques. It was prepared to segment and extract characteristics of oilseeds digital images. In this context, we have applied the segmentation method based on Graph Theory and taking into account the relationship between the pixels belonging to the object and the background of the image. Besides, geometric descriptors have been included to allow an enrichment of the information obtained. Results have shown the useful of the model for the selection of oilseeds aiming to gains in productivity and quality.
Bridging DH and Humanistic HCI

Bowker’s Age of Potential Memory describes a new era characterized by a culture of knowledge production that fosters and stifles certain forms of statements depending on the logics that subtend them. Through processes of ubiquitous data collection, analysis, and feedback, individuals are increasingly reduced to users; users are re-created as data doubles or data doppelgangers, post hoc, through the aggregation and analysis of their data traces. This discursive transformation of the human that will arise in relation to living alongside and through these doubles or doppelgangers is difficult to understand within the framework of extant disciplinary silos. And yet methods that connect disciplines are emerging. To realize these connections, translational work is required. This paper explores the complementarity of digital humanities (DH) and humanistic human-computer interaction (hHCI) through the lens of distant reading. I focus on distant reading—topic modelling in particular—because of its methodological popularity and relation to discourse. I argue that distant reading comprises a useful connection between these two young domains: a pivot that allows for the inter- or transdisciplinary study of the future human through the analysis of its potential sociotechnical, discursive compositions.
The novel secure testament methodology for cryptocurrency wallet using mnemonic seed

Recently, cryptocurrencies such as Bitcoin, Ethereum, etc, have rapidly been recognized as an asset, often called a digital asset. Comparing to the real assets, it would be handled carefully due to its own characteristics that are completely different. The disadvantage of such digital assets, on the one hand, is that whoever knows the private key of the wallet, they can easily take over that digital asset. On the other hand, if the wallet’s owners are dead without passing credential information to others, that asset will be lost forever. We thus propose the novel secure testament methodology for a digital asset such as Bitcoin to solve the mentioned problems. The mechanism is based on managing the mnemonic seed of the wallet so that an inheritor will obtain the mnemonic seed in a secure way. Two mechanisms of the proposed methodology are implemented. The first is to save the mnemonic seed in a secure repository. Therefore, no one can access to the cryptocurrency wallet while the owner still being alive and does not wish anybody to access. The second is to deliver the mnemonic seed securely to legitimate inheritor when the owner is suspected to be dead or disappeared. Finally, based on our experiment, the results show that the aforementioned problems can be solved. Furthermore, the proposed mechanism can help the owner of the asset to managing the confidentiality of their assets.
Data-Driven Performance Monitoring of Dynamical Systems Using Granger Causal Graphical Models

Abstract Data-driven analysis and monitoring of complex dynamical systems have been gaining popularity due to various reasons like ubiquitous sensing and advanced computation capabilities. A key rationale is that such systems inherently have high dimensionality and feature complex subsystem interactions due to which majority of the first-principle based methods become insufficient. We explore the family of a recently proposed probabilistic graphical modeling technique, called spatiotemporal pattern network (STPN) in order to capture the Granger causal relationships among observations in a dynamical system. We also show that this technique can be used for anomaly detection and root-cause analysis for real-life dynamical systems. In this context, we introduce the notion of Granger-STPN (G-STPN) inspired by the notion of Granger causality and introduce a new nonparametric technique to detect causality among dynamical systems observations. We experimentally validate our framework for detecting anomalies and analyzing root causes in a robotic arm platform and obtain superior results compared to when other causality metrics were used in previous frameworks.
Application of Augmented Reality in Thermal Control Implementation of Satellite Pipe

Abstract This paper reviews the traditional thermal control implementation of satellite pipe in AIT process. As the thermistors and heating devices are too small compared to the satellite 3D model, the 3D design input is inconvenient to use. In order to improve efficiency and quality of thermal control implementation of satellite pipe, a system based on AR technology is designed. With the help of the system, the code and specification of the thermal control components are clear at a glance, so an ordinary operator can verify the polarity correctness of the thermal control implementation and the efficiency can be doubled.
Efficient Fault-Tolerant Syndrome Measurement of Quantum Error-Correcting Codes Based on "Flag"

