Reviewerly/exHarmony huggingface dataset:
A ramp secret sharing scheme from finite fields

A ramp secret sharing scheme is a cryptographic method to encode a secret s into multiple sharess1, s2, … , sn that only from specified subsets of the shares one can recover s. In this paper, we construct a strong rampsecret sharing scheme based on finite fields.
Speeding Up the Engineering of Interactive Systems with Generative AI

This keynote discusses the opportunities and challenges of using Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) as tools for developing interactive systems. We will look at different stages in the development lifecycle of interactive systems and assess the value of AI support. We explore how GenAI and LLMs can potentially speed-up the ideation, requirements elicitation, architecture development, prototyping, implementation, and testing of interactive systems. The talk will outline emerging practices, such as the use of prompts for code and system generation, to facilitate prototyping and accelerate implementation. We will outline fundamental challenges and suggest emerging research directions, and pose research questions. What will software development tools look like in the future? How can we efficiently use AI to develop interactive systems without compromising quality? We also speculate about the implications of these developments for researchers, practitioners, and society. We believe that it will massively accelerate the digital transformation. Interactive AI-based tools for systems and software development will become a major research direction.
DAM: An Agent for Change in Organization to Cope with Disruptive Technology, Shifting Consumer Needs and Expectations

This paper emphasizes that DAM would help organizations respond to external changes in a dynamic environment and would promote internal changes in the organization.Firstly, from the organization's response to external changes, I would analyze the success and failure cases of organizations using a series of incremental and radical innovations to respond to changes in the external environment.And I would put forward the point of view that organizations need DAM to participate in change management in the context of the dynamic digital world.Secondly, in terms of internal changes in the organization, I would explain how DAM helps companies introduce technology, rethink business models, develop new workflows and processes, adjust structural changes and organizational culture, and promote better organization development.Finally, the focus of the discussion part is that for organizations that use DAM, although DAM may have agent power, the importance of individual agent power to the organization could not be ignored.At the same time, organizations also need to pay attention to the impact of identity, organizational identity, technological transformation, and the costs and risks the organization has to bear when introducing DAM strategies or departments.
Research trends in the field of natural language processing : A scientometric study based on global publications during 2001-2020

The study provides a quantitative and qualitative description of global research in “Natural Language Processing” ( NLP) using bibliometric methods. The analysis is based on publications data sourced from Scopus database for the period 2001-2020. The purpose of the study is to understand the status of NLP research at the global, national, institutional, and author level. The study highlights the productivity and performance of NLP research on a series of metrics as well as provides a visual view of collaborative network relationship between authors, research institutions, and leading countries using standard software tools. In addition, the study identified the leading players in NLP research such as key countries, institutions, authors, and areas of research. According to the study, the USA leads in global publications output as well as it leads in terms of relative citation index.
FEATURES OF PROFILE TRAINING OF A MODERN SPECIALIST

The article is devoted to the study and substantiation of theoretical and methodological provisions related to the formation, development and management of knowledge in modern conditions. Modern educational learning models should be aimed at further education, including through the formation and integration of competencies, ensuring logical interconnection and continuity at each stage of the educational system; at the same time, the models themselves should be formed by compiling and analyzing the requirements of not only the state, but also the labor market and society as a whole, taking into account not only highly specialized, but also personal, multidisciplinary and generalized competencies
DIGITALIZATION OF EDUCATION: RISKS AND PROSPECTS

The article analyzes the problems of implementing digital learning at all levels of the education system in the modern era of cyber-socialization of society and the numerous problems, risks and prospects associated with this process. In the process of improving national education, modern technologies are acquiring an increasing role every year, the introduction of which contributes to the modernization and development of education, as well as to the improvement of the quality of training of future specialists and the convergence of education with science. At the same time, such technologies require a revision of existing approaches to educational activities, as well as an analysis of their impact on society and individual social groups. In this regard, the study of issues of digitalization of education and its social consequences seems to be a very relevant area of scientific research. The purpose of this study is to identify the main problems and determine the prospects for digitalization in the field of education.
THE DETERMINANTS OF CURRENCY EUROIZATION IN THE BALKANS

The study aims to explore the euroization phenomenon in the Balkans, or more precisely, the main determinants of currency euroization in the selected seven Balkan countries (Albania, Bosnia and Herzegovina, Bulgaria, Croatia, North Macedonia, Romania, and Serbia) between 2000 and 2019. Currency euroization is proxied by two variables: foreign currency deposits and foreign currency denominated liabilities to total liabilities. This study has altogether showed that the rates of deposit and liabilities euroization had stayed persistent over a period of 20 years, with very small variations, even during the period of the 2008 crisis and its aftermath. The results of panel data analysis have showed that deposit euroization is influenced by consumer price index, real interest rate and trade openness. For liability euroization, trade openness and real effective exchange rate have proved to be statically significant variables, but with negative and positive effect respectively.
Inverted Non-Maximum Suppression for More Accurate and Neater Face Detection

