Reviewerly/exHarmony huggingface dataset:
Astrometry and Time-Resolved Photometry From Streaks Using Calibrated Ultra-Wide Field of View Cameras

Extracting the astrometry and photometry of an observed object in an optical image can be complicated by a number of factors. Wide-angle lenses can introduce significant distortion into images. Popular methods for correcting for distortion assume basic symmetries in optical systems that may not be reflected in reality. Additionally, the photometric intensity and corresponding uncertainty of a streaking object should be extracted in an information optimal manor. This paper approaches both of these problems separately. First, an empirical method for image calibration based on a Delaunay triangulation with vertices defined by observed and catalog star locations is outlined. This method is theoretically capable of less than 0.8 pixel residual error across the image assuming correct association between the observed stars and catalog. Next a method is discussed for information optimal streak extraction based on breaking an observed streak into constant intensity streak segments. Both an analytical and computational method for determining the appropriate number of segments are discussed. The method for extracting the streak is then demonstrated against a sub-optimal method to show that the method extracts all observable information in the streak. These algorithms improve over existing methods for extracting streaking space object information from wide-angle images.
Cyclic DenseNet for Tumor Detection and Identification

How to correctly and efficiently detect and identify tumors has always been a core issue in the medical field. In order to improve the accuracy and efficiency of tumor detection, we proposed a DenseNet-based tumor recognition and classification model Cyclic DenseNet. First, Cyclic DenseNet inherits the fully-connected architecture of DenseNet and fully exploits the image features. Secondly, Cyclic DenseNet introduces the group concept and uses Group Normalization instead of Batch Normalization, in order to improve the stability of the parameters. At the same time, we extract the features deep after each Dense Block, which reinforces the features reuse and strengthens the advantages of DenseNet. Finally, in order to reduce the high time overhead caused by the reuse of multiple features, we adopt the optimal block extraction method, which greatly improves the training efficiency. The experimental results show that this method can effectively improve the accuracy and efficiency of tumor detection and recognition. Compared with the existing algorithms, it has achieved better results in the area under the receiver operating curve (ROC) and other criteria.
ShodhGangotri

ShodhGangotri is an open access repository of Indian research in progress which is a new initiative that complements ShodhGanga – the reservoir of Indian theses. ShodhGangotri hosts electronic version of approved synopses and research proposals submitted to the universities in India by research scholars for registering themselves for the PhD programme and reveals the trends and directions of ongoing research in India and helps to avoid duplication of research. This chapter examines the current status of ShodhGangotri in different aspects namely universities, issue date, discipline, and state. University-wise analysis shows that 48% of the total number of synopses is submitted by Shri Jagdishprasad Jhabarmal Tibrewala University, 8.9% by Swami Ramanand Teerth Marthwada University, 8.7% by Dayalbagh Educational Institute, and 5.7% by Mahatma Gandhi University, and they occupy 1 to 4 positions, respectively. The chapter concludes with a suggestion that all universities and research institutions should make it mandatory to submit approved synopses and research proposals at the time of their PhD registration to make it experience the pulse of ongoing Indian research.
Design of Anonymous Communication Protocol Based on Group Signature

The onion routing protocol enables low-latency anonymous communication over the public network. Currently, many onion routing protocols have been proposed to enable anonymous connections between users, such as Tor. Even though existing onion routing protocols appear to have a secure structure, there are still some disadvantages in terms of efficiency and security. This paper proposes a new onion routing protocol based on certificateless group signature, which uses the new designed certificateless group signature scheme to achieve anonymous authentication of messages. In the protocol, this paper modifies the data encapsulation format of the transmission, and uses only the key shared with the receiver to encrypt the data payload in a single layer. The proposed protocol not only reduces the encryption and decryption operations of the data packets in the transmission process, but also improves the forwarding efficiency of the message, and further realizes the anonymous authentication and integrity of the message forwarding.
A Fast-Growing Sequence Inspired by TREE(k)

While watching a video “The Enormous TREE(3)” from the Numberphile series [8], the author of this paper was fascinated by how fast the “tree sequence” can grow. A tree is defined as a collection of...
DEVELOPING LEARNERS’ COGNITIVE AUTONOMY ON THE BASIS OF ‘GOOGLE MAPS’ GEO-SERVICE AND TEACHER’S PERSONAL SITE WHILE CONDUCTING ADDITIONAL ENGLISH CLASSES

The article examines the possibilities for using ‘Google Maps’ service on the English teacher’s personal site to promote cognitive autonomy of middle- and senior-grade students of general education institutions in extra-curriculum education. The paper provides examples of using ‘Google Maps’ technology in the English language teaching and proposes an algorithm for teachers and students to generate Google maps within the framework of project-based activity. The study indicates that such technologies provide serious advantages: students’ active involvement into educational process, raising motivation, efficient organization of educational space.
OOD Case Study: More Security Requirements

