Reviewerly/exHarmony huggingface dataset:
Connected monads weakly preserve products

Abstract If F is a (not necessarily associative) monad on Set , then the natural transformation $$F(A\times B)\rightarrow F(A)\times F(B)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>F</mml:mi><mml:mo>(</mml:mo><mml:mi>A</mml:mi><mml:mo>×</mml:mo><mml:mi>B</mml:mi><mml:mo>)</mml:mo><mml:mo>→</mml:mo><mml:mi>F</mml:mi><mml:mo>(</mml:mo><mml:mi>A</mml:mi><mml:mo>)</mml:mo><mml:mo>×</mml:mo><mml:mi>F</mml:mi><mml:mo>(</mml:mo><mml:mi>B</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> is surjective if and only if $$F(\varvec{1})=\varvec{1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>F</mml:mi><mml:mo>(</mml:mo><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mo>)</mml:mo><mml:mo>=</mml:mo><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:mrow></mml:math> . Specializing F to $$F_{\mathcal {V}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>F</mml:mi><mml:mi>V</mml:mi></mml:msub></mml:math> , the free algebra functor for a variety $$\mathcal {V},$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>V</mml:mi><mml:mo>,</mml:mo></mml:mrow></mml:math> this result generalizes and clarifies an observation by Dent, Kearnes and Szendrei in 2012.
Spectrum allocation model for cognitive wireless networks based on the artificial bee colony algorithm

Cognitive radio through dynamic spectrum allocation allows an efficient use of the radio-electric spectrum. It is a key subject for the performance of cognitive radio networks. The purpose of the present article is to develop a spectrum allocation model for cognitive wireless networks based on the Artificial Bee Colony algorithm and assess its performance in spectrum occupancy traces obtained from monitoring the spectrum using the energy detection technique. Results show a reduction in the number of spectral handoff with no excessive execution times.
The Effects of Motivation, Empowerment and Organizational Culture on Organizational Commitment of Municipalities in Jordan

Serving quality services to the customers and various stakeholders is the main motto of municipalities in Jordan with regard to support socio economic development within its particular jurisdiction.However, in the Jordanian context, the municipalities are identified to have massive debts and this indicates that self-generated revenue is not sufficient for municipalities to accept their mandated roles as engine of growth for the community.Moreover, several municipalities are allied with poor governance, wastage as well as corruption and maladministration as evidenced from upsurge in the size of indebtedness.Thus, this study investigates the effects of Motivation, Empowerment and Organizational Culture (OC) on organizational commitment (DCO) of municipalities in Jordan.Very few empirical research has been conducted on this ground using these variables in a single model in Jordan.Hence, a structured survey was conducted and selected 340 government officials via cluster random sampling.The hypotheses were tested using SEM-PLS3 based on social exchange theory.Based on the statistical results, there is a significant effect of Motivation, Empowerment and Organizational Culture (OC) on organizational commitment (DCO).Subsequently, the findings evoked that there is a dire need to focus on Motivation, Empowerment and Organizational Culture to get better commitment from the municipalities in Jordan.
THE IMAGE OF AN UKRAINIAN LECTURER AT THE TURN OF THE 20TH – 21ST CENTURIES: EXPERIENCE HISTORICAL OF THE BEGINNING OF THE TRANSFORMATION OF THE EDUCATIONAL SYSTEM

&#x0D; &#x0D; &#x0D; The article unveils the teaching penalty against the background of the transformation of the higher education system of Ukraine at the end of the twentieth century - at the beginning of the twenty-first century, an attempt is made to present a holistic view of it from the point of view of the emotional and psychological state, motivation, and assessment of its consciousness. Information is also provided on external factors that have a direct impact on the effectiveness of the teaching environment. Its general quantitative and qualitative characteristics are also provided. It is concluded that the image of the Ukrainian teacher in the indicated period was determined by the state characteristic of a transitional society with the inertia of the manifestations of post-Soviet consciousness. This, in turn, determined their value series and motivation, which still often gravitated to the paradigm of the past. It turned out to be a permanent support group for the transformation of the higher education system - young teachers as natural carriers of innovation.&#x0D; &#x0D; &#x0D;
A Novel Fast Dimension‐Reducing Ranked Query Method with High Security for Encrypted Cloud Data LIU Lianggui and LIU Zhengjin (School of Information Science and Technology, Zhejiang Sci‐Tech University, Hangzhou 310018, China)

