Reviewerly/exHarmony huggingface dataset:
Traditional Knowledge Tracing Models for Clustered Students

Against the background of the worldwide COVID-19 pandemic, online learning has currently become one of the dominant educational forms.For more effective online learning, knowledge tracing that can dynamically estimate the knowledge state of learners should be paid more attention.This study gives an overall introduction of knowledge tracing models.We illustrate the development of the Bayesian Knowledge Tracing model and clarify its concrete mathematic principle.In addition, an individualized method based on clustered students for the Bayesian Knowledge Tracing model is initially proposed, which changes the individualization level from a group of all students to subgroups.To confirm whether this individualized method can be generalized to other knowledge tracing models, we also test it on logistic knowledge tracing models.Therefore, we provide an introduction about the principles of three logistic knowledge tracing models.We evaluate our method on the four models with two international public educational datasets.The results show that all knowledge tracing models for clustered students outperform the original models without clustering.The Bayesian Knowledge Tracing model and item response theory for clustered students both gain good improvement in their performance, while the Performance Factors Analysis for clustered students has only few improvements.The reason for the experimental results is discussed at the end.Overall, this paper provides a general method for further promoting the performance of traditional knowledge tracing models that requires more studies.
IMPLEMENTASI ASSOCIATION RULE MINING UNTUK MENENTUKAN POLA KOMBINASI MAKANAN DENGAN ALGORITMA APRIORI

OH5 Hash Cafe is a business that is engaged in the food sector and there is a lot of competition in doing business that is increasingly difficult to do so it is necessary to develop a strategy, this study aims to determine the pattern of food combinations, the method used in this research is the Apriori Algorithm to be able to find out and processed using the Rapid Miner 9.7 software in determining food combination patterns, the Apriori Algorithm is an interesting type of association rule in data mining and an interesting association analysis to produce an efficient algorithm that is high frequency pattern analysis, an association can be identified with two benchmarks, namely: Support and Cofindence. Support is the percentage of item combinations in the database, while Confidence is the strong relationship between items in the association rule.&#x0D; &#x0D;
Integration of legacy applications into "Eni" scientific research ecosystem

The article describes the development of a digital platform for the Eni research ecosystem, based on a microservice approach that automatically scales the computing resources of the system when working with big data. A brief description of the structure of the digital platform, its main components and the functionality of the basic subsystems is provided. Two types of digital platform consumers are presented - legacy and platform applications. Their capabilities and limitations are shown. The subsystem of distributed computing is described, which provides continuous management and monitoring of the microservice architecture of the platform, in particular, it is responsible for: load balancing, service discovery, system recovery after failures, end-to-end authentication, "canary rollouts", access control. The high-performance computing subsystem is presented. It includes models and methods for organizing parallel calculations on various hardware devices, such as a multiprocessor system, a cluster of computing devices connected by a local network, and a graphics processor. As software solutions for organizing parallel computing, multithreaded data processing technologies, MPI messaging interface, and CUDA technology are used. Also described is a data mining subsystem designed to deploy different types of neural networks with different architectures, including direct distribution, convolutional, recurrent and generative neural networks. The integration of the legacy application MD-SLAG-MELT v13.0 into the ecosystem of scientific research using platform integration adapters is presented. The architectures and the main components of the original software package and the integrated software package MD-SLAG-MELT v14.0 are presented. The results of load testing with the analysis of performance metrics and response time of an inherited application, when processing big data, are presented.
Design of Program Objective Structure in Marine Ecotourism Development

Ecotourism has become one of the tourist's favourites.Bangsring Underwater (Bunder) tourism is marine ecotourism in Banyuwangi East Java, that has increased tourist visits.Bunder's management involves the local community.Community engagement is expected to have a significant impact on the better management of natural resources.However, the implementation of ecotourism management from various elements other than society encounters some difficulties, especially in balancing between the objectives of improving people's quality of life, the pressure of tourism activities in the marine environment, or the utilization of wisely maintained resources.This research aims to design the objective structure of the program and determine what factors are the key to the development of marine ecotourism.The research method uses library studies and expert assessments to assess the subelements of the program's objectives, and the next stage uses Interpretive Structural Modeling analysis to design structures and determine critical factors for marine ecotourism development.The results of the research analysis found there are four levels of goal structure, and the vital elements in the development of marine ecotourism are improving people's quality of life, improving service services, realizing sustainable resource utilization, realizing maritime tourism management arrangements, expanding employment, and increasing people's income.The balance of objectives for sustainable coastal resources, tourism business performance, and community wellbeing is important to the common consciousness.
PENERAPAN METODE FILTER GAUSSIAN DALAM PERBAIKAN KUALITAS CITRA SATELIT MODIS

