Reviewerly/exHarmony huggingface dataset:
한국어 학습자의 사과 담화 구성 양상 연구 - 사과하기 역할극 말뭉치를 중심으로 -

This study aims to analyze the discourse construction of apologies in the conversations of Korean learners. To achieve this goal, this study collected spoken data from the role play of Korean learners; this included an official business transaction, personal business transaction, and personal community situation. Also, we investigated the role-play data after setting up the components of an apology discourse construction as “head act,” “a confession, or admission of responsibility,” “an explanation,” “an offer of repair,” “promise of forbearance,” and “an appreciation.” When we examined the quantitative frequency of the components of an apology, the “head act” played a key role in the components of discourse, and then the others followed, and the frequency was different in specific situations. Moreover, we did not find less prototypicality of the frequency in official business transaction situations compared to personal community situations. Furthermore, in this study, we tried to examine the development of the data quantitatively by dividing it into “introduction - evolvement - completion;” and as a result, we found characteristics in each situation, and some linguistic and pragmatics problems in the attempts of Korean learners.
Organizing the network of regional centers “Sirius” to support talented children and peculiarities of the network training of teachers

The features of the activities of regional centers for identifying, supporting and developing the abilities and talents of children and youth, which are created taking into account the experience of the Talent and Success Foundation of the Sirius Educational Center (Sochi), are analyzed. The possibilities of transfer technologies for reproducing the model of talent development in children in the regions of Russia are analyzed according to the model of the Sirius educational center. The features of the organization of educational activities on the basis of regional network of resource centers of the federal project "Success of every child" of the national project "Education" are revealed. The problems of the legal foundations of creating regional centers are analyzed taking into account the experience of the Talent and Success educational fund. The organization of the activities of regional centers in the full project cycle is analyzed - the identification, support and development of abilities and talents among children and youth, monitoring for further development and the creation of infrastructure for the further development of educational programs for children and youth who have shown outstanding abilities. The network partnership with industrial and technological companies in the regions for the development of educational infrastructure is analyzed. The management structure of the Sirius network of regional centers is characterized, as well as the creation of an expert council to support regional centers.
GraphMI: Extracting Private Graph Data from Graph Neural Networks

As machine learning becomes more widely used for critical applications, the need to study its implications in privacy turns to be urgent. Given access to the target model and auxiliary information, the model inversion attack aims to infer sensitive features of the training dataset, which leads to great privacy concerns. Despite its success in grid-like domains, directly applying model inversion techniques on non-grid domains such as graph achieves poor attack performance due to the difficulty to fully exploit the intrinsic properties of graphs and attributes of nodes used in Graph Neural Networks (GNN). To bridge this gap, we present \textbf{Graph} \textbf{M}odel \textbf{I}nversion attack (GraphMI), which aims to extract private graph data of the training graph by inverting GNN, one of the state-of-the-art graph analysis tools. Specifically, we firstly propose a projected gradient module to tackle the discreteness of graph edges while preserving the sparsity and smoothness of graph features. Then we design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference. With the proposed methods, we study the connection between model inversion risk and edge influence and show that edges with greater influence are more likely to be recovered. Extensive experiments over several public datasets demonstrate the effectiveness of our method. We also show that differential privacy in its canonical form can hardly defend our attack while preserving decent utility.
The curious case of posts on Stack Overflow

Community website for programming related Q&A (Question and Answer), Stack Overflow serves as a popular platform to ask questions and respond from other community members. Over the period, user ...
Counterfactual Graph Learning for Link Prediction.

Learning to predict missing links is important for many graph-based applications. Existing methods were designed to learn the observed association between two sets of variables: (1) the observed graph structure and (2) the existence of link between a pair of nodes. However, the causal relationship between these variables was ignored and we visit the possibility of learning it by simply asking a counterfactual question: would the link exist or not if the observed graph structure became different? To answer this question by causal inference, we consider the information of the node pair as context, global graph structural properties as treatment, and link existence as outcome. In this work, we propose a novel link prediction method that enhances graph learning by the counterfactual inference. It creates counterfactual links from the observed ones, and our method learns representations from both of them. Experiments on a number of benchmark datasets show that our proposed method achieves the state-of-the-art performance on link prediction.
70 Treatment-risk paradox in non st-elevation acute coronary syndromes: how does the anaemia contribute?

