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The purpose of this article is to clarify current and widespread misconceptions about the properties of blockchain technologies and to describe challenges and avenues for correct and trustworthy design and implementation of distributed ledger system (DLS) or Technology (DLT).

The authors contrast the properties of a blockchain with desired, however emergent, properties of a DLS, which is a complex and distributed system. They point out and justify, with facts and analysis, current misconceptions about the blockchain and DLSs. They describe challenges that these systems will need to address and possible solution avenues for achieving trustworthiness.

Many of the statements that have appeared on the internet, news and academic articles, such as immutable ledger and exact copies, may be misleading. These are desired emergent properties of a complex system, not assured properties. It is well-known within the distributed systems and critical software community that it is extremely hard to prove that a complex system correctly and completely implements emergent properties. Further research and development for trustworthy DLS design and implementation is needed, both practical and theoretical.

This is the first known published attempt at describing current misconceptions about blockchain technologies. Further collaborative work, discussions, potential solutions, evaluations, resulting publications and verified reference implementations are needed to ensure DLTs are safe, secure, and trustworthy.

Interdisciplinary teams with members from academia, business and industry, and from disciplines such as business, entrepreneurship, theoretical and practical computer science, cybersecurity, finance, mathematics and statistics, must be formed. Such teams must collaborate with the objective of developing strategies and techniques for ensuring the correctness and security of future DLSs in which our society may become dependent.

The value and originality of this article is twofold: the disproving, through fact collection and systematic analysis, of current misconceptions about the properties of the blockchain and DLSs, and the discussion of challenges to achieving adequate trustworthiness along with the proposal of general avenues for possible solutions.

The purpose of this paper is to evaluate the relative effects of three facets or connectors argued to be vital for learners in successful e-learning outcomes in developing economies.

Data were collected through a survey involving 130 learners. A stratified sampling technique was employed. Regression analyses making use of linear, multiple and PROCESS macro in Statistical Package for the Social Sciences (SPSS) were used to analyze data.

Technological self-efficacy and social presence are the most important facets needed by participants for effective learning in higher education institutions in developing countries. Learning tools meant to enhance teaching and learning and also contribute to learner satisfaction.

The findings of the study provide insights to academic administrators to pay close attention to the three connectors in order to ensure quality learning. The findings guide higher learning institutions to adequately and selectively pay attention to the three connections. Deliberate efforts focusing on students' situations, opinions and concerns are vital for learner satisfaction in developing economies.

This study represents a first attempt to examine the effect of the “right connections” for effective learning in developing economies, using a quantitative approach. The findings bring into attention the role of assessing learner inputs and virtual environment in boosting the effectiveness of e-learning. The findings also result in a model that should lead to increased learner satisfaction through the implementation of right connections. The study “disputes” the relevance of a universal e-learning system.

The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm.

Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized.

This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm.

Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast.

As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.

The purpose of this work is to bridge FL and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. FL is an emerging collaborative machine learning technique that trains a model across multiple devices or servers holding private data samples without exchanging their data. The locally trained results are aggregated by a centralized server in a privacy-preserving way. However, there is an assumption where the centralized server is trustworthy, which is impractical. Fortunately, blockchain technology has opened a new era of data exchange among trustless strangers because of its decentralized architecture and cryptography-supported techniques.

In this study, the author proposes a novel design of a smart agent inspired by the smart contract concept. Specifically, based on the proposed smart agent, a fully decentralized, privacy-preserving and fair deep learning blockchain-FL framework is designed, where the agent network is consistent with the blockchain network and each smart agent is a participant in the FL task. During the whole training process, both the data and the model are not at the risk of leakage.

A demonstration of the proposed architecture is designed to train a neural network. Finally, the implementation of the proposed architecture is conducted in the Ethereum development, showing the effectiveness and applicability of the design.

The author aims to investigate the feasibility and practicality of linking the three areas together, namely, multi-agent system, FL and blockchain. A blockchain-FL framework, which is based on a smart agent system, has been proposed. The author has made several contributions to the state-of-the-art. First of all, a concrete design of a smart agent model is proposed, inspired by the smart contract concept in blockchain. The smart agent is autonomous and is able to disseminate, verify the information and execute the supported protocols. Based on the proposed smart agent model, a new architecture composed by these agents is formed, which is a blockchain network. Then, a fully decentralized, privacy-preserving and smart agent blockchain-FL framework has been proposed, where a smart agent acts as both a peer in a blockchain network and a participant in a FL task at the same time. Finally, a demonstration to train an artificial neural network is implemented to prove the effectiveness of the proposed framework.

This study aims to offer a bibliometric and coding analysis of blockchain articles published in the accounting, auditing and accountability fields.

The data were collected using the Scopus database and a bibliometric and qualitative coding analysis with the keywords “blockchain” and “accounting” or “auditing” or “accountability.” Of the 514 initial sources, 93 peer-reviewed papers, book chapters and conference proceedings in the areas of business, management and accounting were finally selected. Nonscientific sources such as nonpeer-reviewed books and white papers were excluded.

