Reviewerly/exHarmony huggingface dataset:
Utilizing the Logistic Regression Model in Analyzing the Categorical Data of Economic Effects

The categorical data has a significant role in representing statistical binary variables, and they are analyzed by means of grouping the response variable into ordered categories. Thereby, the dependent variable becomes of type binary qualitative variable. The data related to the financial position of world countries is classified within the categorical data. This work is to study the economic effects of an individual's different factors on determining the richness or poorness levels of a selected population of countries. Moreover, a logistic regression model is to be created to estimate these levels. As a sample of research, the categorical data relevant to the financial status of 20 Arabic countries were drawn from the website of the World Bank, WB. In addition, for comparison purpose, another similar set of categorical data was generated by MATLAB too. The paper has been based on two hypotheses, first is the well-known regression models, like the ordinary least squares or maximum likelihood, are not accurate in case of binary qualitative variables. Second, is utilizing the logistic regression model as an alternative model to achieve the paper goal. The paper results, for both WB data and MATLAB data, have successfully proved the ability of the logistic regression model in manipulating the categorical data and predicting the coefficients of the corresponding regression models.
Evaluasi Kesiapan Modernisasi Sistem Irigasi di Daerah Irigasi Krueng Jreu Kabupaten Aceh Besar

Irrigation modernization as a fundamental transformation of irrigation water resources management which aims to increase the utilization of human resources and services provided to a farmer in an effective, efficient, and sustainable manner based on the five pillars of irrigation modernization. This study aims to evaluate the readiness of modernization Krueng Jreu Irrigation Area based on the 5 pillars of irrigation modernization. The research method was observation, interview, and research documentation. The results showed that the index value of modernization readiness in Krueng Jreu on the pillars of water availability with a value of 10, the pillars of irrigation infrastructure with a value of 19.8. Pillars of the irrigation management system with a value of 14, the pillars of irrigation management institutions with a value of 9 and the pillars of human resources for irrigation management with a value of 6. The total value of the index for modernization readiness in Krueng Jreu is 58.8 which is categorized as sufficient, irrigation modernization is postponed and must make improvements within 1-2 years.
Vyacheslav Koleichuk as the Engine of the Russian Kinetic Art. Imaginary dialogue at the exhibition

The State Tretyakov Gallery hosts a significant exhibition “Laboratory of the Future. Kinetic Art in Russia”. Its significance, the influence of the artistic phenomenon of kinetic art itself on domestic art of the 20th and 21st centuries has not yet been fully determined. The exhibition emphasizes kinetic art as one of the central national trends in experimental artistic creativity of the 20th century, even as some kind of a tradition. On the one hand, the exhibition would have been impossible without the participation of the creators of the Russian avant-garde, the founders of abstract art, the creators of the first abstract sculptures and dynamic structures: V. V. Kandinsky, K.S. Malevich, El Lissitzky, V. E. Tatlin, A. M. .Rodchenko. On the other hand, recognized masters, inventors of kinetic art in the USSR in the 1960s and 1970s, creators of the synthetic works of art combining the sound, color, form, images and motion are also important: Lev Nusberg’s “Group Movement” in Moscow and “KB Prometheus” under the leadership of Bulat Galeev in Kazan, the first kinetic construction at the USSR Exhibition of Economic Achievements Francisco Infante and the dynamic installation “Atom” by Vyacheslav Koleichuk, experiments with electronic sound and acoustics of the Experimental Studio of Electronic Music of Evgeny Murzin, the Theremen Center, created by Andrei Smirnov, space projects by Vyacheslav Loktev installations with light and sound in Leningrad by August Lanin 1. One of the key figures in this artistic process was the architect, designer, researcher, inventor, constructor and teacher Vyacheslav Fomich Koleichuk (1941–2018). This imaginary dialogue is covering some of the inventions of the artist, developing the traditions of Russian kinetic art, expanding the artistic space of modern design and architecture 2.
Security and Privacy of Sensitive Data in Cloud Computing Using RSA

Today's world internet is being used by almost everyone. Numerous file exchanges take place online including many official documents. These files require some sort of security mechanisms while being transmitted over the Internet. Technology has done a great deal for changing the way we live and do business today. In the fast-moving world we need something essential for fast computation. Along with the popular use of computer, Information security has also become one of the problems which need to be solved. Many security issues like the malware authors, information leakage, endangerment and unauthorized exploitation need to be taken into account. To control those issues, crypto-security is necessary. More Applications started to use Rivest–Shamir–Adleman (RSA). However, Since RSA on large blocks is computationally intensive and largely byte-parallel. System will parallel perform the encryption and decryption process in a distributed environment and the performance analysis shows improvement in terms of execution time and provides the security.
Application of Genome Sequence Based on Entropy Coding

