Reviewerly/exHarmony huggingface dataset:
MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders

In this paper, we present a novel neuroevolutionary method to identify the architecture and hyperparameters of convolutional autoencoders. Remarkably, we used a hypervolume indicator in the context of neural architecture search for autoencoders, for the first time to our current knowledge. Results show that images were compressed by a factor of more than 10, while still retaining enough information to achieve image classification for the majority of the tasks. Thus, this new approach can be used to speed up the AutoML pipeline for image compression.
Quantum-resistant digital signatures schemes for low-power IoT

Quantum computers are on the horizon to get to a sufficient size. These will then be able to break most of the encryption and signature schemes currently in use. This is the case for human interface devices as well as for IoT nodes. In this paper i am comparing some signature schemes currently in the process of standardization by the NIST. After explaining the underlying basis on why some schemes are different in some aspects compared to others i will evaluate which currently available implementations are better suited for usage in IoT use-cases. We will come to further focus on the most promising schemes FALCON and Dilithium, which differ in one signifiant aspect that makes FALCON worse for signing but very good for verification purposes.
An Analysis of English Material for Tourism Industry at Vocational High School 4 Sampit Based on ESP Approach

Teaching material is an important aspect in teaching and learning process, because it gives significant impacts toward reaching the teaching and learning goal. English has become an important subject to master by the Tourism Industry students of Vocational High School 4 Sampit. Because most tourism industries use English as a global language, hence, English teaching material should be related to vocational school students’ needs for their future workplace. This study aimed to analyze the compatibility of the English teaching material with the students’ needs. This study was a descriptive qualitative study employing content analysis. Questionnaire was used as the research instrument in collecting the data. The data were analyzed by using ESP approach based on Richards and Rodger’s theory (2001). The findings show that the English teaching materials used in Tourism Industry Program at Vocational High School 4 Sampit were not related to the students’ needs. The materials were too general for Vocational High School students. The result suggested that English material for Vocational High School 4 Sampit must be developed based on students’ needs of tourism industry.
Correlations and Transfer Functions

We introduce the concepts of waveform correlations, and the way in which they can be exploited to extract information from seismograms. We show how correlation procedures can be used to determine time and phase delays. We then consider the closely related topic of transfer functions between aspects of the wavefield, and this leads into a discussion of the ways by which seismograms can be compared – a topic of importance in the comparison of observations and simulations. We also consider the nature of receiver functions and the correlation of teleseismic signals at a receiver to yield information on local structure.
Evaluating Gin Rummy Hands Using Opponent Modeling and Myopic Meld Distance

Gin Rummy is a popular two-player card game involving choices to draw and discard cards to form sets of matching cards. Unlike other popular games such as Chess, Poker, and Go, there is little formal artificial intelligence research about how to make good decisions when playing Gin Rummy. In this paper, we develop an agent that plays Gin Rummy through a combination of known and expected card values, modeling the opponent to predict their cards of interest, and a conservative approach to assessing when to end the hand. In addition to discussing our observations about Gin Rummy that inspired our agent's design and how the agent works, we evaluate the relative importance of various features employed by our agent by competing agents which implement various subsets of those features.
Уровни и структура ИКТ-компетентности педагогических кадров учреждений высшего образования для целей электронного обучения

The article discusses the issue of the information and communication competence of a high school teacher and the underlying competencies necessary for the effective organization and practical application of e-learning. We propose to differ the basic, professional pedagogical and special pedagogical levels of information and communication competence of teachers on the basis on the experience of the NIHE in improving the qualifications of high school teaching staff, analysis of technological features and types of e-learning. Each level has two groups of technological and communicative competences, their main elements are described.
Workflow of Autonomic Smart Devices Using Internet of Things

Autonomic IoT systems require variable conduct at runtime to adjust to various framework settings. Building reasonable models that range both plan time and runtime is along these lines fundamental for such systems. Be that as it may, existing methodologies separate the fluctuation model from the social model, prompting synchronization issues, for example, the requirement for dynamic reconfiguration, what's more, reliance management. A few methodologies characterize a fixed number of conduct variations and are along these lines inadmissible for exceptionally factor settings. This paper expands the semantics of the DX-MAN service model to consolidate fluctuation with conduct. The model permits the plan of composite services that characterize an endless number of work process variations, which can be picked at runtime with no reconfiguration instrument. We depict our model's autonomic abilities by utilizing a contextual analysis in the area of smart homes.
미하일 바흐친(Mikhail Bakhtin) 카니발이론의 유희성을 중심으로 본 베트멍(Vetements)의 패션 밈 특성

