Reviewerly/exHarmony huggingface dataset:
Consideration on Credibility of the Speaker : By analyzing the perception of university students focusing on the categories and content elements of credibility

This study aims to comprehensively examine the practical aspects of credibility as well as the academic and theoretical characteristics of credibility through a survey of the perceptions of university students on credibility. In addition, while analyzing the social atmosphere and context reflected in the perception of credibility, it attempts to derive implications for the direction of communication education in university liberal arts education.<BR> In this study, the constituent elements of credibility that college students think are classified into affective, cognitive, behavioral, integrated, and social categories according to their characteristics, and after presenting each detailed element, the characteristics of the characters mentioned in each example were examined.<BR> Looking at people with credibility by field, entertainers(20.4%) > lecturers(11.7%) > administrators(9.7%) > writers(7.8%) > YouTubers(6.8%), politicians(6.8%), professionals(6.8%) > Businessmen(4.9%) followed by facilitators(3.9%), professors and researchers(3.9%), athletes(1.9%), and religious people(1.9%).<BR> This paper categorizes the constituent elements of public confidence through the analysis of university students" perceptions of the public confidence of the speaker, and presents the constituent elements, and suggests an educational plan based on the hierarchy and diffusion between each category.<BR> This study is meaningful in that it can be used in the development of liberal arts education contents to increase communication competency according to changing times as a basic data for communication education for university students in the future.
Comparing Ground 3D DCIP and Airborne Inductive IP over the Hickey's Pond High Sulphidation Epithermal Target

Summary The results of a ground 3D DCIP survey are compared to AIIP results obtained from airborne EM over the Hickey’s Pond high sulphidation Au-Cu epithermal target on Burin Peninsula, NL. Athough there is good correlation between the 3D DCIP and airborne IP results, there are some differences: notably the AIIP results appear to correlate with drainages, and are likely also influenced by lake bottom sediments. AIIP time-constants are also 2 to over 3 orders of magnitude smaller than expected for ground IP data. But this is related to the different base frequencies used and understood to be reflecting much finer grained sources in AIIP than what the DCIP system is responding to. Even with these differences, the AIIP data appears to spatially correlate well in areas with the ground-based data. The ground 3D IP data have better depth of investigation and respond to the larger grained sulphide sources that are the exploration target.
매김법 제약과 어찌말 매인이름씨 변천과의 상관성

The purpose of this study is to verify the correlation between the constrain of adnominal ending and the transition of adverbial dependent noun. The result could be summarized as follows.Firstly, classified the adverbial dependent nouns of medieval and modern korean language according to their degree of change, and then examined the integration patterns with the adnominal ending. The list is divided into annihilated list, grammatical list, generated list, and maintained list according to the degree of change. As a result of checking the constraint of adnominal ending, the annihilation list and the grammatical list were integrated only with the adnominal ending. The generated list had a different constraint according to the list, and the maintained list was generally unrestricted.Next, confirmed the relationship between adnominal ending and dependent noun transition. For this, questions related to the adnominal ending constraint were presented. First, whether the restriction of adnominal ending is related to the change of dependent noun disappearance or grammaticalization. Second, whether change is proceeded by specific adnominal ending. Third, whether the generated list is related to adnominal ending constraint , And fourth, there are adnominal ending constraints in the list that are maintained, and what are the changes if they are related to the adnominal ending constraint.In summary, can confirm that the adnominal ending constraint and the evolution of dependent nouns are relevant. The choice of adnominal ending seemed to be related to the semantic nature of dependent nouns. And, if there is an adnominal ending constraint among the generated list and the maintained list, can confirm that there is a possibility of change.
The role of the teacher’s acmeological competence in the context of trilingual education

The article discusses the concept of the «acmeological competence» and its role in the context of trilingual education. The policy of trilingual education is described, the insufficient level of the English language profi-ciency among secondary school teachers is noted and the need for the development of a multilingual person-ality is revealed. The authors come to the conclusion that in order to realize this need, it is impossible to do without the acmeological competence, which stimulates the development of such personal qualities as self-realization, self-development and self-improvement. This suggests that a person should constantly develop himself in order to achieve good results in professional activities. However, the processes of self-realization, self-development and self-improvement are not particularly important tasks for all people. These processes become important when a person faces the problem of solving professional or life problems. Thus, the au-thors highlight the fact that every adult should set himself difficult tasks in order to achieve personal and pro-fessional growth. In addition to the acmeological competence, the article examines such concepts as «acmeological ability», «professionalism of activity» and «professionalism of the individual», which also play an important role in the professional formation and in the development of personality. In general, ac-cording to the authors, the development of a multilingual personality, which is a task in the trilingual educa-tion, requires a teacher to be highly motivated in the professional activity.
Learning from multiscale wavelet superpixels using GNN with spatially heterogeneous pooling

