Reviewerly/exHarmony huggingface dataset:
Minimum-Time Path Planning for Autonomous Landing of UAV on Aerial Drone Carrier

The motivation for this work stems from the need to develop trajectory planning algorithms for small UAVs with limited resources. The goal is to address one of the most critical problems in UAV design, namely weight constraints. Therefore, in order to reduce the weight, small batteries are used which affect the flight duration and overall performance. To increase the operating time during a mission, frequent recharging is typically required. To solve this problem, an autonomous air-to-air take-off and landing on a drone carrier for recharging is proposed. The main objective of this study is to calculate the minimum time and thus the shortest maneuver route between the UAV and the drone carrier. To achieve this goal, the path planning algorithm is utilized through the closed-loop direct optimal control approach to calculate the optimal time, which is defined as a cost function. In addition to the optimization techniques, the singular perturbation technique (SPT) and the Hamiltonian are used to solve and refine the optimal control problem (OCP). Finally, the numerical simulation results in both open and closed loop are presented and the performance of the proposed algorithm is evaluated.
SOFTWARE SUBSYSTEM FOR SOLVING NP-COMPLEX COMBINATORIAL LOGIC PROBLEMS ON GRAPHS

The paper is devoted to the development of the software for solving NP-hard and NP-hardcombinatorial-logical problems on graphs. The paper contains a description of graphs combinatorial-logical problems. New multilevel search architectures such as simple combo, parallel combo,two-levels, integrated, and hybrid are proposed to effectively address them. These architecturesare based on methods inspired by natural systems. The key difference between these architecturesis the division of search into two or three levels and the use of various algorithms for evolutionarymodeling and bioinspired search on them. This allows obtaining sets of quasi-optimal solutions toperform parallel processing and partially eliminate the problem of premature convergence. The articleprovides a detailed description of the developed software subsystem and its modules. As modulesin the subsystem, there are five developed architectures and a set of developed algorithms for evolutionarymodeling and bioinspired search, such as evolutionary, genetic, bee, ant, firefly and monkey.Thanks to its modular structure, the subsystem has the ability to design more than 50 different searchcombinations. This makes it possible to use all the advantages of bioinspired optimization methodsfor efficiently solving NP-complex combinatorial-logical problems on graphs. To confirm the effectivenessof the developed software subsystem, a computational experiment was carried out on testexamples. The series of tests and experiments carried out have shown the advantage of using a softwareproduct for solving combinatorial-logical problems on graphs of large dimension, in comparisonwith known algorithms, which indicates the prospects of using this approach. The timecomplexity of the developed algorithms is O(nlogn)) at best, and O (n3) at worst.
Educational collaboration of Russian libraries: The possibilities and prospects

Collaboration is one of the most efficient forms of cooperation within the system of continuing library and information education. The study is based on general scientific methods, including that of institutional analysis. The study subject is collaboration as an instrument of interaction and joint efforts, wшер the results to be actualized through exchange of knowledge, experience and skills in a specific educational ecosystem. The study object is collaboration within the system of continuing library professional education. With resource, organizational and process approaches, the essence of the concept of "collaboration" is identified; the focus is made on its special institutional structure and role in efficient interaction between the subjects of knowledge economy. The essence of the definition of "educational collaboration" is revealed, taking into account the inherent features and various individual approaches to its interpretation. The experience in building the collaboration of library continuing education providers, i.e. "LENINKA" Corporate University of the Russian State Library and Department of Continuing Professional Education of the State Public Scientific and Technological Library of the Russian Academy of Sciences Siberian Branch, is analyzed (20192020). The special role of collaboration in providing continuing professional education to Russian library specialists is emphasized. It is demonstrated that collaboration in education facilitates co-organization of professional communities, render their activities more efficient and highly demanded within the digital economy and knowledge society.
Skin color correction via convolutional neural networks in 3D fringe projection profilometry

Fringe Projection Profilometry (FPP) with Digital Light Projector technology is one of the most reliable 3D sensing techniques for biomedical applications. However, besides the fringe pattern images,often a color texture image is needed for an accurate medical documentation. This image may be acquired either by projecting a white image or a black image and relying on ambient light. Color Constancy is essential for a faithful digital record, although the optical properties of biological tissue make color reproducibility challenging. Furthermore, color perception is highly dependent on the illuminant. Here, we describe a deep learning-based method for skin color correction in FPP. We trained a convolutional neural network using a skin tone color palette acquired under different illumination conditions to learn the mapping relationship between the input color image and its counterpart in the sRGB color space. Preliminary experimental results demonstrate the potential for this approach.
What Is Negation in a System 2020?

