Reviewerly/exHarmony huggingface dataset:
Can Blockchain based Supply Chain Create a Stock Market Reaction? Evidence from an Event Study

This study investigates Blockchain based Supply chain (BC-SC) announcements impact on the stock market. Using a sample of 104 announcements between 2016 and 2021, we conduct an international event study to determine the market reaction. We find that an overall positive market reaction of 1.45% to all BC-SC announcements on the announcement day. In addition, we find that the stock market reaction slightly differs when different SC challenges are targeted by the announcing firm. When blockchain is used for traceability purposes, the market response is less positive when compared to other SC challenges like resolving disputes, maintaining cargo integrity and data security. We further find that firms that are part of BC industrial collaborations experience a more positive stock market response, when compared to firms using private or public BC networks. When looking into firm characteristics, we find that innovative firms experience greater positive reaction to BC-SC announcements. Interestingly, we find that smaller firms do not experience a strong positive market response. Leveraging the diverse industries in our sample, we investigated the effect of early adoption of BC-SC with respect to the industry. The late BC-SC adoption in the manufacturing sector only received a significantly positive market reaction. Overall, it seems that the market views many BC initiatives as adding to the competitiveness and long-term profitability of organizations. It also shows that BC, as a multi-stakeholder technology should be evaluated from both a strategic and operational perspective. Other implications and research directions are also identified; especially given the limitations of the study and the relatively early BC-SC supply chain developments.
Ways of Forming the Control and Evaluation Competencies of a Future Teacher

The article analyzes the problem of control and evaluation competencies of a teacher. Based on the analysis of domestic and foreign literature, different views on the essence and content of the concepts of «control and evaluation competencies» were established. As the analysis has shown, the problem of objectivity of assessment is one of the key pro blems of pedagogical qualimetry. Currently, science is engaged in an intensive search for objective methods and means of control, evaluation criteria, measurement procedures, tools, etc. One of the directions of the search is related to the definition of the essence of control and evaluation competencies in the context of a competence-based approach in education. The article presents the results of the analysis of psychological, pedagogical and methodological literature devoted to the problem of the formation of control and evaluation competencies (COC), highlights its functions, clarifies the classification of relevant competencies. Based on the analysis of scientific and methodological literature, the activities of teachers of pedagogical universities and the work of school teachers, the authors developed a model for the formation of control and evaluation competencies. The structure and content of the model are disclosed: a) monitoring by students of the control and evaluation activities of university teachers and teachers, b) teaching students the theoretical foundations of control and evaluation competencies, c) development of control and evaluation competencies in pedagogical practice, d) control and self-control of the formation of control and evaluation competencies among students. The article also shows the ways and means of forming the control and evaluation competencies of future teachers, the effectiveness of the methodology of forming the control and evaluation competencies of future teachers is experimentally substantiated. This is proved by the results of the experiment.
Managing Agentic Technology: Distinguishing and Equalizing Human and Material Agencies

Organizations are increasingly deploying algorithmic agents to accomplish their core functions. Thus, a critical question is how to manage algorithmic agents to achieve organizational goals. Agentic technologies act without human intervention. The human management of algorithmic agents is, in its most basic form, a practice of managing material agency. Through qualitative analysis of 16 years (2005-2021) of archival data on English Wikipedia, the study explicates the process of managing algorithms in organizations. Our analysis revealed five principles that either prioritized human agency over material agency or equalized the two. To prioritize humans, the organizational structures, norms, and policies are designed to support human work while limiting the speed and space of algorithmic actions. When humans and algorithms are treated equally, the consequences rather than mechanisms of actions are scrutinized and algorithms are managed only after negative impacts occur. These findings provide initial insights regarding the management of algorithms in organizations through the agency perspective.
Privacy-Preserving and Robust Federated Deep Metric Learning