We propose a novel Fast dimensionreducing ranked query method (FDRQM) with high security for encrypted cloud data. We use Principal component analysis (PCA) algorithm to improve the speed of data encryption and search efficiency. Moreover, a random threshold of accumulated contribution rate of principal components is set to realize the randomness of data dimension reduction and improve data security further. Besides, we introduce a unit matrix before dimension reduction of index, which can not only improve the security of the system, but also ensure the accuracy of query. We demonstrate that our algorithm is more effective and efficient than existing algorithms.
Enhancing the secrecy of a cryptographic key generated using synchronized artificial neural networks1

The main options for the formation of a shared secret using synchronized artificial neural networks and possible patterns of behavior of a cryptanalyst are considered. To solve the problem of increasing the confidentiality of the generated shared secret, if it is used as a cryptographic key, it is proposed to use the mixing a certain number of results of individual synchronizations (convolution). As a mixing function, we consider the convolution of the vectors of network weights by bitwise addition modulo 2 of all the results of individual synchronizations. It is shown that the probability of success of a cryptanalyst is reduced exponentially with an increase of the number of terms in the convolution and can be chosen arbitrarily small. Moreover, the distribution law of the generated key after convolution is close to uniform and the uniformity increases with the number of terms in the convolution.
Some results of studying the impact of digital technologies upon learning activities

The subject of this research is the implementation of digital technologies in educational process of a university. The goal consists in studying the impact of digital technologies upon the students&amp;rsquo; learning activities. The article presents the results of questionnaire-based survey among students by the three question pools. In the course of research, the author examines such aspects of the problem, as the positive and negative impact of technologies upon learning activities of the students of digital generation. Particular attention is given to consideration of students&amp;rsquo; attitude on digitalization of higher education. The opinions of pedagogues on the results of conducted research are presented. The scientific novelty lies in mainstreaming the question on the negative impact of digital technologies upon learning activities of the modern generation of students that deserves special attention. On the background of common passion of the scholars of researchers and pedagogues for the ideas of digitalization of education, when digital technologies are viewed as virtually the key factor for modernization of educational process; second come the problems of growing pathological dependence of youth on digital technologies, undesired to switch to digitalized educational process to the disadvantage of communication in social networks and pleasant pastime online. The problem of the negative effect of digital technologies on learning activities must be recognized in order to find the ways for its solution.
PARTICIPATORY MAPPING FOR DISASTERS IN TULUNGREJO VILLAGE, BUMIAJI SUB-DISTRICT, BATU CITY

The ecological situation in Batu has a significant potential disaster, one of the areasthat have this is Tulungrejo village in the Bumiaji sub-district of Batu city. In general,there have been thirty-two disaster outbreaks in the Bumiaji sub-district; the areawith the most disaster risk is Tulungrejo village. Therefore, as an awareness ofemergency response situations, participatory mapping for disaster potential risk isessential to conduct by using the information of local people. This research appliedthe Participatory Geographic Information System (Pgis) as the mapping approach.The purpose of using this approach is to produce a disaster risk potential map asthe reference in determining the priority of disaster risk reduction based on theinformation that exists in the local area. This participatory mapping is an essentialpart of creating the social-technical resilience by the community.
Enhancement of Security within OpenStack – Some measures

Cloud computing is being used rapidly by the businesses around the world.Businesses are migrating the existing IT infrastructure to Cloud based Infrastructure offed third party service providers due to cost and availability of the latest technology at their door front.While that being the Security of the data and software stored on the Cloud has been a real threat and still many issues in relation to the security enforcement within cloud based infrastructure has been the real issue that must be tackled so that the users of the cloud based infrastructure are sure of the confidentiality of their data and software.OpenStack is open source software that can be used by anybody for building private cloud.Use of OpenStack minimizes the cost required almost to nil for building once own private clouds.Many security related issues crops up when Open stack is used due existence of several vulnerabilities Security enhancements are required to make Open Stack fully reliable in maintaining the secrecy and confidentially of the data stored in Open Stack.This paper elaborates on the security lapses exposed within OpenStack and proposes various enhancements to the existing provisions so that OpenStack is fully secured without compromising on Service level conditions.The Existing Architectures of open stack are expanded through addition of more components and the interactions between the new and existing components
A fast text similarity measure for large document collections using multireferencecosine and genetic algorithm

One of the critical factors that make a search engine fast and accurate is a concise and duplicate free index. In order to remove duplicate and near-duplicate (DND) documents from the index, a search engine needs a swift and reliable DND text document detection system. Traditional approaches to this problem, such as brute force comparisons or simple hash-based algorithms, are not suitable as they are not scalable and are not capable of detecting near-duplicate documents effectively. In this paper, a new signature-based approach to text similarity detection is introduced, which is fast, scalable, and reliable and needs less storage space. The proposed method is examined on standard text document datasets such as CiteseerX, Enron, Gold Set of Near-duplicate News Articles, and other similar datasets. The results are promising and comparable with the best cutting-edge algorithms considering accuracy and performance. The proposed method is based on the idea of using reference texts to generate signatures for text documents. The novelty of this paper is the use of genetic algorithms to generate better reference texts.
Enhancing the Quality of Service of Cloud Computing in Big Data using Virtual Private Network and Firewall in Dense Mode

