Reviewerly/exHarmony huggingface dataset:
A Network with Composite Loss and Parameter-free Chunking Fusion Block for Super-Resolution MR Image

MRI is often influenced by many factors, and single image super-resolution (SISR) based on a neural network is an effective and cost-effective alternative technique for the high-resolution restoration of low-resolution images. However, deep neural networks can easily lead to overfitting and make the test results worse. The network with a shallow training network is difficult to fit quickly and cannot completely learn training samples. To solve the above problems, a new end-to-end super-resolution (SR) method is proposed for magnetic resonance (MR) images. Firstly, in order to better fuse features, a parameter-free chunking fusion block (PCFB) is proposed, which can divide the feature map into <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>n</mi> </math> branches by splitting channels to obtain parameter-free attention. Secondly, the proposed training strategy including perceptual loss, gradient loss, and L1 loss has significantly improved the accuracy of model fitting and prediction. Finally, the proposed model and training strategy take the super-resolution IXISR dataset (PD, T1, and T2) as an example to compare with the existing excellent methods and obtain advanced performance. A large number of experiments have proved that the proposed method performs better than the advanced methods in highly reliable measurement.
Codes, Patterns and Shapes of Contemporary Online Antisemitism and Conspiracy Narratives – an Annotation Guide and Labeled German-Language Dataset in the Context of COVID-19

Over the course of the COVID-19 pandemic, existing conspiracy theories were refreshed and new ones were created, often interwoven with antisemitic narratives, stereotypes and codes. The sheer volume of antisemitic and conspiracy theory content on the Internet makes data-driven algorithmic approaches essential for anti-discrimination organizations and researchers alike. However, the manifestation and dissemination of these two interrelated phenomena is still quite under-researched in scholarly empirical research of large text corpora. Algorithmic approaches for the detection and classification of specific contents usually require labeled datasets, annotated based on conceptually sound guidelines. While there is a growing number of datasets for the more general phenomenon of hate speech, the development of corpora and annotation guidelines for antisemitic and conspiracy content is still in its infancy, especially for languages other than English. To address this gap, we have developed an annotation guide for antisemitic and conspiracy theory online content in the context of the COVID-19 pandemic that includes working definitions, e.g. of specific forms of antisemitism such as encoded and post-Holocaust antisemitism. We use the guide to annotate a German-language dataset consisting of $\sim \! 3,700$ Telegram messages sent between 03/2020 and 12/2021.
Representing and Determining Argumentative Relevance in Online Discussions: A General Approach

Understanding an online argumentative discussion is essential for understanding users' opinions on a topic and their underlying reasoning. A key challenge in determining completeness and persuasiveness of argumentative discussions is to assess how arguments under a topic are connected in a logical and coherent manner. Online argumentative discussions, in contrast to essays or face-to-face communication, challenge techniques for judging argument relevance because online discussions involve multiple participants and often exhibit incoherence in reasoning and inconsistencies in writing style. We define relevance as the logical and topical connections between small texts representing argument fragments in online discussions. We provide a corpus comprising pairs of sentences, labeled with argumentative relevance between the sentences in each pair. We propose a computational approach relying on content reduction and a Siamese neural network architecture for modeling argumentative connections and determining argumentative relevance between texts. Experimental results indicate that our approach is effective in measuring relevance between arguments, and outperforms strong and well-adopted baselines. Further analysis demonstrates the benefit of using our argumentative relevance encoding on a downstream task, predicting how impactful an online comment is to certain topic, comparing to encoding that does not consider logical connection.
Performance Comparison of Passage Retrieval Models according to Korean Language Tokenization Methods

Recently, NLP (Natural Language Process) is required in question-answering technology in many fields such as chatbots and AI speakers. Accordingly, companies and communities are conducting research on Open Domain Question Answering (ODQA), which is required to identify which question and answer questions should be used in a vast amount of data sets. In fact, in the case of question-and-answer learning, there are cases in which multilingual methods are included in most cases, but performance can be further improved by using a Korean-only model. Therefore, this paper focuses on information retrieval, which is a method that includes answers to questions in the ODQA Framework and tries to find a method unique to Korean. In this paper, we propose to apply the token for each query to the model using the Korean method using the BM25 (Best Matching) and DPR (Dense Passage Retrieval) models. To verify the above evaluations, KorQuAD v1 dataset is used. Accordingly, Korean BM25 can obtain the highest score for morpheme-based tokenization, and additionally, adding and maintaining other tokens according to the model performed well, and as a result of learning with the same model, DPR has more sub-tokens than the conventional method.
An Empirical Analysis of Non-Immersive Virtual Reality on Behavioral Intentions: Moderating Role of Openness to Experience