One technology that uses satellite capabilities that can capture the image of an area is the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor, the resulting image may experience noise (noise) this is caused when sending through the transmission line, the image is too dark or bright, the image less sharp or blurred. For that we need a method of noise reduction such as remote sensing through satellite imagery. The purpose of this study is to apply the Gaussian lowpass filter method in improving the quality of fashionable satellite imagery in order to have good image quality. The results of PSNR show the highest value on the MOD_04 image, with smoother image results, the resulting PSNR value is 19.7791 higher than other images, where the higher the PSNR value, the better the resulting image.
Low-Resource Translation as Language Modeling

We present our submission to the very low resource supervised machine translation task at WMT20. We use a decoder-only transformer architecture and formulate the translation task as language modeling. To address the low-resource aspect of the problem, we pretrain over a similar language parallel corpus. Then, we employ an intermediate back-translation step before fine-tuning. Finally, we present an analysis of the system’s performance.
Declarative Dashboard Generation

Systems of systems are highly dynamic software systems that require flexible monitoring solutions to be observed and controlled. Indeed, operators have to frequently adapt the set of collected indicators according to changing circumstances, to visualize the behavior of the monitored systems and timely take actions, if needed. Unfortunately, dashboard systems are still quite cumbersome to configure and adapt to a changing set of indicators that must be visualized. This paper reports our initial effort towards the definition of an automatic dashboard generation process that exploits meta-model layouts to create a full dashboard from a set of indicators selected by operators.
Continuidad con los procesos pedagógicos de los estudiantes durante la pandemia ocasionada por el COVID-19

Introduction: distance learning solutions and approaches which teachers have conveyed, through all means possible to the students and families, from the moment in which COVID-19 preventive isolation measures were initiated, in support of education continuity at home, is a reality that concerns us at this time to manage the situation of education delivery in Colombia.  Objective: to analyze two questions asked to parents on the types of connectivity and academic processes to continue to provide educational services to students during the coronavirus pandemic. Methodology: we worked on two research hypotheses. SPSS Statistics 25.0 and the Kruskal –Wallis Hypothesis Test were used for data processing. Results: it was determined that the related frequencies for the type of connectivity, was 475 for those who had home internet access, 83 had a data plan on their cell phone, and 22 did not have any type of connectivity, thus the great majority of the students´ families had home internet access, followed by data plans in their cell phones, which allow them to follow the teaching processes by virtual modes. Conclusion: according to the Kruskal-Wallis test assess for significant differences there was no difference between the various types of communication among parents and high school grades at the General Santander educational institution. The most widely used is home internet. Regarding to how parents prefer student academic processes to be continued they chose their children to stay home. This was evident at all high school levels.
Evaluating the Impact of COVID-19 on Cyberbullying through Bayesian Trend Analysis

COVID-19's impact has surpassed from personal and global health to our social life In terms of digital presence, it is speculated that during pandemic, there has been a significant rise in cyberbullying In this paper, we have examined the hypothesis of whether cyberbullying and reporting of such incidents have increased in recent times To evaluate the speculations, we collected cyberbullying related public tweets (N = 454, 046) posted between January 1st, 2020 - June 7th, 2020 A simple visual frequentist analysis ignores serial correlation and does not depict changepoints as such To address correlation and a relatively small number of time points, Bayesian estimation of the trends is proposed for the collected data via an autoregressive Poisson model We show that this new Bayesian method detailed in this paper can clearly show the upward trend on cyberbullying-relatedtweets since mid-March 2020 However, this evidence itself does not signify a rise in cyberbullyingbut shows a correlation of the crisis with the discussion of such incidents by individuals Our work emphasizes a critical issue of cyberbullying and how a global crisis impacts social media abuse and provides a trend analysis model that can be utilized for social media data analysis in general © 2020 ACM
Neural Machine Translation between similar South-Slavic languages.