This study reveals a promising and multidisciplinary field of research dominated by scholars and less by practitioners. Qualitative research, especially discourse analysis, is the most used method among authors. This study gives some useful insights about blockchain's definition and characteristics, business models, processes involved, connection with other technologies and relationships with accounting theories. Among the most interesting insights, the results confirm that technology as an external force can create an intersection among several research areas: accounting, auditing, accountability, business, management, computer science and engineering fields. Finally, in terms of research themes, although blockchain has a clear effect on auditing accounting, the links with the area of accountability are less clear and validated.

This study highlights the current state of the field, combining methodological approaches and providing valuable future research insights. Additionally, it is also a starting point for professionals to fully understand blockchain's characteristics and potential with a constructive and systemic approach.

In conducting a multi-disciplinary, multi-degree study of all 83 higher education accrediting organizations in the United States and the 605 academic programs associated with them, our goal was to uncover patterns in the presence of leadership and general workforce competencies identified within the stated learning outcomes employed by these accrediting organizations. Our findings suggest strong variability across categories of leadership competence related to workforce competencies, where skills related to reasoning and communication were emphasized much more heavily than others such as intrapersonal development. These findings emerged across all postsecondary degree levels, from pre-baccalaureate to graduate programs, raising important questions for the leadership development of post-secondary students. Keywords: outcomes assessment, student leadership, professional development, leadership education, workforce development, competencies.

While colleges and universities often make the case that preparing students for future career success is critical, studies that examine the empirical support for the assertion curiously lag behind the advanced rhetoric. This paper will showcase research findings based on an analysis of 36,327 learning outcomes addressed within all 83 higher education accrediting organizations in the United States, representing 605 distinct postsecondary academic programs. Our goal was to uncover any patterns of emphasis in particular workforce and leadership competencies embedded within those learning outcomes and examine the extent to which those competencies are represented similarly across postsecondary degree levels.

Given the cryptocurrency market boom in recent years, this study aims to identify the factors influencing cryptocurrency pricing and the major gaps for future research.

A systematic literature review was undertaken. Three databases, Scopus, Web of Science and EBSCOhost, were used for this review. The final analysis comprised 88 articles that met the eligibility criteria.

The influential factors were identified and categorized as supply and demand, technology, economics, market volatility, investors’ attributes and social media. This review provides a comprehensive and consolidated view of cryptocurrency pricing and maps the significant influential factors.

This paper is the first to systematically and comprehensively review the relevant literature on cryptocurrency to identify the factors of pricing fluctuation. This research contributes to cryptocurrency research as well as to consumer behaviors and marketing discipline in broad.

The purpose of this paper is to contribute to the debate on blockchain (BC) adoption for preventing counterfeiting by investigating BC systems where different options for BC feeding and reading complement the use of BC technology. By grounding on the situational crime prevention, this study analyses how BC systems can be designed to effectively prevent counterfeiting.

This is a multiple-case study of five Italian wine companies using BC to prevent counterfeiting.

This study finds that the desired level of upstream/downstream counterfeiting protection that a brand owner intends to guarantee to customers through BC is the key driver to consider in the design of BC systems. The study identifies which variables are relevant to the design of feeding and reading processes and explains how such variables can be modulated in accordance with the desired level of counterfeiting protection.

The cases investigated are Italian companies within the wine sector, and the BC projects analysed are in the pilot phase.

The study provides practical suggestions to address the design of BC systems by identifying a set of key variables and explaining how to properly modulate them to face upstream/downstream counterfeiting.

This research applies a new perspective based on the situational crime prevention approach in studying how companies can design BC systems to effectively prevent counterfeiting. It explains how feeding and reading process options can be configured in BC systems to assure different degrees of counterfeiting protection.

This paper aims to study the node modeling, multi-agent architecture and addressing method for the material conscious information network (MCIN), which is a large-scaled, open-styled, self-organized and ecological intelligent network of supply–demand relationships.

This study models the MCIN by node model definition, multi-agent architecture design and addressing method presentation.

The prototype of novel E-commerce platform based on the MCIN shows the effectiveness and soundness of the MCIN modeling. By comparing to current internet, the authors also find that the MCIN has the advantages of socialization, information integration, collective intelligence, traceability, high robustness, unification of producing and consuming, high scalability and decentralization.

Leveraging the dimensions of structure, character, knowledge and experience, a modeling approach of the basic information can fit all kinds of the MCIN nodes. With the double chain structure for both basic and supply–demand information, the MCIN nodes can be modeled comprehensively. The anima-desire-intention-based multi-agent architecture makes the federated agents of the MCIN nodes self-organized and intelligent. The MCIN nodes can be efficiently addressed by the supply–demand-oriented method. However, the implementation of the MCIN is still in process.

This paper lays the theoretical foundation for the future networked system of supply–demand relationship and the novel E-commerce platform.

The authors believe that the MCIN, first proposed in this paper, is a transformational innovation which facilitates the infrastructure of the future networked system of supply–demand relationship.

Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.

In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.

The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.

To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.