<h3>Background</h3> It is not long since researchers started to shed light on management disparities like treatment-risk paradox in NSTE-ACS in emerging countries, including Iraq. We find it is crucial to address the possible contributing factors for this phenomenon, one of the proposed underlying causes for treatment-risk paradox is anaemia. <h3>Purpose</h3> This study aimed to investigate the impact of anaemia on decision and timing of intervention in NSTE-ACS. <h3>Methods</h3> Admitted patients with NSTE-ACS were prospectively recruited, their baseline characteristics, management strategy (invasive vs conservative) and timing to intervention if done; all were recorded. Then they were grouped into anaemic (Hb&lt;13 in male, &lt;12 in female) vs non-anaemic, GRACE risk score was calculated, so stratified into low, intermediate and high with scores &lt;109, 109-140 and &gt;140 respectively. <h3>Results</h3> Total n.=183, 32.8% were anaemic, anaemic patients were older (62.6±10.9 vs 56.8±11.6, p=0.001), more to be females (45% vs 24.4%, p=0.005) and more to report prior history of IHD (65% vs 44.7%, p=0.01), anaemic patients were less to be smokers versus non -anaemic counterparts (23.3% vs 37.4%, p=0.04). Anaemic group were more to have dyspnea at presentation (48.3% vs 26.8%, p=0.004). Anaemic patients were more at high GRACE risk class (35% vs 30.1%) and more to develop acute heart failure (AHF) (35% vs 15.4%, p=0.003) than their non-anaemic counterparts, yet, they were less to be treated invasively (48.3% vs 72.4%, p=0.001). However, at high risk class they were more to be catheterized than non-anaemics and more to be catheterized within &lt;72 hours (54.5% vs 43.8%) while at low risk class non-anaemics were more to be catheterized within &lt; 72 hours (34.7% vs 28.6%), see <b>figure 1</b>. <h3>Conclusion</h3> Anaemia in NSTE-ACS occurs more in elderly, females and high GRACE risk class patients, anaemic patients had higher rate of AHF and were less to be catheterized in general, yet more to be catheterized if they were at high risk class but with delayed timing. Future studies are warranted to provide further insights on risk stratification and targeted therapies for those patients. <h3>Conflict of Interest</h3> None
Learning the Dynamic Change of User Interests from Noise Web Data

The web is noise, inconsistent and irrelevant by nature, finding useful information that defines interest of user has become a challenge. Noisy web data is currently considered as data that is not part of the main web page content. However, not every item of information that forms part of the main web content meets the interests of web users. Existing research acknowledges that there is a need to propose machine learning tools capable of addressing problems with data available on the web and what users are interested in. This research work presents an investigation of current research work proposed in minimizing the levels of noisy data on the web. It examines their contribution as well as existing challenges. It proposes an approach that learns web data in relation to interestingness of users. The proposed approach considers the dynamic change of user interest as well as evolving web data. This is to ensure not only reduction of noise in web data but also decrease of useful information otherwise identified and eliminated as noise by existing tools. The practical application of the proposed tool will contribute towards creating dynamic web contents that are seamless to dynamic user interests. Experiments conducted in this research shows that noise web data reduction process is driven by evolving user interests. As a result, what is currently identified and eliminated as noise can be useful when user interests and their changes over time are considered
The Frontiers of Deep Learning for Earth System Modelling&amp;#160;

&amp;lt;p&amp;gt;This talk gives an overview of cutting-edge artificial intelligence applications and techniques for the earth-system sciences. We survey the most important recent contributions in areas including extreme weather, physics emulation, nowcasting, medium-range forecasting, uncertainty quantification, bias-correction, generative adversarial networks, data in-painting, network-HPC coupling, physics-informed neural nets, and geoengineering, amongst others. Then, we describe recent AI breakthroughs that have the potential to be of greatest benefit to the geosciences. We also discuss major open challenges in AI for science and their potential solutions. This talk is a living document, in that it is updated frequently, in order to accurately relect this rapidly changing field.&amp;lt;/p&amp;gt;
Control cost and quantum speed limit time in controlled almost exact state transmission in open systems

We investigate the influence of the environment noise on the control cost and quantum speed limit time (QSLT) in the process of quantum state transmission through a spin chain under pulse control. The chain is immersed in its surrounding non-Markovian, finite temperature heat baths. We find that almost exact state transmission (AEST) can be realized in weak system-bath coupling, low temperature, and strong non-Markovian baths under effective external control. Correspondingly, the control cost decreases with decreasing bath temperature, system-bath coupling strength. Non-Markovianity from the baths can also help to reduce the cost. There exists a trade-off between the control cost and transmission fidelity. Higher fidelity requires higher cost. In addition, the minimum cost has been found to obtain AEST. Furthermore, we discuss the QSLT during the process of AEST. It is noticeable that non-Markovianity from the baths plays a very important role. More non-Markvoian baths correspond to shorter QSLT for weak system-bath coupling and low temperature. However, in the strong coupling and high temperature regime, more Markovian baths correspond to shorter QSLT.
Authentication Protocol for Anti Counterfeiting System using NFC