This paper describes the ADAPT-DCU machine translation systems built for the WMT 2020 shared task on Similar Language Translation. We explored several set-ups for NMT for Croatian–Slovenian and Serbian–Slovenian language pairs in both translation directions. Our experiments focus on different amounts and types of training data: we first apply basic filtering on the OpenSubtitles training corpora, then we perform additional cleaning of remaining misaligned segments based on character n-gram matching. Finally, we make use of additional monolingual data by creating synthetic parallel data through back-translation. Automatic evaluation shows that multilingual systems with joint Serbian and Croatian data are better than bilingual, as well as that character-based cleaning leads to improved scores while using less data. The results also confirm once more that adding back-translated data further improves the performance, especially when the synthetic data is similar to the desired domain of the development and test set. This, however, might come at a price of prolonged training time, especially for multitarget systems.
A Dataset for Anaphora Analysis in French Emails

In 2019, about 293 billion emails were sent worldwide every day. They are a valuable source of information and knowledge for professionals. Since the 90’s, many studies have been done on emails and have highlighted the need for resources regarding numerous NLP tasks. Due to the lack of available resources for French, very few studies on emails have been conducted. Anaphora resolution in emails is an unexplored area, annotated resources are needed, at least to answer a first question: Does email communication have specifics that must be addressed to tackle the anaphora resolution task? In order to answer this question 1) we build a French emails corpus composed of 100 anonymized professional threads and make it available freely for scientific exploitation. 2) we provide annotations of anaphoric links in the email collection.
A Study Protocol for Checking Validity of Evaluation of Temporal Parameters of Gait Using Microsoft Kinect Azure in Normal Healthy Population

Introduction: 5In healthy population, temporal parameters of gait like step time and stride time arefrequently linked with gait cycle in healthy populations but can be obstructive, difficult and time taking tomeasure. This study is to check the efficacy of kinect azure for evaluating of temporal parameters of gait innormal healthy people. A kinect based recording application was started &amp; use to detect , tracks human postanalytical motions. Kinect is low cost, unobstructive and accurate gait analysis application with various useslike monitoring, diagnosis, rehabilitation &amp; management. The end result of this study is to fed a state systemthat analysis the present state from which the assessment are withdrawn.Method: The research has been structured as an observational study. The total of 132 participants will betaken from AVBRH, sawangi Meghe for study as per inclusion and exclusion criteria. With intervention theperiod of study will be 6 months. It holds single period, concurrent validity evaluation comparing temporalgait parameters derived from the Kinect system.Discussion: This study protocol aims to evaluate the Validity of evaluation of temporal parameters of gaitusing Microsoft Kinect Azure. The study’s expected outcome will concert on the evaluation of temporalparameters of gait using Microsoft Kinect Azure in normal healthy population.
Sequence to Sequence Coreference Resolution

Until recently, coreference resolution has been a critical task on the pipeline of any NLP task involving deep language understanding, such as machine translation, chatbots, summarization or sentiment analysis. However, nowadays, those end tasks are learned end-to-end by deep neural networks without adding any explicit knowledge about coreference. Thus, coreference resolution is used less in the training of other NLP tasks or trending pretrained language models. In this paper we present a new approach to face coreference resolution as a sequence to sequence task based on the Transformer architecture. This approach is simple and universal, compatible with any language or dataset (regardless of singletons) and easier to integrate with current language models architectures. We test it on the ARRAU corpus, where we get 65.6 F1 CoNLL. We see this approach not as a final goal, but a means to pretrain sequence to sequence language models (T5) on coreference resolution.
Effects of higher levels of qubits on control of qubit protected by a Josephson quantum filter

Abstract A Josephson quantum filter (JQF) protects a data qubit (DQ) from the radiative decay into transmission lines (TLs) in superconducting quantum computing architectures. A transmon, which is a weakly nonlinear harmonic oscillator rather than a pure two-level system, can play a role of a JQF or a DQ. However, in the previous study, a JQF and a DQ were modeled as two-level systems neglecting the effects of higher levels. We theoretically examine the effects of the higher levels of the JQF and the DQ on the control of the DQ. It is shown that the higher levels of the DQ cause the shift of the resonance frequency and the decrease of the maximum population of the first excited state of the DQ in the controls with a continuous wave (cw) field and a pulsed field, while the higher levels of the JQF do not. Moreover, we present optimal parameters of the pulsed field, which maximize the control efficiency.
Language Attribution of an Unmarked Text Corpus

