Reviewerly/exHarmony huggingface dataset:
Image recognition in unmanned aviation using modern programming languages

The paper considers the different methods of image recognition in unmanned aviation using modern programming languages. It is shown that the new era in aviation is characterized by new challenges and threats, as well as uncertainty, and it is not always possible to identify a threat through standard means of control. The authors summarize the various methodologies of analysis and justify the algorithm for recognition zones of video observation of possible icing of the surface of the aircraft. The tested methods, in general, were divided into three groups: the preliminary filtering and image preparation, the logical processing of the results of the filtering and—machine learning which in general are divided into three groups. The filtering that allows highlighting of images in the recognition area, linearization, the transformation of “Hafa” and filtering contours as a separate class of filters were selected as the main methods of filtering images. The authors propose to use a device that can determine possible areas of icing of aircraft using airborne meteorological radar. The problem is the ratio of the image, which was before the icing, and the changes in this image in the presence of ice.
A new class of p -ary regular bent functions

<p style='text-indent:20px;'>Bent functions have many important applications in cryptography and coding theory. This paper considers a class of <inline-formula><tex-math id="M2">\begin{document}$ p $\end{document}</tex-math></inline-formula>-ary functions with the Dillon exponent of the form <p style='text-indent:20px;'><disp-formula> <label/> <tex-math id="FE1"> \begin{document}$ f(x) = \sum\limits_{i = 0}^{q-1}(Tr^n_1(a_1x^{(r i+s)(q-1)})+Tr^n_1(a_2x^{(r i+s)(q-1)+\frac{q^2-1}{2}}))+bx^{\frac{q^2-1}{2}}, $\end{document} </tex-math></disp-formula> <p style='text-indent:20px;'>where <inline-formula><tex-math id="M3">\begin{document}$ n = 2m $\end{document}</tex-math></inline-formula>, <inline-formula><tex-math id="M4">\begin{document}$ q = p^m $\end{document}</tex-math></inline-formula>, <inline-formula><tex-math id="M5">\begin{document}$ p $\end{document}</tex-math></inline-formula> is an odd prime, <inline-formula><tex-math id="M6">\begin{document}$ a_1,a_2\in \mathbb{F}_{p^n} $\end{document}</tex-math></inline-formula>, and <inline-formula><tex-math id="M7">\begin{document}$ b\in \mathbb{F}_p $\end{document}</tex-math></inline-formula>. With the help of Kloosterman sums, we present an explicit characterization of these <inline-formula><tex-math id="M8">\begin{document}$ p $\end{document}</tex-math></inline-formula>-ary regular bent functions for the case <inline-formula><tex-math id="M9">\begin{document}$ gcd(s-r,\frac{q+1}{2}) = 1 $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M10">\begin{document}$ gcd(r,q+1) = 1 $\end{document}</tex-math></inline-formula> or <inline-formula><tex-math id="M11">\begin{document}$ 2 $\end{document}</tex-math></inline-formula>. Our results generalize results of Li et al. [IEEE Trans. Inf. Theory 59 (2013) 1818-1831].
Evaluating the Impact of Text Duplications on a Corpus of More than 600,000 Clinical Narratives in a French Hospital.

A significant part of medical knowledge is stored as unstructured free text. However, clinical narratives are known to contain duplicated sections due to clinicians' copy/paste parts of a former report into a new one. In this study, we aim at evaluating the duplications found within patient records in more than 650,000 French clinical narratives. We adapted a method to identify efficiently duplicated zones in a reasonable time. We evaluated the potential impact of duplications in two use cases: the presence of (i) treatments and/or (ii) relative dates. We identified an average rate of duplication of 33%. We found that 20% of the document contained drugs mentioned only in duplicated zones and that 1.45% of the document contained mentions of relative dates in duplicated zone, that could potentially lead to erroneous interpretation. We suggest the systematic identification and annotation of duplicated zones in clinical narratives for information extraction and temporal-oriented tasks.
Future of machine translation

Machine Translation (MT) has seen fluctuating periods of growth and attention since the 1950s, with the primary leading paradigms shifting from rule-based (RBMT) to statistical (SMT), and now neural (NMT). This chapter aims to analyse the seeds of these paradigms through a 1949 memorandum by Warren Weaver that presents five hypothetical approaches to MT. They are: word-for-word translation; disambiguation using micro context (i.e. co-text); an approach based on formal logic; a cryptography approach to translation as decoding; and translation based on invariants (universal symbolic representation of meaning). This chapter shows how some developments external to the translation industry have enabled the realization of Weaver’s vision and discusses various connections among the seeds, the enabling developments and the three machine-translation paradigms. The chapter concludes with a discussion of possible directions for the future of MT and its connection with human translation.
Greening of education in modern society