This study is used to examine the impact of virtual reality in real estate and the effects of offeringconsumers non - immersive Virtual reality (VR) experience through broadlyexisting technology, to allow them to viewreal estate products. This research also highlights such technologies to enhance the customer’s experience, thoughts, and attitudes generally in a vastly competitive real estate industry. Data have been collected from the consumer of thereal estate sector. 302 consumers filledout the questionnaires and finally, valid responses 206 have been used for the PLS Structural Equation Modeling (SEM) analysis. The systematic results discovered better "visiting" experiences and more positive attitudes toward both the products and the agency. Further, results postulate that knowledge of properties mediates the positive relationship between the visiting real estate properties through VR and behavioral intention. Furthermore,Openness to experience moderates the positive effect between visiting real estate properties and behavioral intention. This studyprovidesa valuable implication on a realistic level, this study also stressed the importance of offering consumers an enhanced experience of promoted products for practitioners to positively impact consumer's attitudes and behavioral intentions.
High-precise posture measurement for measuring freeform surface with computer generated hologram compensation

Abstract： To realize the high-precision position measurement of freeform surfaces， this paper proposes an optic-mechanical reference positioning method that employs a position measurement model.First， an opti• cal-mechanical reference positioning method based on a coordinate measuring machine and computer-gener• ated holography is proposed.Then， using a spherical mounted retroreflector (SMR) target ball， cat eye， and reference ball as the benchmarks， three benchmark position measurement models are established on the basis of wave aberration theory and parallax effect.The functional relationship between the position er• ror and the wavefront aberration in the reference area is obtained， and the three position measurement mod•
Design and Development of a Web-Based Medical Record Retention Information System at Polyclinic X Jember Regency

Medical record retention activities that have been carried out at Polyclinic X Jember Regency have encountered obstacles in sorting files that are still manual, starting with the officer checking the month and year of the patient's last visit to the Polyclinic's SIM Clinic, then taking several medical records on the storage rack and recording the medical record number for sorting. There are quite a lot of files on the storage rack, making officers have to sort the files one by one. The type of research used R&amp;D with the Waterfall system development method, namely Requirement Definition, System and Software Design, Implementation and Unit Testing, Integration and System Testing, also Operation and Maintenance. Data collection techniques using interviews and observations with 2 respondents, namely the head of polyclinic and medical records officers. This study aims to design and create a web-based medical record retention information system by minimizing errors in sorting medical record files and facilitating the performance of medical record officers in the filing section. The result of this research is a web-based medical record retention information system created using the PHP programming language.
A Data Fusion Framework for Multi-Domain Morality Learning

Language models can be trained to recognize the moral sentiment of text, creating new opportunities to study the role of morality in human life. As interest in language and morality has grown, several ground truth datasets with moral annotations have been released. However, these datasets vary in the method of data collection, domain, topics, instructions for annotators, etc. Simply aggregating such heterogeneous datasets during training can yield models that fail to generalize well. We describe a data fusion framework for training on multiple heterogeneous datasets that improve performance and generalizability. The model uses domain adversarial training to align the datasets in feature space and a weighted loss function to deal with label shift. We show that the proposed framework achieves state-of-the-art performance in different datasets compared to prior works in morality inference.
An investigation of feed-forward and feed-back eye movement training in immersive virtual reality

The control of eye gaze is critical to the execution of many skills. The observation that task experts in many domains exhibit more efficient control of eye gaze than novices has led to the development of gaze training interventions that teach these behaviours. We aimed to extend this literature by i) examining the relative benefits of feed-forward (observing an expert’s eye movements) versus feed-back (observing your own eye movements) training, and ii) automating this training within virtual reality. Serving personnel from the British Army and Royal Navy were randomised to either feed-forward or feed-back training within a virtual reality simulation of a room search and clearance task. Eye movement metrics – including visual search, saccade direction, and entropy – were recorded to quantify the efficiency of visual search behaviours. Feed-forward and feed-back eye movement training produced distinct learning benefits, but both accelerated the development of efficient gaze behaviours. However, we found no evidence that these more efficient search behaviours transferred to better decision making in the room clearance task. Our results suggest integrating eye movement training principles within virtual reality training simulations may be effective, but further work is needed to understand the learning mechanisms.
Smart Education System Enhancing Collaborative Learning with Virtual Reality and Cloud-Edge Computing Task Scheduling Algorithm