The notion of negation is basic to any formal or informal logical system. When any such system is presented to us, it is presented either as a system without negation or as a system with some form of negation. In both cases, we are supposed to know intuitively whether there is no negation in the system or whether the form of negation presented in the system is indeed as claimed. To be more specific, suppose Robinson Crusoe writes a logical system with Hilbert-type axioms and rules, which includes a unary connective $$*A$$ . He puts the document in a bottle and lets it lose at sea. We find it and take a look. We ask: is the connective “ $$*$$ ” a negation in the system? Yet the notion of what is negation in a formal system is not clear. When we see a unary connective $$*A$$ , (A a wff) together with some other axioms for some additional connectives, how can we tell whether $$*A$$ is indeed a form of negation of A? Are there some axioms which the connective “ $$*$$ ” must satisfy in order to qualify $$*$$ as a negation?
Comprehensive Fault Diagnosis of Distribution Network Based on Four Axis Characteristics of "Time, Signal, Algorithm and Sample"

With the increasing demand for reliable power supply of distribution network in economic society, how to realize sensitive and reliable perception of holographic information of distribution network and accurate fault diagnosis of urban distribution network has become the key to realize intelligent distribution network. Aiming at the problem of sensitive sensing and accurate positioning of distribution network, this paper proposes a comprehensive fault diagnosis method for distribution network based on "time, signal, algorithm and sample". The fault features of time, signal, algorithm and sample are used for accurate analysis. The multi-level fault analysis method of switch, feeder and substation is used to build a multi-dimensional fault feature analysis library, and a comprehensive fault diagnosis model of distribution network is established, which integrates data fusion, feature extraction and intelligent decision-making. Using waveform correlation, empirical mode decomposition technology and other fault feature extraction methods, as well as the introduction of depth of historical sample analysis method, a multi-objective normalized distribution network comprehensive fault diagnosis method is established, which provides an effective means for accurate fault identification of smart distribution network and improves power supply reliability.
10.6 micron interferometry and beyond

As the first graduate student coming from Korea at the Optical Sciences Center 0n January 1976, I was quite fascinated by the genuine “Openness” and “Diversities” not only in the surroundings of Tucson, but also among the people and programs at OSC. Under the guidance of Dr. James C. Wyant, various longwavelength interferometric systems were developed using 10.6 micron CO2 laser for testing large optics, IR transmitting optics, rough surfaces, etc. This presentation describes some of the early developments in optical testing and measurements in real-time, high-speed interferometry and profiling technologies with some anecdotes.
Public Opinion Prediction based on Markov Model

With the rapid development of the Internet in our country, the information on the Internet has spread rapidly like an explosion. The convenience of the Internet shortens the time it takes for people to exchange information, which is likely to cause the outbreak of hot-spot incidents of online public opinion. The public opinion orientation on the Internet will directly affect the real thoughts and behaviors of offline people. In order to avoid the occurrence of major influential public opinion events, timely grasping the hot topics discussed by people, and paying attention and positive guidance are important means of maintaining social harmony and stability. Markov logic network is a first-order logic knowledge base with additional weights for formulas. From the perspective of probability theory, Markov logic network provides a concise description language for Markov network, and can flexibly introduce a large amount of domain knowledge into Markov network. Therefore, this article studies the prediction model of public policy public opinion based on Markov model.
Data-based analysis of the optimal direction of a photovoltaic system for remote sensor nodes for a case study in Germany

Solar based self-sufficient smart sensor nodes are broadly used for remote monitoring purpose especially for applications, where the assessment for human is hostile. For the power supply of these smart sensor nodes, solar panels and battery are usually used. In order to collect the solar energy more efficiently, the solar penal of the sensor node should be directed optimal. In this paper a study at Schnarrenberg with the coordinate [48.8°, 9.2°] in Germany has been done, to determine the optimal tilt and orientation angle, which maximize the received solar energy at an inclination in the worse months, nevertheless without weakening of the solar energy of other months. Finally, with the calculated average monthly solar energy on this inclination, the minimum value was taken to determine the peak sun hours to size of the solar panel. Our approach is evaluated by applying the hourly solar global and diffuse irradiance on the horizontal surface from the open metadata of the German Weather Service, between 2013 and 2017. The evaluation results can be used for distributing sensor nodes along high voltage overhead lines in the framework of PrognoNetz research project.
AutoLL: Automatic Linear Layout of Graphs based on Deep Neural Network