Unmarked text corps will increasingly appear with the growth of information on the web. Automated analysis of Big Data in search engines, scientific and commercial applications requires detailed information about the object under study. In the case of text bodies, information on the language of the documents is extremely important. Working with the scanned texts the situation is even more complicated. In this paper, the idea of using the fractal-inspired irregularity to attribute the language of the text is being further developed. A methodology for the attribution is proposed and an experiment based on 10 European languages is conducted. The proposed approach has shown its effectiveness and promise. A selection of approximately 4000 characters (1 page of text) allows you to uniquely attribute the language of the text.
Pengembangan E-Modul Bangun Datar Sederhana Berbasis Problem Based Learning (PBL) Menggunakan Aplikasi Kvsoft Flipbook Maker Untuk Siswa Kelas III

Abstract: This research is motivated by the need for electronic-based e-module teaching materials combined with the PBL approach in order to understand and attract students' interest in mathematics. The purpose of this study was to develop mathematics teaching materials, especially in simple flat shapes in grade III elementary schools and to measure the feasibility, practicality and effectiveness of the developed media. This study uses the ADDIE model. Data collection using validation sheets and response questionnaires. This research uses qualitative and quantitative data analysis techniques. The PBL-based flat-build e-module using the kvisoft flipbook maker application produced was declared feasible or valid with a score of 85.82%. The result of the questionnaire response was a score of 3.78 which indicates a good category. The results of the effectiveness of the evaluation test in class III students obtained a value of 90.47. So that this e-module can increase student motivation.
Deep learning-based fringe pattern transformation method for phase calculation

Fringe projection profilometry (i.e., FPP) has been one of the most popular techniques in three-dimensional (i.e., 3-D) measurement. In FPP, it is necessary to obtain accurate desired phase by using a small number of fringes in dynamic measurement. Recently, fringe pattern transformation method (i.e., FPTM) is proposed based on deep learning, which can achieve accurate 3-D measurement using a single fringe, but the phase error is still higher than the phase-shifting algorithm. In this paper, the phase error of FPTM is analyzed and the relationship between it and local depth change rate is illustrated firstly. Then, the accuracy of FPTM can be improved by using more fringes. Compared with traditional methods, FPTM can achieve higher precision 3-D measurement when less fringes are used.
Deep Learning Based Character Recognition Platform in Complex Situations

Recently, various character recognition techniques are advanced and used a lot in real situations. In the real complex situation, it is difficult to recognize characters because of Korean, English of various fonts and background noise. In this paper, we shall present character bounding box module, character recognition module and background elimination module to solve problems. We shall show that the character recognition platform is used in complex situations.
E-learning e futuri studenti in mobilità internazionale. Riflessioni su aspetti e potenzialità di un corso di lingua italiana

The A1 online Italian course offered by the CLA (University Linguistic Centre) of the University of Perugia, is one of the results achieved during various research projects, which has contributed, on the one hand, to the internationalization of the Institution, on the other, to the enhancement of digital technologies providing future university mobility students with the opportunity to acquire linguistic-cultural knowledge, even before the beginning of their mobility exchange programme in Italy. The experience reported in this article reflects on an evolving work, describing its design phase – course structure, selection and creation of linguistic and didactic materials, tools available in the Moodle open-source learning platform – and its subsequent phases of course activation and verification. Throughout the entire project, we focused on two fundamental aspects: inspiring and maintaining student motivation in addition to constructing an assisted, and above all, interactive self-learning path.
S×C4IoT: A Security-by-contract Framework for Dynamic Evolving IoT Devices

The Internet of Things (IoT) revolutionised the way devices, and human beings, cooperate and interact. The interconnectivity and mobility brought by IoT devices led to extremely variable networks, as well as unpredictable information flows. In turn, security proved to be a serious issue for the IoT, far more serious than it has been in the past for other technologies. We claim that IoT devices need detailed descriptions of their behaviour to achieve secure default configurations, sufficient security configurability, and self-configurability. In this article, we propose S×C4IoT, a framework that addresses these issues by combining two paradigms: Security by Contract (S×C) and Fog computing. First, we summarise the necessary background such as the basic S×C definitions. Then, we describe how devices interact within S×C4IoT and how our framework manages the dynamic evolution that naturally result from IoT devices life-cycles. Furthermore, we show that S×C4IoT can allow legacy S×C-noncompliant devices to participate with an S×C network, we illustrate two different integration approaches, and we show how they fit into S×C4IoT. Last, we implement the framework as a proof-of-concept. We show the feasibility of S×C4IoT and we run different experiments to evaluate its impact in terms of communication and storage space overhead.