Federated learning, in contrast to traditional learning paradigms, has demonstrated its unique advantages in providing intelligence at the edge. However, existing federated learning approaches focus on the end-to-end classification tasks requiring a simple collaboration procedure where each participant can perform its local training independently. Unfortunately, there are still many tasks relying on learning the distinguishable feature metrics with respect to all the data, which is a different collaboration procedure across training participants. For example, the model for people identification has to ensure the feature representing a person is dissimilar to those representing others. To enable such federated learning for deep metrics (a.k.a federated deep metric learning) is challenging due to the data privacy and procedure robustness issues. With the consideration of these two challenges, this work proposes a novel computing framework for federated deep metric learning. This framework leverages the system-algorithm co-design to address privacy concerns via the Trusted Execution Environment (SGX enclave) and Differential Privacy mechanism. It also introduces a large-scale federated protocol which can robustly and efficiently deal with practical factors like the network fluctuation. We implement and evaluate our computing framework with two settings. One is a real-world implementation with a large number of mobile devices, while the other one is in our controllable environment for conducting experiments in various tasks. Our evaluation results show that our computing framework is able to train federated deep metric learning models with excellent scalability, data privacy preserving, and considerable accuracy even in exception conditions.
Towards sustainable social media-based international entrepreneurship during the COVID-19 pandemic

Sustainable entrepreneurship is acknowledged to be the process of exploiting economic, social, and ecological possibilities which have been receiving increasing attention from organisations and scholars. On the other hand, digital platforms are considered as a critical means for this purpose since they have eliminated the international borders. Investing in other countries and attracting ventures from them is an imperative factor towards a developed market. Besides, all mentioned concepts have been influenced by the COVID-19 pandemic and numerous challenges have been created which should be solved. To this end, this research investigates the challenges towards sustainable social media-based international entrepreneurship during the COVID-19. Hence, by reviewing the literature, mentioned challenges are extracted and then finalised by experts’ opinions. Next, these challenges are screened by modified hesitant fuzzy Delphi. The most highlighted ones are then analysed by hesitant fuzzy decision-making trial and evaluation laboratory-based analytic network processes and the weights are determined. Subsequently, the study focuses on illustrating the effect of the severe detected obstacles on the six components of the entrepreneurship ecosystem; policy, finance, culture, supports, human capital, and markets by hesitant fuzzy cognitive maps. The results demonstrate that online internationalisation is the most critical challenge and finance is the most centric element.
A Cost-Effective and Scalable Processing of Heavy Workload with AWS Batch

Recent technological advancements in the IT field have pushed many products and technologies into the cloud. In the present scenario, the cloud service providers mainly focus on the delivery of IT services and technologies rather than throughput. In this research paper, we used a scalable cost-effective approach to configure AWS Batch with AWS Fargate and CloudFormation and implemented it in order to handle a heavy workload. The AWS service configuration procedure, GitHub repository, and Docker desktop applications have been clearly described in this work. A cost-effective configuration and architecture of AWS Batch processing are given to provide high throughput. The processing of heavy workload by AWS Batch is represented in terms of execution time and the result shows that the concurrent execution reduces the execution time. To enhance the throughput heavy workload using batch processing an "Amazone FSx for Lustre" can also be used.
Investigating the Predictability of QoS Metrics in Cellular Networks

Applications on mobile devices face varying network conditions in cellular networks. The connected radio cell is often changing, especially with moving devices. Different access technologies, varying signal strengths, or distance to the connected radio tower influence the Quality of Service (QoS) of mobile applications. Existing technologies like buffering or adaptive video streaming work reactive, i.e., they react to a decreasing download bitrate. In contrast, these technologies and mobile applications in general could benefit from early knowledge of the expected connection quality.This work investigates the predictability of QoS metrics in cellular networks based on the experience of previous measurements. For this, we developed an Android app to measure download bitrates with minimal data consumption. We performed over 90 000 measurements using a single network operator and analyzed how precise QoS indicators like packet round trip times and download bitrates can be predicted. We developed a methodology to predict the expected download bitrate along a route and present our approach of aggregating measurements into hexagons of dynamic size. The core contributions of this work are (i) a methodology and implementation of systematic measurement data collection, (ii) an open data publication of our measurement data set, and (iii) an approach for predicting QoS metrics in cellular networks based on aggregated measurements. Our results show, that our approach is able to predict the downlink bitrate, the packet round trip time (ping), or DNS query duration along a given route.
Analysis of Data Performance that Reduces Resource Utilization Overheads and Increases the Efficiency