Linear layouts are a graph visualization method that can be used to capture an entry pattern in an adjacency matrix of a given graph. By reordering the node indices of the original adjacency matrix, linear layouts provide knowledge of latent graph structures. Conventional linear layout methods commonly aim to find an optimal reordering solution based on predefined features of a given matrix and loss function. However, prior knowledge of the appropriate features to use or structural patterns in a given adjacency matrix is not always available. In such a case, performing the reordering based on data-driven feature extraction without assuming a specific structure in an adjacency matrix is preferable. Recently, a neural-network-based matrix reordering method called DeepTMR has been proposed to perform this function. However, it is limited to a two-mode reordering (i.e., the rows and columns are reordered separately) and it cannot be applied in the one-mode setting (i.e., the same node order is used for reordering both rows and columns), owing to the characteristics of its model architecture. In this study, we extend DeepTMR and propose a new one-mode linear layout method referred to as AutoLL. We developed two types of neural network models, AutoLL-D and AutoLL-U, for reordering directed and undirected networks, respectively. To perform one-mode reordering, these AutoLL models have specific encoder architectures, which extract node features from an observed adjacency matrix. We conducted both qualitative and quantitative evaluations of the proposed approach, and the experimental results demonstrate its effectiveness.
Partial Distillation of Deep Feature for Unsupervised Image Anomaly Detection and Segmentation

Unsupervised image anomaly detection and segmentation is challenging but important in many fields, such as the defect of product inspection in intelligent manufacturing. The challenge is that, the labeled anomalous data is few and only normal data is available, causing the distribution of the anomaly unknowable. Unsupervised methods based on image-reconstruction and feature-embedding have been recently studied for anomaly detection and segmentation, while there still exists the problems in term of the reconstruction quality and testing performance. Inspired by the application of pre-trained feature-embedding in anomaly detection, this paper proposes a novel framework called Partial Distillation of Deep Feature (PDDF) for unsupervised image anomaly detection and segmentation. Only normal images are used to partially distill the knowledge of the deep features extracted by the pre-trained network at training. The difference (dissimilarity) between the distilled feature with the pre-trained one is applied to score the uncertainty of anomaly at testing. The PDDF achieves the comparing results compared with the state-of-the-art methods on MVTec AD dataset, with the area under the receiver operating characteristic curve (ROCAUC) of 96.7% for unsupervised anomaly detection, 96.9% and the area under the per-region-overlap curve (PROAUC) of 91.8% for unsupervised anomaly segmentation.
Deep Learning Causal Attributions of Breast Cancer

In this paper, a deep learning-based approach is applied to high dimensional, high-volume, and high-sparsity medical data to identify critical casual attributions that might affect the survival of a breast cancer patient. The Surveillance Epidemiology and End Results (SEER) breast cancer data is explored in this study. The SEER data set contains accumulated patient-level and treatment-level information, such as cancer site, cancer stage, treatment received, and cause of death. Restricted Boltzmann machines (RBMs) are proposed for dimensionality reduction in the analysis. RBM is a popular paradigm of deep learning networks and can be used to extract features from a given data set and transform data in a non-linear manner into a lower dimensional space for further modelling. In this study, a group of RBMs has been trained to sequentially transform the original data into a very low dimensional space, and then the k-means clustering is conducted in this space. Furthermore, the results obtained about the cluster membership of the data samples are mapped back to the original sample space for interpretation and insight creation. The analysis has demonstrated that essential features relating to breast cancer survival can be effectively extracted and brought forward into a much lower dimensional space formed by RBMs.
Universum Based Nonparallel Kernelized Extreme Learning Machine for Binary Classification

Extreme Learning Machine (ELM) is proved to be fast, cost-effective and efficient for solving the binary classification problems. On the other hand, learning with the universum samples is also considered useful as these samples are incorporated as the prior information with the classifier. In our proposed work, we design the novel Universum based nonparallel kernelized Extreme Learning Machine (UNPKELM) which is a variant of Extreme Learning Machine. The two ELMs are learned and trained with the two nonparallel hyperplanes and hence make a robust classifier. This work also presents the mathematical formulation of UNPKELM and equations are derived to determine the output weights. Our proposed classifier UNPKELM is evaluated using 10 benchmark datasets and its performance (G-Mean) is compared to KELM, WKELM, CCR-KELM which verifies its effectiveness.
Transformative Computing Based on Advanced Human Cognitive Processes