An adaptive learning model for English vocabulary through a machine learning is proposed in this paper.The four main types of user information, including basic student information, quiz information, course video viewing information, and forum interaction information, are processed through feature engineering, and a better model on sparse data is proposed through comparison on different models, and the prediction accuracy of the model is improved through natural language processing techniques, to achieve feedback on user learning efficiency through user data and provide teachers and students with the corresponding teaching and learning suggestions for teachers and students.It is found that the quiz information has more influence than the course video viewing information, and the accuracy is improved by about 3% compared with TF-IDF after introducing word embedding.The use of mobile for English learners to learn to read in a fragmented learning context enables targeted training in weak areas of English reading, thus improving different aspects of learners' reading skills.
Data mining, sui generis right and aggregators. Some reflections after the judgment of the court of justice of the european union in case CV-Online Latvia v Melons

European Intellectual Property Law provides a special intellectual property (IP) right –the sui generis right– for those database makers that made a substantial investment when creating the database. This IP right is infringed in those cases where the defendant creates a risk that the database maker will lose incomes, thereby depriving that database maker of revenue which should have enabled him to redeem the cost of the investment in setting up and operating the database. However, in recent Melons case, the Court of Justice of the European Union redefines the sui generis right in relation to online aggregators. It highlights the fact that access to information and competition concerns have to enter the sui generis right’s infringement test. It is the first time that this Court balances the interest of the database maker and those of the other parties, as part of the infringement test. We have to keep in mind European caselaw on aggregators and scraping since scraping tools are playing and will play a key role in the development of artificial intelligence
Water 4.0 in Hungary: Prospects and Cybersecurity Concerns

This paper discusses the increasing significance of smart water management, within the context of the fourth industrial revolution and the associated cybersecurity risks, particularly in Hungary and Central Europe.By examining the current state of smart water management and analyzing the various cybersecurity threats, this study seeks to raise awareness around the need for enhanced security measures, in this critical sector.The research methodology is primarily based on a through literature review and secondarily, on related data analysis.The paper identifies several cybersecurity challenges and potential solutions for smart water management and finally suggests future research directions, to ensure the safe and sustainable development of this critical infrastructure.
Maintenance Of Local Languages Through Assistance Of Local Content Teachers In Elementary School

Â One of the opportunities to strengthen the resilience of local languages is through local content subjects. The unavailability of reference standards in developing local content curricula in primary schools confuses teachers in projecting the targets to be achieved. The unavailability of the competency standard formulation has caused the field of study of local content to differ from one elementary school to another. Currently, the field of study of local content is taught by teachers who do not have special qualifications. Some teachers are appointed with religious education, sports education, art education. With this background, teachers often do not have references to representative local languages have not mastered the rules of local languages well. Under these conditions, training and mentoring efforts are needed. This service activity is carried out with a strategy starting with need assessment, FGD, curriculum formulation, teacher assistance, and monitoring and evaluation. This service aims to awaken teacher and student awareness about the importance of maintaining local languages and references in learning local content in the form of draft teaching materials.Â
Adding RF situational awareness to robotic simulation systems

Various tools are now available to assist the roboticist in developing autonomy algorithms for tasks such as path planning or collision avoidance. Many tools support the integration of live or simulated RGB cameras, LIDAR, radar, and IMU sensors. This paper will describe adding an RF sensor. The proposed RF sensor detects radio and locates emitters in the environment for the purpose of collision avoidance. We outline an approach to share data to help locate and avoid collisions. The protocol is designed to maximize safety, privacy, security, timeliness, and other desirable properties discussed in the paper. Preliminary results are shown to illustrate the concepts.
Fast processing of underwater active polarimetric dehazing imaging without prior knowledge

