Reviewerly/exHarmony huggingface dataset:
Modification of the “Piramidal” algorithm of the small time series forecasting

It is proposed a modification of the “piramidal” algorithm of small time series forecasting “Piramidal” approach was developed in recent years, numerical results show advantages of this method in comparison with known approaches to extrapolation, based on the using of polynomials, including Newton's extrapolation But this approach was tested only on deterministic time series In this paper piramidal approach is applied to construct prognoses in the case where the time series contains a random component It is studied the procedure for constructing the forecast value in accordance with the pyramidal method and improved the main criteria of this method The main idea of the method improving is to find special patterns in the table of finite differences The improved method is used for the number of patients with COVID-19 forecasting in Ukraine © 2021 Copyright for this paper by its authors Use permitted under Creative Commons License Attribution 4 0 International (CC BY 4 0) CEUR Workshop Proceedings (CEUR-WS org)
The History and Enlightenment ofthe World Marine Geological Survey

The major historical events not only impact the global pattern in society, politics and economy, but also exsert profound effect over science and technology. Based on researches about the development of theories and technoloy in marine geology during and after the major historical events, such as the past four technological revolutions and World War Ⅰ and Ⅱ, we first divided the development history of the world marine geological survey into 5 periods (emergence period, initial period, development period, mature period and upgrade period). We reviewed the objectives, characteristics and main achievements in the field of marine geological surveys in different periods in the history. Then, we summarized the law of development in world marine geological survey and discussed about the enlightenment. In the end, we put forward our suggestions on the development of China's marine geology in light of its current scientific, political and economic situation.
Machine Learning Based Recommendation System: A Review

The digital era has created an extreme choice paradigm with an explosion of endless content. A user who is juststarting on the platform or looking for a creature can be lost in this ocean. Therefore, it is necessary to design asystem that can guide users as per their interest. To overcome this problem, the Recommendation System (RS)came into existence. RS is a tool used to recommend items as per user’s interests. The benefits of the RS cannotbe exaggerated, given the potential impact to improve many of the problems associated with widespread use andover-selection in many web applications. In recent years, Machine learning (ML) shows great interest in differentresearch areas, such as computer vision and Natural Language Processing (NLP), not only because of its stellarperformance but also because of its attractive feature of demonstrating learning from scratch. The effect of MLtechniques can be seen while applying these techniques to the prediction and recommender system. This paperpresented a comprehensive survey on recommendation techniques used in conjunction with the ML approach inmany domains. This work aims to find the shortcoming of available RS for different fields and the areas thatrequire more effort to attain higher accuracy.
Quantum-Access Security of the Winternitz One-Time Signature Scheme

Quantum-access security, where an attacker is granted superposition access to secret-keyed functionalities, is a fundamental security model and its study has inspired results in post-quantum security. We revisit, and fill a gap in, the quantum-access security analysis of the Lamport one-time signature scheme (OTS) in the quantum random oracle model (QROM) by Alagic et al. (Eurocrypt 2020). We then go on to generalize the technique to the Winternitz OTS. Along the way, we develop a tool for the analysis of hash chains in the QROM based on the superposition oracle technique by Zhandry (Crypto 2019) which might be of independent interest.
A New Interval Type-2 Fuzzy Logic System Under Dynamic Environment: Application to Financial Investment

This paper proposes a new interval type-2 fuzzy logic system (IT2 FLS) for financial investment with time-varying parameters adaptive to real-time data streams by using an on-line learning method based on a state-space framework. Particularly, our state-space approach regards parameters of IT2 FLSs as state variables to sequentially learn by Bayesian filtering algorithms under dynamic environment, where time-series data are continuously observed with occasional structural changes. Moreover, our proposal is helpful for financial applications, which often involve various practical complex constraints, because general state-space model makes it possible to flexibly deal with non-linearities. In our empirical experiment with time-series data of financial assets, our approach is applied to on-line parameter learning of type-1 and type-2 FLSs for portfolio decision making. As a result, it is shown that the IT2 FLS holds its advantage against the type-1 FLS, even though the both type-1 and type-2 models have the adaptive time-varying parameters, which is an unexplored topic for empirical studies of this area.
Classifying Spanish se constructions: from bag of words to language models

Spanish se constructions are a complex linguistic phenomenon that challenges Natural Language Processing (NLP) tasks such as part-of-speech or dependency relation tagging. Se is a high-frequency word that appears in nine different types of syntactic constructions and adds information of diverse nature depending on the context. Thus, to solve the problem Spanish se constructions poses in an efficient way, this study proposes a tagging system for se applied to a corpus composed of 2,140 sentences. This corpus is used in a classification experiment where 9 classifiers based on machine learning models and a dependency parser are tested. Results show that pre-trained language models based on transformers architecture reach the highest accuracy (0.83) and f-score (0.70) values.
A Review Paper on Las Vegas and Monte Carlo Algorithms