Fault-tolerant syndrome measurement plays an important role in the process of quantum error correction, and considerable effort had been taken for reducing the physical overhead of syndrome measurement which including the ancilla qubits, the CNOT gates and the time-slots. The two extra qubits syndrome measurement technique, known as "flag"-style syndrome measurement, cuts down the number of extra qubits to the utmost. However, it works slowly because it measures only one syndrome at a time. We extend the technique to extract all syndromes of the distance-3 quantum error-correcting code at once. We propose a new method that increases the parallelism of the syndrome measurement circuit and reduces the time overhead by allocating and adjusting the order of CNOT gates for measuring data block reasonably, which we call dynamic time-slot allocation scheme, and which is applicable to both Hamming codes and color codes. For a CSS quantum error-correcting code with the number of stabilizers m and the maximum weight w, we only need 2m extra qubits and 2 × (w+2) time-slots for one-shot measurement of all syndromes.
Machine Learning-Enabled Adaptation of Information Fusion Software Systems

Real-time control systems must fuse information from multiple sensors to perform mission tasks in dynamic environments. The volatility of these environments can cause sensor degradation or failure, reducing the accuracy and reliability of the information fusion. The fusion system must automatically adapt to these environmental changes to minimize their effect while still completing its objectives. Further complicating the situation, many systems contain non-adaptive legacy software, so a generic adaptation framework must be able to adapt the system&#x0027;s software without any semantic knowledge. In this paper, we present an approach that uses semantic-agnostic program transformation to expand the range of behavior of an Unmanned Underwater Vehicle (UUV) information fusion system and optimize its behavior against mission objectives using machine-learning techniques. An analysis of our approach showed that adapting a UUV with our adaptation framework resulted in a 50&#x0025; increase in its ability to search for an object under water and return safely while perturbed by battery failures.
Non-rigid Image Feature Matching by Structure Constraints

In this paper, we propose a non-rigid feature matching approach for image registration. The non-rigid feature matching approach is formulated as a maximum likelihood (ML) estimation problem. The feature points of one image are represented by Gaussian mixture model (GMM) centroids, and are fitted to the feature points of the other image by moving coherently to encode the global structure. We constructed two local structure descriptors of connectivity matrix and Laplacian coordinate to preserve the local structure of these feature points. Furthermore, the expectation maximization (EM) algorithm is applied to solve for this ML problem. Experiments on public datasets and real images demonstrate that the proposed approach has better performance than current state-of-the-art methods.
The Splendors and Miseries of Open Access Scientific Publishing in Ukraine

The letter represents the author’s opinion on the reasons and background of the actively developing practice of unconscientious open access scientific publishing, as well as briefly discussing the current condition of academic publishing and indexation in Ukraine.
Effective satellite image enhancement based on the discrete wavelet transform

Satellite image enhancement and restoration is scientifically possible by applying image processing and other soft computing techniques. In this study, a comparison of various existing satellite image resolution enhancement techniques in wavelet domain has been done. I propose a new satellite image enhancement technique based on DCWT-GWO. The novel technique employs the duel tree complex wavelets which, in relation to the classical wavelets, permit a faster execution of the wavelet transforms and furnish incredible flexibility in the generation of the wavelets. An effective system for noise reduction have designed in the backdrop of diverse categories of noise, where an optimal threshold is chosen for each sub band at diverse scales by means of the Gray Wolf optimisation (GWO) algorithm. The optimal threshold will denoise the image effectively. The fitness of the optimisation algorithm determine the quality of the image.
A Damage Identification System Based on Deep Learning

Deep Learning has achieved good results in many tasks, especially computer vision tasks such as image classification, object detection and semantic segmentation. And these tasks are usually part of a practical application such as automatic driving and face payment. In this paper, we propose an architecture and implementation of vehicle damage identification system based on deep learning. The system consists of damage identification module, mponent segmentation module and image alignment module. With this system, we only need to provide the the close and long view photos of the car, and the system will automatically identify damage and calculate the claim amount.
COLLECTION OF BOOKS OF THE CYRILLIC FONT OF THE XVII – EARLY XX CENTURE IN THE IRKUTSK REGIONAL STATE UNIVERSAL SCIENTIFIC LIBRARY NAMED AFTER I. I. MOLCHANOV-SIBIRSKY: THE EXPERIENCE OF STUDYING AND CATALOGIZATION

