Reviewerly/exHarmony huggingface dataset:
Cartoon-Style Image Rendering Transfer Based on Neural Networks

Cartoon rendering of images is a challenging nonphotorealistic image rendering task, which aims to transform real photos into cartoon-style nonphotorealistic images while preserving the semantic content and texture details of the original photos. Based on the understanding of the characteristics of cartoon images and analysis of the defects of established approaches, this paper improves existing methods. The convolutional neural networks’ powerful image processing ability and attention mechanism are utilized to get more fine-grained image features, making the result of rendering more realistic. This paper mainly studies how neural networks can better process cartoon pictures, adjust parameters, and choose training methods. Furthermore, the article proposes a new solution of cartoon-style image rendering based on deep learning. The actual test results of real original images have shown that the model is suitable for cartoon-style image rendering.
Underwater image restoration via background light estimation and depth map optimization

In underwater images, the significant sources of distortion are light attenuation and scattering. Existing underwater image restoration technologies cannot deal with the poor contrast and color distortion bias of underwater images. This work provides a new underwater image restoration approach relying on depth map optimization and background light (BL) estimation. First, we build a robust BL estimation model that relies on the prior features of blurriness, smoothness, and the difference between the intensity of the red and blue-green channels. Second, the red-light intensity, difference between light and dark channels, and disparity of red and green-blue channels by considering the hue are used to calculate the depth map. Then, the effect of artificial light sources on the underwater image is removed using the adjusted reversed saturation map. Both the subjective and objective experimental results reveal that the images produced by the proposed technology provide more remarkable visibility and superior color fidelity.
Experiences of Game-Based Learning and Reviewing History of the Experience Using Player's Emotions

In this paper, we discuss whether the history of a learning experience, containing action and emotion information, is useful for review of the experience in game-based learning using virtual space. We developed a game-based story generation system that automatically generates scripts in real time by using a player's emotions and actions. The system has two functions: a game-based experiential learning environment and automatic story generation. The system provides the player with a virtual world and a virtual tool operated by using a hand controller and a display. The system recognizes the player's real-time emotions through facial expressions, and it outputs reactions based on these emotions and actions via a knowledge-based system when the player operates the tool. Then, it outputs scripts based on the emotions and the history of actions. We evaluated the system by conducting experiments with university students as subjects. As a result, subjects found the stories generated by this system interesting because they were based on the player's experience in the game and used the player's behavioral history and emotions. If we consider this as a record of the learning experience, the learning history is an impressive record accompanied by emotions. Thus, historical information that records the actions and emotions of the learner in real time is considered effective because it allows the learner to recall his or her own experiences after the game experience. The results suggest that the historical information, including the learner's real-time actions and emotions, is helpful for review in learning. There is a possibility that experiential learning through games using virtual spaces, such as the one used in this study, will become widespread in the future. In such cases, it will be necessary to examine the learning effects of using historical information with emotions. Therefore, we believe that the results and discussions in this study will be useful for experiential learning using virtual spaces.
Online voting system using block chain technology

Voting in online is a reasonable option in contrast to both the conventional paper polling form framework and the generally utilized electronic voting machine. An electronic voting site must be safe and secure. Electing honesty, as well as vote straightforwardness and elector protection, are exceptionally significant contemplations. This paper proposes an electronic voting system based on blockchain that overcomes some of the drawbacks of current democratic systems. A P2P (peer-to-peer) Network is utilized to impart a distributed ledger with voting transactions to make a private blockchain. The application is planned so that the client is totally absent of the intricacy of the fundamental design. Every elector is distinguished by their Aadhar number, which has been supported by the public authority. The application utilizes this number to ensure that every citizen just gets one vote. At the point when a vote is submitted as a transaction, it brings each of the peers up to date. Votes are encrypted and hashed prior to being added to the blockchain, which fortifies security and produces a chain of blocks, since each peer both a private key and a public key.
DDDM: A Brain-Inspired Framework for Robust Classification