Security design principles introduced in Chap. 24 are applied to the Address Book Application (ABA) and Personal Finances case studies. Various design models are used to express the security design in each case study. Sample code is used to describe some implementation details.
Jacques de Vaucanson (1709–1782)

Jacques de Vaucanson is a famous French automata (especially androids) inventor in 18th century, the two automata he builds, “the Flute Player” and “the Pipe Player” are the first two androids in the world which base on the method of imitation, and his automaton “the Digesting Duck” could show natural digestion process as real ones. Vaucanson opens a golden era for android making from the middle 18th century to the first half of 19th century. In the Encyclopédie méthodique par ordre des matières, Denis Diderot (1713–1784) and Jean Le Rond d’Alembert (1717–1783) once describes the Flute Player as the prototype of android which could perform human functions (Diderot et al. in Encyclopédie, ou Dictionnaire raisonné des sciences, des arts et des métiers, par une société de gens de Lettres. F. Frommann Verlag – G. Holzboog, Stuttgart, pp. 896–897, 1990 [1]). Vaucanson also integrates his automata making experience into loom design and invented the world’s first fully automatic loom, this loom offers inspiration for Joseph Marie Jacquard (1752–1834) to invent the extensively used Jacquard loom of that time.
Inverse Problems of Chemical Kinetics on Volpert's Graph

The present study is devoted to the mathematical and computer modeling of the kinetics of complex chemical reactions. The subject of this work is the inverse problems of the chemical kinetics of catalytic reactions. A graph and theoretic technique that allows one to determine the functional dependences of kinetic parameters directly from the graph of the reaction mechanism is developed. Based on the proposed algorithm, a program for determining the basis of nonlinear parametric functions of kinetic parameters is created. The program implements a graph and theoretic interpretation of the mechanisms of complex chemical reactions for constructing stationary kinetic models of catalytic reactions. Using this technique, the mechanism of methane vapor conversion on a nickel catalyst was studied.
Class Imbalance Data-Generation for Software Defect Prediction

The imbalanced nature of class in software defect data, which including intra-class imbalance and inter-classes imbalance, increases the difficulty of learning an effective defect prediction model. Most of sampling and example generation approaches just focused on inter-class imbalanced defect data, and they are not effective to handle the issue of intra-class imbalance. This paper proposed a distribution based data generation approach for software defect prediction to deal with inter-class and intra-class imbalanced data simultaneously. First, the classified sub-regions are clustered according to the distribution in the sample feature space. Second, the data are generated by corresponding strategies according to different distribution in sub-regions, where the inter-class balance is achieved by increasing the number of defective samples, and the intra-class balance is achieved by generating different density of data in different sub-regions. Experiment results show that the proposed method can reduce the impact of data imbalance on defect prediction and improve the accuracy of software defect prediction model effectively by generating inter-class and intra-class balanced defects data.
Hybrid Particle Swarm Optimization with Science Cosine Algorithm and Mathematical Equations for Enhancing Robot Path Planning

This paper introduces a hybrid metaheuristic algorithm that combines Particle Swarm Optimization (PSO) algorithm with Sine Cosine algorithm and Mathematical equations. The algorithm makes a contribution to optimization field by providing better strategy for finding the global minimal value, enhancing exploration and exploitation features, speeding up the converge rate over the tested benchmark optimization problems. The results show that combining SCA and ME with PSO in a new hybrid algorithm called PSE. However, the new algorithm overcome the drawbacks of PSO and it effectively solve high dimensional optimization problems. PSE algorithm is being applied in order to enhance robot path planning, robots can find a high efficiency specification objective when powered by hybrid algorithm. The objective of optimization is to reduce the path lengths to target. Autonomous path planning is necessary to prevent obstacles during the motion of robot. The result show that the algorithm proposed is able to give better performance in reaching targets.
Experimental Analysis of Approaches to Multidimensional Conditional Density Estimation

Recently several original methods for conditional density estimation (CDE) have been developed. The abundance of information comprised by the full conditional density of target variables is great when compared to the regression or quantile regression estimates. Still, there are only few independent experimental investigations of these methods, especially concerning a multidimensional target variable, and this paper aims to address this issue. We consider several approaches such as kernel density estimation, reduction to binary classification, Naïve Bayes, Bayesian Network, “varying coefficient” approach, random forests and Approximate Bayesian Computation applied to a conditional density estimation problem. We examine these methods when applying to various datasets together with the dependency of the methods’ performance on different parameters including the number of irrelevant covariates, smoothness, and flatness of the distribution. Considered datasets include artificial models with required properties and with the known exact value of CDE evaluation measure and a real-world dataset arisen from the problem of structure recognition by XANES spectra, which is reduced to a regression task with a complex multimodal probability distribution of the target variable. The special attention is paid to the computation of the evaluation measure as the methods based on the direct optimization of the loss employ its imprecise but fast approximation which results in the poor prediction quality for datasets with a small target variance.
An automatic grading system for electronic medical records with neural network

