Reviewerly/exHarmony huggingface dataset:
Research on risk visualization and warning technology of distribution network based on convolutional neural network

With the expanding topology and equipment scale of distribution network, a huge amount of alarm information is gathered in the distribution network master station, which urgently needs efficient computing power for identification and analysis. In this paper, based on the powerful hardware system of distribution network cyber physical system and advanced event processing capability of convolutional neural network (CNN), we have formed a state sensing and risk warning technology to realize multi-dimensional information integrated processing, visualization and reasoning to support fault information aggregation and processing. Taking the actual main station alarm service as an example, it accurately identifies a large amount of alarm information, realizes numerical analysis, threshold comparison, trend analysis, automatic warning of abnormal figure states, and pushes fault alarms to grid operation and maintenance personnel in real time using visualization interaction methods. The accuracy of equipment identification and defect identification is verified based on real-world distribution network scenarios, supporting the realization of distribution network operation monitoring and fault handling.
Analyzing the Traffic of MANETs using Graph Neural Networks

Graph Neural Networks (GNNs) have been taking role in many areas, thanks to their expressive power on graph-structured data. On the other hand, Mobile Ad-Hoc Networks (MANETs) are gaining attention as network technologies have been taken to the 5G level. However, there is no study that evaluates the efficiency of GNNs on MANETs. In this study, we aim to fill this absence by implementing a MANET dataset in a popular GNN framework, i.e., PyTorch Geometric; and show how GNNs can be utilized to analyze the traffic of MANETs. We operate an edge prediction task on the dataset with GraphSAGE (SAG) model, where SAG model tries to predict whether there is a link between two nodes. We construe several evaluation metrics to measure the performance and efficiency of GNNs on MANETs. SAG model showed 82.1% accuracy on average in the experiments.
Heterogeneous Graph Sparsification for Efficient Representation Learning

Graph sparsification is a powerful tool to approximate an arbitrary graph and has been used in machine learning over homogeneous graphs. In heterogeneous graphs such as knowledge graphs, however, sparsification has not been systematically exploited to improve efficiency of learning tasks. In this work, we initiate the study on heterogeneous graph sparsification and develop sampling-based algorithms for constructing sparsifiers that are provably sparse and preserve important information in the original graphs. We have performed extensive experiments to confirm that the proposed method can improve time and space complexities of representation learning while achieving comparable, or even better performance in subsequent graph learning tasks based on the learned embedding.
Enlarging the notion of additivity of resource quantifiers

Whenever a physical quantity becomes essential to the realization of useful tasks, it is desirable to define proper measures or monotones to quantify it. In quantum mechanics, coherence, entanglement, and Bell nonlocality are examples of such quantities. Given a quantum state $\ensuremath{\varrho}$ and a quantifier $\mathcal{E}(\ensuremath{\varrho})$, both arbitrary, it is generally a hard task to determine $\mathcal{E}({\ensuremath{\varrho}}^{\ensuremath{\bigotimes}N})$. However, if $\mathcal{E}$ turns out to be additive, we simply have $\mathcal{E}({\ensuremath{\varrho}}^{\ensuremath{\bigotimes}N})=Ne$ with $e=\mathcal{E}(\ensuremath{\varrho})$. In this paper we generalize this useful notion through the inner product $\mathcal{E}({\ensuremath{\varrho}}^{\ensuremath{\bigotimes}N})=\stackrel{P\vec}{N}\ifmmode\cdot\else\textperiodcentered\fi{}\stackrel{P\vec}{e}$, where $\stackrel{P\vec}{e}=[\mathcal{E}({\ensuremath{\varrho}}^{\ensuremath{\bigotimes}{i}_{1}}),\mathcal{E}({\ensuremath{\varrho}}^{\ensuremath{\bigotimes}{i}_{2}}),...,\mathcal{E}({\ensuremath{\varrho}}^{\ensuremath{\bigotimes}{i}_{q}})]$ is a vector whose $q$ entries are the figure of merit under study calculated for some numbers of copies smaller than $N$ ($1\ensuremath{\le}{i}_{1}&lt;{i}_{2}&lt;\ensuremath{\cdots}&lt;{i}_{q}&lt;N$) and $\stackrel{P\vec}{N}=({N}_{{i}_{1}},{N}_{{i}_{2}},...,{N}_{{i}_{q}})$ is a string of numbers that depends only on $N$ and on the set of integers ${{i}_{j}}$. The (vector) linear dependence enables a full analytic treatment, and the determination of asymptotic results ($N\ensuremath{\rightarrow}\ensuremath{\infty}$), which may or may not be regular. We show that the one shot distillable entanglement of certain spherically symmetric states can be quantitatively approximated by such an augmented additivity.
Generative Adversarial Networks for Inpainting Occluded Face Images

