The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a mid-sized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this article is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed. Accordingly, the reader will become familiar with the pivotal issues of IoUT and BMD processing, whilst gaining an insight into the state-of-the-art applications, tools, and techniques. Finally, we analyze the architectural challenges of the IoUT, followed by proposing a range of promising direction for research and innovation in the broad areas of IoUT and BMD. Our hope is to inspire researchers, engineers, data scientists, and governmental bodies to further progress the field, to develop new tools and techniques, as well as to make informed decisions and set regulations related to the maritime and underwater environments around the world.

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Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey

최근 따뜻한 R&D, 사회이슈 해결형 연구개발과 같이 사회문제에 대응하는 혁신에 대한 관심이 증대되고 있다. 이러한 혁신활동은 기술개발 및 획득이 아니라 사회문제 해결 그 자체에 초점을 맞추고 문제인식-연구개발-실증-실용화-서비스 제공을 종합적으로 고려하는 새로운 기획·관리·평가체제를 요구하고 있다. 또한 프라이버시 침해문제, 디지털 중독문제, 식품 안전성 문제, 나노입자의 환경·보건 안전성 문제 등 과학기술혁신 과정에서 발생할 수 있는 부정적 효과와 기술위험에 대한 사회적 관심이 증대되면서 이에 대한 적극적 대응이 요구되고 있는 실정이다. 이에 안전하면서도 사회문제 해결에 기여할 수 있는 ‘사회에 책임지는 과학기술혁신(Responsible Research and Innovation: 이하 RRI)’ 추진 시스템 구축에 대한 관심이 증대되고 있으며, 이를 효과적으로 구현하기 위해서는 각 기술 부문에서 RRI의 구체적인 내용과 지향점을 연구하고 사회적 합의를 이끌어가는 작업이 필요하다.

 본 연구는 EU 국가를 중심으로 전개되고 있는 RRI를 위한 프로그램을 살펴보고, RRI의 의의와 시사점을 다음과 같이 도출해내고 있다.

 [의의] 1. 신기술을 통한 사회문제 해결 활성화 촉진
 2. 기술 재해 등 기술로 인한 사회적·윤리적 문제에 대한 대응 강화
 3. 시민 참여 및 커뮤니케이션을 통한 연구·혁신의 신뢰성 확보
 4. 기술과 사회의 상호작용을 다루기 위한 학제 간 연구의 중요성 제고 

[시사점] 1. RRI는 과학기술혁신 활동에 대한 새로운 프레임워크를 제공하여 새로운 연구추진 방식과 체제를 제시하고

 과학기술과 사회의 대화 촉진
 2. 저출산·고령화, 양극화, 에너지·환경문제, 재난의 빈발 등과 같은 사회문제에 대한 과학기술의 대응력 강화
 3. 사회문제 해결형 혁신모델과 정책 방향 제시
 4. 사회-기술기획의 구체적인 활동 방향 제시와 추진체제 모델 제시

사회에 책임지는 과학기술혁신 :Responsible Research and Innovation 논의 동향

The education system has dramatically changed from physical to online due to COVID-19 Millions of learners around the world have been affected due to the pandemic caused by coronavirus The emergence of COVID-19 has forced educational institutions to divert all strategies from physical to online platforms for the safety of the students, instructors, and all the other staff members The processes of examining students and their assessment strategies are heavily affected due to the sudden adoption of online education than physical education at campus The research in this paper discusses issues relevant to online assessments and the strategies used by the universities due to COVID-19 in the region of UAE The research collected data through an online questionnaire from 312 undergraduate university students in UAE to understand their behaviour Furthermore, their performance of online assessment is gauged through the analysis of exams transcripts and comparison is made with their previous GPA and CGPA to understand the effects of COVID-19 on their overall performance Semi-structured interviews were conducted to discuss issues related to e-assessment and recommendations are made to improve the online assessment process in universities The research proves that students have benefited from the online assessment © 2021 by the authors

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The shift to online assessment due to covid-19: An empirical study of university students, behaviour and performance, in the region of UAE

Checking the identity of students and authorship of their online submissions is a major concern in Higher Education due to the increasing amount of plagiarism and cheating using the Internet. The literature on the effects of e-authentication systems for teaching staff is very limited because it is a novel procedure for them. A considerable gap is to understand teaching staff’ views regarding the use of e-authentication instruments and how they impact trust in e-assessment. This mixed-method study examines the concerns and practices of 108 teaching staff who used the TeSLA - Adaptive Trust-based e-Assessment System in six countries: UK, Spain, Netherlands, Bulgaria, Finland and Turkey. The findings revealed some technological, organisational and pedagogical issues related to accessibility, security, privacy and e-assessment design and feedback. Recommendations are to provide: a FAQ and an audit report with results, to raise awareness about data security and privacy, to develop policies and guidelines about fraud detection and prevention, e-assessment best practices and course team support.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/bjet.12733