Despite their outstanding performance in a broad spectrum of real-world tasks, deep artificial neural networks are sensitive to input noises, particularly adversarial perturbations. On the contrary, human and animal brains are much less vulnerable. In contrast to the one-shot inference performed by most deep neural networks, the brain often solves decision-making with an evidence accumulation mechanism that may trade time for accuracy when facing noisy inputs. The mechanism is well described by the Drift-Diffusion Model (DDM). In the DDM, decision-making is modeled as a process in which noisy evidence is accumulated toward a threshold. Drawing inspiration from the DDM, we propose the Dropout-based Drift-Diffusion Model (DDDM) that combines test-phase dropout and the DDM for improving the robustness for arbitrary neural networks. The dropouts create temporally uncorrelated noises in the network that counter perturbations, while the evidence accumulation mechanism guarantees a reasonable decision accuracy. Neural networks enhanced with the DDDM tested in image, speech, and text classification tasks all significantly outperform their native counterparts, demonstrating the DDDM as a task-agnostic defense against adversarial attacks.
Controllable Text Generation for Open-Domain Creativity and Fairness

Recent advances in large pre-trained language models have demonstrated strong results in generating natural languages and significantly improved performances for many natural language generation (NLG) applications such as machine translation and text summarization. However, when the generation tasks are more open-ended and the content is under-specified, existing techniques struggle to generate long-term coherent and creative content. Moreover, the models exhibit and even amplify social biases that are learned from the training corpora. This happens because the generation models are trained to capture the surface patterns (i.e. sequences of words), instead of capturing underlying semantics and discourse structures, as well as background knowledge including social norms. In this paper, I introduce our recent works on controllable text generation to enhance the creativity and fairness of language generation models. We explore hierarchical generation and constrained decoding, with applications to creative language generation including story, poetry, and figurative languages, and bias mitigation for generation models.
One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model

Semi-weakly supervised semantic segmentation (SWSSS) aims to train a model to identify objects in images based on a small number of images with pixel-level labels, and many more images with only image-level labels. Most existing SWSSS algorithms extract pixel-level pseudo-labels from an image classifier - a very difficult task to do well, hence requiring complicated architectures and extensive hyperparameter tuning on fully-supervised validation sets. We propose a method called prediction filtering, which instead of extracting pseudo-labels, just uses the classifier as a classifier: it ignores any segmentation predictions from classes which the classifier is confident are not present. Adding this simple post-processing method to baselines gives results competitive with or better than prior SWSSS algorithms. Moreover, it is compatible with pseudo-label methods: adding prediction filtering to existing SWSSS algorithms further improves segmentation performance.
Towards an Ontology Network in Finance and Economics

Finance and economics are wide domains, where ontologies are useful instruments for dealing with semantic interoperability and information integration problems, as well as improving communication and problem solving among people. In particular, reference ontologies have been widely recognized as powerful tools for representing a model of consensus within a community to support communication, meaning negotiation, consensus establishment, as well as semantic interoperability and information integration. In domains like economics and finance, which are too large and complex to be represented as a single, large and monolithic ontology, it is necessary to create an ontological framework, built incrementally and in an integrated way, as a network. Therefore, in this paper we introduce OntoFINE, an Ontology Network in Finance and Economics that organizes and integrates knowledge in the realm on finance and economics, serving as a basis to several applications. We discuss the development of OntoFINE and present some of its applications.
Post-quantum signature with preimage chameleon hashing

In this work, we propose a generalization of the concept of chameleon hash first described in [Krawczyk and Rabin 1998], which we call preimage chameleon hash. While in the conventional chameleon hash, the trapdoor allows a user to compute second preimages, in this generalization, it is possible to compute first preimages. We show how to adapt the post-quantum chameleon hash from [Cash et al. 2010] to a preimage chameleon hash and use this modified construction to build a new signature scheme based on [Mohassel 2011]. A preimage chameleon hash allows the signer to encode in its signature chosen information to be checked during verification. We prove our signature scheme to be strongly unforgeable under a chosen message attack (SUF-CMA).
SMARTER Teamwork: System for Management, Assessment, Research, Training, Education, and Remediation for Teamwork

Before joining the teams and training lab at IST, she conducted research on expatriate assignments and the resulting socialization, career capital gains, and knowledge transfer that occur in the international context.Currently, Shirley is continuing to pursue expatriate research in addition to working on teamwork and training research in the medical domain.In specific, Dr. Sonesh is working on investigating clinical decision making
THE INFLUENCE OF L1 ON L2 IN TRANSLATION: ERRORS MADE BY YOGYAKARTA STATE UNIVERSITY STUDENTS