Convolutional neural network (CNN) recognizers have made substantial progress in face recognition. Existing recognizers have a lot of power over un-occluded faces, but their performance suffers when it comes to recognizing occluded faces directly. As occlusions cause a lack of visual and recognition signals. The face inpainting task is complicated as it requires generating new pixels for the missing regions of the face image. Generative adversarial networks (GAN) are more suitable for this task when we have to reconstruct visually plausible occlusions in face inpainting. The GAN model is able to generate and inpaint the missing regions of the image. In this paper, we have developed a methodology that makes use of GAN and contextual attention to inpaint images. This image inpainting has applications in the area of face recognition, face animation, and generating synthetic data.
Translanguaging in Youtube Channel: A Case Study of Nessie Judge

This study aims at describing the components of translanguaging, the reason for using translanguaging, and the functions of translanguaging in YouTube videos. This research used the descriptive qualitative method. The data were collected from the YouTube channel of Nessie Judge. First, the researcher was transcribed the utterances into written text. Then, identified the transcript and classified into each components of translanguaging. After that, reduced data in the form of table. The components are divided according to the utterances. Then, the researcher was interpreted the reasons of translanguaging in Nessie Judge’s YouTube videos. In addition, the researcher was found the speech function of translanguaging used by Nessie Judge in her YouTube videos. The results of this study show there are two components of translanguaging that Nessie used in her YouTube videos. The translanguaging’s components used in the three videos were dominated by 83% code-switching, and 17% translating and interpreting. The difference between translanguaging and code switching lies in the concept. The concept of translanguaging gives freedom for anyone to use more than two languages without any restrictions, while code-switching has its own limitations. The result of this study also finds there are six reasons why Nessie Judge used translanguaging. The most dominant reason of Nessie Judge used translanguaging is talking about a particular topic with 78%. In addition, there are five speech functions Nessie Judge used translanguaging. The most dominant speech function of Nessie Judge used in the three videos is 74% metalinguistic function.
Automatic Generation of Visualizations for Machine Learning Pipelines

Visualization is very important for machine learning (ML) pipelines because it can show explorations of the data to inspire data scientists and show explanations of the pipeline to improve understandability. In this paper, we present a novel approach that automatically generates visualizations for ML pipelines by learning visualizations from highly-upvoted Kaggle pipelines. The solution extracts both code and dataset features from these high-quality human-written pipelines and corresponding training datasets, learns the mapping rules from code and dataset features to visualizations using association rule mining (ARM), and finally uses the learned rules to predict visualizations for unseen ML pipelines. The evaluation results show that the proposed solution is feasible and effective to generate visualizations for ML pipelines.
Prototyping Deep Learning Applications with Non-Experts: An Assistant Proposition

Machine learning (ML) systems based on deep neural networks are more present than ever in software solutions for numerous industries. Their inner workings relying on models learning with data are as helpful as they are mysterious for non-expert people. There is an increasing need to make the design and development of those solutions accessible to a more general public while at the same time making them easier to explore. In this paper, to address this need, we discuss a proposition of a new assisted approach, centered on the downstream task to be performed, for helping practitioners to start using and applying Deep Learning (DL) techniques. This proposal, supported by an initial testbed UI prototype, uses an externalized form of knowledge, where JSON files compile different pipeline metadata information with their respective related artifacts (e.g., model code, the dataset to be loaded, good hyperparameter choices) that are presented as the user interacts with a conversational agent to suggest candidate solutions for a given task.
MCDA Framework for Edge-Aware Multi-Cloud Hybrid Architecture Recommendation

Deploying applications on hybrid clouds with computational artifacts distributed over public backends and private edges involve several constraints. Designing such deployment requires application architects to solve several challenges, spanning over hard regulatory policy constraints as well as business policy constraints such as enablement of privacy by on-prem processing of data to the extent the business wants, backend support of privacy enabling technologies (PET), sustainability in terms of green energy utilization, latency sensitivity of the application. In this paper, we propose to optimize hybrid cloud application architectures, while taking all those factors into consideration, and empirically demonstrate the effectiveness of our approach. To the best of our knowledge, this work is the first of its kind.
Blockchain Technology as an Accelerator for the Development of Digitalization in the Financial Sector of the Economy