A novel genome sequence entropy coding algorithm based Context modeling is proposed. In this paper, the genome sequence data of bioinformatics features is sliced and reformed into 4 clusters according with biological meanings: the coding sequence cluster, the intron cluster, the RNA cluster and the residual cluster. According to the specific biological characteristics of the sequences in each set were pretreated respectively, then the sequences are compressed with entropy coding algorithm. The experiment shows that it is a good method to deal with the problem that than original DNA compression algorithms of the long DNA sequences, so it can make compression ratio and entropy coding efficient higher.
Dynamical analysis, circuit realization, and application in pseudorandom number generators of a fractional-order laser chaotic system*

A new five-dimensional fractional-order laser chaotic system (FOLCS) is constructed by incorporating complex variables and fractional calculus into a Lorentz–Haken-type laser system. Dynamical behavior of the system, circuit realization and application in pseudorandom number generators are studied. Many types of multi-stable states are discovered in the system. Interestingly, there are two types of state transition phenomena in the system, one is the chaotic state degenerates to a periodical state, and the other is the intermittent chaotic oscillation. In addition, the complexity of the system when two parameters change simultaneously is measured by the spectral entropy algorithm. Moreover, a digital circuit is design and the chaotic oscillation behaviors of the system are verified on this circuit. Finally, a pseudo-random sequence generator is designed using the FOLCS, and the statistical characteristics of the generated pseudo-random sequence are tested with the NIST-800-22. This study enriches the research on the dynamics and applications of FOLCS.
Bangladeshi Banknote Recognition in Real-time using Convolutional Neural Network for Visually Impaired People

Visually impaired people face extreme difficulties in recognizing paper currencies for day-to-day transactions as the texture and shape of many currencies are very similar. In this paper, an automated system for identifying the Bangladeshi banknotes using a convolutional neural network has been proposed for helping visually impaired people. A new dataset has been created consisting of more than 70,000 images of currently available Bangladeshi banknotes. The development of the system includes dataset building, creating and training the convolutional neural network model, and testing it in real-time. For verifying the efficacy of the proposed method, it has been tested in various backgrounds. The system can identify the eight banknotes used in Bangladesh with an average accuracy of 92% and exhibit the result with both textual and auditory output. Moreover, the system is invariant of the orientation and sides of notes. Visually impaired people will be able to easily use it in daily transactions.
IDENTIFIKASI CITRA UKIRAN ORNAMEN TRADISIONAL BALI DENGAN METODE MULTILAYER PERCEPTRON

Balinese ornament carving are a cultural heritage that is owned by especially the Balinese people. However, especially Balinese people only know the shape of the carving without knowing the name and characteristics of the Balinese traditional carving ornaments. Based on these problems, the researchers have a solution to research about Balinese Ornament Carving Identification by utilizing digital image processing technology. In this study uses Gabor Filter as a feature extraction from the carved image that used and Multilayer Perceptron as a classifier. There are 18 (eighteen) classes of Balinese carving ornaments use in this study with a total of dataset is 268 (two hundred and sixty eight). The purpose of this study was to determine the level of identification accuracy of Balinese ornament carving with Multilayer Perceptron method. In the implementation using digital image processing technic with Multilayer Perceptron method was based on backpropagation learning algorithm with 10560 neuron input layers, 50 neuron hidden layers, and 18 neuron output layers as classifier obtained the accuracy for testing is 43%. Classification testing based on k-fold cross validation with K=5 results in average accuracy of 41.14% with optimum accuracy of 56% and accuracy testing with Confusion Matrix obtained the accuracy 43.3%, sensitivity 42.68% and specificity 96.87%.
Forward Super-Resolution: How Can GANs Learn Hierarchical Generative Models for Real-World Distributions