Cloud Computing entails accessing and storing programs and data over the internet instead of the hard drive of a personal computer.Over the Internet, it is the practice of software and hardware to pass a service.Cloud gives the ability to consumers to access big data and use applications from every device that can have access to the internet, however, the key problem is security and this can be solvable by a firewall and Virtual Private Network.Recently, research has been accomplished in deploying firewalls and Virtual Private Networks with parameters of throughput and load in sparse mode.In this paper, an examination of firewall and Virtual Private Network is considered based on average throughput, average packet loss and average end-to-end delay in dense mode.To examine the performance of cloud computing without Firewall and Virtual Private Network, with firewall only, and with firewall and Virtual Private Network is the research goal.The simulation results have shown that Firewall and Virtual Private Network offers better security through a wide investigation with slight distress in the cloud performance.
Advertorials for Vocational Senior Secondary School (VSSS) in Indonesia: A Multimodal Analysis

This paper analyses four advertorial texts about phone product in the magazines.Advertorials provide the new-image advertisement into promotion activity.It reflects a strong multimodality.The practice of multimodality becomes the new trends that attract the attention of people.A multimodal analysis was chosen since the practice of multimodality surrounds the texts.However, the focus discussion in this paper is the visual semiotic systems that are reflected in the texts.This paper brings the results of the analysis to classroom practice, mainly to teach the brand new promoting agents to the vocational senior secondary school in Indonesia.
Evaluation of Different Methods for Non-Metric Camera Calibration

Calibration of a non-metric digital camera is a procedure that aims the modeling of systematic errors caused by lens distortion due to manufacturing and assembly process. This procedure must be carried out in order to improve the accuracy of a project. In addition, in photogrammetric measurements it is essential to comprehend the interior orientation parameters to model the distortions and generate trustful cartographic products. The camera calibration is needed more often for non-metric camera due to its low geometric stability. In the case of a commercial off-the-shelf digital camera, its interior orientation parameters are sensitive to external exposure and other factors, these characteristics creates the necessity of calibrating the sensor before any data acquisition. There are difference on the calibration methods, where some approaches requires more time, more elaborated data and sophisticated algorithms, such as the calibration using ground control points, on the other hand, there are faster and automated approaches that applies computer vision to reduce any human interaction. In this paper, the quality of these two different approaches for camera calibration was investigated. The first calibration is called “GCP-based” and it is based on georeferenced data processed with commercial software, and the second calibration is called ``Chessboard-based” and applies computer vision algorithms to estimate the parameters using a planar chessboard with black and white pattern and known dimensions. As result, the planimetric RMSE were compared with the reference coordinates, better accuracy was obtained with Agisoft PhotoScan software, with a RMSE of 1.4 cm.
Bayesian ODE Solvers: The Maximum A Posteriori Estimate

It has recently been established that the numerical solution of ordinary differential equations can be posed as a nonlinear Bayesian inference problem, which can be approximately solved via Gaussian filtering and smoothing, whenever a Gauss--Markov prior is used. In this paper the class of $\nu$ times differentiable linear time invariant Gauss--Markov priors is considered. A taxonomy of Gaussian estimators is established, with the maximum a posteriori estimate at the top of the hierarchy, which can be computed with the iterated extended Kalman smoother. The remaining three classes are termed explicit, semi-implicit, and implicit, which are in similarity with the classical notions corresponding to conditions on the vector field, under which the filter update produces a local maximum a posteriori estimate. The maximum a posteriori estimate corresponds to an optimal interpolant in the reproducing Hilbert space associated with the prior, which in the present case is equivalent to a Sobolev space of smoothness $\nu+1$. Consequently, using methods from scattered data approximation and nonlinear analysis in Sobolev spaces, it is shown that the maximum a posteriori estimate converges to the true solution at a polynomial rate in the fill-distance (maximum step size) subject to mild conditions on the vector field. The methodology developed provides a novel and more natural approach to study the convergence of these estimators than classical methods of convergence analysis. The methods and theoretical results are demonstrated in numerical examples.
Ranger: Boosting Error Resilience of Deep Neural Networks through Range Restriction.