Abstract Automatically grading electronic medical records from clinical researchers is an important task on healthcare research. In this paper, we present a convolutional neural network framework to grade medical records by grading note. We regard the scoring process as a text pattern classification task with mapped sentences from grading concept rule to medical records. This framework involves two stages. The first stage is key medical concept matching between grading note and medical records. The second stage is text pattern classification which can predict whether the key concept in grading note is correct, missing or incorrect. The result shows that our neural network model performs better than other traditional machine learning grading methods. Our system makes a great progress for text pattern classification accuracy and performs much better baselines on grading process.
A General Approach to Distributed and Privacy-Preserving Heuristic Computation

Multi-agent planning (MAP) has recently gained traction in both planning and multi-agent system communities, especially with the focus on privacy-preserving multi-agent planning, where multiple agents plan for a common goal but with private information they do not want to disclose. Heuristic search is the dominant technique used in MAP and therefore it is not surprising that a significant attention has been paid to distributed heuristic computation, either with or without the concern for privacy. Nevertheless, most of the distributed heuristic computation approaches published so far are ad-hoc algorithms tailored for the particular heuristic. In this work we present a general, privacy-preserving, and admissible approach to distributed heuristic computation. Our approach is based on an adaptation of the technique of cost partitioning which has been successfully applied in optimal classical planning. We present the general approach, a particular implementation, and an experimental evaluation showing that the presented approach is competitive with the state of the art while having the additional benefits of generality and privacy preservation.
EFFICIENCY OF THE GAMIFICATION METHOD IN EDUCATIONAL INSTITUTIONS OF THE MINISTRY OF INTERNAL AFFAIRS OF THE RUSSIAN FEDERATION (BY THE EXAMPLE OF THE “LASERTAG” GAME)

The article examines peculiarities of implementing the gamification method in educational process. Within the framework of pedagogical analysis, the paper reveals principles and conditions of gamification, describes methods and techniques. The authors identify aspects of using the “Lasertag” game at the lessons and describe gaming techniques, which can be used in individual or group work. It is shown that “Lasertag” is a reliable and efficient tool to form learners’ professional competences in educational institutions of the Ministry of Internal Affairs of the Russian Federation.
Guided Refine-Head for Object Detection

In recent years, multi-stage detectors improve the accuracy of object detection to a new level. However, due to multiple stages, these methods typically fall short in the inference speed. To alleviate this problem, we propose a novel object detector—Guided Refine-Head, which is made up of a newly proposed detection network called Refine-Head and a knowledge-distillation-like loss function. Refine-Head is a two-stage detector, and thus Refine-Head has faster inference speed than multi-stage detectors. Nonetheless, Refine-Head is able to predict bounding boxes for incremental IoU thresholds like a multi-stage detector. In addition, we use knowledge-distillation-like loss function to guide the training process of Refine-Head. Therefore, besides fast inference speed, the proposed Guided Refine-Head also has competitive accuracy. Abundant ablation studies and comparative experiments on MS-COCO 2017 validate the superiority of the proposed Guided Refine-Head. It is worth noting that Guided Refine-Head achieves the AP of 38.0% at 10.4 FPS, surpassing Faster R-CNN by 1.8% at the similar speed.
Research on Repairing Historical Photos of Damaged Scratches Based on Computer Technology

Historical photographs record the true face of a moment in the development of human history, they have authenticity, vividness, and unique values. However, due to various factors, aging and damage will occur. With the development of computer technology, the restoration technology is more used in photo restoration and virtual restoration of cultural relics. This paper first analyzes the principle of repairing photo archives based on computer technology, and then uses the combination of statistics and computer image processing technology to detect and repair the scratches in historical photographs. And the paper establishes a model repair framework, which provides a new idea for the repair of such historical photos. The experimental results show that the method has a significant repair effect.
A New Smoothing Based Image Recolorization Method