The necessity of transition to ecologically oriented civilization, conditionality of formation of system of knowledge in education education and culture is shown. The system of values of ecocentric orientation is offered. In the conditions of transitive modern society the complex of ecologically significant skills is formed at receiving education and is transformed into internal beliefs of the individual. It is argued that the modern reality requires immediate initiation and development of ecological education of youth at all stages and levels of education: the state of the environment can not wait for full formation of generations, which is subject-oriented environmental education in early childhood, and does not allow time for long-term experiments.
Deep Belief Network–Based Learning Algorithm for Humanoid Robot in a Pitching Game

A cognition learning algorithm based on a deep belief network and inertia weight Particle Swarm Optimization (PSO) is presented and examined in a humanoid robot. The psychology concepts were adopted from Thinking, Fast and Slow by Daniel Kahneman. The human brain comprises two systems, System 1 and System 2. Based on their characteristics, System 1 and System 2 handle different tasks during cerebration. In this study, Deep Belief Network (DBN) is trained to construct the function of System 1 for the rapid reaction. On the other hand, PSO is applied to build System 2 for the slow and complicated brain behavior. Through the cooperation of System 1 and System 2, the proposed cognition learning algorithm can apply the psychology theories to allow the humanoid robot for learning the suitable pitching postures autonomously. In the experiments conducted in this study, the robot was trained for only five selected points and was then asked to throw precisely to nine points. The proposed algorithm provided 100% accuracy in the robot pitching game. The feasibility of the proposed algorithm was thus verified.
Are Federated Cloud Sharing Systems Sustainable?: <i>On Dynamic Sharing Markets and Their Stability</i>

The recent emergence of the small cloud (SC), both in concept and in practice, has been driven mainly by issues related to service cost and complexity of commercial cloud providers (e.g., Amazon) employing massive data centers. However, the resource inelasticity problem faced by the SCs due to their relatively scarce resources might lead to a potential degradation of customer QoS and loss of revenue. A proposed solution to this problem recommends the federated sharing of resources between competing SCs to alleviate the resource inelasticity issues that might arise. Based on this idea, a recent effort proposed SC-Share, a performance-driven static market model for competitive small cloud environments that results in an efficient market equilibrium jointly optimizing customer QoS satisfaction and SC revenue generation. However, an important question with a non-obvious answer still remains to be answered, without which SC sharing markets may not be guaranteed to sustain in the long-run - is it still possible to achieve a stable market efficient state when the supply of SC resources is dynamic in nature?. In this article, we take a first step to addressing the problem of efficient market design for single SC resource sharing in dynamic environments. We answer our previous question in the affirmative through the use of Arrow and Hurwicz's disequilibrium process in economics, and the gradient play technique in game theory that allows us to iteratively converge upon efficient and stable market equilibria.
PENGEMBANGAN BAHAN AJAR ELEKTRONIK BERBASIS FLIPBOOK MAKER PADA MATERI PROGRAM LINEAR KELAS X SMK

&lt;p&gt;Learning activities at SMK Muhammadiyah Kesesi are still less use of instructional media, teachers still use the available package books in school and power point at the time of learning so that students feel bored. This research develops electronic teaching materials based on flipbook maker linear class X material program at SMK Muhammadiyah Kesesi. The purpose of this research is to know the validity and the practicability of the developed electronic materials. The research conducted using the Borg &amp;amp; Gall development model included in Putra (2012:120-121). The steps of the research are: 1) research and data collection; 2) development of initial product type / shape; 3) initial field trials; 4) field implementation test; 5) product improvement. There are 8 validator in this research and there are 28 students as respondent. Based on the research results obtained the average total criteria from the validator V a  4, 1. According to the validation criteria, it can be conclud that the electronic teaching materials using kvisoft flipbook maker application on the material linear, class X SMK is said to be valid. While the calculation of the value of the practicality of electronic materials using kvisoft flipbook maker application on material linear class X SMK obtained from the student response questionnaire is 85,9 %. It means, the developed electronic teaching material is declared to be practical. Therefore, it can be concluded that the electronic teaching materials using kvisoft flipbook maker application on material linear class X SMK is suitable to use as one of the learning resources.&lt;/p&gt;
LFM Signal Analysis Based on Improved Lv Distribution