This study investigates the errors made by Indonesian English learners within crosslinguistic interference between L1 and L2 (Indonesian and English). The data were gathered by making the respondents translate 6 Indonesian sentences which are commonly used during class presentation into proper English. Such task was aimed at investigating the interlingual preposition errors induced by the process of transfer between the target language (English) and the source language (Indonesian). The translation task was assigned in 22 October 2018 to 19 adult EFL learners of English Education Magister’s Program of Yogyakarta State University semester I. They were asked to translate the sentences from Indonesian into English within the allocated time. The task was conducted directtly by writing down the translation on a piece of paper. The data was then analyzed through Coder’s error analysis theory. The findings suggested that these particular English foreign language learners made the fewest errors on preposition omission – and more errors on wrong use and redundancy of prepositions.
Significance of Big Data Analytics in Higher Education Institutions of Saudi Arabia

Research is an observatory study addressing the importance of big data analytics in higher education institutions of Saudi Arabia. Quantitative data collection method was deployed to collect data from King Abdulaziz University and then it was analyzed by using PLS-SEM. The study has proven that there is a high requirement of applying big data analytics on the university networks to separate the useable data from the unusable data. This will improve network performance and make the network more stable. The study has contributed to the knowledge base theory significantly.
Memprediksi Penjualan Pada Toko Hanifah Metode C.45

Drug stocks in pharmacies are important information for the sales process. The existing stock is not in accordance with the needs of consumers, the distribution of drugs that are less needed in the stock will cause losses because the drug has expired due to too long stored in the warehouse. Another problem is that obat cannot predict drugs that are needed a lot, to overcome these problems needed a prediction system. These problems can be solved by the decision tree method for the prediction of drug supplies. The concept of the Decision Tree Algorithm is to convert data into decision trees and decision rules. System development with Waterfall model, using PHP programming language, My SQL database, system design using object oriented approach, system testing using Black Box for functionality test, validity testing with rapid miner tools. The result of the development of the system is a prediction of drug sales at pharmacies. The result of black-box testing is that all developed systems work properly. The results of the validity test by comparing the old system with the new system with the rapid miner, using 30 transaction samples the accuracy is 89% which means the system has good performance.
The choice of the individual way of teacher’s professional development

In the article, as part of the research of the modern teachers training system dynamics, the need for in-depth consulting of educational needs of teachers different categories is substantiated. The categories of teachers that require a special approach when creating an individual educational route are indicated. The meaning of the mentoring development, coaching in the teachers training system is revealed. The role of off-system resources is shown.
Digital pedagogical pillars in the system of teachers’ additional professional education

The article discusses the use of digital pedagogical pillars in the system of teachers’ additional education caused by the challenges of the modern information society. The similarities and differences of such pillars with the electronic educational resource are shown. Various options for implementing of such pillars in education process are proposed.
Challenges of the modern school in the context of digital education

Active transformation of general education, a focus on learning new skills of the XXI century can be subject to the implementation of modern digital technologies in the educational process, which correspond to the needs of the economy and society, especially the development of modern pupils and interesting modern generation. The article discusses the issues of the readiness of educational organizations for the transition to digital education and the widespread use of new digital technologies in the educational process. The main advantages of the active use of digital technologies in education for solving problems that cannot be solved or poorly solved by a modern Russian school based on traditional technologies are presented.
Hiding Sensitive Itemsets Using Sibling Itemset Constraints

Data collection and processing progress made data mining a popular tool among organizations in the last decades. Sharing information between companies could make this tool more beneficial for each party. However, there is a risk of sensitive knowledge disclosure. Shared data should be modified in such a way that sensitive relationships would be hidden. Since the discovery of frequent itemsets is one of the most effective data mining tools that firms use, privacy-preserving techniques are necessary for continuing frequent itemset mining. There are two types of approaches in the algorithmic nature: heuristic and exact. This paper presents an exact itemset hiding approach, which uses constraints for a better solution in terms of side effects and minimum distortion on the database. This distortion creates an asymmetric relation between the original and the sanitized database. To lessen the side effects of itemset hiding, we introduced the sibling itemset concept that is used for generating constraints. Additionally, our approach does not require frequent itemset mining executed before the hiding process. This gives our approach an advantage in total running time. We give an evaluation of our algorithm on some benchmark datasets. Our results show the effectiveness of our hiding approach and elimination of prior mining of itemsets is time efficient.
Comprehensive research on Facemask Detection using CNN for the recent COVID-19 outbreak

