Reviewerly/exHarmony huggingface dataset:
Efficient Entanglement Transfer Between Light and Cold-atom Quantum Memories

The transfer of quantum states of light and their mapping into stationary quantum nodes is one of the building blocks for future quantum networks and envisionned applications [1] . Central to this endeavor is the distribution of entanglement between the material nodes, which opens a variety of major applications [2] . For instance, to realize long-distance quantum communications one needs to divide the distance into shorter quantum repeater links connecting entangled memories. Subsequent entanglement swapping operations enable an exponential improvement in distribution time. In this context, the efficiency of the entanglement mapping is a key parameter.
AUTOMATED NEWS CLASSIFICATION USING MACHINE LEARNING

The way of news consumption has changed drastically since the past as 87% of the respondent of a survey has cited online as their primary news source which makes the news article more accessible as ever for the readers, but the accessibility may overwhelm the readers due to the vast amount of news that is published online every day. Thus, having news classification is important but with the amount of the news published, we need the help of the machine learning to classify the news article in which this project was set to do. The objective of this project is to find the best machine learning model that can be used for the news classification, developing process for online news extraction and finally developing an automated system to extract and classify the news articles. Support Vector Machine (SVM) with TF-IDF Vectorizationmethod was found to be the best machine learning model for news article classification and online news article extraction process was done using Python script. After that, extracted articles are used to develop the machine learning model with an accuracy of 89.92%. The developed model was then used for the classification process in the automated news classification program which was build in Python as well. At the end, this project has helped to develop an automated news classification system will be helpful for the readers as they are able to view the news articles that are interesting to them.
Newspaper use among undergraduate students at a college library at the University of Education, Winneba. Ghana

Newspapers are among the information resources found in all types of libraries because they constitute an important source of information for users. The CODeL Library, recognising newspapers as an important source of information, subscribes to the newspapers in the print format. Visitors to the library use the newspapers to advance their knowledge. In light of that it is essential to examine the use of newspapers among undergraduate students. Therefore, the study sought to examine the use of newspapers by undergraduate students who patronise the library. The main instrument for data collection was questionnaires which included both open and closedended. A sample size of 437 was selected through a simple random technique and was used for the study. The students who were involved were from different faculties at the north campus were asked to respond to the questionnaire after the purpose of the study was duly explained to them. The analysed data was presented in frequency tables and percentages. The findings of the study showed that respondents acknowledged newspapers as good sources of information, useful for academic purposes, improves their writing and enhances their academic work. Generally, the study established that respondents have a positive attitude towards newspaper reading. In respect of challenges, the study revealed inadequate copies of newspapers and the inability to borrow among others. Recommendations made included, among others, adjusting newspaper collection development policies to include electronic newspaper subscriptions and creating another space solely for newspapers to allow for discussions. Citation: Larson, A. G. (2021). Newspaper use among undergraduate students at a college library at the University of Education, Winneba. Ghana.International Journal of Technology and Management Research (IJTMR), Vol. 6 (1): Pp.139-153.Received: March 22, 2021Accepted: September 1, 2021
A color image encryption algorithm based on DNA encoding and bit-level permutation

A color image encryption algorithm based on DNA coding and bit-level scrambling is proposed to solve the problems of poor effect and low security in the current color image encryption schemes.Firstly, the color image is preprocessed based on bite-level scrambling rules. Then the statistical information of the RGB plane itself was changed by means of DNA coding and cyclic shift. Finally, S-box is used for nonlinear replacement of pixel values after DNA decoding.Theoretical analysis and simulation results show that the algorithm can provide a larger key space, the cipher image also has higher information entropy, and has good security.
Building a Digital Library in a Manufacturing Company

Every company, both government and private, which runs business activities, cannot be separated from document recording/archiving. Documents are created to record everything related to various activities ranging from attendance data, purchase data, sales data, financial data, audit data, training data and others. Good documentation and archiving will make it easier for each section to be audited by both internal auditors and external auditors. With good documentation management, ideas, ideas and thoughts that have been done previously will be stored and can be reopened when needed. The rapid development of information technology has a positive impact on document storage/archiving methods. Currently, document storage/archiving can be overcome by changing the form of paper printed documents into digital form and stored on storage media. In manufacturing companies, currently there are still many companies that archive documents by storing them in their respective departments so that the availability of shelves in that department increases and this has an impact on the availability of space. So that document access can be done centrally, archiving is needed like a library. Documents that need to be archived at manufacturing companies include SOPs, production data, sample test data, ISO data, customer audit data, supplier audit data, training data, book/reference data and others.
An Analysis of Students Responses in the use of Edmodo Applications at the Fifth semester in English Language Education Department of Sultan Ageng Tirtayasa University