CNN-based face detection methods have achieved significant progress in recent years.In addition to the strong representation ability of CNN, post-processing methods are also very important for the performance of face detection.In general, the face detection method predicts several candidate bounding-boxes for one face.NMS is used to filter out inaccurate candidate boxes to get the most accurate box.The principle of NMS is to select the box with a higher score as the basic box and then delete the box which has a large overlapping area with the basic box but has a lower score.However, the current NMS method and its improved versions do not perform well when face image quality is poor or faces are in a cluster.In these situations, even after NMS filtering, there is often a face corresponding to multiple predicted boxes.To reduce this kind of negative result, in this paper, we propose a new NMS method that operates in the reverse order of other NMS methods.Our method performs well on lowquality and tiny face samples.Experiments demonstrate that our method is effective as a post-processor for different face detection methods.The source code has been released on https://github.com/.
Artificial Intelligence, Robotics and Law

The book covers the basic concepts of Artificial Intelligence and Robotics’ legal functions. It develops the foundation that what types of law and regulation are exist and how law will applies in the newest field. This book examines the concepts of Artificial Intelligence and Machine Learning to understand the historical development of Artificial Intelligence. How emerging robot and Artificial Intelligence technologies are being regulated in different countries, how Machine Learning base action can be liable under the law? To understand the principle of Ethics and its importance in Machine Learning, Policy guideline for national security, what is the legal status of Robots under the IP framework? Economic impact of transforming business activities and How Machine Learning tools play a role in public decision-making, and Artificial Intelligence increase transparency? Each chapter explains the concept with a relevant example and legal policies that design further research discussions.
IMAGE DESCRIPTION GENERATOR USING DEEP LEARNING

Image description generator using Deep Learning can create an image's content using properly constructed, meaningful English sentences.The user's camera or mobile phone is continuously used to capture images in real-time.Our models extract features from an image using a convolutional neural network (CNN).To create a reliable image description in English, these Features Are Provided to A Recurrent Neural Network (RNN) Or A Long Short-Term Memory (LSTM) Network.We Use A CNN To Extract Features from The Image.CNNs are the state-of-the-art methods for object recognition and detection and have been used and studied for a variety of image tasks.In more detail, we extract features from the Fc7 Layer of the VGG-16 Network that has been trained on ImageNet and is well suited for object detection for all input images.Due to computational limitations in LSTM, we first obtain a 4096-dimensional image feature vector and then reduce it using principal component analysis (PCA) to a 512-dimensional image feature vector.These characteristics are fed into the LSTM network to produce a description of the image in accurate English, which might then be converted to audio using text-to-speech technology.
A Glossary for Knowledge Organization Systems Terminology

This paper describes the creation of a terminological glossary for the subject domain of knowledge organization systems (KOS). The glossary addresses the need for unambiguous conceptual information about the subject matter for students majoring in information science. It aims to create a glossary of definitions for concepts of different types of KOS. The theoretical and methodological foundations are based on corpus linguistics for the construction of the glossary. The methodology is characterized as an exploratory, descriptive, and applied study, using the ISKO Encyclopedia of Knowledge Organization (IEKO) as a source for data collection and terminology. The cut-off was based on the KOSs listed in NISO Z39.19 (2005; R2010) and ISO 25964 (2011; 2013). It is hoped that the terminological glossary created will improve students’ understanding of the different KOSs, by compiling definitions and giving them greater semantics.
Explainable AI: Techniques and Tools for Interpreting Deep Learning Models

Explainable AI (XAI) refers to the development of artificial intelligence models and algorithms that can be understood and explained by humans. The goal of XAI is to create AI systems that not only produce accurate results but also provide insights into their decision-making process. In recent years, the use of machine learning and other AI technologies has become increasingly prevalent in a variety of fields, including healthcare, finance, and transportation. However, many AI models are often regarded as "black boxes," meaning that the internal workings of the model are not transparent, and it is difficult for humans to understand how the model arrived at a particular decision.
A Study on the Construction Process of Nonsan Catholic church in the Rev. Singer.