Counterfeit medicines are defined by World Health Organization (WHO) as those that are fraudulently and deliberately unlabelled with identity. Various products that are counterfeited, cause problems to various manufacturing companies such as automotive parts, jewelry, cosmetics, software food and beverage, etc. Pharmaceutical products have serious threats from it. Counterfeit medicines do not offer any countermeasure to diseases, that is why the people, who use these medicines, suffer a lot. This problem poses a threat to lawful manufacturing companies since it results in revenue loss.
Half-day Panoramic view on Acoustic Materials - 1 - Fabien Chevillotte

The youth group of the new EAA (European Acoustics Association) Technical Committee on Acoustic Materials, the YAN (Young Acoustician Network) and the YRAM (Young Researchers in Acoustic Metamaterials) organized a joint event for young researchers (masters/PhD/post-docs) on the topic of acoustic materials on Monday, May 10, 2021. The program included 6 accessible lectures to grasp different aspects of the topic: Introduction to porous materials - Fabien Chevillotte (Matelys) Current (and future) trends in acoustic materials - Bart Van Damme (Empa) From granular media to magneto granular crystals - Georgios Theocharis (LAUM) Viscoelasticity in porous materials - Lucie Rouleau (CNAM) Bio-based acoustic materials for building applications - Clement Piegay (CEREMA) Biomedical ultrasound applications of acoustics holograms - Noe Jimenez (I3M, UPV) Links: EAA TC on Acoustic Materials: https://euracoustics.org/technical-committees/tc-on-acoustic-materials/tc-on-acoustic-materials EAA YAN: https://euracoustics.org/yan YRAM: https://yram.org
The Development of Students’ Discovery Learning Worksheet Based on Android in Buffer Solution

The Development of Students’ Discovery Learning Worksheet Based on Android in Buffer Solution. The objectives of this research were to develop and determine the feasibility of Android-based learning media and to describe its characteristics, teachers’ and students’ responses to the student worksheets discovery learning based on Android in buffer solution. This research used Borg and Gall research design and development which only focused on the first five stage. The data were analyzed using descriptive statistics analysis. The expert validation showed that the average percentage on content suitability aspect, construction and readability were 89,44%, 92,85% and 92,94% and considered as very high criteria consecutively. The teachers’ responses on suitability, construction and readability aspects were 92,59%, 93,96% and 94,51% and considered as very high criteria respectively. Therefore, students’ responses on readability and attractiveness were 96,03% and 98,05% and considered as very high criteria. Based on these results, the student worksheets based on Android generated by this research is valid and worthy as sources of learning. Keywords: learning media, discovery learning model, student worksheets, android, buffer solution.
Perancangan Sistem Informasi Akuntansi Berbasis Web (Studi Kasus : Bengkel Audio Mobil Konsep Auto Accessories)

Car Audio Workshop The concept of Auto Accessories is a shop that is engaged in trading that sells equipment such as audio, accessories, and car seats, and services that sell wages for equipment purchased by customers. At the Car Audio Workshop all the recording of purchases and sales transactions and merchandise inventory is still done manually so that it causes a lot of errors in the making of financial statements and also errors in inventory of goods that are detrimental in business decisions. Based on the existing problems, an accounting information system and inventory of goods are made to support the Car Audio Workshop in carrying out its business. Accounting information systems are built using the PHP programming language and MySQL database. The method used is inventory valuation method that is first in first out (FIFO) because this method is suitable for the sales department where when making a sale, the goods sold are the earliest items purchased. With the existence of this system, the recording of financial statements and every transaction and amount of inventory becomes easier and more practical. This system is tested by conducting black box testing and user acceptance test (UAT) so that it can find out how user acceptance of the information system developed.Keywords: PHP, MySQL, FIFO,  Black Box and User Acceptance Test (UAT).
Intelligent Multi-Keyword Ranked Searchable Security Algorithm

enterprises are moving their valuable information to the cloud because of the advantage that storage as a service brings. Reliability is at the heart of the cloud service and relationship. We use the sense of security when establishing trust. Trust is established through cryptography. Searchable encryption is a method to provide security. Literature research workers have been studying searchable encryption schemes. We will focus on advanced techniques that leverage CRSA and B-Tree to enhance level of trust. We conducted a search on encrypted data using the Azure cloud platform.
NDPNet: A novel non-linear data projection network for few-shot fine-grained image classification