The article is devoted to the thematic collection of books of the Cyrillic font of the XVII – early XX century, stored in the Department of Historical and Cultural Heritage of the Irkutsk Regional Library named after I. I. MolchanovSibirsky. The library staff formed this collection by means of identifying books from the general book storage, donations and purchases from private individuals in the period from 1976 to 2018. It presents data about the history of the collection formation, the main sources and stages of books receipt, the characteristic of its current state: number, analysis by year, place and type of publication. Particular attention is paid to books acquired at the end of XIX – early XX century. Circumstances of the pre-revolutionary library stock formation (until 1920 – the Irkutsk City Public Library) and books of the Cyrillic font purchased during this period are considered. Books accepted according to the Decree of the Council of People's Commissars of 1918 «On the order of requisition of libraries, book warehouse and books in general» are analyzed. On the example of books belonging to the library of the Irkutsk Ascension Monastery, the role of preserved ownership and dedicatory inscriptions in the study of the history of this library and the history of the individual specimens is considered. Descriptions of the four versions of the monastery library bookplates and some inscriptions with notes about the receipt history of books are provided. Inscriptions on four editions of the XVIII century are examined in detail as examples of books with the unique history. Texts of inscriptions are reproduced in the original orthography (excepting obsolete letters). All books are associated with famous personalities in the history of the region and Russia as a whole. Among them: the Bishop of Irkutsk and Nerchinsk, Saint Sophronius (Kristalevsky; 1703–1771), Hieromonk Herman, etc. The article is an interim result of a two-year study and description of the collection. The result of the work will be the release of a printed catalog.
K-Prototype Clustering for the Person of Temporary Probation and Parole and Criminal Release Based on Rough Set

Those released from prison, who committed a crime at least once, have higher possibility to commit crimes more seriously. Thus, it's socially significant to reduce the recidivism rate of those people. In this paper, we process the criminals' data obtained from Iowa's public database with two steps. Firstly, Considering the data characteristics of the criminals-many redundant items, we use the rough set attributes reduction algorithm based on probability distribution to reduce the data. Secondly, for clustering the data objects with mixed numeric and categorical attributes, we use the K-prototypes clustering algorithm. Experiment shows the model works well, which could support subsequent prediction work.
Exploiting Linear Manifold Features With Parts-Based Representation in Various Scenes

Image recognition in complex scenes is a big challenge in computer vision. Manifold learning has become one of the most popular tools in the application of data dimensionality reduction and image recognition due to its efficiency in retrieving the intrinsic geometric features of image data. In this paper, we propose a new manifold feature extracting model based on the nonnegative matrix factorization (NMF) for image clustering in various scenes. In this model, Pearson distance with multiple manifold regulation constraints are adopted as the objective function to derive NMF based learning algorithms for the feature capturing of high dimensional data. With a variable neighborhood size in the learning, the proposed model can learn the linear features and at the same time learn the local similarity of images in multi-scale neighborhoods of a graph space. For different settings of learning parameters λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lx</sub> and λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sx</sub> , tests show that the proposed algorithms can efficiently retrieve low dimensional structures of images. Test results on four different image datasets demonstrate that the algorithms can achieve the state of art performance on the clustering of images in different types of scene.
Vision Sharing Method of Network Robot Based on Deep Learning

With the development of science and technology, robots are evolving from scratch, from low-level systems to high-level systems. The future of intelligent robots is towards intelligent and emotional development, and finally to achieve the goal of human-computer coexistence. Network robot is the main direction of future development. It is a robot controlled by computer network. Human-computer interaction technology, visual sharing and remote monitoring technology, data transmission and communication are the focus of their research. Therefore, the purpose of this paper is to explore the research of vision sharing method based on the theory of robotic algorithm, which leads to different thinking about the future development direction of robots. This paper will adopt the research method of concrete analysis of specific problems to make data comparison and draw conclusions. The results of this study show that as a typical representative of advanced manufacturing equipment, the network intelligent robot will have a large industrial development space and broad market prospects. At the same time, the country should draw lessons from the successful practices of foreign advanced robot industry development, formulate the development strategy and related policies of the robot industry, which is the key to the success or failure of China’s robot industry development.
Selecting the Best Optimal Feature Correspondence with Deep Learning