Generative adversarial networks (GANs) are among the most successful models for learning high-complexity, real-world distributions. However, in theory, due to the highly non-convex, non-concave landscape of the minmax training objective, GAN remains one of the least understood deep learning models. In this work, we formally study how GANs can efficiently learn certain hierarchically generated distributions that are close to the distribution of real-life images. We prove that when a distribution has a structure that we refer to as Forward Super-Resolution, then simply training generative adversarial networks using stochastic gradient descent ascent (SGDA) can learn this distribution efficiently, both in sample and time complexities. We also provide empirical evidence that our assumption "forward super-resolution" is very natural in practice, and the underlying learning mechanisms that we study in this paper (to allow us efficiently train GAN via SGDA in theory) simulates the actual learning process of GANs on real-world problems.
Analysis and Improvement of Heterogeneous Hardware Support in Docker Images

Docker images are used to distribute and deploy cloud-native applications in containerised form. A container engine runs them with separated privileges according to namespaces. Recent studies have investigated security vulnerabilities and runtime characteristics of Docker images. In contrast, little is known about the extent of hardware-dependent features in them such as processor-specific trusted execution environments, graphics acceleration or extension boards. This problem can be generalised to missing knowledge about the extent of any hardware-bound instructions within the images that may require elevated privileges. We first conduct a systematic one-year evolution analysis of a sample of Docker images concerning their use of hardware-specific features. To improve the state of technology, we contribute novel tools to manage such images. Our heuristic hardware dependency detector and a hardware-aware Docker executor hdocker give early warnings upon missing dependencies instead of leading to silent or untimely failures. Our dataset and tools are released to the research community.
The Spectrum of Fisher Information of Deep Networks Achieving Dynamical Isometry

The Fisher information matrix (FIM) is fundamental to understanding the trainability of deep neural nets (DNN), since it describes the parameter space's local metric. We investigate the spectral distribution of the conditional FIM, which is the FIM given a single sample, by focusing on fully-connected networks achieving dynamical isometry. Then, while dynamical isometry is known to keep specific backpropagated signals independent of the depth, we find that the parameter space's local metric linearly depends on the depth even under the dynamical isometry. More precisely, we reveal that the conditional FIM's spectrum concentrates around the maximum and the value grows linearly as the depth increases. To examine the spectrum, considering random initialization and the wide limit, we construct an algebraic methodology based on the free probability theory. As a byproduct, we provide an analysis of the solvable spectral distribution in two-hidden-layer cases. Lastly, experimental results verify that the appropriate learning rate for the online training of DNNs is in inverse proportional to depth, which is determined by the conditional FIM's spectrum.
Simultaneous Attack on CNN-Based Monocular Depth Estimation and Optical Flow Estimation

Thanks to the excellent learning capability of deep convolutional neural networks (CNNs), CNN-based methods have achieved great success in computer vision and image recognition tasks. However, it has turned out that these methods often have inherent vulnerabilities, which makes us cautious of the potential risks of using them for real-world applications such as autonomous driving. To reveal such vulnerabilities, we propose a method of simultaneously attacking monocular depth estimation and optical flow estimation, both of which are common artificial-intelligence-based tasks that are intensively investigated for autonomous driving scenarios. Our method can generate an adversarial patch that can fool CNN-based monocular depth estimation and optical flow estimation methods simultaneously by simply placing the patch in the input images. To the best of our knowledge, this is the first work to achieve simultaneous patch attacks on two or more CNNs developed for different tasks.
Predicting the Number of Reported Bugs in a Software Repository

The bug growth pattern prediction is a complicated, unrelieved task, which needs considerable attention. Advance knowledge of the likely number of bugs discovered in the software system helps software developers in designating sufficient resources at a convenient time. The developers may also use such information to take necessary actions to increase the quality of the system and in turn customer satisfaction. In this study, we examine eight different time series forecasting models, including Long Short Term Memory Neural Networks (LSTM), auto-regressive integrated moving average (ARIMA), and Random Forest Regressor. Further, we assess the impact of exogenous variables such as software release dates by incorporating those to the prediction models. We analyze the quality of long-term prediction for each model based on different performance metrics. The assessment is conducted on Mozilla, which is a large open-source software application. The dataset is originally mined from Bugzilla and contains the number of bugs for the project between Jan 2010 and Dec 2019. Our numerical analysis provides insights on evaluating the trends in a bug repository. We observe that LSTM is effective when considering long-run predictions whereas Random Forest Regressor enriched by exogenous variables performs better for predicting the number of bugs in the short term.
Initial Seed Value Effectiveness on Performances of Data Mining Algorithms