This study aims to determine students responses after using Edmodo Applications in English learning. The objectives of this research is to identify student’s responses on the use of Edmodo in English Language. This study adopt the Qualitative research. Case study is used to be an appropriate method for this research as proposed by Cresswell ( 2012). The students close-ended Questionnaire, and students interview were used to collect the data. The data were analyzed qualitatively. The close-ended Questionnaire and interview student were employed to collect the data of students responses in use of Edmodo Applications. The results showed that the use of Edmodo Applications in English Learning are , 84% of 45 students were strongly agree that Edmodo was easy to use as their English learning media. The features that have been designed in Edmodo is simple so that it easier to use for students as a tool in English learning. The use of Edmodo in English learning process makes students more easier to understand the material given by the teacher based on the apps features in Edmodo that supported students in learning process.
Generative Compositional Augmentations for Scene Graph Prediction

Inferring objects and their relationships from an image in the form of a scene graph is useful in many applications at the intersection of vision and language. In this work, we consider a challenging problem of compositional generalization that emerges in this task due to a long tail data distribution. Current scene graph generation models are trained on a tiny fraction of the distribution corresponding to the most frequent compositions, e.g. . However, test images might contain zero- and few-shot compositions of objects and relationships, e.g. . Despite each of the object categories and the predicate (e.g. 'on') being frequent in the training data, the models often fail to properly understand such unseen or rare compositions. To improve generalization, it is natural to attempt increasing the diversity of the training distribution. However, in the graph domain this is non-trivial. To that end, we propose a method to synthesize rare yet plausible scene graphs by perturbing real ones. We then propose and empirically study a model based on conditional generative adversarial networks (GANs) that allows us to generate visual features of perturbed scene graphs and learn from them in a joint fashion. When evaluated on the Visual Genome dataset, our approach yields marginal, but consistent improvements in zero- and few-shot metrics. We analyze the limitations of our approach indicating promising directions for future research.
Machine learning based state estimation of turbulent flows: robustness for noisy input

Neural networks (NNs) are widely recognized as a powerful tool for fluid flow analyses thanks to a nonlinear operation inside NNs. We here investigate the fundamental difference between NNs and conventional linear methods by considering state estimation of turbulent flows. As an example, we consider the estimation of the velocity field in a turbulent channel flow from a streamwise wall shear stress input by using convolutional neural network (CNN) and linear stochastic estimation (LSE). The nonlinear CNN outperforms the linear CNN and the LSE thanks to nonlinear activation functions. In addition, parametric studies inside them are also performed by focusing on a robustness against a noisy input. The present study reveals pros and cons of both linear and nonlinear methods toward practical turbulent estimations.
The Accuracy and Shortcomings of Google Translate Translating English Sentences to Indonesia

Google Translate is a free and practical online translation service that allows millions of people around the globe to translate words, phrases, sentences, and paragraphs into an intended target language. However, in 2015, some Google Translate users in Indonesia, filed complaints, asserting that the machine was often inaccurate, speculating that it could only translate languages at the micro-level of words and phrases, rather than complete sentences or paragraphs. This research works to examine the accuracy as well as the shortcomings of Google Translate, in the context of English to Indonesian translations, in order to critically engage the complaints made by Google users. For the purpose of this study, 80 English sentences were translated using Google Translate and assessed for accuracy using a table adapted from Memsource criteria. Both the original sentences and their translated versions were analyzed using a sentence pair matrix to determine the machine’s failings and areas for improvement. The results challenged those initial speculations which suggested Google Translate is only effective with words and phrases. On the contrary, Memsource proved to be a useful tool in demonstrating a reasonable level of accuracy, accurately translating 60.37% of Indonesian-English sentences and vice versa.
Towards a Grounded Theory for a Development Process Model for Machine Learning Based Systems

The software industry has experienced the integration of artificial intelligence capabilities into applications, facing new challenges regarding software development. Despite research and industry contributions providing lessons learned and best practices, no study proposed a reference process for developing this type of software, and practitioners still struggle to establish a working process. Through a Grounded Theory study involving practitioners with experience in machine learning (ML) projects, this paper presents an emerging theory of how ML-based systems are developed. The reported results comprise key elements of a reference development process with its respective phases and activities.
Students’ Learning Motivation In Classroom And Distance-Based Formats