Outbreaks of Corona Virus Disease 2019 (Covid-19) have had a profound effect on humanity. Covid-19 infection infected an estimated 172 million people worldwide by May 2021. Wearing a face mask, according to the World Health Organization (WHO), could significantly prevent the spread of Covid19. To combat the spread of Covid-19, governments from several countries have called for the implementation of the "no mask, no service" policy. Even improper use of a facemask, however, can disrupt the purpose and spread the virus. As a result, to remedy this situation and ensure public safety, a face mask monitoring system, also known as a mask detection algorithm, is not essential. Face mask detection algorithms are a small set of detection algorithms used to find objects on the scene. Keywords— Facemask detection, Covid-19, convolution neural network, Covid-19 deep neural network, machine learning, object detection
THE POTENTIAL OF ENGLISH LEARNING VIDEOS IN FORM OF VLOG ON YOUTUBE FOR ELT MATERIAL WRITERS

YouTube is the most popular video sharing websites where millions of videos can be found online, especially videos for learning a language. Students can use YouTube to get many language learning videos to help them study the language. Not only getting many videos that can be used as teaching materials in the classroom, teachers can also create their own English video for their students or even for everyone. Designing an English learning video is an opportunity for teachers to make videos based on their teaching context. One type of content that can have a big opportunity to develop is English learning video in form of Vlog. Vlog has a huge number of audiences, especially for Indonesian learners. This research is aimed at analyzing the potential of English learning video in form of Vlog for ELT material writers.
The <i>Vera Icona</i> Installation and Performance: A Reflection on Face Surveillance in Contemporary Society

Abstract The Vera Icona installation-performance considers the relationship between face and image in contemporary society. It is inspired by the legend of the Vera Icona, an “icon made without hands” (said to have come into existence miraculously rather than by human creation), which is part of the political and philosophical history of images, from the religious icon to contemporary video. The installation-performance proposes a critical analysis of Jerusalem’s surveillance society. Using artificial intelligence techniques such as deep learning in order to replicate the mise en abyme art technique, the installation blends faces of those being observed with the faces of observers. It invites viewers to consider themselves at the center of their own universe, far from established norms and controlling societies.
The Use of the Cocomelon YouTube Channel as a Medium for Introducing Children's English Vocabulary

YouTube is a video-watching application that is very popular in today's society. Therefore, people can access YouTube anywhere and anytime. Not only ordinary videos, but nowadays, YouTube is also used for academic purposes for children. Children can also learn English by watching stories, music, or YouTube videos. Various videos on YouTube can be helpful. Be accessed easily, one of which is through the Cocomelon Channel. This study aims to explain using the Cocomelon YouTube channel as a learning medium for vocabulary recognition for children aged 6-12 years. This research is a case study with a qualitative method in which the researchers take observation and interview as the data collection technique. The participants in this study were children aged 6 to 12 years. From the children's feedback and interviews with parents, the result shows that using Cocomelon's YouTube channel can affect the addition of new vocabulary for children.
INTEGRATE: A methodology to facilitate critical care research using multiple, linked electronic health records at population scale.

Critical Care is a specialty in medicine providing a service for severely ill and high-risk patients who, due to the nature of their condition, may require long periods recovering after discharge. Consequently, focus on the routine data collection carried out in Intensive Care Units (ICUs) leads to reporting that is confined to the critical care episode and is typically insensitive to variation in individual patient pathways through critical care to recovery.A resource which facilitates efficient research into interactions with healthcare services surrounding critical admissions, capturing the complete patient's healthcare trajectory from primary care to non-acute hospital care prior to ICU, would provide an important longer-term perspective for critical care research.To describe and apply a reproducible methodology that demonstrates how both routine administrative and clinically rich critical care data sources can be integrated with primary and secondary healthcare data to create a single dataset that captures a broader view of patient care.To demonstrate the INTEGRATE methodology, it was applied to routine administrative and clinical healthcare data sources in the Secure Anonymised Data Linking (SAIL) Databank to create a dataset of patients' complete healthcare trajectory prior to critical care admission. SAIL is a national, data safe haven of anonymised linkable datasets about the population of Wales.When applying the INTEGRATE methodology in SAIL, between 2010 and 2019 we observed 91,582 critical admissions for 76,019 patients. Of these, 90,632 (99%) had an associated non-acute hospital admission, 48,979 (53%) had an emergency admission, and 64,832 (71%) a primary care interaction in the week prior to the critical care admission.This methodology, at population scale, integrates two critical care data sources into a single dataset together with data sources on healthcare prior to critical admission, thus providing a key research asset to study critical care pathways.