The development of the Internet has led to the emergence of an internet meme culture using duplication. This phenomenon has appeared in the fashion field as ‘fashion memes’ and its playful expression has helped it to spread. Vetements is one of the well-known brands that actively incorporates fashion memes into their fashion. The purpose of this study was to examine the playfulness of Mikhail Bakhtin’s Carnival Theory and identify the playful identity, design characteristics, and implications of Vetements’ fashion memes in light of Bakhtin’s playfulness. The research methods were literature research and collection case analysis, and empirical content research was conducted in parallel. The scope of this study was fashion memes shown in the products from 2017 S/S to 2019 F/W. The study results showed that Bakhtin''s playfulness could be divided into “playfulness in the destruction of hierarchy”, “playfulness in grotesque imagery” and “playfulness in the emphasis on the lower bodily stratum”. Based on these findings, the playful identity of Vetements’ fashion meme images can be described as follows. First, it breaks down the existing order of fashion brands like the destruction of hierarchy in Bakhtin’s theory. Second, it is grotesque; it is strange, eerie, yet ridiculous at the same time. Third, it is a playful metaphor of contamination, filth, and lower stratum. The modes of embodiment of such identity include Vetements’ collaboration with famous brands, the use of cheap items, and the expression of genderless ideas.
EVOLUTION OF OPEN SCIENCE TOOLS AND TECHNOLOGIES

Building a global information space and Knowledge Society, the European community is developing a priority area for research and innovation policy – the Open Science, which, among other things, provides open access to publications and research results. When developing this direction in Ukraine, it is expedient to take into account the experience of successful countries, global transformations and trends, digital progress, and historical lessons. The article’s purpose is to substantiate the periodization and determine the stages of evolution of open science tools and technologies in the world. The article presents some results of the research within the project «Cloud-oriented systems of open science in teaching and professional development of teachers» (registration number 2020.02/0310) implemented with grant support from the National Research Fund of Ukraine. Theoretical methods are used: historical-pedagogical method, generalization of scientific and scientific-methodical sources, analysis and systematization of concepts, works of Ukrainian and foreign scientists, experts in the field of digitalization of education and science. The periodization of the evolution of open science tools and technologies has been developed. The first stage was characterized by a sharp increase in data and knowledge flows, the emergence of the first initiatives to ensure the availability of scientific and cultural achievements of humankind. The second stage was characterized by the intensification of the process of transition of journals and scientific repositories to the open format, the development and dissemination of software to support open access, the development of cloud computing and Web 2.0 technology. The result of the third period was the intensive development of scientific-educational environments, due to the processes of digitization, increasing trends: open access, archiving of articles, data exchange.
Asymptotic Analysis of Convolutional Neural Network with Minimization of Computational Resources for Real Time Short Video Applications

Real time deep learning on video streams made a challenging task for video analysis on the edge devices. A lot of research revolving deep neural networks which solve computational problems very well and there is also improvements in computational power. That helps move the images and train huge networks and neural networks is trying to solve or video analysis trying to solve is getting an understanding of the video doing video analysis for video understanding understand what we see in the video. CNN are employed with vision algorithms, where the image is convolved iteratively by many of the kernelsto extract the features behind it. The paper approach deals with using adaptive recognizer for the wide class of system with non-linearity functions in variation environment. The standard online algorithm used is gradient descent for adjusting weights of the convolutational neural network. The learning set is assigned applying bounded condition to infinite. To analyze the input variation variables with its limiting actions, asymptotic analyzers have been used. The limiting conditions assure the convergence of these algorithms globally in the nonlinear structure. The main factor is that not necessary of using penalty term to achieve sequence of the weights with the boundless. Overall computational resources aimed at minimizing energy consumption with memory through the derivation of activation function and back propagation with matrix multiplication is used as optimization algorithm which is same as feed forward pass algorithm.
DEEP LEARNING APPROACH FOR AUTOMATED SCREENING OF MALARIA PARASITE