Image recolorization is a process of generating new synthetic images for given reference images and input images. Color characteristics and geometrical structure details of the synthetic images are transferred from reference images and input images, respectively. In classical image recolorization models, the total variation (TV) regularizer is usually used to suppress noise and graininess during the process of estimating synthetic images. However, the TV regularizer usually produces pseudo contours known as staircase-like artifacts and cannot preserve some important structure details well. To solve these problems, in this paper a new smoothing based image recolorization model is proposed, in which a fractional-order TV regularizer is designed. Moreover, an edge protection process is also proposed which can further improve the preservation performance of image tiny details. Numerical results demonstrate that our proposed model can effectively protect image tiny details of image recolorization results, while the staircase-like artifacts are avoided.
Aspects of using a numerical simulator for a robot position-orientation matrix determination

Abstract The paper presents the mathematical model of a parallel topology robot of FP 3 +3·RRS+MP 3 type, used in order to determine the position-orientation matrix of the robot’s mobile reference system. By using a numerical simulator, positions of the characteristic point are plotted in the 3D space.
Comprehensive Testing of Software and Hardware Data Security Tools Using Virtualization

The article considers the need to use modern virtualization tools in the process of developing, assembling and testing software and hardware data security tools. Such data security tools include hardware components that implement key security functions, but also impede the use of number of testing tools. The use of virtualization tools for software and hardware data security tools allows to perform security checks, which are difficult to test on physical computing hardware, for example: security functions that run independently of the operating system of computing facility or before starting the user session. However, using virtualization tools may degrade the “purity” of the testing process, so it is necessary to ensure that software implementation of the hardware devices’ virtual connection interfaces and virtual machine components comply with existing standards and specifications. Depending on the connection interface of hardware components of data security tools, it is possible to use the built-in capabilities of virtualization tools or existing AMD IOMMU and Intel Vt-d technologies to redirect these components to a virtual environment.
Substantiation of the trajectory planning algorithm for unmanned tracked vehicle

Abstract Mobility operation of an unmanned transport vehicle (UTV) when operating on rough terrain is a difficult task. The most difficult is the problem of the tracked UTV motion. Simplification of the controlled motion simulation, ambiguity of the track – terrain interaction parameters, the random nature of external impacts lead to fuzziness of the status parameters of the UTV as a controlled object. The article proposes a method for trajectory planning of the UTV motion, taking into account the nonholonomic nature of the track – terrain interaction. Within this objective, three aspects of the research stand out sharply. First, refinement of the tracked vehicle mathematical model. Secondly, the Voronoi diagram method is the basis of the algorithm for motion trajectory planning. Thirdly, the parameters of interaction with the external environment, in particular, the dependences of the coefficient of resistance to side slip, being obtained in real time, allow us to synthesize a correcting matrix that provides for increased motion stability. The effectiveness of the proposed approach is planned to be assessed by UTV controlled motion simulation and during experimental investigations.
Implementasi Model Pembelajaran Doscovery Learning pada Pembelajaran Keterampilan Berbicara di SD Inpres Kompleks BTN IKIP

Indonesian skills are four interrelated skills namely listening skills, skills, reading skills, and writing skills. Of the four skills, one of the most important skills in daily life is skills which are an effective oral communication medium. As Tarigan, (2008: 3) says speaking is language Speaking skills in elementary school are the core of the learning process in schools, because with learning to speak students can communicate inside and outside the classroom in accordance with their development. This fact becomes the foundation in the effort to implement the Discovery Learning learning model to see the extent of its effect on skills. The goal to be achieved is to find a picture of the application of the Discovery Learning learning model in Indonesian Language learning. To find out the picture of the results of students' skills at SD Inpres BTIP IKIP Complex, Rappocini District, Makassar City. To determine the effect of the application of the Discovery Learning learning model to students' skills at SD Inpres BTN IKIP Complex, Rappocini District, Makassar City. The results showed that the application of the Discovery Learning learning model affected the skills of students at SD Inpres BTIP IKIP Complex, Rappocini District, Makassar City. This is proven by statistical analysis using the Paired Sample t-Test showing that the value of Sig. (2-tailed) is smaller than 0.05, which means that Ho is refused Ha accepted. It is recommended to use innovative learning models in learning.
Big Data Based E-commerce Search Advertising Recommendation

Search engine marketing promoted by search engine companies, e,g., Google and Baidu, and the acknowledgment of brand promotion supported by the search engine have breaking through the limitation of traditional marketing model. However, with the ever-increasing complexity of internet ecosystem, how to improve the recommendation efficiency of e-commerce search advertisements has been conducting a joint academic/industry challenge. To address this issue, through analyzing the popular treatment schemes of search advertising, a recommendation scheme for e-commerce search advertisements using Spark based big data framework is proposed in this paper, which presents a solid solution to achieve high relevant recommendation for network users’ searching behaviors and information needs while implementing the tripartite benefit of network users, advertising platforms and advertisers. The conducted experiments have been shown to demonstrate the performance.