Reviewerly/exHarmony huggingface dataset:
Postgraduate Course “History and Philosophy of Science”: What? How? What for?

The article addresses the problem of teaching the course of history and philosophy of science. This course has been taught to graduate students of Russian universities for more than 15 years. But disputes over its content and expediency still do not subside, but only flare up with renewed vigor. Those disputes were in three directions. The author proposes to return to the discussion of the existing problems in each of the areas of criticism of postgraduate discipline. These are: 1) the content of the subject from the standpoint of the coordination of its historical and philosophical parts (What?); 2) pedagogical and methodological support of training (How?); and 3) the ultimate goals of postgraduate training in general (For what?). The questions “how?” and “ what for?” are included in a broader context – the formation of the competence of a future scientist, his readiness to solve complex and non-trivial scientific problems. Reflections outlined in the article are built into the overall picture of post-graduate training in universities that has developed to date. The author sums up his reflections by calling for non-stop communication in a mode of continuous “recursive adjoining”. Such a strategy, in his opinion, remains the most reliable means of highquality promotion of both teaching and training of graduate students in general.
An Extensive Analysis of Machine Learning Based Boosting Algorithms for Software Maintainability Prediction

Software Maintainability is an indispensable factor to acclaim for the quality of particular software.It describes the ease to perform several maintenance activities to make a software adaptable to the modified environment.The availability & growing popularity of a wide range of Machine Learning (ML) algorithms for data analysis further provides the motivation for predicting this maintainability.However, an extensive analysis & comparison of various ML based Boosting Algorithms (BAs) for Software Maintainability Prediction (SMP) has not been made yet.Therefore, the current study analyzes and compares five different BAs, i.e., AdaBoost, GBM, XGB, LightGBM, and CatBoost, for SMP using open-source datasets.Performance of the propounded prediction models has been evaluated using Root Mean Square Error (RMSE), Mean Magnitude of Relative Error (MMRE), Pred(0.25),Pred(0.30),& Pred(0.75)as prediction accuracy measures followed by a non-parametric statistical test and a post hoc analysis to account for the differences in the performances of various BAs.Based on the residual errors obtained, it was observed that GBM is the best performer, followed by LightGBM for RMSE, whereas, in the case of MMRE, XGB performed the best for six out of the seven datasets, i.e., for 85.71% of the total datasets by providing minimum values for MMRE, ranging from 0.90 to 3.82.Further, on applying the statistical test and on performing the post hoc analysis, it was found that significant differences exist in the performance of different BAs and, XGB and CatBoost outperformed all other BAs for MMRE.Lastly, a comparison of BAs with four other ML algorithms has also been made to bring out BAs superiority over other algorithms.This study would open new doors for the software developers for carrying out comparatively more precise predictions well in time and hence reduce the overall maintenance costs.
Quantum direct secret sharing using Bell state

Abstract In this paper, we propose an efficient multiparty quantum direct secret sharing scheme based on Bell state and Bell measurements. In our scheme, the secret information can be directly encoded into the second particles of Bell states through the local unitary operations. In order to increase the number of participants, we use the controlled-NOT gate to produce the final multipartite entangled states. In the reconstruction phase, each participant performs Bell measurements for their particles and obtains the final key strings. Furthermore, they cooperate to recover the secret information. In addition, we give a concrete example with four participants to demonstrate more clearly the integrity of our scheme. Finally, we demonstrate that our protocol can resist both collusion attack and intercept-and-resend attack.
Analysis of Decoherence in Linear and Cyclic Quantum Walks

We theoretically analyze the case of noisy Quantum walks (QWs) by introducing four qubit decoherence models into the coin degree of freedom of linear and cyclic QWs. These models include flipping channels (bit flip, phase flip and bit-phase flip), depolarizing channel, phase damping channel and generalized amplitude damping channel. Explicit expressions for the probability distribution of QWs on a line and on a cyclic path are derived under localized and delocalized initial states. We show that QWs which begin from a delocalized state generate mixture probability distributions, which could give rise to useful algorithmic applications related to data encoding schemes. Specifically, we show how the combination of delocalzed initial states and decoherence can be used for computing the binomial transform of a given set of numbers. However, the sensitivity of QWs to noisy environments may negatively affect various other applications based on QWs.
Using Fuzzy Equivalence Relations to Model Position Specificity in Sequence Kernels