Abstract: A randomized calculation is a calculation that utilizes a level of irregularity as a feature of its rationale. The calculation ordinarily utilizes consistently irregular bits as an assistant contribution to manage its conduct, in the expectation of accomplishing great execution in the case over every conceivable decision of arbitrary bits. Formally, the calculation's execution will be an irregular variable controlled by the arbitrary bits; in this manner, either the running time or the yield (or both) are arbitrary factors. One needs to recognize calculations that utilization the irregular info so they generally end with the right answer, yet where the normal running time is limited (Las Vegas calculations, for instance Quicksort), and calculations which get an opportunity of creating an off base outcome (Monte Carlo calculations, for instance the Monte Carlo calculation for the MFAS problem) or neglect to deliver an outcome either by flagging a disappointment or neglecting to end. Keywords: Las Vegas, Monte Carlo, Randomized Algorithm, arbitrary bits, Quicksort Cite this Article: Shivani Bansal, Krutibash Nayak. A Review Paper on Las Vegas and Monte Carlo Algorithms. Journal of Advances in Shell Programming. 2019; 6(3): 1–4p.
DeepTective

This paper presents DeepTective, a deep learning approach to detect vulnerabilities in PHP source code. Our approach implements a novel hybrid technique that combines Gated Recurrent Units and Graph Convolutional Networks to detect SQLi, XSS and OSCI vulnerabilities leveraging both syntactic and semantic information. We evaluate DeepTective and compare it to the state of the art on an established synthetic dataset and on a novel real-world dataset collected from GitHub. Experimental results show that DeepTective achieves near perfect classification on the synthetic dataset, and an F1 score of 88.12% on the realistic dataset, outperforming related approaches. We validate DeepTective in the wild by discovering 4 novel vulnerabilities in established WordPress plugins.
Earthquake Alerts Go Live in the Pacific Northwest

Oregon and Washington residents will receive an alert on their cell phones if they are in danger from an incoming quake.
東洋初のロボット、《學天則》に表れた 西村真琴の思想

In 1928 Makoto Nishimura exhibited a robot called “Gakutensoku” that means “follow the laws of the universe and nature” at the exposition held to commemorate Emperor Showa’s throne. Although it has been evaluated as the first robot in the Orient, the “Gakutensoku” is not made of metal like Western European robots, but it is made in a form that can not move alone. So At the Chosun Expo, no attention was paid. Whereas Western robots were high-end machines that were born as cutting-edge machines that replace human labor, But the Gakutensoku Produced a robot that conveys its thoughts to humans, which has nothing to do with labor. Nishimura emphasizes that robots are the same as human beings, and that he consider how humans should live and what humanity is through robots. Today, the symbiosis between man and machine is emphasized. These attitude of Nishimura is very important for us in dealing with machines.
Issues in the fight against cybercrime

The rapid development and expansion of the use of information and communication technologies (ICT) is accompanied by an increase in the number of cybercrime and the potential for harm. The issues of combating cybercrime, which have become a topical and large-scale topic of discussion in modern times, and on the other hand, the activities of the Electronic Security Center in the fight against cybercrime in the Republic of Azerbaijan are commented. Key words: cybercrime, Electronic Security Center, hacker, effective fight
Gamificación e innovación educativa: apuntes para la práctica

Responder a las exigencias de la educacion en este momento, implica que el docente, se convierta en un innovador de sus practicas aulicas y, sobre todo, se identifique por el esfuerzo constante de contribuir o concebir el proceso de ensenanza y aprendizaje en una experiencia ludica mediada por las tecnologias de la informacion y las comunicaciones. Las experiencias y reflexiones que se hacen de este proceso se identifican con la gamificacion, asumida con insertidumbre, en un principio, pero que viene ganando terreno en el proceso educativo en general. En este trabajo se explican algunas consideraciones acerca de este proceso, en un intento loable por aportar a la practica y se dejan planteadas a lo largo del analisis exigencias y aspectos metodologicos que guiaran la gamificacion como parte del ejercicio de innovacion en la practica. Con este proposito se dedican espacios para confrontar criterios y asumir desde la posicion que se defiende las posibilidades de establecer relaciones entre gamificacion e innovacion.Palabras clave: Ludica, gamificacion, innovacion educativa. ABSTRACTResponding to the demands of education at this time implies that the teacher becomes an innovator of the ear practices and, above all, identifies by the constant effort to contribute or conceive the teaching and learning process in a playful experience mediated by information and communication technologies. The experiences and reflections that are made of this process are identified with gamification, assumed with insecurity, at first, but which has been gaining ground in the educational process in general. In this paper, some considerations about this process are explained, in a laudable attempt to contribute to the practice, and the demands and methodological aspects that will guide gamification as part of the practice of innovation exercise are raised throughout the analysis. For this purpose, spaces are dedicated to confront criteria and assume from the position that defends the possibilities of establishing relationships between gamification and innovation.Keywords: Playful, gamification, educational innovation.
Learning prediction intervals for regression: Generalization and calibration