With the emerging adoption of deep neural networks (DNNs) in the HPC domain, the reliability of DNNs is also growing in importance. As prior studies demonstrate the vulnerability of DNNs to hardware transient faults (i.e., soft errors), there is a compelling need for an efficient technique to protect DNNs from soft errors. While the inherent resilience of DNNs can tolerate some transient faults (which would not affect the system's output), prior work has found there are critical faults that cause safety violations (e.g., misclassification). In this work, we exploit the inherent resilience of DNNs to protect the DNNs from critical faults. In particular, we propose Ranger, an automated technique to selectively restrict the ranges of values in particular DNN layers, which can dampen the large deviations typically caused by critical faults to smaller ones. Such reduced deviations can usually be tolerated by the inherent resilience of DNNs. Ranger can be integrated into existing DNNs without retraining, and with minimal effort. Our evaluation on 8 DNNs (including two used in self-driving car applications) demonstrates that Ranger can achieve significant resilience boosting without degrading the accuracy of the model, and incurring negligible overheads.
Common Generative Adversarial Network Types and Practical Applications

Generative Adversarial Networks (GAN) which are analyzed in this study are among many deep learning methods which have been developed to overcome the restrictions of generic deep learning algorithms such as Restricted Boltzmann Machines (RBM), Deep Boltzmann Machines (DBM) and Variational Autoencoders (VAE). GAN models and structures can create new unique data from the collected data bases. These data bases can contain thousands of data and different types of data. The variations of these methods are mostly used for deep learning applications such as image restoration and creation, signal processing, and detection of cyber-attacks. In the literature, there are many different types of GANs. In this paper, it was focused on two GAN methods which are the Least Squares Generative Adversarial Networks (LSGAN), and Deep Convolutional Generative Adversarial Networks (DCGAN). These methods have been developed to improve the performance of the traditional GAN algorithm and solve various problems by satisfying different requirements. In this study, the architectures, usage types, properties and numeric definitions about these two methods were given and also the differences between them were analyzed. After that, the practical applications of these algorithms in the literature which have been used for creating new and unique data from the collected data were also discussed in this paper. 5 literature studies for LSGAN and 2 literature studies for DCGAN were given. Finally, we have compared the obtained results of these methods and explain which method can be used for which type of application. As seen from the researches, the applications that these methods can be applied are different from each other.
Developing Security Climate in Educational Organizations in the Cyber Era

Abstract The rapid development of information technology opens the barriers between regions and countries. These developments urge globalization. In educational organizations the involvement of information and communication technology in the world of education is a necessity to improve quality and competitiveness. On the other hand the development of information technology also provides threats including the desire of the parties to take a number of valuable information that is transacted on the internet. This study aims to overcome the problem of information security preventively by developing climate security in information technology-based educational organizations. This study uses a qualitative approach with a grounded theory method. Case organizations are faculty X and faculty Y at university Z. Data collection using interviews. Subjects interviewed were heads of academic fields, heads of information technology units, information technology officers. The results of the study are found manifestation of climate security. In case organizations that develop climate security, caution, accountability, ethical compliance and integrity are found. With these behaviors employees and leaders show an atmosphere of safety at work in the form of an atmosphere of mutual remembrance, compliance not to commit fraud, modeling to work safely, strictness of rules and sanctions, and leadership that prioritizes information safety.
System Design of a Student Relationship Management System Using the Internet of Things to Collect the Digital Footprint

Information systems plays an important role in the development of many perspectives on the part of higher education institutions, especially in the management of students' lives.Systems must be accessible and meet the needs of students, and allow higher education institutions to receive accurate and appropriate information.The purpose of this research was to design a system in the form of a student relationship management system (SRMS) using the Internet of Things (IoT) to collect digital footprint.These include the provision of an overview service station for monitoring students, the determination of relevant actors, the IoT process diagram, a sequence diagram and an entity relationship diagram.The results of the evaluation showed an overall very high level of appropriateness and a very high level in terms of the overall appropriateness of the usability of the system.All of the system design aspects for developing the system for collecting the digital footprint of higher education institutions to support student services and student behavior were shown to be appropriate.
Image Demoireing with Learnable Bandpass Filters

Image demoireing is a multi-faceted image restoration task involving both texture and color restoration. In this paper, we propose a novel multiscale bandpass convolutional neural network (MBCNN) to address this problem. As an end-to-end solution, MBCNN respectively solves the two sub-problems. For texture restoration, we propose a learnable bandpass filter (LBF) to learn the frequency prior for moire texture removal. For color restoration, we propose a two-step tone mapping strategy, which first applies a global tone mapping to correct for a global color shift, and then performs local fine tuning of the color per pixel. Through an ablation study, we demonstrate the effectiveness of the different components of MBCNN. Experimental results on two public datasets show that our method outperforms state-of-the-art methods by a large margin (more than 2dB in terms of PSNR).