Neural networks have become the standard for image classification tasks. On one hand, convolutional neural networks (CNNs) achieve state-of-the-art performance by learning from a regular grid representation of images. On the other hand, graph neural networks (GNNs) have shown promise in learning image classification from an embedded superpixel graph. However, in the latter, studies have been restricted to SLIC superpixels, where 1) a single target number of superpixels is arbitrarily defined for an entire dataset irrespective of differences across images and 2) the superpixels in a given image are of similar size despite intrinsic multiscale structure. In this study, we investigate learning from a new principled representation in which individual images are represented by an image-specific number of multiscale superpixels. We propose WaveMesh, a wavelet-based superpixeling algorithm, where the number and sizes of superpixels in an image are systematically computed based on the image content. We also present WavePool, a spatially heterogeneous pooling scheme tailored to WaveMesh superpixels. We study the feasibility of learning from the WaveMesh superpixel representation using SplineCNN, a state-of-the-art network for image graph classification. We show that under the same network architecture and training settings, SplineCNN with original Graclus-based pooling learns from WaveMesh superpixels on-par with SLIC superpixels. Additionally, we observe that the best performance is achieved when replacing Graclus-based pooling with WavePool while using WaveMesh superpixels.
"Alexa, Can I Program You?": Student Perceptions of Conversational Artificial Intelligence Before and After Programming Alexa

Growing up in an artificial intelligence-filled world, with Siri and Amazon Alexa often within arm's - or speech's - reach, could have significant impact on children. Conversational agents could influence how students anthropomorphize computer systems or develop a theory of mind. Previous research has explored how conversational agents are used and perceived by children within and outside of learning contexts. This study investigates how middle and high school students' perceptions of Alexa change through programming their own conversational agents in week-long AI education workshops. Specifically, we investigate the workshops' influence on student perceptions of Alexa's intelligence, friendliness, aliveness, safeness, trustworthiness, human-likeness, and feelings of closeness. We found that students felt Alexa was more intelligent and felt closer to Alexa after the workshops. We also found strong correlations between students' perceptions of Alexa's friendliness and trustworthiness, and safeness and trustworthiness. Finally, we explored how students tended to more frequently use computer science-related diction and ideas after the workshops. Based on our findings, we recommend designers carefully consider personification, transparency, playfulness and utility when designing CAs for learning contexts.
Integrated Studies Intrigue to Mineral Prospectivity. Applicability to Kikombo, Dodoma, Tanzania

Summary Detailed prospecting studies were carried out in Kikombo, Dodoma, Central Tanzania to examine the possible mineralization zones through delineating subsurface structures and characterizing geochemical distribution of various elements by integrating the results of the methods applied. The prospecting methods utilized include geological methods, regional aeromagnetic survey, local ground magnetic survey, airborne gravity survey, induced polarization survey and geochemical survey. Geological observation shows the area’s underneath geology to be composed of tonalitic — orthogneiss rock types whose regolith is composed of brown sand which is light as well as reddish. Gravity survey results suggest high gravity anomaly to most of the occupied area. Aeromagnetic surveys reveal high magnetic anomaly to most of the occupied area with NE-SW structural trending patterns. Local ground magnetic survey reveals to lack prominent structures (faults) that are deep and localized, however, the shallow structures were revealed showing similar trend of NE -SW. The depth to the magnetic anomaly of various methods employed goes up to 494.1 m. IP results indicate high resistive and chargeable bodies at the basement with some faults striking NE-SW. Geochemical survey shows the concentration of elements such as As, Au, Ca, Cr, Cu, Fe, K, Mg, Mn, P, S, Ti, V, Zn and Zr while suggesting a strong positive correlation of Mn and Cu.
Building Our Application

We’ve been working in the safety of our component workspace. Unfortunately, unless our product is a component library, our users won’t receive much value from our components unless we place them in our real application. In this chapter, we’re going to take everything we’ve made so far using Storybook and apply it to a real application. Our approach with Storybook is a bit different from how many applications are built, but as we’ll see in this chapter, when we’re ready to build our actual application, everything falls into place pretty quickly.
Generating Artificial Core Users for Interpretable Condensed Data