In this paper, we propose a parameter estimation method named improved Lv distribution (ImLVD) for linear frequency modulated (LFM) signals. In this method, based on the scaling principle, we present a two-scale estimation strategy to acquire low computational cost. The two-scale estimation strategy includes coarse estimation operation and fine estimation operation. To implement the strategy, improved scaled Fourier transform (ISFT) and Chirp-Z transform (CZT) operation are used, where the ISFT can be implemented by fast Fourier transform (FFT) and complex multiplications. By changing the frequency searching range of ISFT, the ISFT is used in different frequency ranges. To improve the anti-noise, we present an improved parameter selection criterion which can reduce the noise correlation effectively. In this paper, the implementation, anti-noise performance, and computational cost are analyzed. Through simulations and analyses, we demonstrate that the ImLVD outperforms the compared algorithms.
Women, politics and Twitter: Using machine learning to change the discourse

Including diverse voices in political decision-making strengthens our democratic institutions. Within the Canadian political system, there is gender inequality across all levels of elected government. Online abuse, such as hateful tweets, leveled at women engaged in politics contributes to this inequity, particularly tweets focusing on their gender. In this paper, we present ParityBOT: a Twitter bot which counters abusive tweets aimed at women in politics by sending supportive tweets about influential female leaders and facts about women in public life. ParityBOT is the first artificial intelligence-based intervention aimed at affecting online discourse for women in politics for the better. The goal of this project is to: $1$) raise awareness of issues relating to gender inequity in politics, and $2$) positively influence public discourse in politics. The main contribution of this paper is a scalable model to classify and respond to hateful tweets with quantitative and qualitative assessments. The ParityBOT abusive classification system was validated on public online harassment datasets. We conclude with analysis of the impact of ParityBOT, drawing from data gathered during interventions in both the $2019$ Alberta provincial and $2019$ Canadian federal elections.
Lecturers' Productivity in Private Universities, Kazakhstan

The productivity levels of lecturers and what causes an increase or decrease in them have been the subjects of debate and concentration among academics, practitioners and researchers for many decades.This makes it more volatile because of the complex nature of the numerous different industries involved.In the education industry itself for example, there have been numerous antecedents which have been found to have an effect on productivity levels.The aim of this research is to identify the factors and assist in closing the gaps of knowledge in increasing lecturer productivity, especially in the private universities.The main purpose of this study was to identify the significant relationship of work-life balance, job responsibility, competency, motivation and professionalism on lecturer productivity in private universities Kazakhstan.Hypotheses created to support these objectives results indicated that all tested independent variables have significant relationship with lecturer productivity except for no significant relationship indicated for competency and professionalism based on correlation analysis.This study considers these antecedents on lecturer productivity in the Private universities being the fastest growing industry in Kazakhstan.This implies that the organizational human resource policies may need a re-look on creating better benefits, lecturer assistance program, job design, training and development and organizational culture.These findings suggest that in this complex and dynamic environment, lecturer productivity is a means of organizational success and sustainability.
Content of multimedia orientations of students: risks and ways of their pedagogical overcoming in the framework of social partnership

The purpose of the study is to analyze the content of multimedia orientations of students who are real users of virtual space of the university. In the article next problems are exposed:1)new concept is ”content of multimedia orientations of students”;2)multimedia risks of students;3)the methods of overcoming of multimedia rrisks; 4) the model of social and pedagogical partnership; 5)the degree of multimedia orientations of students; 6) comparison of the obtained results with the research data related to the determination of students’ religious negativity, the phenomenon of which was reflected in their preferences in the selection of virtual information. The article discusses the relationship of the fourth industrial revolution and the digital economy; virtualistics and information and communication technologies; e-education and distance learning in the context of foreign and domestic scientific discourse, which allowed the introduction of new concepts in the field of professional pedagogy. Particular attention is paid to the methodological justification of cooperation between the aggregate subjects: students and teachers in the process of overcoming multimedia risks on the basis of the developed innovative model of social and pedagogical partnership in the system of higher professional education.
Parasite cloud service providers: on-demand prices on top of spot prices

On-demand resource provisioning and elasticity are two of the main characteristics of the cloud computing paradigm. As a result, the load on a cloud service provider (CSP) is not fixed and almost always a number of its physical resources are not used, called spare resources. As the CSPs typically don't want to be overprovisioned at any time, they procure physical resources in accordance to a pessimistic forecast of their loads and this leads to a large amount of spare resources most of the time. Some CSPs rent their spare resources with a lower price called the spot price, which varies over time with respect to the market or the internal state of the CSP. In this paper, we assume the spot price to be a function of the CSP's load. We introduce the concept of a parasite CSP, which rents spare resources from several CSPs simultaneously with spot prices and rents them to its customers with an on-demand price lower than the host CSPs' on-demand prices. We propose the overall architecture and interaction model of the parasite CSP. Mathematical analysis has been made to calculate the amount of spare resources of the host CSPs, the amount of resources that the parasite CSP can rent (its virtual capacity) as well as the probability of SLA violations. We evaluate our analysis over pricing data gathered from Amazon EC2 services. The results show that if the parasite CSP relies on several host CSPs, its virtual capacity can be considerable and the expected penalty due to SLA violation is acceptably low.
Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild

We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. The method is based on an autoencoder that factors each input image into depth, albedo, viewpoint and illumination. In order to disentangle these components without supervision, we use the fact that many object categories have, at least in principle, a symmetric structure. We show that reasoning about illumination allows us to exploit the underlying object symmetry even if the appearance is not symmetric due to shading. Furthermore, we model objects that are probably, but not certainly, symmetric by predicting a symmetry probability map, learned end-to-end with the other components of the model. Our experiments show that this method can recover very accurately the 3D shape of human faces, cat faces and cars from single-view images, without any supervision or a prior shape model. On benchmarks, we demonstrate superior accuracy compared to another method that uses supervision at the level of 2D image correspondences.
CLASSY: A Conversational Aware Suggestion System

Over the last few years, pervasive systems have seen some interesting development. Nevertheless, human&ndash;human interaction can also take advantage of those systems by using their ability to perceive the surrounding environment. In this work, we have developed a pervasive system &ndash; named CLASSY &ndash; that is aware of the conversational context and suggests documents potentially useful to the users based on an Information Retrieval system, and proposed a new scoring approach that uses semantics and distance based on proximity data in order to classify the relationship between tokens.
A Novel Enhancement Method for Medical Image using Double Density Wavelet and Stationary Wavelet Transforms

Contrast illumination of Magnetic Resonance (MR) and Computed Tomography (CT) images at local regions is low, and edge overlapping for various tissues in imaging is higher. In this paper, a novel medical image enhancement method dedicated to overcoming the problem stated above is proposed by investigating Double Density Wavelet Transform (DDWT) and Stationary Wavelet Transform (SWT). The process is followed by a new enhanced contrast, illumination module, and linear combination strategy. DDWT decomposes the original image, and the Inverse Discrete Wavelet Transform (IDWT) computes the reconstructed image. SWT again decomposes the original image at four sub-bands including LL, LH, HL, and HH. The suggested statistical module is then applied to the low-frequency subband (LL) for improving the contrast and illumination region of interest (ROI) and incorporated with high-frequency sub-bands by Inverse SWT (ISWT). Finally, the linear combination was obtained for the resulting enhanced image to maintain the illumination for the details of the image. The proposed algorithm was verified with various datasets and validated for not only subjective but objective evaluation as well. It exhibited high performance for contrast and brightness suitable for diagnostic purposes.
Protecting Biomedical Data Against Attribute Disclosure.

Modern medical research requires access to patient-level data of significant detail and volume. In this context, privacy concerns and legal requirements demand careful consideration. Data anonymization, which means that data is transformed to reduce privacy risks, is an important building block of data protection concepts. However, common methods of data anonymization often fail to protect data against inference of sensitive attribute values (also called attribute disclosure). Measures against such attacks have been developed, but it has been argued that they are of little practical relevance, as they involve significant data transformations which reduce output data utility to an unacceptable degree. In this article, we present an experimental study of the degree of protection and impact on data utility provided by different approaches for protecting biomedical data from attribute disclosure. We quantified the utility and privacy risks of datasets that have been protected using different anonymization methods and parameterizations. We put the results into relation with trivial baseline approaches, visualized them in the form of risk-utility curves and analyzed basic statistical properties of the sensitive attributes (e.g. the skewness of their distribution). Our results confirm that it is difficult to protect data from attribute disclosure, but they also indicate that it can be possible to achieve reasonable degrees of protection when appropriate methods are chosen based on data characteristics. While it is hard to give general recommendations, the approach presented in this article and the tools that we have used can be helpful for deciding how a given dataset can best be protected in a specific usage scenario.
A novel efficient multiple encryption algorithm for real time images

In this study, we propose an innovative image encryption Techniques based on four different image encryption Algorithm. Our methodology integrates scrambling followed by Symmetric and Asymmetric Encryption Techniques, to make the image meaningless or disordered to enhance the ability to confront attack and in turn improve the security. This paper mainly focused on the multiple encryption Techniques with multiple keys on a single image by dividing it into four blocks. So instead of using one Encryption method a combination of four different Encryption Algorithm can make our image more secure. The Encryption is done first by using DNA as secret key, second by using RSA, third by DES and fourth by Chebyshev. The pros and cons for all the Encryption methods are discussed here. Proposed methodology can strongly encrypt the images for the purpose of storing images and transmitting them over the Internet. There are two major benefits related with this system. The first benefit is the use of Different Algorithm with different keys. The second benefit is that even though we are using four different Algorithm for a single image, the time taken for encryption and decryption is few seconds only. Our method is methodically checked, and it shows an exceptionally high level of security with very good image quality.