In today, the size of the data is increasing at a random speed. So, this leads to processing of Big data. When we compare this in business applications where the volume of data is huge and at the same time it should be processed in efficient manner. Traditional system fails to process the bigdata because most of the data in bigdata is unstructured. To improve performance in distributed data processing resource utilization plays vital role. There are resource gaps develop while execution occurs. This is more frequent in heterogeneous environment. In the previous techniques there is wastage or not efficient usage of resources. To process data in distributed environment multiple platforms used such as Apache Hadoop, Apache Spark etc. Here we develop new algorithm that reduces the usage of resources and increases the performances. The algorithm implemented in Apache Spark distributed environment. The experimental results indicate efficient utilization of resources and increase in performance.
Text Summarization using Extractive and Abstractive Techniques

There have been considerable advancements in Text Summarization over the last few years. There are two ways to text summarization: one is based on natural language processing (NLP), and the other is based on deep learning. In the realm of NLP, text summarization is the most intriguing and challenging task. NLP stands for Natural Language Processing, which studies and manipulates human language by computers. Because of the massive increase in information and data, it has become critical. Text summarization is creating a thorough and meaningful summary of text from various sources such as books, news stories, research papers, and tweets, among others. Large text documents that are difficult to summarize manually are the research subject. The job of condensing a piece of text into a shorter version, minimizing the size of the original text while maintaining key informative components and content meaning, is known as summarising. Because human text summarising is a time-consuming and intrinsically tiresome process, automating it is gaining popularity and hence acts as a potent stimulus for academic study.
Formation and Development of an Educational Ecosystem for People with Disabilities

The chapter of the monograph is devoted to the consideration of the prospects for building educational ecosystems for people with disabilities. New requirements for the system of education of persons with disabilities in modern conditions have been determined, directions for the development of digitalization in education have been identified. The author analyzes new reserves in the training of people with disabilities to understand this historical time, systemic activity in the context of increased interaction, inclusion, new competencies, professional behavior and responsibility in the learning process. The chapter considers the integration processes in the education of persons with disabilities, the implementation of inclusive educational programs, including remotely, the opportunities and prospects for them to receive higher statuses, and a general improvement in their quality of life.
A NISQ Method to Simulate Hermitian Matrix Evolution

As a universal quantum computer requires millions of error-corrected qubits, one of the current goals is to exploit the power of noisy intermediate-scale quantum (NISQ) devices. Based on a NISQ module-layered circuit, we propose a heuristic protocol to simulate Hermitian matrix evolution, which is widely applied as the core for many quantum algorithms. The two embedded methods, with their own advantages, only require shallow circuits and basic quantum gates. Capable to being deployed in near future quantum devices, we hope it provides an experiment-friendly way, contributing to the exploitation of power of current devices.
Modelling Bimodal Data Using a Multivariate Triangular-Linked Distribution

Bimodal distributions have rarely been studied although they appear frequently in datasets. We develop a novel bimodal distribution based on the triangular distribution and then expand it to the multivariate case using a Gaussian copula. To determine the goodness of fit of the univariate model, we use the Kolmogorov–Smirnov (KS) and Cramér–von Mises (CVM) tests. The contributions of this work are that a simplistic yet robust distribution was developed to deal with bimodality in data, a multivariate distribution was developed as a generalisation of this univariate distribution using a Gaussian copula, a comparison between parametric and semi-parametric approaches to modelling bimodality is given, and an R package called btld is developed from the workings of this paper.
A New Fail-Stop Group Signature over Elliptic Curves Secure against Computationally Unbounded Adversary

If an adversary has unlimited computational power, then signer needs security against forgery. Fail Stop signature solves it. If the motive of the signature is to hide the identity of the signer who makes signature on behalf of the whole group then solution is Group signature. We combine these two features and propose “A new Fail Stop Group Signature scheme (FSGSS) over elliptic curves”. Security of our proposed FSGSS is based on “Elliptic curve discrete logarithm problem” (ECDLP). Use of elliptic curve makes our proposed FSGSS feasible to less bandwidth environment, Block chains etc. Due to security settings over elliptic curves, efficiency of proposed scheme increases in terms of computational complexity.
Implementation of Elliptical Curve Cryptography Based Diffie-Hellman Key Exchange Mechanism in Contiki Operating System for Internet of Things