Currently, data analysis processes based on individual, personalized recording techniques are used more and more. Such processes allow for intelligent data analysis considering only those elements that are of significant importance to a specific recipient. In addition, they provide the possibility of applying individual analysis results to deep data analysis processes. In this paper, such possibilities of data analysis will be described, with particular emphasis on the use of transformational computing technology taking into account advanced human cognitive processes.
The Law of Blasphemy vis-a-vis the Modern World: Violation of Freedom of Speech and Expression

Blasphemy is one of the world's most mishandled laws with regards to suppressing the voices and violating the freedom of speech and expression, the researchers, frequently go under a downpour of inquiries over its legitimateness in the present modern foundations of the world. While regularly it is viewed as a reasonable restriction over the Freedom of Speech and Expression, multiple times it is additionally viewed as a deterrent in the improvement of a logical temper among individuals, and an approach of forcing the religious beliefs and virtues of one community over others. In this modern reality where Freedom of Speech is generally considered as a fundamental and human right, just as Freedom of Religion; nations in spite of being a pluralist, with a unique assorted variety in its populace, have a law against blasphemy under the pretense of Hate speech. This paper traces the footprints of the Blasphemy Laws from its inception, to its existence in Islam to Western World; and why it was adopted in Muslim countries; and at the same time analyses how far Blasphemy Law in the world is supported, by comparing the Islamic Blasphemy with Western Blasphemy. This article also has a special mention of India's Blasphemy Laws and its stance in today's modern era.
Cost Optimization of Software Quality Assurance

Software quality assurance constitutes an important share to total development over heads of an organization. Mainly anticipates around fifty percent to the costs could be endorsed for error recognition, and elimination that could be the capable area for charges optimization. Various practices could be usage for advance the quality of software. The main question in that context is then how to use these diverse methods in a charge reduction way. The main difficulty specified by quite a few researchers are prices and benefits of software quality assurance and also interrelationships to prompt causes are still not totally understood. The classic shows extra general than current logical mockups that will be capable to grip changed kinds of methods. The projected classic is more real than comparable economic mockups of software quality assurance. Since this one contains the practical aspects could influencers or recognition. The related data to the mockups is gathered through different writings to provide regular standards on behalf of different methods and faults. This will permit cooler software to model that carryout and found on the experimental records of mockup to focus the compassion examination consequential to the quantitative command in standing to the involvement issues. To conclude, a tactic to recognized efficiency constituents of a structure built proceeding processes of full strategy mockups permits concentrating the software quality assurance to those modules. An entire authentication stayed not truthful since the prototypical needs standardization completed a number of plans in a corporation to produce sensible expectations. More readings were accomplished in many areas and assessing altered methods in changed stages. Altogether, those readings delivered an obliging response on the prototypical and too funded to the frame of an acquaintance on the efficiency of the methods.
Curricular Improvement Through Course Mapping: An Application of the NICE Framework

Curriculum mapping is a useful exercise to ensure that necessary skills are integrated into a curriculum, that courses satisfy relevant state and national standards, to benchmark a program and ensure performance, to demonstrate the quality of a program to accrediting bodies, and to serve as a guide for programmatic activities such as credit transfer.This paper reports the result of an exercise to map the courses in a Computer and Information Technology Program at a large midwestern university with the National Initiative for Cybersecurity Education (NICE) Cybersecurity Workforce Framework (NICE Framework).The NICE Framework was selected because it the primary reference for public, private, and academic sectors for cybersecurity practice.It is a nationally recognized taxonomy for cybersecurity skills, abilities, and work roles.The process started with a mapping of the NICE Framework to the overall course structure.This was done to understand the scope of the program's coverage of the competencies identified in the framework.The NICE Framework takes the approach of cybersecurity work and breaks down the field into seven categories, which are then broken down into specialty areas and work roles with their attendant knowledge, skills, and abilities.The learning outcomes and topics covered in each class were identified.This information was then used to narrow down the categories to the competencies covered by each class.After mapping the overall courses to the categories and specialty areas, the courses were then mapped to the competency groups and competencies.Each course was then broken down into its topic areas and these were used to complete a more granular mapping of each course to knowledge, skills, and abilities as defined by the NICE framework.This paper reports the results of the mapping to the NICE Framework.This paper then discusses the alignment of the program with the NICE Framework and puts forth recommendations to improve the alignment.