Recent work has shown that in a dataset of user ratings on items there exists a group of Core Users who hold most of the information necessary for recommendation. This set of Core Users can be as small as 20 percent of the users. Core Users can be used to make predictions for out-of-sample users without much additional work. Since Core Users substantially shrink a ratings dataset without much loss of information, they can be used to improve recommendation efficiency. We propose a method, combining latent factor models, ensemble boosting and K-means clustering, to generate a small set of Artificial Core Users (ACUs) from real Core User data. Our ACUs have dense rating information, and improve the recommendation performance of real Core Users while remaining interpretable.
Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification

In this paper, we propose a novel method that trains pass-phrase specific deep neural network (PP-DNN) based auto-encoders for creating augmented data for text-dependent speaker verification (TD-SV). Each PP-DNN auto-encoder is trained using the utterances of a particular pass-phrase available in the target enrollment set with two methods: (i) transfer learning and (ii) training from scratch. Next, feature vectors of a given utterance are fed to the PP-DNNs and the output from each PP-DNN at frame-level is considered one new set of generated data. The generated data from each PP-DNN is then used for building a TD-SV system in contrast to the conventional method that considers only the evaluation data available. The proposed approach can be considered as the transformation of data to the pass-phrase specific space using a non-linear transformation learned by each PP-DNN. The method develops several TD-SV systems with the number equal to the number of PP-DNNs separately trained for each pass-phrases for the evaluation. Finally, the scores of the different TD-SV systems are fused for decision making. Experiments are conducted on the RedDots challenge 2016 database for TD-SV using short utterances. Results show that the proposed method improves the performance for both conventional cepstral feature and deep bottleneck feature using both Gaussian mixture model - universal background model (GMM-UBM) and i-vector framework.
Fock state interferometry for quantum enhanced phase discrimination

We study Fock state interferometry, consisting of a Mach-Zehnder Interferometer with two Fock state inputs and photon-number-resolved detection at the two outputs. We show that it allows discrimination of a discrete number of apriori-known optical phase shifts with an error probability lower than what is feasible with classical techniques under a mean photon number constraint. We compare its performance with the optimal quantum probe for M-ary phase discrimination, which unlike our probe, is difficult to prepare. Our technique further allows discriminating a null phase shift from an increasingly small one at zero probability of error under ideal conditions, a feature impossible to attain using classical probe light. Finally, we describe one application to quantum reading with binary phase-encoded memory pixels.
Iterated graph neural network system

We present Iterated Graph Neural Network System (IGNNS), a new framework of Graph Neural Networks (GNNs), which can deal with undirected graph and directed graph in a unified way. The core component of IGNNS is the Iterated Function System (IFS), which is an important research field in fractal geometry. The key idea of IGNNS is to use a pair of affine transformations to characterize the process of message passing between graph nodes and assign an adjoint probability vector to them to form an IFS layer with probability. After embedding in the latent space, the node features are sent to IFS layer for iterating, and then obtain the high-level representation of graph nodes. We also analyze the geometric properties of IGNNS from the perspective of dynamical system. We prove that if the IFS induced by IGNNS is contractive, then the fractal representation of graph nodes converges to the fractal set of IFS in Hausdorff distance and the ergodic representation of that converges to a constant matrix in Frobenius norm. We have carried out a series of semi supervised node classification experiments on citation network datasets such as citeser, Cora and PubMed. The experimental results show that the performance of our method is obviously better than the related methods.
Learning a Generative Motion Model from Image Sequences based on a Latent Motion Matrix

We propose to learn a probabilistic motion model from a sequence of images for spatio-temporal registration. Our model encodes motion in a low-dimensional probabilistic space - the motion matrix - which enables various motion analysis tasks such as simulation and interpolation of realistic motion patterns allowing for faster data acquisition and data augmentation. More precisely, the motion matrix allows to transport the recovered motion from one subject to another simulating for example a pathological motion in a healthy subject without the need for inter-subject registration. The method is based on a conditional latent variable model that is trained using amortized variational inference. This unsupervised generative model follows a novel multivariate Gaussian process prior and is applied within a temporal convolutional network which leads to a diffeomorphic motion model. Temporal consistency and generalizability is further improved by applying a temporal dropout training scheme. Applied to cardiac cine-MRI sequences, we show improved registration accuracy and spatio-temporally smoother deformations compared to three state-of-the-art registration algorithms. Besides, we demonstrate the model's applicability for motion analysis, simulation and super-resolution by an improved motion reconstruction from sequences with missing frames compared to linear and cubic interpolation.
EU Commission Proposal: Regulatory Package on Crypto-Assets