Metric-based few-shot fine-grained image classification (FSFGIC) aims to learn a transferable feature embedding network by estimating the similarities between query images and support classes from very few examples. In this work, we propose, for the first time, to introduce the non-linear data projection concept into the design of FSFGIC architecture in order to address the limited sample problem in few-shot learning and at the same time to increase the discriminability of the model for fine-grained image classification. Specifically, we first design a feature re-abstraction embedding network that has the ability to not only obtain the required semantic features for effective metric learning but also re-enhance such features with finer details from input images. Then the descriptors of the query images and the support classes are projected into different non-linear spaces in our proposed similarity metric learning network to learn discriminative projection factors. This design can effectively operate in the challenging and restricted condition of a FSFGIC task for making the distance between the samples within the same class smaller and the distance between samples from different classes larger and for reducing the coupling relationship between samples from different categories. Furthermore, a novel similarity measure based on the proposed non-linear data project is presented for evaluating the relationships of feature information between a query image and a support set. It is worth to note that our proposed architecture can be easily embedded into any episodic training mechanisms for end-to-end training from scratch. Extensive experiments on FSFGIC tasks demonstrate the superiority of the proposed methods over the state-of-the-art benchmarks.
Chaotic diffusion of the fundamental frequencies in the Solar System

The long-term variations of the orbit of the Earth govern the insolation on its surface and hence its climate. The use of the astronomical signal, whose imprint has been recovered in the geological records, has revolutionized the determination of the geological time scales (e.g. Gradstein & Ogg 2020). However, the orbital variations beyond 60 Myr cannot be reliably predicted because of the chaotic dynamics of the planetary orbits in the Solar System (Laskar 1989). Taking into account this dynamical uncertainty is necessary for a complete astronomical calibration of geological records. Our work addresses this problem with a statistical analysis of 120 000 orbital solutions of the secular model of the Solar System ranging from 500 Myr to 5 Gyr. We obtain the marginal probability density functions of the fundamental secular frequencies using kernel density estimation. The uncertainty of the density estimation is also obtained here in the form of confidence intervals determined by the moving block bootstrap method. The results of the secular model are shown to be in good agreement with those of the direct integrations of a comprehensive model of the Solar System. Application of our work is illustrated on two geological data: the Newark-Hartford records and the Libsack core.
PoDT: A Secure Multi-chains Consensus Scheme Against Diverse Miners Behaviors Attacks in Blockchain Networks

As cross-chain technologies make the interactions among different blockchains (hereinafter "chains") possible, multi-chains consensus is becoming more and more important in blockchain networks. However, more attention has been paid to the single-chain consensus schemes. The multi-chains consensus with trusted miners participation has been not considered, thus offering opportunities for malicious users to launch Diverse Miners Behaviors (DMB) attacks on different chains. DMB attackers can be friendly in the consensus process of some chains called mask-chains to enhance trust value, while on other chains called kill-chains they engage in destructive behaviors of network. In this paper, we propose a multi-chains consensus scheme named as Proof-of-DiscTrust (PoDT) to defend against DMB attacks. Distinctive trust idea (DiscTrust) is introduced to evaluate the trust value of each user concerning different chains. A dynamic behaviors prediction scheme is designed to strengthen DiscTrust to prevent intensive DMB attackers who maintain high trust by alternately creating true or false blocks on kill-chains. On this basis, a trusted miners selection algorithm for multi-chains can be achieved at a round of block creation. Experimental results show that PoDT is secure against DMB attacks and more effective than traditional consensus schemes in multi-chains environments.
A Multimodal Social Semiotic Analysis of Emoji's Use with Reference to COVID-19

Emojis pose significant challenges for researchers in the various platforms of social media. Their meanings are sometimes far-fetched and difficult to grasp. In spite of their assumed universality, the sense of an emoji may change from one language to another and from culture to culture. This paper investigates the use of emojis in everyday language via a multimodal social-semiotic analysis investigating the collaboration of different semiotic modes with the context of situation to generate the intended meaning out of the multimodal features of emojis.  It tries to adopt aquantitative research methodology relying on two questionnaires to understand the meaning potentials of these significantfactors of social media.60 participants have been collected, 30 students (15 male and 15 female students) and 30 academics (15 male and 15 female academics) of Al-Maarif University College. The test is designed to find out the extent of awareness and recognition between students and academics in terms of meanings of emojis during Covid-19 pandemic and whether or not misunderstanding of their meanings may affect their intended messages to achieve successful communication. The study tries to find out any possible variations between the two parties understudy. The questionnairesare designed to find out how the participants apply their knowledge of emoji meanings in their communication. The Findings have revealed that there are many variations in the way emojis are handled by each group and an increase in using covid-19 special emojis other than the rest of emojis as a context-based usage.