Reviewerly/exHarmony huggingface dataset:
Implementasi Kombinasi Caesar Cipher dan Hill Cipher Menggunakan Modifikasi Sandi Morse Untuk Pengamanan Pesan Berbasis Teks

The security and confidentiality of a message or document sent to other people must be guaranteed its confidentiality and security, so that the message or document is not misused by parties who do not have an interest in the message. Security and confidentiality of messages or documents can be kept confidential and secure by using cryptography. data security and confidentiality issues are very important from an information. Because of the importance of this information, the person concerned will question whether the authenticity of the information is still maintained or whether the information has been hijacked. So in the process of securing data and information with cryptography, of course it requires a qualified algorithm to maintain the confidentiality of the information, so that the purpose of this research is how to maintain the confidentiality of information by converting information into codes known only to the sender and receiver by using cryptographic techniques and steganography. Then the process of securing the data or information the researcher uses a combination of Caesar Chiper and Hill Chiper which is modified into Morse code then the resulting CipherText results will be saved into a JPEG file using the LSB (Least Significant Bit) Steganography method. The process of storing messages or ciphertext into image files will make it difficult for irresponsible people or cryptoanalysts to solve messages sent in the form of images.
Research Guides: SJU Library Streaming Video Resources: Lumière

Overview of the streaming video content providers currently available for SJU campus members through the library.
Transfer Learning Grammar for Multilingual Surface Realisation

Deep learning approaches to surface realisation are often held back by the lack of good quality datasets. These datasets require significant human effort to design and are rarely available for low-resource languages. We investigate the possibility of cross-lingual transfer learning of grammatical features in a multilingual text-to-text transformer. We train several mT5-small transformer models to generate grammatically correct sentences by reordering and inflecting words, first using monolingual data in one language and then in another language. We show that language comprehension and task-specific performance of the models benefit from pretraining on other languages with similar grammar rules, while languages with dissimilar grammar appear to disorient the model from its previous training. The results indicate that a model trained on multiple languages may familiarize itself with their common features and, thus, require less data and processing time for language-specific training. However, the experimental models are limited by their entirely text-to-text approach and insufficient computational power. A complete multilingual realisation model will require a more complex transformer variant and longer training on more data.
Coherent Quantum Engine

Coherence plays a key role in quantum information, which is why it has been considered as a potential resource for quantum machines, with the aim of surpassing classical ones [, , , , , ]. In Refs. [, ], the quantum coherence in the working substance is injected by the drive while in Ref. [] it comes from the bath which is non-thermal. However there has been no experimental implementation of such a quantum machine using a single qubit as working substance so far.
Multi-turn Dialogue Generation Using Self-attention and Nonnegative Matrix Factorization

Abstract Recently, neural generative models have shown significant potential in the field of human-computer interaction, especially the dialogue systems. In this paper, we propose a new model for multi-turn dialogue generation, which uses the self-attention mechanism to extract relevant information in the dialogue history, and utilizes the nonnegative matrix factorization(NMF) to learn topic vectors from an external corpus. The response generated by the model is not only affected by the dialogue context, but also by the corresponding topic vectors. We conduct experiments on a public dataset to evaluate the performance of our model. Experimental results demonstrate that our model has the ability to generate more diverse and context-sensitive responses.
An Improved Face Recognition System Using PCA and KNN

In this paper, we improve a face recognition system using Principal Component Analysis (PCA) to extract features from the face images and reduce the dimensionality of each image and K nearest neighbor to classify data. Both methods have been used together to have the most accuracy. Experiments have been carried on Olivetti Research Laboratory Human Face Database (ORL). Face images are in grayscale and in different poses. Results that have been calculated and shown in the last section, illustrates the improvement of our proposed method.
Research Reflections on SPOC Based Teaching Model and Practice in Chinese College English Education (2014-2020)

This research takes a systematic investigation about recent publications on domestic SPOC based teaching models and practice in Chinese College English Education from 2014 to 2020. It discovers that the year of 2018 was supposed to be a turning point and the year of 2019 made both quantitative and qualitative changes. Plus, non-empirical studies have widened the gap with empirical researches since 2015. In addition, the research area showed a tendency of diversification, such as blended teaching, design of teaching mode, movable terminal, and learners' factor.
A Novel Filter For the Detection of Object Boundary and Edges in Noisy and Non-Noisy Images