Finding a correspondence between a pair of feature sets is an important problem with several applications ranging from camera calibration to panoramic image generation. When this problem is formulated as a transportation (or, minimum-cost network flow) problem, the optimal solution induces the sets of feature correspondences. Considering the case where multiple optimal solutions exist for a given transportation problem, each optimal solution yields different feature correspondences. The objective of this paper is to utilize deep learning to select the optimal solution, which best preserves local neighborhood information in such cases. Instead of a hand-crafted optimal solution selection as done by the previous work [6], we propose a novel deep learning-based optimal solution selection. The experimental evaluation of the proposed work including a comparison with the previous framework demonstrates both the effectiveness and efficiency of the overall approach.
Multi-model Investigative Exploration of Social Media Data with BOUTIQUE: A Case Study in Public Health

We present our experience with a data science problem in Public Health, where researchers use social media (Twitter) to determine whether the public shows awareness of HIV prevention measures offered by Public Health campaigns. To help the researcher, we develop a investigative exploration system called BOUTIQUE that allows a user to perform a multistep visualization and exploration of data through a dashboard interface. Unique features of BOUTIQUE includes its ability to handle heterogeneous types of data provided by a polystore, and its ability to use computation as part of the investigative exploration process. In this paper, we present the design of the BOUTIQUE middleware and walk through an investigation process for a real-life problem.
Perception of Biology Student Universitas Negeri Medan towards Biology Cell Learning Based on E-Learning

Abstract The purpose of this research is to know the students’ perceptions of learning biology cell based on e-learning. This descriptive research was done by questionnaire in collecting the data. The source of the data was students of biology of FMIPA Unimed class of 2018. Based on the questioners that have been analyzed, 80,76% students feel that e-learning support them in learning teaching process and 96,15% argue that e-learning can help them to access subject matter. But 50% students face some problems to interact, not only with other students but also with lecturer. Further research is needed to develop e-learning content in biology cell to make it easier for students to understand and to improve the interaction between student and lecturer in e-learning.
A Novel BPSO-Based Optimal Features for Handwritten Character Recognition Using SVM Classifier

The handwritten character recognition is a line of research within the image processing for which there have been developed many techniques and methodologies. Its main objective is to identify a character from a digitized image that is represented as a set of pixels. This paper presents a handwritten character recognizing framework with the help of rows and column-wise segmentation, feature extraction using discrete wavelet transform (DWT) and histogram of oriented gradients (HOG) features and support vector machine (SVM)-based classifier. Further research is extended for optimal feature selection using BPSO for the training of SVM to reduce the machine overhead maintain near to same accuracy. Achieved accuracy of the proposed method is 99.23%.
An Improved Word Similarity Measure For Ontological Context

For various Natural Language Processing (NLP) use cases, it is desirable to know the significance of the text. Various methods based on path, corpus and knowledge measures are used to find out the similarity among words. Different word similarity approaches are analysed in this paper. The widely accepted approach is Wu and Palmer similarity measure. But it has a major disadvantage. It produces less similarity score for those pair of words which are in same hierarchy in the ontology, whereas according to the contextual meaning, these words are more connected and so should have more similarity score. This paper lists the shortcomings of Wu and Palmer formula and presents a remodelled formula to improve the scores of such pair of words. The remodelled formula uses logarithm bringing the depth of the words in the ontology under a uniform scale. wup- Wu and Palmer, LCS- Least Common Subsumer(the most clearly identified concept or word which is an ancestor of two words in the ontology), Sim <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">remodelled</sub> - Remodelled Wu and Palmer formula.
Analyzing Evolution Patterns of Object-Oriented Metrics

Software evolution is mandatory to keep it useful and functional. However, the quality of the evolving software may degrade due to improper incorporation of changes. Quality can be monitored by analyzing the trends of software metrics extracted from source code as these metrics represent the structural characteristics of a software such as size, coupling, inheritance etc. An analysis of these metric trends will give insight to software practitioners regarding effects of software evolution on its internal structure. Thus, this study analyzes the trends of 14 object-oriented (OO) metrics in a widely used mobile operating system software, Android. The study groups the OO metrics into four dimensions and analyzes the trends of these metrics over five versions of Android software (4.0.2-4.3.1). The results of the study indicate certain interesting patterns for the evaluated dimensions, which can be helpful to software practitioners for outlining specific maintenance decisions to improve software quality.