We study the generation of prediction intervals in regression for uncertainty quantification. This task can be formalized as an empirical constrained optimization problem that minimizes the average interval width while maintaining the coverage accuracy across data. We strengthen the existing literature by studying two aspects of this empirical optimization. First is a general learning theory to characterize the optimality-feasibility tradeoff that encompasses Lipschitz continuity and VC-subgraph classes, which are exemplified in regression trees and neural networks. Second is a calibration machinery and the corresponding statistical theory to optimally select the regularization parameter that manages this tradeoff, which bypasses the overfitting issues in previous approaches in coverage attainment. We empirically demonstrate the strengths of our interval generation and calibration algorithms in terms of testing performances compared to existing benchmarks.
Sovereign Digital Currencies: Reshaping the Design of Money and Payments Systems

This paper focuses on how technology might reshape payments going forward. It considers the policy issues and choices associated with crypto-currencies, stablecoins and sovereign digital currencies and emphasises that there is no single model for sovereign digital currency design.While Bitcoin and its progenies could be safely ignored by regulators, Facebook’s proposal for Libra, a global stablecoin, brought an immediate and potent response from regulators globally. Any proposal by the private sector to move into the creation of currency — the traditional preserve of sovereigns — was always likely to trigger such a regulatory response, as well as the launch of sovereign digital currencies by other major central banks. While China has moved first, dozens of other countries are now investigating their own central bank digital currencies or other forms of sovereign digital currency. This paper argues that central banks should first focus not on rolling out novel new forms of sovereign digital currencies, but rather on transforming their payment systems. In time, domestic money and payment systems are expected to evolve so that central banks cooperate with (new and old) private entities to launch digital currencies that better underpin monetary and payment systems at the domestic, regional and international levels.
대학생의 교양교육 질적 수준 분석 연구

The purpose of this study was to provide suggestions for improvement of university students’general education by examining the relationship between perception of general education, educational experience, learning outcomes, campus life, and facilities. For this study, data from 756 students of A university located in Gyeonggi-do were analyzed. The main results were as follows. First, the importance of general education was highly recognized and overall satisfaction on general education was above average. Second, educational experience was above average, but the average for high-level abilities in class was relatively low. Third, the average of communication ability and interpersonal relationship ability was relatively high in learning outcomes. Fourth, campus life was below average, and it was recognized that exchanges with professors were less frequent than exchanges between students. Fifth, perception of campus facilities, cyber education system was evaluated the lowest. Lastly, the correlation between all variables was positively high, and educational experience, learning outcomes, and campus facilities had a significant influence on the perception of general education. It is expected that this study will be used as basic data for improving the operation of general education for university students or as a reference for related policies.
Computational Reproducibility of Named Entity Recognition methods in the biomedical domain

Unsupervised Named Entity Recognition (NER) approaches do not depend on labelled data to function properly but rather on a source of knowledge, in which promising candidates can be looked up to find the corresponding concept. In the biomedical domain knowledge source like this already exists; namely the Unified Medical Language System (UMLS). In this paper, three different unsupervised NER models using UMLS, namely MetaMap, cTakes and MetaMapLite are evaluated and compared from the results published by Demner-Fushman, Rogers and Aronson (2017) and Reategui and Ratte (2018). The Unsupervised Biomedical Named Entity Recognition framework (UB-NER) is developed, with which the results of the experiments of the three models, five datasets and two NER tasks are presented.
Fast Privacy-Preserving Punch Cards.

Loyalty programs in the form of punch cards that can be redeemed for benefits have long been a ubiquitous element of the consumer landscape. However, their increasingly popular digital equivalents, while providing more convenience and better bookkeeping, pose a considerable risk to consumer privacy. This paper introduces a privacy-preserving punch card protocol that allows firms to digitize their loyalty programs without forcing customers to submit to corporate surveillance. We also present a number of extensions that allow our scheme to provide other privacy-preserving customer loyalty features. Compared to the best prior work, we achieve a $14\times$ reduction in the computation and a $11\times$ reduction in communication required to perform a hole punch, a $55\times$ reduction in the communication required to redeem a punch card, and a $128\times$ reduction in the computation time required to redeem a card. Much of our performance improvement can be attributed to removing the reliance on pairings or range proofs present in prior work, which has only addressed this problem in the context of more general loyalty systems. By tailoring our scheme to punch cards and related loyalty systems, we demonstrate that we can reduce communication and computation costs by orders of magnitude.
Expert System for Early Detection of Disease in Corn Plant Using Naive Bayes Method: Expert System for Early Detection of Disease in Corn Plant Using Naive Bayes Method