Noise removal, Object detection and Edge identification in an image is an active research problem in Computer Vision, Machine Vision and Digital Image Processing (DIP). Edge detection is considered as one of the basic steps in the DIP for the detection of the object. Edge discovery of the object is one of the big challenges when we have an image with a complex and clutter background or image having a significant amount of noise. Due to the noise in an image their always be a chance of non-true edge generation. The traditional operator like sobel, robert, canny, prewitt, log is able to find the edge pixel but not shown the good result when we focus on a proper boundary and edge detection for the objects in noisy images. In this paper author's proposed a novel filter, which gives the good result in comparison of traditional operators in a qualitative or quantitative parameter. For experimental result analysis, a qualitative and quantitative comparison is done with another well-known operator like sobel, robert, prewitt etc. All experimental analysis is done with the randomly selected images from the MSRA dataset (Salient Object data-set). Noisy images are prepared by mixing the artificial noise using MATLAB with there default value of mean and variance and further these images are used to evaluate the performance of our proposed filter in presence of noise.
Cek SimilarityOriginality: The Effectiveness of Interactive E-Book for Self-Study and Increasing Students’ Critical Thinking Skills in Electromagnetic Radiation Topic

This research aims to develop interactive e-book on electromagnetic radiation topic, which can be used for self-study, and to increase students critical thinking skill. The methodology in this research is based on ADDIE model research and development (R&D) design which consist of analysis, design, development, implementation, and evaluation. The data resource was the students and teachers of XII grade of senior high schools in Lampung Province. The instrument used were questionnaire and test instrument. Data analysis technique was conducteddescriptively with interval using n-gain, independent t-test, and paired t-test. The research showed that interactive e-book on electromagnetic radiation was effective for self-study and to increase critical thinking skill of the students significantly at a confidence level of 95%, with Ngaining experimental group = 0.76 which was higher than in control group = 0.55. Therefore, it can be concluded that the product which was developed namely interactive e-bookon electromagnetic radiation is effective for self-study and to increase students' critical thinking skill.
Corporate Social Responsibility and Traditional Practices Recognized as Violence Against Women in Nigeria’s Oil Region

We examine the impact of multinational oil companies’ (MOCs’) corporate social responsibility (CSR) on traditional practices recognized as violence against women and girls (VAWG) in Nigeria’s oil region. Results from the use of a combined propensity score matching and logit model indicate that MOCs’ CSR play a significant role in empowering women and girls with information and education to protect their human rights. This implies that CSR offers an opportunity for MOCs to help address prevalence of child early and forced marriage, female genital mutilation/cutting, sex trafficking, virginity testing, and taboos through a business case for stakeholders’ human right protection.
From back to the roots into the gated woods: Deep learning for NLP

Deep neural networks have revolutionized many fields, including Natural Language Processing. This paper outlines teaching materials for an introductory lecture on deep learning in Natural Language Processing (NLP). The main submitted material covers a summer school lecture on encoder-decoder models. Complementary to this is a set of jupyter notebook slides from earlier teaching, on which parts of the lecture were based on. The main goal of this teaching material is to provide an overview of neural network approaches to natural language processing, while linking modern concepts back to the roots showing traditional essential counterparts. The lecture departs from count-based statistical methods and spans up to gated recurrent networks and attention, which is ubiquitous in today’s NLP.
Creating Biographical Networks from Chinese and English Wikipedia

With the rise of digital humanities, historians explore how to intellectually engage with textual sources given the available computational tools of today. The ENP-China project employs Natural Language Processing methods to tap into sources of unprecedented scale with the goal to study the transformation of elites in Modern China (1830-1949). One of the subprojects is extracting various kinds of data from biographies and, for that, we created a large corpus of biographies automatically collected from the Chinese and English Wikipedia. The dataset contains 228,144 biographical articles from the offline Chinese Wikipedia copy and is supplemented with 110,713 English biographies that are linked to a Chinese page. We also enriched this bilingual corpus with metadata that records every mentioned person, organization, geopolitical entity and location per Wikipedia biography and links the names to their counterpart in the other language. This data structure allows the researcher to analyze the relationships between biographies via shared contents and compare networks in different language settings. In this paper we will describe our methodology for building this new dataset. The first step was to use automatic text classification for extracting Chinese biographies. We trained a binary classifier to detect biographies on manually classified examples and used a subset of unseen texts to assess its accuracy. The second step used Named Entity Recognition to generate metadata and extract relations from the links in Wikipedia. Furthermore, we will delve into the method for building networks from this dataset. We argue that depending on the specific research question, different networks may be built. Using the metadata, researchers can create various kinds of networks to suit their needs. On top of releasing this dataset as an enriched bilingual corpus, we will provide an online interface to query and explore it. Our interface benefits from the bipartite graph structure (it can be seen as a network of documents and entities) and applies the same exploration and clustering strategy as in Cillex.
La grammatica della fantasia arriva in Brasile: problemi e difficoltà nella prima traduzione in lingua portoghese di Gianni Rodari