The scattering and absorption of suspended particles in water severely restrict the quality of underwater imaging. Several works have been proposed to improve the dehazing effect, but their efficiency was not considered. However, the processing efficiency of dehazing is an important indicator of the computing cost in an active application. An improved dehazing method that analyzes and optimizes the estimation accuracy of polarization parameters in the traditional underwater polarization imaging model is proposed. The degree of linear polarization of the target object light and the backscattered light and the transmission coefficient in the scattering medium are jointly estimated by the stochastic gradient descent method. The experimental results indicate that the proposed method can better preserve image details at a faster speed than the traditional underwater dehazing imaging method, especially in high-turbidity water environments. The average speed of the algorithm for restoring a 1024 × 768 pixel image is about 6.2 ms.
Progress In Implementing The Framework For Enhanced Fund Engagement On Governance

This paper provides an interim update on implementation of the 2018 Framework for Enhanced Fund Engagement on Governance.This update is in advance of a comprehensive formal review of the Framework scheduled for mid-2021.This interim update finds that implementation of the Framework is well underway.Specifically: (i) a new centralized process to systematically analyze governance and corruption vulnerabilities for all Fund members-a core aspect of the Framework-has been put in place; (ii) text mining analysis indicates that governancerelated terms in staff reports have increased by more than four-fold relative to the average over 2008-17, with such references rising across all types of countries and becoming more correlated with governance weaknesses, though such word counts are unable to assess changes in the quality or traction of staff's advice; (iii) more qualitative analysis suggests that the Framework has supported deeper discussions on governance and anti-corruption issues in Article IV reports, often in accompanying Selected Issues papers or detailed annexes on governance issues; (iv) Fund-supported programs have leveraged the Framework to design specific conditionality related to governance and anti-corruption reforms, with governance improvements now being a core objective of many programs; (v) technical assistance and training to assist countries in strengthening governance and anti-corruption efforts is being stepped up, including governance diagnostic missions, which undertake detailed analysis of governance weaknesses and propose prioritized solutions; (vi) much work is being done to produce analytical work, outreach to the public and civil society, and collaborate with other international organizations on governance issues; and (vii) so far 10 advanced economies have participated in the voluntary assessment of their efforts to stem transnational corruption by effectively criminalizing the bribery of foreign officials and preventing the concealment of the proceeds of corruption in their countries.Continued efforts are needed to support the Framework's implementation.Key challenges include ensuring sustained engagement by the Fund, building further ownership and efforts by country authorities, supporting country teams in these complex discussions, and filling data gaps.Securing additional volunteers for the assessment of transnational aspects of corruption will also be essential.
Cutting-edge Research on Cryptography Theory in Response to Changes in Computing Environments

Triggered by the case that a government employee successfully forged a bank card in 1982, NTT established the first research group on cryptography around the same time.NTT Secure Platform Laboratories has contributed to building a firm theoretical ground for cryptography while developing cryptographic technologies that can respond to the changes in evolving communication and computing environments.In this article, research activities of NTT Secure Platform Laboratories on cryptography and information security technologies-in preparation for the emergence of rapidly developing quantum computers-are discussed.
THE USE OF CROSSWORD PUZZLE ON THE STUDENTS’ ACHIEVEMENT IN LEARNING VOCABULARY

Vocabulary have a big contribution to support the successful students’ of learning English, because without grammar is not big problem in communication, but without vocabulary is large problem in communication with students. It means that vocabulary is very important thing in learning English. This research was conducted the use of crossword puzzle on the students’ achievement in learning vocabulary. The minimum completeness criteria of english lesson SMA Negeri 1 Badar =60. Based on the research mean score of students test on pre test =44,57. The mean score on posttest improved become = 78,04 and it reached the minimum completeness criteria score. There were 33,47 point the difference between pre test to the post test score. The calculation of data by using t test and found the t-observation=3,285 then compared it with ttable=2,074 of level alpha = 5%. It meant that tobservation was higher that t-table =3,285&gt;2,074. So, the researcher concluded that there were achievement on student use of crossword puzzle on the Students’ in learning vocabulary on the X students’ of SMA Negeri 1 Badar.
ANOMALY DETECTION - REVIEW OF METHODS, TOOLS AND ALGORITHMS

This paper contains review of algorithms, methods and tools nowadays used for anomaly detection.Anomaly detection is used in many domains of science and industry, some authors classify anomaly detection as data mining and data science tool, others state it is decision support tool under artificial intelligence domain and indeed the use cases of anomaly detection are very different.The article describes the main algorithms used for anomaly detection from perspective of theory of computer science and practical use cases of anomaly detection in different domains of industry. Several paragraphs are dedicated to the frameworks used by one of the most popular and powerful anomaly detection tools available in the market - Microsoft Anomaly detector.