본고는 1961년에 완공된 논산성당(루르드의 성모 성당) 건립의 배경 과 추진 과정을 고찰한다. 논산지역은 18세기 말부터 천주교 신앙이 전 파되었고, 1921년 본당 승격으로 이어졌다. 이후 지속적으로 성장한 교 세는 강경본당이 분리되고, 6.25전쟁이 발발하였음에도 불구하고 1950 년대 말에 이르러 3천명에 육박하였다. 당시 주임이었던 생제 신부는 많은 신자들을 수용할 수 있는 새 성당 건립을 추진하였다. 원설계는 프랑스인 Lucien Beun이 하였고, 실시설계는 한국인 건축가 이희태가 맡았다. 공사는 1958년 5월 정지작업을 시작으로 1960년 2월 초석 축복, 이어서 1961년 10월 11일에 봉헌식을 가졌다. 성당 제단부 에는 신비적 아름다움을 더하기 위하여 프랑스 유리화 장인 앙투안 베 사크가 제작한 ‘루르드의 성모’ 발현 장면이 스테인드글라스로 장식되 었다. 성당의 건립에는 다양한 인물들의 협조와 노력이 있었다. 건축에 직접 참여한 건축가와 예술가 외에도 이들을 소개하고 비용을 부담한 후원자This paper examined the construction process of Nonsan Catholic Church (Notre Dame de Lourdes), which was completed in 1961. In the Nonsan area, the Catholicity spread from the end of the 18th century, and Catholic communities are promoted as parish in 1921. Since then, the congregation has been expanded and gathered 3,000 believers by the end of the 1950s even though Ganggyeong Parish has been split and the Korean War has broken out. Father Singer, who was the head of the church at the time, propelled the construction of a new church that could accommodate a large number of believers. It was originally designed by Frenchman Lucien Beun, and the final design was done by Korean architect Lee Hee-tae. The construction has begun in May, 1958 with flattening the land, and blessing ceremony has been held in Feb, 1960 and dedicatory ceremoy in Oct 11, 1961. The scene of ‘Notre Dame de Lourdes’ was decorated with stained glass created by French glass-painting artist Antoine Beyssac to add mystical beauty in the church altar. For the construction of the church, many people supported and poured efforts. In addition to the architects and artists who participated in the construction, there were sponsors who introduced them and paid expenses, and a great support of the Vatican. As representative sponsors, there were Chang-myon, who was the vice president at the time, Thams Chang, dean of Fine Arts, Seoul National University, and priests and believers from Hesdin Church, the hometown of Father Singer. And with the active support of Father Singer and Nonsan believers, the church was successfully completed. Domestic and foreign renowned politicians and the old villagers sacrified and cooperated for the establishment of Nonsan Catholic Church and it has become an internal component of the monu-mental architecture. In addition, Nonsan Church is the first modernist church built in Chung- nam. As modernist architecture, which is practical and simple aesthetics for city reconstruction was popular in the West after the war, Nonsan Catholic Church is an important example that led the modernist church architecture early in Korea.
Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) Modeling in Forecasting Covid-19 Cases in Indonesia

Covid-19 is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The spread of Covid-19 in Indonesia has grown rapidly that the World Health Organization (WHO) has declared Covid-19 a pandemic. Covid-19 cases have spread to 34 provinces in Indonesia. Covid-19 data in Indonesia involves space and time so the appropriate modeling is the space-time model. Space-time modeling of the Covid-19 case in 34 provinces in Indonesia using the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) model has not been carried out. The purpose of this research is to get the best GSTARIMA model and forecat the Covid-19 case for the next several times. This model incorporates time and location interdependence with different parameters for each location. Identification of the order of the AR and MA was carried out through the STACF and STPACF plots. For simplicity of interpretation, the spatial order is chosen first order. In this study, the queen contiguity and the inverse distance location weighting matrix were used. The parameter estimation used is Ordinary Least Square (OLS). The results show that the best model for predicting Covid-19 cases in 34 provinces in Indonesia is the GSTARIMA model (1,1,0)1 using an inverse distance weighting matrix with the smallest RMSE value of 1.22.Keywords: Covid-19, GSTARIMA, Queen Contiguity, Inverse Distance, OLS.
Strengthening the Link Between Organizational Culture and Employee Loyalty: Exploring the Mediating Effects of Employee Trust and Job Satisfaction

Purpose: This research investigates the role of employee trust and job satisfaction in the relationship between organizational culture and employee loyalty.&#x0D; Design/methodology/approach: The study was conducted among 355 healthcare professionals working in a private healthcare system in southern cities of Vietnam, including doctors, nurses, and other medical staff. The research method employed was partial least squared structure equation modeling (PLS-SEM) with SmartPLS software for data analysis.&#x0D; Findings: The findings indicate that organizational culture has a direct positive impact on employee loyalty and also affects employee trust and job satisfaction positively. Furthermore, employee trust and job satisfaction act as intermediaries and positively influence employee loyalty toward the organization.&#x0D; Research limitations/implications: The research acknowledges the limitation of the limited sample size and encourages further exploration of these relationships in other contexts. In addition, this study provides valuable insights for the management board of private healthcare organizations in southern cities of Vietnam on how to improve employee loyalty by cultivating a positive organizational culture, promoting employee trust, and enhancing job satisfaction.&#x0D; Originality/value: Overall, this research contributes significantly to the existing literature by examining the interplay between employee trust, organizational culture, job satisfaction, and employee loyalty.
Automated Curation and AI Workflow Management System for Digital Pathology.