After 2000s, Computer capacities and features are increased and access to data made easy. However, the produced and recorded data should be meaningful. Transformation of unprocessed data into meaningful information can be done with the help of data mining. In this study, classification methods from data mining applications are studied. First, the parameters that make the results of the same data set different were investigated on 4 different data mining tools (Weka, Rapid Miner, Knime, Orange), It has been tested with 3 different algorithms (K nearest neighborhood, Naive Bayes, Random Forest). In order to evaluate the performance of the data set while creating the classification models, the data set was divided into training data and test data as 80% -20%, 70% -30% and 60-40%. The accuracy, roc and precision values was used to test the performance of the classifying data. While classifying, the effect of algorithm parameters on the results is observed. The most important of these parameters is the initial seed value. The initial seed is a value using especially in classification algorithms that determines the initial placement of the data and directly affects the result. In this respect, it is very important to determine the initial seed value correctly. In this study, initial seed values between 0 and 100 were evaluated and it was shown that the classification could change the accuracy value approximately by 5%.
Virtual Classroom Usage and User Perception for English Learning as a Second Language at Universities in Lima, Peru

A more extensive use of virtual tools in teaching-learning processes needs a professors’ and students’ proactive attitude, positive experiences, and improvement in their implementation. A cross-sectional study was carried out to know the perceptions of private university students from Lima on the use of the necessary elements for an English language virtual classroom. The sample was composed of 150 accounting students from five universities, whose perceptions on virtual classroom elements were assessed and compared to their previous experiences. Most students were in favor of using the virtual classroom and its different elements, and those who had a previous experience with virtual classrooms were more in favor of using reminders, chats, forums, e-mails and online exercises (p&lt;0,05). In sum, most students were in favor of using the virtual classroom and having a previous experience favors a more positive perception towards online learning tools.
Training of future primary school teachers for the formation of functional literacy in pupils

The main problem area of the article is the current issues of pedagogical science concerning training of future primary school teachers for the formation of functional literacy in pupils. The emergence of these issues is determined by globalization and integration processes in the educational system of Ukraine and in the world, social transformations that affect the content of primary education, as well as modern humanitarian, scientific and technological development. This requires each individual to show functional literacy. Based on this, there is an urgent need for fundamental changes in postclassical education, caused by modern European theories, educational models, the latest tools of professional activity of primary school teachers who are responsible for the provided educational services. These changes are necessary in the context of the recommendations provided by leading European and international educational organizations, and are confirmed by the results of international PISA research. The purpose of the research involves establishing the essence of the basic concept of “functional literacy” and its structure. The research found a link between functional literacy and soft skills. The program of the experiment provides for the diagnosis of the formation of functional literacy in future primary school teachers. Questionnaire and testing of final year students’ in specialty 013 Primary Education of Bohdan Khmelnytsky National University of Cherkasy (Ukraine) confirmed the assumption that in most students functional literacy is formed at the middle level.
Pyramid Medical Transformer for Medical Image Segmentation

Deep neural networks have been a prevailing technique in the field of medical image processing. However, the most popular convolutional neural networks (CNNs) based methods for medical image segmentation are imperfect because they model long-range dependencies by stacking layers or enlarging filters. Transformers and the self-attention mechanism are recently proposed to effectively learn long-range dependencies by modeling all pairs of word-to-word attention regardless of their positions. The idea has also been extended to the computer vision field by creating and treating image patches as embeddings. Considering the computation complexity for whole image self-attention, current transformer-based models settle for a rigid partitioning scheme that potentially loses informative relations. Besides, current medical transformers model global context on full resolution images, leading to unnecessary computation costs. To address these issues, we developed a novel method to integrate multi-scale attention and CNN feature extraction using a pyramidal network architecture, namely Pyramid Medical Transformer (PMTrans). The PMTrans captured multi-range relations by working on multi-resolution images. An adaptive partitioning scheme was implemented to retain informative relations and to access different receptive fields efficiently. Experimental results on three medical image datasets (gland segmentation, MoNuSeg, and HECKTOR datasets) showed that PMTrans outperformed the latest CNN-based and transformer-based models for medical image segmentation.
Long-distance quantum key distribution based on the physical loss control

Existing quantum cryptography is resistant against secrecy-breaking quantum computers but suffers fast decay of the signal at long distances. The various types of repeaters of propagating quantum states have been developed to meet the challenge, but the problem is far from being solved. We step in the breach and put forth long-distance high secrecy optical cryptography, creating the fast quantum key distribution over distances up to 40,000 kilometers. The key element of the proposed protocol is the physical control over the transmission line.