The book Grammar of Fantasy: An Introduction to the Art of Inventing Stories was published in Italy in 1973 and is the first work by Gianni Rodari to be translated and published in Brazil, in 1982. The 1980s, in Brazil, represented a peculiar period for children’s literature and the publication of Rodari’s work is not accidental, but is part of a specific educational project. This volume is pedagogical, didactics and, at the same time, metalinguistic (this aspect led to many difficulties in translating into Portuguese). By choosing a foreignization strategy, the Brazilian translation is not easy to read. Being a book designed for both adults and young people, one wonders whether the translation choices have influenced the type of reception and whether they also influenced the publication of Rodari’s other works in Brazil, which, apart from one exception, occurs almost twenty years later, already in this new century.
Online Transportation and Logistics Program: A Case Study

Transportation and its affiliated sister channels like warehousing, port/terminal operations, logistics, and supply chain provide exciting as well as lucrative career options for those with an awareness and aptitude for the rigors of these professions. Progressing in this field requires formal education, which has traditionally meant an interruption in the existing career. This paper gives an insight into the experiences of a university as it adopted the online delivery of its courses for this niche student body. The narration and concluding analysis are presented in the form of a SWOT analysis. The threats and weaknesses faced by the institution were considerable, but the inherent strengths and the opportunities of online education create an overall positive outcome.
SCIENTIFIC AND PRACTICAL BASIS FOR THE INTRODUCTION OF ELEMENTS OF DUAL TEACHING IN PROFESSIONAL TRAINING OF FUTURE TEACHERS

The article discusses the integration of dual teaching elements into the process of vocational training for future teachers. From the 30s of the twentieth century to the beginning of the twenty first century, the reflection of the elements of dual teaching in the educational process of pedagogical higher education institutions is based. Although it does not mention the introduction of the elements of dual education, it is noted that in recent years, due to the emphasis on the training of future teachers, special attention is paid to practical classes, internships. From 2014, domestic dissertation research and research projects covering dual learning will be thoroughly analyzed. Questionnaires were received from university graduates and employers. It will also be revealed that there are currently opportunities to integrate dual teaching elements into the process of future teacher training.
SECURITY OF USING A BANK CARD WITH ADDITIONAL PROTECTION MEASURES AND DIFFERENT FORMS OF SUBMISSION OF SMS2 / SMM2 CODE

It is difficult to imagine the modern world without a convenient opportunity to pay for goods on the Internet. However, the convenience faced the problem of stealing card data during transactions. Cybercriminals come up with more and more ways to steal money. Therefore, more advanced protection technologies must be developed. At the moment, many original solutions are offered, but they all have their advantages and disadvantages. The paper discusses some of the methods of protection and analyzes the possibility of their implementation, price, practicality and degree of protection.
MAPREDUCE FUZZY C-MEANS ENSEMBLE CLUSTERING WITH GENTLE ADABOOST FOR BIG DATA ANALYTICS

Big data clustering is one of the significant processes employed in numerous application domains. Existing clustering algorithms do not cope with large-scale data, resulting in higher false positive rate. In order to cluster such large datasets with higher accuracy, MapReduce gradient descent gentle AdaBoost clustering (MGDGAC) technique is proposed. The MGDGAC technique designs MapReduce fuzzy C-means (MFCM) clustering where the large dataset is initially subdivided into a number of chunks which are executed in parallel on different nodes to effectively perform clustering processes with minimal time. The data with larger membership value are grouped in the cluster with help of mappers. Then, reducer in MFCM clustering re-estimates the centroid value and iteratively fed to the mapper again until it attains a particular iteration and groups the similar data together. Finally, MGDGAC technique applies gentle AdaBoost with intention of reducing the training error of large data clustering.
Refined Mode-Clustering via the Gradient of Slope

In this paper, we propose a new clustering method inspired by mode-clustering that not only finds clusters, but also assigns each cluster with an attribute label. Clusters obtained from our method show connectivity of the underlying distribution. We also design a local two-sample test based on the clustering result that has more power than a conventional method. We apply our method to the Astronomy and GvHD data and show that our method finds meaningful clusters. We also derive the statistical and computational theory of our method.
Topology-Inspired Method Recovers Obfuscated Term Information From Induced Software Call-Stacks