Malaria is a life-threatening disease caused by Plasmodium parasites that infect the red blood cells (RBCs).According to the 2018 World Health Organization (WHO) report, an estimated 435,000 malaria- related deaths are reported globally. Children under 5 years of age are reported to be the most vulnerable, accounting for 61% of the estimated death counts. Early diagnosis and treatment is the most effective way to prevent the disease. Manual identification and counting of parasitized cells in microscopic thick/thin-film blood examination remains the common, but burdensome method for disease diagnosis. Its diagnostic accuracy is adversely impacted by inter/intra-observer variability, particularly in large-scale screening under resource-constrained settings.The primary aim of the paper is to overcome these inter/intra-observer variability or errors that occur as a result of these by automating this process using Deep Learning Algorithms like Convolutional Neural Network (CNN) in such a way so as to have the maximum accuracy. These CNN’s can be used to detect whether a Red Blood Cell stained with a chemical Giemsa is infected with Malaria or not. The Malaria Cell Image Dataset provided by Kaggle consisting of a total of 27,558 images of the blood cells is being used to train the model.This data can be used to train various pre trained CNN models like ResNet- 152, WideResNet101-2 as they have low top-1 and top-5 errors. Instead of using the pretrained models as it is, we can unfreeze the already existing layers and modify the layers to suit our dataset. The dataset will also be tested using SGD+Nesterov+Momentum,Adam AdaGrad, AdaMax optimizers and the best combination would be used for the final model.This CNN model would then be either deployed directly in an Android Phone or can be deployed in a remote web server to which images can be sent, processed at the server and results sent back
Reflections on distance education technologies: culture of education and new challenges on a global scale

Since the beginning of 2020, the entire world has faced a terrible threat and problems caused by a new type of virus (COVID-19) which dispelled the myth of stability in the modern era. Now in Kazakhstan, as in many countries, there is a complicated situation with the coronavirus. Humanity is facing the issue of survival: the pandemic has reached a global scale. The crisis of 2020 has become unique, special and unprecedented in the modern history of mankind. The author shows how the Kazakh educational system has built its work in the new conditions and formulates the tasks that modern education faces today.
Development of Maritime English Learning Construct in Navigation Functions at Politeknik Ilmu Pelayaran Makassar

Development of Maritime English Learning Construct in Navigation Function of Politeknik Ilmu Pelayaran. Learning in Politeknik Ilmu Pelayaran must be able to adapt to the latest needs in preparing graduates to have knowledge, understanding and skills of Maritime English in navigation functions so that they meet the minimum requirements as a commercial shipping officer as stipulated in STCW. This study aims to determine how the construct of Maritime English learning in navigation functions with a model developed at Politeknik ilmu Pelayaran. The study was conducted with a model development approach and questionnaires were used to collect data. The research sample were student officers of the Diklat Pelaut I, Politeknik Ilmu Pelayaran. The sampling technique used is proportional random sampling. The data were analyzed by qualitative and quantitative method.
Applications of SKREM-Like Symmetric Key Ciphers

In a prior paper we introduced a new symmetric key encryption scheme called Short Key Random Encryption Machine (SKREM), for which we claimed excellent security guarantees. In this paper we present and briefly discuss how some other cryptographic applications besides plain text encryption can benefit from the same security guarantees. We task ourselves with and succeed in showing how Secure Coin Flipping, Cryptographic Hashing, Zero-Leaked-Knowledge Authentication and Authorization and a Digital Signature scheme which can be employed on a block-chain, can all be achieved using SKREM-like ciphers, benefiting from their security guarantees. We also briefly recap SKREMlike ciphers and the core traits which make them so secure. The realizations of the above applications are novel because they do not involve public key cryptography. Furthermore, the security of SKREMlike ciphers is not based on hardness of some algebraic operations, thus not opening them up to specific quantum computing attacks.
A Study On The Effects Of U.S. Events On Sentiment Relating To Minority Groups On Twitter

