Towards Next Generation Teaching, Learning, and Context-Aware Applications for Higher Education: A Review on Blockchain, IoT, Fog and Edge Computing Enabled Smart Campuses and Universities

[Abstract] Smart campuses and smart universities make use of IT infrastructure that is similar to the one required by smart cities, which take advantage of Internet of Things (IoT) and cloud computing solutions to monitor and actuate on the multiple systems of a university. As a consequence, smart campuses and universities need to provide connectivity to IoT nodes and gateways, and deploy architectures that allow for offering not only a good communications range through the latest wireless and wired technologies, but also reduced energy consumption to maximize IoT node battery life. In addition, such architectures have to consider the use of technologies like blockchain, which are able to deliver accountability, transparency, cyber-security and redundancy to the processes and data managed by a university. This article reviews the state of the start on the application of the latest key technologies for the development of smart campuses and universities. After defining the essential characteristics of a smart campus/university, the latest communications architectures and technologies are detailed and the most relevant smart campus deployments are analyzed. Moreover, the use of blockchain in higher education applications is studied. Therefore, this article provides useful guidelines to the university planners, IoT vendors and developers that will be responsible for creating the next generation of smart campuses and universities.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431G/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

Conceptual modeling in the era of Big Data and Artificial Intelligence: Research topics and introduction to the special issue

Since the first version of the Entity–Relationship (ER) model proposed by Peter Chen over forty years ago, both the ER model and conceptual modeling activities have been key success factors for modeling computer-based systems. During the last decade, conceptual modeling has been recognized as an important research topic in academia, as well as a necessity for practitioners. However, there are many research challenges for conceptual modeling in contemporary applications such as Big Data, data-intensive applications, decision support systems, e-health applications, and ontologies. In addition, there remain challenges related to the traditional efforts associated with methodologies, tools, and theory development. Recently, novel research is uniting contributions from both the conceptual modeling area and the Artificial Intelligence discipline in two directions. The first one is efforts related to how conceptual modeling can aid in the design of Artificial Intelligence (AI) and Machine Learning (ML) algorithms. The second one is how Artificial Intelligence and Machine Learning can be applied in model-based solutions, such as model-based engineering, to infer and improve the generated models. For the first time in the history of Conceptual Modeling (ER) conferences, we encouraged the submission of papers based on AI and ML solutions in an attempt to highlight research from both communities. In this paper, we present some of important topics in current research in conceptual modeling. We introduce the selected best papers from the 37th International Conference on Conceptual Modeling (ER’18) held in Xi’an, China and summarize some of the valuable contributions made based on the discussions of these papers. We conclude with suggestions for continued research.The research reported in this paper was partially funded by the ECLIPSE-UA (RTI2018-094283-B-C32) and the AETHER-UA (PID2020-112540RB-C43) Projects from the Spanish Ministry of Science and Innovation

Faktor penambahbaikan kriteria kebolehgunaan bagi portal kerajaan Malaysia

Penggunaan internet menjadi trend baru pengguna untuk memperoleh maklumat dan pengetahuan terutamanya perkhidmatan dari agensi kerajaan. Justeru, pembangun portal perlu memberi fokus ke atas kualiti antara muka portal bagi memasti interaksi pengguna yang lancar dan fleksibel. Isu kebolehgunaan yang timbul semasa pembangunan dapat menjejas fungsi antara muka portal yang disedia. Ketidakpatuhan kepada garis panduan kebolehgunaan semasa pembangunan, rujukan yang kompleks dan tidak merangkumi kriteria kebolehgunaan sebenar menjadi punca kepada kegagalan menghasil antara muka yang boleh guna. Satu model pengukuhan kriteria kebolehgunaan dibangun sebagai langkah penambahbaikan dan garis panduan sedia ada yang dikuat kuasa diteliti bagi mengatasi isu kebolehgunaan ini. Hasil daripada penambahbaikan mengenal pasti tujuh kriteria kebolehgunaan baharu; Fungsi dan Kesesuaian, Sokongan dan Panduan, Ralat dan Pemulihan, Kawalan dan Kestabilan, Perisian dan Perkakasan, Proses Reka Bentuk dan Penilaian serta Kerahsiaan. Kriteria ini tidak disenarai sebagai kriteria wajib patuh oleh agensi penguatkuasa dan ianya dijangka mampu mempengaruhi reka bentuk kebolehgunaan portal sektor awam