The authors carried out a review of studies devoted to the problems of the forced transition to distance learning during the pandemic. They highlighted the main directions of analysis and described advantages and disadvantages of this learning format. Two directions of psychological and pedagogical research of distance learning are presented: the change in the objective characteristics of the educational environment and the learners’ subjective perceptions and reflections. The types of ideas about the motivation to learning and quasi-professional activities are characterized. The authors made a conclusion about insufficient study of changes in students’ learning motivation in the distance-based format. The paper presents a description of the study of the learning motives of the first year students in engineering training areas, which made it possible to assess the components of learning motivation in classroom and distance-based formats. The applied technique revealed the features of cognitive activity, achievement motivation, and the display of anxiety and anger. The students were grouped according to the level of learning motivation demonstrated in the classroom format. The study revealed the absence of students with an extremely negative attitude towards learning in the distance-based format. A significantly decreased intensity of the anxiety and anger cases was revealed. The empirical data generally showed the relative stability of the main motives of learning activity and the positive dynamics of emotions during the transition to distance learning in the sample group under survey. The authors made a conclusion that similar monitoring of students from other training areas is necessary.
TOURISM AUGMENTED REALITY APPLICATION (TARA)

Industrial 4.0, the revolution of technology is coming influencing all aspect of human life. Augmented reality technology is one of the pillars of industrial 4.0 can give revolution into the world by giving the visual view into the real environment in real time. With this main feature of augmented reality technology will be a great help for human doing especially in planning where they can have better view of outcome of the project before it happen. For example, the constructions of tourist attraction like waterpark with be better with the help of augmented reality technology. Usually, the presentation of the construction project will be in documentation like the expected result image of the project. But with augmented reality, the output of project will be clearer about the specification and architecture structure of the building. By doing this, the inventor and stakeholder of the project will be confident to work on the project due to clear vision of the expected result of the project. This is the reason why this technology needs to fully utilize in all sectors in industry including tourism sector. This technology can help the tourist by gaining new experience in gaining information either when they are travelling or not. In other word is in attractive and interactive ways. By using the mobile based application where the uses continuously grow in all countries, this tourism augmented reality application can help the users to gain information of the tourism place by the mediums of 3 dimensional objects, videos, images, audios and more just by scanning the target images that be set using Android smartphone camera. This technology not just help the tourist to gain knowledges in attractive ways, it also can be one of the ways to promote the country’s tourist attraction places locally and globally at the same time support the development of our country.
ELLIPSDF: Joint Object Pose and Shape Optimization With a Bi-Level Ellipsoid and Signed Distance Function Description

Autonomous systems need to understand the semantics and geometry of their surroundings in order to comprehend and safely execute object-level task specifications. This paper proposes an expressive yet compact model for joint object pose and shape optimization, and an associated optimization algorithm to infer an object-level map from multi-view RGB-D camera observations. The model is expressive because it captures the identities, positions, orientations, and shapes of objects in the environment. It is compact because it relies on a low-dimensional latent representation of implicit object shape, allowing onboard storage of large multi-category object maps. Different from other works that rely on a single object representation format, our approach has a bi-level object model that captures both the coarse level scale as well as the fine level shape details. Our approach is evaluated on the large-scale real-world ScanNet dataset and compared against state-of-the-art methods.
Speech Recognition for Medical Conversations Health Record (MCHR)

This paper provides an overview of major technical perspectives and recognizes the fundamental progress of the conversion of speech to text and also provides an overview of the techniques developed at each point of the speech-to-text conversion classification, a system that automatically transcribes conversations between doctor and patient. MCHR systems are known to store clinical data to capture the patient’s electronic medical health record (EMHR) across time (Rajkomar et al. in NPJ Digit Med 1, 18 (2018) [1]). It eradicates the need to manually monitor the previous paper medical records of a patient and helps ensure consistency and standardization of data. When extracting medical data, electronic medical health records are more efficient for analyzing potential patterns and long-term improvements in a patient's clinical condition. MCHR systems manage doctor–patient conversational data recording as both an audio and text for the use of doctors in health data analysis. These systems help in analyzing clinical data in later stages or match patterns for other patients with similar problems or symptoms. Additional usage is from the conversation we can extract diagnosis information, drug prescriptions and dosage details which can be presented to doctors for verification and approval. This will reduce doctors’ time; nowadays in large hospitals, patient loads are high and doctors are unable to perform prescription entry using text input in hospital management systems, as it’s a time-consuming process. This paper presents the state-of-the-art in voice recognition and discusses how to build a clinical prescribing system for healthcare domain.
Blockchain Technology and Financial Regulation: A Principle-Based Approach to the Regulation of ICOs