On 24<sup>th</sup> September 2020, the EU Commission has published a package of legislative proposals with the aim to create an EU framework on markets in crypto-assets, tokenisation of traditional financial assets and the use of distributed ledger technology (DLT) in financial services as part of the EU digital finance strategy. It includes an addendum to the “MiFID II” Directive 2014/65/EU clarifying that the existing definition of “financial instruments” includes “instruments issued by means of distributed ledger technology”; a proposal for a Regulation on Markets in Crypto-assets (MiCA) to cover crypto-assets falling outside existing EU financial services regulation, including “stablecoins” as well as e-money tokens; a proposal for a Regulation on a Pilot Regime for Market Infrastructures based on Distributed Ledger Technology an “EU passport” for cryptoassets. Related legislative procedures are already on track including general recommendations and first specific motions for amendments of the Commission’s proposals by the European Parliament. It can be expected that the final outcome will be a comprehensive MiFID-type framework (MiCA) on markets in crypto-assets.
Technology Focus: Seismic (February 2021)

It was gratifying to see that, despite the challenges inflicted by the pandemic this past year, technologies in the seismic world continued to advance. As an example, the machine-learning theme again received considerable focus. As I reviewed all the SPE seismic papers this time, the most noticeable thing was the diversity of themes and case histories that were covered. Thus, in addition to selecting three papers to be synopsized this year, I expanded the list of recommended readings to showcase that diversity. For instance, gravity and electromagnetics (and their integration with seismic) are also featured. I hope you will find this broad range of topics useful.
Enhancing Human-Machine Teaming for Medical Prognosis Through Neural Ordinary Differential Equations (NODEs)

Machine Learning (ML) has recently been demonstrated to rival expert-level human accuracy in prediction and detection tasks in a variety of domains, including medicine. Despite these impressive findings, however, a key barrier to the full realization of ML's potential in medical prognoses is technology acceptance. Recent efforts to produce explainable AI (XAI) have made progress in improving the interpretability of some ML models, but these efforts suffer from limitations intrinsic to their design: they work best at identifying why a system fails, but do poorly at explaining when and why a model's prediction is correct. We posit that the acceptability of ML predictions in expert domains is limited by two key factors: the machine's horizon of prediction that extends beyond human capability, and the inability for machine predictions to incorporate human intuition into their models. We propose the use of a novel ML architecture, Neural Ordinary Differential Equations (NODEs) to enhance human understanding and encourage acceptability. Our approach prioritizes human cognitive intuition at the center of the algorithm design, and offers a distribution of predictions rather than single outputs. We explain how this approach may significantly improve human-machine collaboration in prediction tasks in expert domains such as medical prognoses. We propose a model and demonstrate, by expanding a concrete example from the literature, how our model advances the vision of future hybrid Human-AI systems.
Aplikasi Android Mengenal Tata Surya Berbasis Augmented Reality Sebagai Media Belajar Interaktif Siswa Sekolah Dasar

The use of Android-based smartphones is currently increasing rapidly. This makes application developers compete to create various applications. Augmented Reality (AR) is an environment that inserts 3D virtual objects into a real environment. AR is a visualization technology that is currently being developed in several fields. Augmented Reality application in the field of education developed in this study is to discuss the solar system for elementary school students. By presenting a good application content, children's imagination will develop positively. Many media explain about the solar system, such as books and videos. However, the development of Augmented Reality-based media is expected to provide its own insights for students. This research has successfully developed a prototype of a product or media in the form of a book that can be interacted with the AR application on an Android smartphone. The discussion in this study will focus on the application side designed, not on the interactive book design side. Regarding the interactive book media, visual displays of books which have been developed. Application testing has been done by directly interacting with students at the elementary school level, then they respond to the questionnaire. The results of these tests are, the level of usability of the application gets a good response and is easy for use by elementary school students
Application of Intelligent Clustering Algorithm in Image Processing

In order to solve the limitation of traditional K-means algorithm in dealing with large-scale data, a fast approximate k-means algorithm (FAKM) is proposed based on the approximate k-means algorithm (AKM) and the idea of classifying the cluster centers. The algorithm omits the cluster centers which only obtain a few samples in the AKM clustering results, and makes full use of the cluster centers with dense and stable samples in the cluster, In the iterative process, the number of samples and categories to be clustered is gradually reduced, which improves the speed of the algorithm and simplifies the clustering results. The FAKM algorithm is applied to the actual image retrieval system, and the experimental results show that the retrieval accuracy, retrieval time and clustering time of the system are greatly improved.