Corn is a third main plant after the paddies and squeeze in the world, and occupied a second position after the paddies in indonesia. Corn grows both good in the hot areas and cold with the rainfall and irrigation. But it's often dealing with a corn peasants Other than the pest is less information about the disease that attacked corn plants so that cause productivity becomes reduced. The expert - based expert system that has been built with framework php is the codeigniter and MySQL As the databases and methods used in detecting initial disease on the corn plant is Naive Bayes. The result in the probation is a prodial by a disease on the corn plant is being used as a probe for decision for the farmers'decision. The system that has been built is expected to help the peasants in determine and Choose the right handling and according to the level of 92% of the system.
Arithmetic circuits for quantum computing: a software library

Nowadays, quantum computers are capturing the attention of researchers because of their potential capability to solve problems otherwise not solvable with classical computers. In parallel to the constant research aimed at solving problems related to the physical implementation of such computers, there is also another branch of research trying to develop quantum algorithms. Many of the existing algorithms today need to perform arithmetic operations. In this thesis, the development of a software library of quantum arithmetic circuits - capable of interfacing with the main languages related to the world of Quantum Computing, such as Qiskit, Cirq and T-ket - is presented. With the aim of implementing this software library, the basics of quantum computation were first studied and subsequently an extensive bibliographic research was carried out, aimed at identifying the best performing arithmetic circuits currently available. After determining the circuits to be implemented, the software development has begun using the Python language. During the development of the library, with the aim of making simpler future development, code's modularity - by organizing the general package in many subpackages - and readability - by respecting the guidelines proposed in the PEP8 style guide - were looked for. In addition to what has already been said, a detailed documentation has been produced using Sphinx. Once the development of the library was completed, the functionality of each circuit was tested with simulations on classical computers; moreover, some components were tested on real free-accessible quantum computers, in order to characterize their behavior. Even though the results obtained on real hardware proved that many of the circuits implemented in this library will be reliably executed by more sophisticated and performing quantum computers, this library can be currently employed in an effective way in the simulation of quantum circuits on classical computers.
계량시계열과 인공신경망모형을 이용한 아파트매매가격지수 예측 - 서울과 6대 광역도시를 중심으로 -

Due to the household''s asset structure concentrated on real estate, changes in real estate prices have a great influence on the national economy. Accurate forecasting of house prices not only reduces uncertainty in house prices, but can also help in making relevant policy decisions. The purpose of this study is to estimate housing prices using a time-series model and artificial neural network model for the actual transaction price index for apartment sales in Seoul and six metropolitan cities from January 2006 to November 2019, and forecast with high predictive power. It is in building a model.In this study, by adding bidirectional LSTM and GRU models in addition to the existing weighing time series model and Vanilla LSTM model, we compared the predictive power of the proposed model and presented an artificial neural network model with high predictive power. As a result of empirical analysis, it was confirmed that the artificial neural network model with high predictive power differs depending on the region. After constructing a highly predictive artificial neural network model for each region, it is possible to grasp the effects of macroeconomic variables suitable for the region on the housing price index through free manipulation such as adding and deleting variables to achieve desired policy effects.
Virtual Dressing Room with Web Deployment

Trying different clothes in shops and finally selecting the right one is a time consuming andtedious task. So, a Real-time virtual dressing room is the concept where the customers can buy clotheswithoutwearingthem.Virtual dressingenvironment is the online equivalent of the in-store changingroom. People usually avoid buying wearable clothes online mainly because it's hard to judge whether itwilllookgood on them or not. To solvethis problem,wedecidedtobuildanOnline TrialRoomApplication. Our research is focused on developing an application that uses the system camera to capturea video of the user and then splits the video into individual frames from which the user's body is extracted.Finally using functions to extract information on the placement of joints in the body and to transform,rotate, and scale the wearable image onto the user in real-time. In the literature review, we go throughvarious ways to achieve our goal with their advantages and disadvantages. The project is implemented inFlask Web application with OpenCV a Python Module. The application works on devices with an inbuiltor attached camera, internet, and web browser. This project introduces Augmented Reality-based VirtualTrial Room software that allows users to digitally wear clothes by superimposing 2D clothes over devicesand virtual clothes over the monitored user. The clothing moves and enlarges in response to the user'smovements.2DAugmentedReality is used in this web implementation.