Fuzzing is a systematic large-scale search for software vulnerabilities achieved by feeding a sequence of randomly mutated input files to the program of interest with the goal being to induce a crash. The information about inputs, software execution traces, and induced call stacks (crashes) can be used to pinpoint and fix errors in the code or exploited as a means to damage an adversary’s computer software. In black box fuzzing, the primary unit of information is the call stack: a list of nested function calls and line numbers that report what the code was executing at the time it crashed. The source code is not always available in practice, and in some situations even the function names are deliberately obfuscated (i.e., removed or given generic names). We define a topological object called the call-stack topology to capture the relationships between module names, function names and line numbers in a set of call stacks obtained via black-box fuzzing. In a proof-of-concept study, we show that structural properties of this object in combination with two elementary heuristics allow us to build a logistic regression model to predict the locations of distinct function names over a set of call stacks. We show that this model can extract function name locations with around 80% precision in data obtained from fuzzing studies of various linux programs. This has the potential to benefit software vulnerability experts by increasing their ability to read and compare call stacks more efficiently.
Pembangunan Aplikasi Mobile Podcast Untuk Startup Konekzy

There are many methods of delivering content that can be used by the current communication media. However, there are many methods that have not taken advantage of the efficiency of the media.By utilizing the advantages of audio that can improve memory, it can be applied to all ages of users, and visuals that can be felt by the eye senses combine them so that audiovisual learning is created, audiovisual learning is quite effective in providing delivery content towards users. A good method of use is to use audiovisual learning that facilitates learning or content delivery, with a combination of audio and visas that helps understand content, audiovisuals are modern media that follow the times with a mixture of hearing, sight, and movement. Audiovisual learning in media is now usually called podcasts, podcasts are now not only audio but can be implemented in video form. That way the view of a podcast can take the form of an audio podcast or a video podcast. The term podcast itself is audio or video content available on the internet that can be downloaded via video media players (anywhere, anytime).
Prospects for digitalization of art education

The relevance of the study is due to the fact that in the framework of the state program of digitalization of all spheres of the life of Russians, the learning process in higher education institutions of art and culture has acquired a digital format. In this regard, this study is aimed at assessing the digitalization results of a modern university (using the example of the Higher School of Music of A. Schnittke (Institute) of the Russian State Social University and the trends in this process. The analysis of the results allows concluding that in Russia, the digitalization of higher education leads to an increase in the level of its quality, an increase in the level of mastery of programs by students, an expansion of the range of competencies and opportunities for them to obtain quality education for a wider circle of people.
Priority Based VM Allocation and Bandwidth Management in SDN and Fog Environment

Nowadays, fog assisted cloud is a dominant field of computing where data centers are engaged in providing services to various applications having distinct resource needs and priorities. Congestion in the network causes performance degradation in the applications. Some mission-critical applications need to data transfer even during the congestion period. In Software Defined Networks (SDN) clouds, there is a possibility of reconfiguration of the network flows dynamically to avoid such congestions for critical applications. In this paper, a priority-based virtual machine (VM) placement algorithm is proposed which takes care of the hosts and the network configuration. It tries to place the VMs of high-priority applications closely connected to hosts for reducing the network congestion caused by the other applications. The needed bandwidth for the critical applications is also managed by implementing a priority queue on each network device taken care of by SDN controller. The experiment shows that in multi-tenant applications, the proposed combined approach solves the purpose of high priority applications by allocating sufficient resources and meeting the Quality of Service (QoS) requirements.
SafeNcy - D2.1: Trade-off report on landing site selection and trajectory generation techniques

This document provides the definitions of the models and algorithms used by the solutions developed in the context of the project. The document also describes the types and sources of data needed by the SafeNcy solution.
Active Teaching Methods as Means of Developing Students’ Creativity

In the article, you can get an answer to such questions as the place of a lecture in education and on the basis of this concept about the content of a lecture, what can and cannot be talked about. You cannot give all the material in a lecture. In fact, both an inexperienced teacher and a teacher with vast experience can make one methodological mistake: they want to include all their knowledge in the lecture. The teacher, trying to have time to present all the prepared material, lectures quickly. This of course reduces the effectiveness of the lecture. This note provides specific suggestions and discusses the didactic and psychological conditions for organizing the lecture. This article is recommended for university professors for use in lectures.
Differentially Private Correlation Clustering

Correlation clustering is a widely used technique in unsupervised machine learning. Motivated by applications where individual privacy is a concern, we initiate the study of differentially private correlation clustering. We propose an algorithm that achieves subquadratic additive error compared to the optimal cost. In contrast, straightforward adaptations of existing non-private algorithms all lead to a trivial quadratic error. Finally, we give a lower bound showing that any pure differentially private algorithm for correlation clustering requires additive error of $\Omega(n)$.