This paper compares Initial Public Offerings (IPOs) with Initial Coin Offerings (ICOs) and explores their corresponding risks and limitations, with a view to analyse the extent to which the latter should be subject to the same regulatory framework as the former. After assessing the underlying principles of financial regulations for IPOs, we provide an overview of current approaches to regulating ICOs, with a focus on Europe and the U.S. Drawing from the notion of ‘functional equivalence’, we argue that principle-based regulation could be an effective way of addressing the regulation of ICOs, based on an in-depth risk-analysis approach. Finally, we explore the use of blockchain technology as a ‘regulatory technology’ to incorporate specific guarantees into the technological fabric of an ICO, in order to ensure compliance with the fundamental principles of financial regulation.
Deep Permutation Equivariant Structure from Motion.

Existing deep methods produce highly accurate 3D reconstructions in stereo and multiview stereo settings, i.e., when cameras are both internally and externally calibrated. Nevertheless, the challenge of simultaneous recovery of camera poses and 3D scene structure in multiview settings with deep networks is still outstanding. Inspired by projective factorization for Structure from Motion (SFM) and by deep matrix completion techniques, we propose a neural network architecture that, given a set of point tracks in multiple images of a static scene, recovers both the camera parameters and a (sparse) scene structure by minimizing an unsupervised reprojection loss. Our network architecture is designed to respect the structure of the problem: the sought output is equivariant to permutations of both cameras and scene points. Notably, our method does not require initialization of camera parameters or 3D point locations. We test our architecture in two setups: (1) single scene reconstruction and (2) learning from multiple scenes. Our experiments, conducted on a variety of datasets in both internally calibrated and uncalibrated settings, indicate that our method accurately recovers pose and structure, on par with classical state of the art methods. Additionally, we show that a pre-trained network can be used to reconstruct novel scenes using inexpensive fine-tuning with no loss of accuracy.
MODEL DAN STRATEGI PEMBELAJARAN TEMATIK DALAM NOVEL MADOGIWA NO TOTTOCHAN

Thematic learning is integrated learning that uses themes to link several subjects so that they can provide meaningful experiences to students. Because literary works contain elements of education and teaching, readers, including teachers, can use literature as inspiration in developing appropriate learning models and strategies for their students. One example of literary works that can be used as inspiration by teachers, especially elementary school teachers, is a literary work in the form of a novel entitled Madogiwa no Totto-chan. This novel tells the story of a school that has shaped its students to be good and successful children in the future because it implements a natural learning system that provides direct learning experiences for its students.
Fractional Adaptive Kalman Filter with Colored Correlated Noise

For linear fractional systems with correlated fractional colored process noise and colored measurement noise obtained by Tustin generating function method, a fractional adaptive Kalman filter based on virtual noise technology is proposed in this paper, which can effectively realize the state estimation of such fractional systems. The proposed algorithm is simple and more suitable for practical engineering applications. Simulation results verify the effectiveness and feasibility of the proposed algorithm.
The Challenges Experienced by Professional Teachers in Teaching and Learning on Online Platform

Indonesian professional teachers are demanded to master four basic competencies: spiritual, social, pedagogical, and professional competencies.Professional competency requires teachers to be able to apply information and communication technology (ICT) in both online and offline learning mode.Online learning has been increasingly conducted as the impact of the Covid-19 pandemic.In some areas, the pandemic has forced teachers to use full online learning and to learn new online learning platforms that too many teachers are difficult to do.Further investigation is needed to know how the teachers use the platforms.The observed situation shows that the teachers are still lack understanding in using some online platform.They also find some difficulties in integrating ICT in their teaching and learning.This article discusses teachers' challenges in using ICT, particularly online learning platforms.
Universal Derivations v1.1

Universal Derivations (UDer) is a collection of harmonized lexical networks capturing word-formation, especially derivational relations, in a cross-linguistically consistent annotation scheme for many languages. The annotation scheme is based on a rooted tree data structure, in which nodes correspond to lexemes, while edges represent derivational relations or compounding. The current version of the UDer collection contains thirty-one harmonized resources covering twenty-one different languages.