Against the backdrop of the development of the cryptocurrency market, the popularity of blockchain technology has also grown. However, the unique structure of the technology, characterized by its transparency, immutability and, in most cases, decentralization, allows it to be widely used in various areas of life. The article explores the essence of blockchain technology, as well as the degree of technology integration into the financial sector of the economy. The subject of the research is blockchain technology. The object of research is international finance. The author in the article emphasizes the nature of blockchain technology, and also studies the current situation in the blockchain industry, its implementation in the financial sector of the economy, in particular the activities of commercial banks. The blockchain industry is expanding and improving at a steady pace, finding its constant use in both public and private finance. In this direction, active work is also being carried out in Russia. However, due to a number of key limiting factors, to date, blockchain technology has not been integrated in practice to the extent that it could allow it to change the structures of international or individual national finance. Contrary to expectations, the speed of blockchain implementation is slower than that of the Internet technology, which is often compared to the development of blockchain technology. However, despite this fact, the blockchain has gained a foothold in various aspects of international finance and plays one of the key roles in accelerating the process of digitalization of this sector of the economy.
ACRL candidates for 2023: A look at who’s running

Leo S. Lo is the Dean and Professor of the College of University Libraries and Learning Sciences at the University of New Mexico, a position he has held since September 2021. Prior to this, Lo served as Associate Dean, Learning, Undergraduate Services and Commonwealth Campus Libraries, Pennsylvania State University (2019–2021), Associate University Librarian for Research and Learning, Old Dominion University, (2016–2019) and Head of the McLure Education Library and Associate Professor at the University of Alabama (2014–2016). Elisandro (Alex) Cabada is an Assistant Professor, Emerging Technologies and Immersive Scholarship Librarian, and Director of the Grainger IDEA Lab at the University of Illinois at Urbana-Champaign, a position he has held in some capacity since 2016. Cabada has held several other positions at the University of Illinois at Urbana-Champaign, including Medical and Bioengineering Librarian (2018–2022) and Interim Head, Mathematics Library (2020–2022). Prior to this, Cabada served as Engineering and Innovation Librarian at the University of Minnesota Twin Cities (2017–2018). Before 2017, Cabada was Library Support Staff (2007–2017) and a Library Student Assistant (2000–2005) at the University of Illinois at Urbana-Champaign.
The bar-resonator interaction in mallet percussion instruments: A multi-modal model and experimental validation

In some mallet percussion instruments, like vibraphones and marimbas, acoustical resonators are placed under the tuned bars to enhance acoustic radiation. Despite its wide use in commercial instruments, the vibro-acoustic interaction between the tuned bars and their acoustic resonators has not been studied extensively, and previous modeling attempts often neglect important aspects of the coupling dynamics. This work presents a physical model describing the two-way vibro-acoustic interaction between a vibrating beam and an acoustic resonator. The proposed formulation leads to a system composed of a set of mechanical oscillators coupled to a set of acoustical oscillators, representing bar and resonator modes, respectively. The simplicity of the proposed model provides an intuitive understanding of the physics occurring in real instruments and show how specific design parameters will affect their behavior. The dynamics of the system are analyzed through energy balance, time-domain simulations and eigenvalue analysis, revealing a number of interesting features and highlighting three dimensionless parameters: the ratio of frequencies and quality factors, as well as a coupling “strength” coefficient, dependent on the bar-resonator distance, the placement of the resonator along the bar length, bar and resonator mode shapes, amongst others. The proposed model is also shown to be a useful tool for the design of instruments optimized for multi-modal coupling, where several resonator modes are tuned to bar frequencies. Finally, experimental results are presented to validate various aspects of the proposed model and demonstrate its capacity to emulate real instruments, both qualitatively and quantitatively.
Editorial Introduction: Substructural Logics and Metainferences

The concept of substructural logic was originally introduced in relation to limitations of Gentzen’s structural rules of Contraction, Weakening and Exchange. Recent years have witnessed the development of substructural logics also challenging the Tarskian properties of Reflexivity and Transitivity of logical consequence. In this introduction we explain this recent development and two aspects in which it leads to a reassessment of the bounds of classical logic. On the one hand, standard ways of defining the notion of logical consequence in classical logic naturally induce substructural logics when admitting more than two truth values; on the other hand, these substructural logics give rise to hierarchies of metainferences that can be used to approximate classical logic at different levels.
Validity and Reliability Study of IST 2000R Indonesian Version

In addition to the limited use of the Intelligenz Struktur Test 2000 Revised (IST 2000R), the adaptation process of the IST 2000R into the Indonesian version is thought to influence this test. Therefore, this study aims to conduct a review of the psychometric properties of the IST 2000R Indonesian version. The research method used is quantitative research and 919 participants, aged 17-30 years, M = 380; F=539 were involved as the sample. The data analysis technique used consisted of the construct validity test using Confirmatory Factor Analysis (CFA) and estimation of internal consistency reliability Cronbach Alpha. Analysis showed that fit results after modification indices with a p-value is 0.05675, RMSEA index is 0.023 ( &lt; 0.05), GFI 0.99, CFI 0.99. and NFI 0.98 ( &gt; 0.9). The results of Composite Reliability (CR) on IST 2000R show that the reliability of sub-tests that measure numerical and abstract intelligence is good, while the reliability of verbal sub-test items is low shown by CR is 0.44.
Automation of the generation of scientific statistics and the development of monitoring publishing activity dashboards in real time