This is a study on how U.S. national events affect the sentiment relating to different minority groups on Twitter. First, several groups of minorities were identified for examination. Next, the relationship between hashtag usage and Tweet composition relating to various minority groups was investigated. Using the Twitter API and several Python packages, we were able to gather Tweets relating to our areas of interest over the months of June to August 2020. Average Tweet sentiment was determined to see how that sentiment changed before, during, and after selected events. Three metrics were used to draw conclusions from the collected data. These were average sentiment of the Tweets, percentage of Tweets that were “retweets” versus those composed originally, and number of Tweets that were deemed “relevant” by twitter and therefore available for sample. We found that Tweets relating to the Black Lives Matter and related movements decreased in original tweet composition and average sentiment. Overall, this indicated a negative effect on the sentiment relating to Black Lives Matter initiatives during several related events. Tweets relating to the Coronavirus and people of Asian ethnicity did not change in average sentiment and decreased in original tweet composition in relation to the Coronavirus epidemic. Overall, this also indicated a negative effect on the sentiment relating to Asians due to this world event. Finally, Tweets relating to members of the LGBTQ+ community increased average sentiment, and decreased in original tweet composition during the month of June, commonly associated with “Pride Month”. This indicated a positive effect on the sentiment relating to the LGBTQ+ community during this event. We can come to the overarching conclusion that U.S. national events do not have a direct effect on positive or negative sentiment independently, rather that these events cause a change in the sentiment that can be characterized as either more positive or more negative. People are much more likely to retweet Tweets relating to events that elicit visceral reactions from people. This is a common sign of support. Since the original Tweet often has polarized sentiment due to the emotionally charged content, we can conclude that the events of the study will result in high retweet percentages with polarized sentiment.
Comparison between Different Methods for Noise Cancellation in Medical Image

In the medical community, doctors use different types of medical imaging to diagnose the disease.It is possible that these types lead to noise.The goal is to rid these types of noise of the appearance of the image clearly, to give the specialist proper medical examination.These types are Magnetic Resonance (MR), Computed Tomography (CT), Ultrasounds and X-Ray images.For measuring the denoised image quality we used: Peak to Signal noise (PSNR).This is to facilitate the medical community in obtaining the best results for the diagnosis, describing the disease and its treatment and deal with the patient clearly and comfortably.
An Efficient Probabilistic Multi Labeled Big Data Clustering Model for Privacy Preservation Using Linked Weight Optimization Model

An unsupervised analysis of the classification and clustering of data is one of the most powerful and insightful data mining approaches used in different disciplines to identify homogenous groups of objects based on similarities. In machine learning with the increased generation of data, classification continues to be a key subject. While several literary works are interested in classifying the single label, the enormous dimensions of the data require a new approach. Multi-label clustering has therefore gained considerable attention in the testing community in recent years. This method involves a data instance with different labels and it is useful for many fields, e.g. image analysis, text classification and Big Data privacy security. In this case the classification of the single label is expanded. The high dimensionality of the distributed system needs an efficient and effective data management. Multi Label Classifier divides one or more labels in a set of labels of a particular instance. Multi-label classification is one of the leading data collection methods, where a set of labels is annotated in the data collection for each single instance. In one instance, the nature of multiple labels requires more computer power than classified one-label tasks. A multi-label grouping is often simplified by the method of splitting into one label classification, which avoids the distinction between labels. A Multi-Label Big Data Clustering with Privacy Protection Probability Linked Weight Optimization (MLBDC-PP-LWO) model is provided in this paper. In this proposed work, after the identification of sensitive data from data clusters, sensitive information is protected or generalized. The models proposed are compared to existing models and the findings show that the proposed model privacy preservation levels are more than the traditional methods.
Improved Private and Secure Distributed (Batch) Matrix Multiplication

In this paper, we study the problem of distributed matrix multiplication under various scenarios. Specifically, we focus on a scenario where a user has two matrices A and B and wishes to compute their product with the assistance of several distributed colluding servers, which is referred to as secure distributed matrix multiplication (SDMM). Meanwhile, we also consider its generalization by assuming that the user has two matrix batches, which is referred to as secure distributed batch matrix multiplication (SDBMM) or distributed batch matrix multiplication (DBMM) if the distributed servers are incurious. In a variant of this problem, we consider the problem of private and secure distributed matrix multiplication (PSDMM), where a user having a private matrix A and N non-colluding servers sharing a library of L (L>1) matrices $B^{(0)}, B^{(1)},...,B^{(L-1)}$, for which the user wishes to compute $AB^{(\theta)}$ for some $\theta\in [0, L)$ without revealing any information of the matrix A to the servers, and keeping the index $\theta$ private to the servers. Distributed matrix multiplication under these scenarios is supposed to have wide application potentials, such as machine learning and cloud computing. However, studies of distributed matrix multiplication are still scarce in the literature and there is much room for improvement. In this paper, we propose several new coding schemes for the various distributed matrix multiplication models, including two classes of SDMM codes, a DBMM code, an SDBMM code, and finally a PSDMM code. The proposed codes have a better performance than state-of-the-art schemes in that they can achieve a smaller recovery threshold and download cost as well as providing a more flexible tradeoff between the upload and download costs.