One of the most fundamental issues in computational biology is the classification of biological sequences, particularly nucleotide (DNA and RNA) and amino acid (protein) sequences. Sequence statistics were widely used in the early days of computational biology. Hidden Markov Models (HMM) and Artificial Neural Networks (ANN) were used to improve these methods subsequently (ANN). SVM classifiers are essentially linear classifiers in their most basic form. SVMs differ from other linear classifiers such as perceptrons or logistic regressors in that they maximize the margin between the classes to define the classification function – a principle that is known to be optimal in terms of generalization error limitations. This paper replicates previously published sequence kernels based on the occurrence of specified patterns as well as some position-specific variants. We use fuzzy equivalence relations to model position specificity to develop a generalization of position-specific sequence kernels. This is not just a fascinating link, but it also leads to the creation of new kernels, such as the Elin kernel. We discovered that these kernels enable an explicit representation, allowing for improved computing efficiency and feature extraction.
ElasticHash: Semantic Image Similarity Search by Deep Hashing with Elasticsearch

We present ElasticHash, a novel approach for high-quality, efficient, and large-scale semantic image similarity search. It is based on a deep hashing model to learn hash codes for fine-grained image similarity search in natural images and a two-stage method for efficiently searching binary hash codes using Elasticsearch (ES). In the first stage, a coarse search based on short hash codes is performed using multi-index hashing and ES terms lookup of neighboring hash codes. In the second stage, the list of results is re-ranked by computing the Hamming distance on long hash codes. We evaluate the retrieval performance of \textit{ElasticHash} for more than 120,000 query images on about 6.9 million database images of the OpenImages data set. The results show that our approach achieves high-quality retrieval results and low search latencies.
Optimization of Educational Mobile Game Design 'Ayo Wisata ke Riau' based on User's Perspective

Educational Mobile Game ‘Ayo Wisata ke Riau’ is a mobile-based educational game application that was developed in 2017 on the Android platform. The quality requirements used in this game development have been adjusted for the available Educational Mobile Games. Even though it went well, there were many complaints from users during the direct implementation survey. This becomes material for re-evaluation, whether the game is ready to be disseminated to the public. Based on this occurence, a gap analysis was carried out to see the feasibility and the need for improvements to the game application that had been built. Based on the results of the gap analysis that has been carried out, it is indicated that there is a need for significant improvements in the areas of performance and user satisfaction. So that in this study, a design reengineering was carried out that was focused on improving the two things that were considered crucial, so that the results of the design and implementation that had been improved could provide better performance and user satisfaction scores than previous developments.
Between Time and Discourse A Syntactic Analysis of Italian<i>poi</i>

In this article the different functions of Italian poi are described and analysed from a cartographic perspective. We show that in addition to its use as a temporal adverb ( after , then ), poi can be used as a temporal or logical connective adverb, as a discourse marker and as a modal particle. These functions can be correlated with different positions in the clause and with differences in the internal structure of the element itself. Finally, we identify the syntactic environments and sentence types in which poi occurs in its function as a modal particle, as well as the special interpretations that are associated with its presence.
Social Research about Online Crime: Global Range of Topics and a Systematic Analysis of Research in Lithuania

Social research about online crime is a multi-disciplinary field addressing a wide array of topics since its inception in the 1980s. Based on a broad review of state-of-the-art literature and gaps identified in review publications (Holt, Bossler 2014; Stratton, Powell, Cameron 2017; Maimon, Louderback 2019, and others), in this paper I outline 41 key topic in social research about online crime, classified into four broad categories: 1) research focusing on specific types of online crime, 2) research about perpetrators, victims, and law enforcement, 3) research about online crime discourses and public perceptions, 4) research putting the local and global specifics of online crime into perspective. Based on the topic map, I undertook a systematic review of literature on research about online crime published in Lithuania from the empirical social scientific perspective. The results show that very few such studies are carried out in Lithuania. From 2004 to 2020, 26 publications have been found in total. 10 of them were theoretical briefs, while 16 were based on empirical data. Out of the 41 key topic, 14 were covered in the publications, while 29 or roughly two thirds remained unaddressed. The dominant contributors were legal scholars writing about the social aspects of online crime across a variety of topics, and mostly focusing on specific crime types. The most developed topic was cyberbullying, with contributions by scholars mostly from the fields of psychology and education. To fill in these glaring gaps, it is vital to develop this field of research with an emphasis on both wider and deeper research agendas, complex, valid and reliable research data and critical theoretical approaches, inviting systematic contributions from criminology, sociology, communication and media studies, and political science.
Deep Learning based Cryptanalysis of Stream Ciphers