25 Desafíos de la Modelación de Procesos Semánticos

Process modeling has become an essential part of many organizations for documenting, analyzing and redesigning their business operations and to support them with suitable information systems. In order to serve this purpose, it is important for process models to be well grounded in for- mal and precise semantics. While behavioural semantics of process models are well understood, there is a considerable gap of research into the semantic aspects of their text labels and natural lan- guage descriptions. The aim of this paper is to make this research gap more transparent. To this end, we clarify the role of textual content in process models and the challenges that are associated with the interpretation, analysis, and improvement of their natural language parts. More specifically, we discuss particular use cases of semantic process modeling to identify 25 challenges. For each cha- llenge, we identify prior research and discuss directions for addressing themEl modelado de procesos se ha convertido en una parte esencial de muchas organizaciones para documentar, analizar, y rediseñar sus operaciones de negocios y apoyarlos con información apropiada. Para cumplir este fin, es importante para estos que estén completos dentro de una semántica formal y precisa. Mientras la semántica del comportamiento del modelado de procesos se entiende bien, hay una considerable laguna en la investigación entre los aspectos semánticos de sus rótulos textuales, y las descripciones en lenguaje natural. El objetivo de este artículo es hacer esta laguna en la investigación más transparente. Con este fin, clarificamos el papel del contenido textual en los modelos de proceso, y los retos relacionados con la interpretación, el análisis, y desarrollo de sus partes en lenguaje natural. De forma más específica, debatimos los casos particulares del uso del modelado de procesos semánticos para identificar 25 retos. Para cada reto, identificamos antes de la investigación y debatimos las direcciones para dirigirnos a ellos

25 Challenges of Semantic Process Modeling

Process modeling has become an essential part of many organizations for documenting, analyzing and redesigning their business operations and to support them with suitable information systems. In order to serve this purpose, it is important for process models to be well grounded in formal and precise semantics. While behavioural semantics of process models are well understood, there is a considerable gap of research into the semantic aspects of their text labels and natural language descriptions. The aim of this paper is to make this research gap more transparent. To this end, we clarify the role of textual content in process models and the challenges that are associated with the interpretation, analysis, and improvement of their natural language parts. More specifically, we discuss particular use cases of semantic process modeling to identify 25 challenges. For each challenge, we identify prior research and discuss directions for addressing them

The Effect of Cross-Border E-Commerce on China’s International Trade: An Empirical Study Based on Transaction Cost Analysis

Reducing transaction costs by means of policy intervention could generate comparative advantages and contribute to the growth of international trade. Chinese government agencies have introduced a number of policies in support of rapidly growing cross-border e-commerce to promote China’s international trade. However, the previous literature has not empirically verified the precise effect of these policies on the growth of international trade while focusing on the impact of cross-border e-commerce on trade distance and consumer welfare. To address this gap, this paper investigates the impact of cross-border e-commerce on international trade in the context of China, mainly from the perspective of transaction cost economics in conjunction with the traditional comparative advantage model by analyzing information cost, negotiation cost, transportation cost, tariffs and middlemen cost separately. Firstly, the new theoretical model suggests that cross-border e-commerce may have a positive role in promoting international trade only when the negative impact caused by tariff cost and transportation cost is offset. Secondly, our result shows that cross-border e-commerce has a positive effect on the growth of China’s international trade in each year. However, the positive effect does not show incremental growth over time, possibly as a result of the weak implementation of favorable policies in trade, in addition to global trade shrinking

BlockNet Report: Exploring the Blockchain Skills Concept and Best Practice Use Cases

In order to explore the practical potential and needs of interdisciplinary knowledge and competence requirements of Blockchain technology, the project activity "Development of Interdisciplinary Blockchain Skills Concept" starts with the literature review identifying the state of the art of Blockchain in Supply Chain Management and Logistics, Business and Finance, as well as Computer Science and IT-Security. The project activity further explores the academic and industry landscape of existing initiatives in education which offer Blockchain courses. Moreover, job descriptions and adverts are analyzed in order to specify today's competence requirements from enterprises. To discuss and define the future required competence, expert workshops are organized to validate the findings by academic experts. Based on the research outcome and validation, an interdisciplinary approach for Blockchain competence is developed. A second part focuses on the development of the Blockchain Best Practices activity while conducting qualitative empirical research based on case studies with industry representatives. Therefore, company interviews, based on the theoretical basis of Output 1, explore existing Blockchain use cases in different sectors. Due to the interdisciplinary importance of Blockchain technology, these skills will be defined by different perspectives of Blockchain from across multiple mentioned disciplines. The use cases and companies for the interviews will be selected based on various sampling criteria to gain results valid for a broad scale. The analysis of the various use cases will be conducted and defined in a standardized format to identify the key drivers and competence requirements for Blockchain technology applications and their adoption. On the one hand, this approach ensures comparability, on the other hand, it facilitates the development of a structured and systematic framework.Comment: arXiv admin note: text overlap with arXiv:2102.0322