An approach to the automation of publishing activity reporting has been developed. The approach allows to generate dashboards for monitoring scientometric activity in real time and present lists of publications in any required form (in particular, tabular).
Integrating audio and visual modalities for multimodal personality trait recognition via hybrid deep learning

Recently, personality trait recognition, which aims to identify people’s first impression behavior data and analyze people’s psychological characteristics, has been an interesting and active topic in psychology, affective neuroscience and artificial intelligence. To effectively take advantage of spatio-temporal cues in audio-visual modalities, this paper proposes a new method of multimodal personality trait recognition integrating audio-visual modalities based on a hybrid deep learning framework, which is comprised of convolutional neural networks (CNN), bi-directional long short-term memory network (Bi-LSTM), and the Transformer network. In particular, a pre-trained deep audio CNN model is used to learn high-level segment-level audio features. A pre-trained deep face CNN model is leveraged to separately learn high-level frame-level global scene features and local face features from each frame in dynamic video sequences. Then, these extracted deep audio-visual features are fed into a Bi-LSTM and a Transformer network to individually capture long-term temporal dependency, thereby producing the final global audio and visual features for downstream tasks. Finally, a linear regression method is employed to conduct the single audio-based and visual-based personality trait recognition tasks, followed by a decision-level fusion strategy used for producing the final Big-Five personality scores and interview scores. Experimental results on the public ChaLearn First Impression-V2 personality dataset show the effectiveness of our method, outperforming other used methods.
“SOCIAL BONDS-AN EMERGING TOOL IN FINANCIAL MODELING; APPROACH OF FINANCIAL SUSTAINABILITY”

With the ever-changing needs and goals of society and business, the vision of corporates has reformed inevitably. Giant companies are an integral part of society and hence it has a certain sense of responsibility toward social well-being. Under nancial goals, in the journey past that target, only prot maximization has changed to wealth maximization of investors, and presently the decisions are relying upon a goal counting sustainability. In investment decisions under sustainable nance; Environmental, Social and Global (ESG) factors are most considered. No poverty, well-being of citizens, gender equality, affordable energy, strong infrastructure, climactic action, etc. are some factors considered in ESG. Social bonds are a recent emerging nancial tool aiming at sustainability, where the bond proceeds raised by the organizations are used for projects that have a positive social outcome. Hence the present paper attempts to present some related conceptions of social bonds, their need and growth, and challenges that may enforce in its issue process.
Prospects overview of hybrid brain-computer interface for increasing efficiency of research activity

Neuromanagement strategies are becoming a common trend of self-development, which is quite important for researchers, especially in search work. Assisting one’s own cognitive capabilities increases the effectiveness of typical activities through the full application of cognitive resources, combining several technologies. According to technology development programs, particularly in open science, the next two generations can use neuroplasticity as a new competence.
The impact of the open access policy on the students’ motivation to improve the research quality

The article analyzes the impact of Open Access on students’ motivation to improve their research. With the help of qualitative content analysis of semi-standardized interviews, it was found out that the Open Access policy stimulates both positive and negative motivation, which positively affects the desire for the scientific quality.
The influence of social network signals on the development of library web resources by search words

The relationship between social media and library web resources is deeper than it may seem at first glance, and the effective use of modern tools can improve the search output of a web resource, which in turn will allow the library to effectively perform its information management functions in the modern digital economy .
“Open science”: contradictions and algorithms of implementation

The article presents the results of research that argue the need to work out the concept of “Open Science”, its practical realization based on balancing the demand for the results of scientific activity and their accessibility. This is realized through the development of constructive relationships between subjects of scientific activity, an innovative environment, an adequate information space (access to databases in their context).
Solving Vehicle Equipment Specification Problems with Answer Set Programming

We develop an approach to solving mono- and multi-objective vehicle equipment specification problems considering the corporate average fuel economy standard (CAFE problems, in short) in automobile industry. Our approach relies upon Answer Set Programming (ASP). The resulting system aspcafe accepts a CAFE instance expressed in the orthogonal variability model format and converts it into ASP facts. In turn, these facts are combined with an ASP encoding for CAFE solving, which can subsequently be solved by any off-the-shelf ASP systems. To show the effectiveness of our approach, we conduct experiments using a benchmark set based on real data provided by a collaborating Japanese automaker.