Conventional cryptanalysis techniques necessitate an extensive analysis of non-linear functions defining the relationship of plain data, key, and corresponding cipher data. These functions have very high degree terms and make cryptanalysis work extremely difficult. The advent of deep learning algorithms along with the better and efficient computing resources has brought new opportunities to analyze cipher data in its raw form. The basic principle of designing a cipher is to introduce randomness into it, which means the absence of any patterns in cipher data. Due to this fact, the analysis of cipher data in its raw form becomes essential. Deep learning algorithms are different from conventional machine learning algorithms as the former directly work on raw data without any formal requirement of feature selection or feature extraction steps. With these facts and the assumption of the suitability of employing deep learning algorithms for cipher data, authors introduced a deep learning based method for finding biases in stream ciphers in the black-box analysis model. The proposed method has the objective to predict the occurrence of an output bit/byte at a specific location in the stream cipher generated keystream. The authors validate their method on stream cipher RC4 and its improved variant RC4A and discuss the results in detail. Further, the authors apply the method on two more stream ciphers namely Trivium and TRIAD. The proposed method can find bias in RC4 and shows the absence of this bias in its improved variant and other two ciphers. Focusing on RC4, the authors present a comparative analysis with some existing methods in terms of approach and observations and showed that their process is more straightforward and less complicated than the existing ones.
A Partial Differential Equation-Based Image Restoration Method in Environmental Art Design

With the rapid development of networks and the emergence of various devices, images have become the main form of information transmission in real life. Image restoration, as an important branch of image processing, can be applied to real-life situations such as pixel loss in image transmission or network prone to packet loss. However, existing image restoration algorithms have disadvantages such as fuzzy restoration effect and slow speed; to solve such problems, this paper adopts a dual discriminator model based on generative adversarial networks, which effectively improves the restoration accuracy by adding local discriminators to track the information of local missing regions of images. However, the model is not optimistic in generating reasonable semantic information, and for this reason, a partial differential equation-based image restoration model is proposed. A classifier and a feature extraction network are added to the dual discriminator model to provide category, style, and content loss constraints to the generative network, respectively. To address the training instability problem of discriminator design, spectral normalization is introduced to the discriminator design. Extensive experiments are conducted on a data dataset of partial differential equations, and the results show that the partial differential equation-based image restoration model provides significant improvements in image restoration over previous methods and that image restoration techniques are exceptionally important in the application of environmental art design.
Leveraging Hand-Object Interactions in Assistive Egocentric Vision

Egocentric vision holds great promise for increasing access to visual information and improving the quality of life for blind people. While we strive to improve recognition performance, it remains difficult to identify which object is of interest to the user; the object may not even be included in the frame due to challenges in camera aiming without visual feedback. Also, gaze information, commonly used to infer the area of interest in egocentric vision, is often not dependable. However, blind users tend to include their hand either interacting with the object they wish to recognize or simply placing it in proximity for better camera aiming. We propose a method that leverages the hand as the contextual information for recognizing an object of interest. In our method, the output of a pre-trained hand segmentation model is infused to later convolutional layers of our object recognition network with separate output layers for localization and classification. Using egocentric datasets from sighted and blind individuals, we show that the hand-priming achieves more accurate localization than other approaches that encode hand information. Given only object centers along with labels, our method achieves comparable classification performance to the state-of-the-art method that uses bounding boxes with labels.
Overlook: Differentially Private Exploratory Visualization for Big Data

Data exploration systems that provide differential privacy must manage a privacy budget that measures the amount of privacy lost across multiple queries. One effective strategy to manage the privacy budget is to compute a one-time private synopsis of the data, to which users can make an unlimited number of queries. However, existing systems using synopses are built for offline use cases, where a set of queries is known ahead of time and the system carefully optimizes a synopsis for it. The synopses that these systems build are costly to compute and may also be costly to store.&#x0D; We introduce Overlook, a system that enables private data exploration at interactive latencies for both data analysts and data curators. The key idea in Overlook is virtual synopsis that can be evaluated \emph{incrementally}, without extra space storage or expensive precomputation. Overlook simply executes queries using an existing engine, such as a SQL DBMS, and adds noise to their results. Because Overlook's synopses do not require costly precomputation or storage, data curators can also use Overlook to explore the impact of privacy parameters interactively. Overlook offers a rich visual query interface based on the open source Hillview system. Overlook achieves accuracy comparable to existing synopsis-based systems, while offering better performance and removing the need for extra storage.
Exploring Optimality of Piecewise Polynomial Interpolation Functions for Lung Field Modeling in 2D Chest X-Ray Images