Usability framework for mobile augmented reality language learning

After several decades since its introduction, the existing ISO9241-11 usability framework is still vastly used in Mobile Augmented Reality (MAR) language learning. The existing framework is generic and can be applied to diverse emerging technologies such as electronic and mobile learning. However, technologies like MAR have interaction properties that are significantly unique and require different usability processes. Hence, implementing the existing framework on MAR can lead to non-optimized, inefficient, and ineffective outcomes. Furthermore, state-of-the-art analysis models such as machine learning are not apparent in MAR usability studies, despite evidence of positive outcomes in other learning technologies. In recent MAR learning studies, machine learning benefits such as problem identification and prioritization were non-existent. These setbacks could slow down the advancement of MAR language learning, which mainly aims to improve language proficiency among MAR users, especially in English communication. Therefore, this research proposed the Usability Framework for MAR (UFMAR) that addressed the currently identified research problems and gaps in language learning. UFMAR introduced an improved data collection method called Individual Interaction Clustering-based Usability Measuring Instrument (IICUMI), followed by a machine learning-driven analysis model called Clustering-based Usability Prioritization Analysis (CUPA) and a prioritization quantifier called Usability Clustering Prioritization Model (UCPM). UFMAR showed empirical evidence of significantly improving usability in MAR, capitalizing on its unique interaction properties. UFMAR enhanced the existing framework with new abilities to systematically identify and prioritize MAR usability issues. Through the experimental results of UFMAR, it was found that the IICUMI method was 50% more effective, while CUPA and UCPM were 57% more effective than the existing framework. The outcome through UFMAR also produced 86% accuracy in analysis results and was 79% more efficient in framework implementation. UFMAR was validated through three cycles of the experimental processes, with triangulation through expert reviews, to be proven as a fitting framework for MAR language learning

Data-driven conceptual modeling: how some knowledge drivers for the enterprise might be mined from enterprise data

As organizations perform their business, they analyze, design and manage a variety of processes represented in models with different scopes and scale of complexity. Specifying these processes requires a certain level of modeling competence. However, this condition does not seem to be balanced with adequate capability of the person(s) who are responsible for the task of defining and modeling an organization or enterprise operation. On the other hand, an enterprise typically collects various records of all events occur during the operation of their processes. Records, such as the start and end of the tasks in a process instance, state transitions of objects impacted by the process execution, the message exchange during the process execution, etc., are maintained in enterprise repositories as various logs, such as event logs, process logs, effect logs, message logs, etc. Furthermore, the growth rate in the volume of these data generated by enterprise process execution has increased manyfold in just a few years. On top of these, models often considered as the dashboard view of an enterprise. Models represents an abstraction of the underlying reality of an enterprise. Models also served as the knowledge driver through which an enterprise can be managed. Data-driven extraction offers the capability to mine these knowledge drivers from enterprise data and leverage the mined models to establish the set of enterprise data that conforms with the desired behaviour. This thesis aimed to generate models or knowledge drivers from enterprise data to enable some type of dashboard view of enterprise to provide support for analysts. The rationale for this has been started as the requirement to improve an existing process or to create a new process. It was also mentioned models can also serve as a collection of effectors through which an organization or an enterprise can be managed. The enterprise data refer to above has been identified as process logs, effect logs, message logs, and invocation logs. The approach in this thesis is to mine these logs to generate process, requirement, and enterprise architecture models, and how goals get fulfilled based on collected operational data. The above a research question has been formulated as whether it is possible to derive the knowledge drivers from the enterprise data, which represent the running operation of the enterprise, or in other words, is it possible to use the available data in the enterprise repository to generate the knowledge drivers? . In Chapter 2, review of literature that can provide the necessary background knowledge to explore the above research question has been presented. Chapter 3 presents how process semantics can be mined. Chapter 4 suggest a way to extract a requirements model. The Chapter 5 presents a way to discover the underlying enterprise architecture and Chapter 6 presents a way to mine how goals get orchestrated. Overall finding have been discussed in Chapter 7 to derive some conclusions