Wireless Sensor Networks have gradually upgraded to Internet of Things (IoT) of embedded devices wherein the constrained devices have been connected directly onto the Internet. This transformation has not only facilitated the expansion in connectivity and accessibility of the sensor network but has also enabled one sensor network to interact with other through Internet. Security of IoT devices has been researched extensively. The challenge to transform the complex cryptographic algorithms into lighter and faster has kept researchers on their toes. Contiki-OS is one of the purest implementations of 6LoWPAN and IEEE 802.15.4. That makes Contiki-OS lightest and therefore preferred OS for implementation on ultra-low power sensor nodes. Elliptical Cryptography has proved to be the choice of most of the security researchers for constrained devices. However, there is very limited literature available on implementation of Elliptical Cryptography on Contiki-OS. The open-source libraries available for security implementation have not found to be supporting Cooja simulator of Contiki-OS. In this research work we demonstrate improved results in processing the Elliptical Cryptography Based implementation of Diffie-Hellman Key exchange mechanism in Contiki-OS using Cooja simulator. SECP160K1 curve has been implemented and the results in terms of ECDH computational time have been compared with previously published research works. This research demonstrates improved results in Cooja simulator than previous known results on hardware providing a leap ahead in efficiency of implementation.
Upbringing, Additional Education and Professional Orientation as the Most Important Elements of Modern Education

In the chapter of the monograph, upbringing, additional education and professional orientation of Russian schoolchildren is considered as an activity aimed at personal development through the creation of conditions for self-determination and socialization of the student on the basis of traditional socio-cultural, spiritual and moral values and modern educational technologies.
Target Netgrams: An Annulus-Constrained Stress Model for Radial Graph Visualization

We present Target Netgrams as a visualization technique for radial layouts of graphs. Inspired by manually created target sociograms, we propose an annulus-constrained stress model that aims to position nodes onto the annuli between adjacent circles for indicating their radial hierarchy, while maintaining the network structure (clusters and neighborhoods) and improving readability as much as possible. This is achieved by having more space on the annuli than traditional layout techniques. By adapting stress majorization to this model, the layout is computed as a constrained least square optimization problem. Additional constraints (e.g., parent-child preservation, attribute-based clusters and structure-aware radii) are provided for exploring nodes, edges, and levels of interest. We demonstrate the effectiveness of our method through a comprehensive evaluation, a user study, and a case study.
Impact of the Pandemic on the Learning Process of Foreign Students Studying in Russia and Prospects for Educational Migration

Globalization in the field of science and education has intensified the so-called student migration.However, during the COVID-19 pandemic, universities have been forced to quickly reorganize their educational activities with the help of distance learning technologies.The article is aimed at identifying the COVID-19 impact on student migration and the development of distance learning.The article presents a brief review of the current state of student migration all over the world and identifies the approaches to the concept of student migration, as well as its advantages and disadvantages for all participants in the educational process.In the course of the study authors used several general scientific and special methods: comparison and generalization, applied in the process of analysing researchers' views, the abstract logical method applied in the process of formulating research conclusions.The main method of the research was expert survey.Based on an expert survey, the authors identify the key aspects of the COVID-19 impact on student migration, the conditions necessary for improving the quality of distance learning services for foreign students at the university level, and the ways of enhancing the cognitive activity of foreign students during distance learning in the context of the pandemic.In the conclusions, the author determines that COVID-19 leads to serious structural changes in higher education institutions due to the development and spread of new learning technologies, as well as changes in world educational migration.
Power Dynamics in the Evolution of Blockchain

In this paper, drawing on past IS research on how IT affects power dynamics in organization and networks, we focus on blockchain technologies. After describing and analyzing two mini-cases of blockchain creation and use, we critically discuss the power of all actors involved: blockchain code, creators, developers, maintainers, institutional forces and users. We therefore contribute to the literature on the governance of blockchain creation and use in organizations and networks. We do so by exploring the power dynamics among key socio-technical actors involved in significant decision-making processes in the early lifecycle stages of blockchain. The insights gain from this exploratory study support prior literature that indicates that developers may sit in a privileged position in blockchain ecosystems, while also demonstrating the unique power dynamics among other socio-technical actors present. By contrasting these dynamics with those found in other technologies, e.g., ERP and AI, we help to expose the unique power dynamics that exist in these blockchain-enabled decentralized settings. Potential avenues for future theoretical testing and development, as well as practical implications for managers or organizations engaging with emerging blockchain technologies are provided.