In this paper, a landmark based approach, using five different interpolating polynomials (linear, cubic convolution, cubic spline, PCHIP, and Makima) for modeling of lung field region in 2D chest X-ray images have been presented. Japanese Society of Radiological Technology (JSRT) database which is publicly available has been used for evaluation of the proposed method. Selected radiographs are anatomically landmarked using 17 and 16 anatomical landmark points to represent left and right lung field regions, respectively. Local, piecewise polynomial interpolation is then employed to create additional semilandmark points to form the lung contour. Jaccard similarity coefficients and Dice coefficients have been used to find accuracy of the modeled shape through comparison with the prepared ground truth. With the optimality condition of three intermediate semilandmark points, PCHIP interpolation method with an execution time of 5.04873 s is found to be the most promising candidate for lung field modeling with an average Dice coefficient (DC) of 98.20 and 98.54% (for the left and right lung field, respectively) and with the average Jaccard similarity coefficient (JSC) of 96.47 and 97.13% for these two lung field regions. While performance of Makima and cubic convolution is close to the PCHIP with the same optimality condition, i.e., three intermediate semilandmark points, the optimality condition for the cubic spline method is of at least seven intermediate semilandmark points which, however, does not result in better performance in terms of accuracy or execution time.
A Survey on: Application of Transformer in Computer Vision

In the past few years, convolutional neural networks have been considered the mainstream network for processing images. Transformer first proposed a brand new deep neural network in 2017, based mainly on the self-attention mechanism, and has achieved amazing results in the field of natural language processing. Compared with traditional convolutional networks and recurrent networks, the model is superior in quality, has stronger parallelism, and requires less training time. Because of these powerful advantages, more and more related workers are expanding how Transformer is applied to computer vision. This article aims to provide a comprehensive overview of the application of Transformer in computer vision. We first introduce the self-attention mechanism, because it is an important component of Transformer, namely single-headed attention mechanism, multi-headed attention mechanism, position coding, etc. And introduces the reformer model after the transformer is improved. We then introduced some applications of Transformer in computer vision, image classification, object detection, and image processing. At the end of this article, we studied the future research direction and development of Transformer in computer vision, hoping that this article can arouse further interest in Transformer.
Prediksi Jumlah Penjualan Rumah di Bojongsoang ditengah Pandemi Covid-19 dengan Metode ARIMA

This study aims to determine the accuracy of the ARIMA method with the Carmer matrix in forecasting or predicting the number of house sales in the Bojongsoang area which is still experiencing a period of crisis. The data used in this study is secondary data in the form of time series data on the number of house sales. In the ARIMA method, we perform stationary data, then look for autoregressive (AR), moving average (MA), and ARMA (Autoregressive and Moving Average) values. From the available data, the number of house sales has decreased, therefore forecasting is carried out using the ARIMA (1,1,1) model for future home sales to assist property developers in estimating future development projects. the results of the forecasting carried out using the ARIMA (1,1,1) method, which shows that the prediction of the number of house sales in the Bojongsoang area in the June - December period experienced a stable number of house sales
Analysis of Domestic and International Reactions to Modern Hanbok in Korean Wave : Use of Textmining Methods

As the popularity of Hallyu content at domestic and global has increased day by day, interest in Hanbok seen on dramas, movies and K-pop stages has also increased. However, compared to the high popularity of the Korean Wave, there is a lack of research on domestic and foreign responses to modern clothing shown in the Korean Wave culture. Therefore, collecting domestic and foreign responses to each case, emotional analysis was carried out, which aims to identify domestic and foreign responses to the wearing of modern Hanbok in the Korean Wave culture. The responses of the comments were analyzed using text mining in two ways: positive and negative responses were examined through Wordcloud, which lists key words in frequency order, followed by reading and analyzing the actual comments by reaction. The results of the research analyzed through these methods are as follows. Through these results, we wanted to compare and analyze the domestic and global responses to modern Hanbok shown in Korean Wave culture and the domestic and global responses to the wearing of modern Hanbok by ordinary peaple. Through this, we want to provide basic data on how to globalize Hanbok.