Digital pathology applications present several challenges, including the processing, storage, and distribution of gigapixel images across distributed computational resources and viewing stations. Individual slides must be available for interactive review, and large repositories must be programmatically accessible for dataset and model building. We present a platform to manage and process multi-modal pathology data (images and case information) across multiple locations. Using an agent-based system coupled with open-source automated machine learning and review tools allows not only dynamic load-balancing and cross-network operation but also the development of research and clinical AI models using the data managed by the platform. The platform presented covers end-to-end AI workflow from data acquisition and curation through model training and evaluation allowing for sharing and review. We conclude with a case study of colon and prostate cancer model development utilizing the presented system.
A Cloud-Native Federated Learning Architecture for Telecom Fraud Detection

We propose a cloud-native federated learning (FL) architecture, where a FL server is built with serverless microservices. We demonstrate this FL framework with a Telecom fraud detection use case, which trains a global model across multiple CSPs. Our results show that the global model trained by FL improves up to 23% in F1-score compared to locally trained models.
Understanding Social Relations with Graph-Based and Global Attention

Social relations, as the basic relationships in our daily life, are a phenomenon unique to human society that shows how people interact in society. Social relations understanding is to infer the existing social relationships between individuals in a given scenario, which is crucial for us to analyze social behavior. Existing research methods are usually limited to extracting features of characters and related entities, which limits the scope of attention and may miss important clues such as interactions between characters. In this paper, we propose a global attention mechanism that adaptively grasps scenes, objects, and human interactions for reasoning about social relationships. We propose an end-to-end global attention network, which consists of three modules, namely, a convolutional attention module, a graph inference module, and an attentional inference module. The visual and location information is first extracted by the convolutional attention module as the feature information of the person pairs, then it is made to process the relationships between character nodes on the graph inference network, and finally, the attention is fully utilized to classify the social relationships. Extensive experiments on the PISC and PIPA datasets show that our proposed method outperforms the state-of-the-art methods in terms of accuracy.
Efficient quantum secret sharing scheme based on monotone span program

Abstract How to efficiently share secrets among multiple participants is a very important problem in key management. In this paper, we propose a multi-secret sharing scheme based on the Greenberger Horne Zeilinger (GHZ) state. First, the distributor uses monotone span program to encode the secrets and generate the corresponding secret shares to send to the participants. Then, each participant uses the generalized Pauli operator to embed its own secret share into the transmitted particle. The participant who wants to get the secrets can get multiple secrets at the same time by performing a GHZ-state joint measurement. Furthermore, since this scheme is based on a monotone span program, its access structure is more general than the access structure ( t , n ) threshold. Compared with other schemes, our proposed scheme is more efficient, less computational cost.
Keynote 3 : Be Ready for AI Third Wave!?

We have witnessed the achievements of AI research and its impact on human life and society over the past few decades.Since its inception at the Dartmouth Conference 1956, AI has gone through several boom and bust cycles.Each bust pushed AI research from one boom to yet another prosperous boom.For example, the most recent bust from 1987 to 1993 led to another AI research boom which has lasted almost 20 years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019).During the last two decades, many dreams in AI research have become reality.However, today AI development run into many barriers in meeting the expectations of public in the commercialization of AI technologies such as safedriving, self-abstracting, self-learning, etc.In this talk the speaker will explore where is boundary to impede the AI advancement by reviewing the AI history, and the major achievements from the first and second AI waves.Then speaker will share the vision of next AI wave in order to be ready for AI third wave.We believe in that the AI third wave is coming and will be a much stronger torrent!
Understanding the Support of IoT and Persuasive Technology for Smart Bin Design: A Scoping Review

In this scoping review, we aim to summarize and analyze the latest persuasive design research developments for smart bins. This study initially collected data from 551 scientific papers, and later, based on a selection process, 13 papers that mainly focused on handling smart bins through persuasive designs were included in the final analysis. These 13 papers were rated by four characteristics: research specifications, methodologies, persuasive strategies, and adopted technologies. We argue that, to understand how to design cyber-physical systems for waste management that involve the cooperation of civil society, a promising path includes unraveling how persuasive smart bins designs are being developed and identifying the challenges and opportunities that exist for waste management in cyber-physical collaborative environments, at the intersection of person, place, and technology.