Pedagogical approaches for e-assessment with authentication and authorship verification in Higher Education

Identity verification and proctoring of online students are one of the key challenges to online learning today. Especially for online certification and accreditation, the training organizations need to verify that the online students who completed the learning process and received the academic credits are those who registered for the courses. Furthermore, they need to ensure that these students complete all the activities of online training without cheating or inappropriate behaviours. The COVID-19 pandemic has accelerated (abruptly in certain cases) the migration and implementation of online education strategies and consequently the need for safe mechanisms to authenticate and proctor online students. Nowadays, there are several technologies with different grades of automation. In this paper, we deeply describe a specific solution based on the authentication of different biometric technologies and an automatic proctoring system (system workflow as well as AI algorithms), which incorporates features to solve the main concerns in the market: highly scalable, automatic, affordable, with few hardware and software requirements for the user, reliable and passive for the student. Finally, the technological performance test of the large scale system, the usability-privacy perception survey of the user and their results are discussed in this work.

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Online Student Authentication and Proctoring System Based on Multimodal Biometrics Technology

Assessing the semantic similarity of texts is an important part of different text-related applications like educational systems, information retrieval, text summarization, etc. This task is performed by sophisticated analysis, which implements text-mining techniques. Text mining involves several pre-processing steps, which provide for obtaining structured representative model of the documents in a corpus by means of extracting and selecting the features, characterizing their content. Generally the model is vector-based and enables further analysis with knowledge discovery approaches. Algorithms and measures are used for assessing texts at syntactical and semantic level. An important text-mining method and similarity measure is latent semantic analysis (LSA). It provides for reducing the dimensionality of the document vector space and better capturing the text semantics. The mathematical background of LSA for deriving the meaning of the words in a given text by exploring their co-occurrence is examined. The algorithm for obtaining the vector representation of words and their corresponding latent concepts in a reduced multidimensional space as well as similarity calculation are presented.Assessing the semantic similarity of texts is an important part of different text-related applications like educational systems, information retrieval, text summarization, etc. This task is performed by sophisticated analysis, which implements text-mining techniques. Text mining involves several pre-processing steps, which provide for obtaining structured representative model of the documents in a corpus by means of extracting and selecting the features, characterizing their content. Generally the model is vector-based and enables further analysis with knowledge discovery approaches. Algorithms and measures are used for assessing texts at syntactical and semantic level. An important text-mining method and similarity measure is latent semantic analysis (LSA). It provides for reducing the dimensionality of the document vector space and better capturing the text semantics. The mathematical background of LSA for deriving the meaning of the words in a given text by exploring their co-occurrence is examined. Th...

/pdf/assessing-semantic-similarity-of-texts-methods-and-4s75c0blfh.pdf

Assessing semantic similarity of texts - Methods and algorithms

Choosing the appropriate architecture for a big data project is not a straightforward task. The architect needs to have knowledge in the problem domain, future application requirements, big data technology landscape and architectural patterns. Today’s dynamic business environment enforces constantly evolving big data applications, which often need to process thousands of messages per second. Solutions implementing microservices and Event Driven Architecture (EDA) could provide scalability and extensibility to the required application. The article will outline microservices and EDA challenges, advantages and potential problems concerning big data stream processing.

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Using microservices and event driven architecture for big data stream processing

E-Assessment is an innovative form for the evaluation of learners' knowledge and skills in online education, as well as in blended-learning environments, where part of the assessment activities is carried out online. As e-assessment involves online communication channel between learners and educators, as well as data transfer and storage, security measures are required to protect the environment against system and network attacks. The issue concerning security is challenging from both educational and technical point of views. Such issues are discussed under the scope of the TeSLA project. Educational challenging problems at e-assessment are analyzed and technical architectural recommendations for securing the e-assessment system according to the General Data Protection Regulation are provided.

/pdf/security-challenges-in-e-assessment-and-technical-solutions-5gf07fmgbz.pdf

Security Challenges in e-Assessment and Technical Solutions

Choosing the appropriate architecture and technologies for a big data project is a difficult task, which requires extensive knowledge in both the problem domain and in the big data landscape. The paper analyzes the main big data architectures and the most widely implemented technologies used for processing and persisting big data. Clouds provide for dynamic resource scaling, which makes them a natural fit for big data applications. Basic cloud computing service models are presented. Two architectures for processing big data are discussed, Lambda and Kappa architectures. Technologies for big data persistence are presented and analyzed. Stream processing as the most important and difficult to manage is outlined. The paper highlights main advantages of cloud and potential problems.

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Anna Rozeva

Papers

Pedagogical approaches for e-assessment with authentication and authorship verification in Higher Education

Assessing semantic similarity of texts - Methods and algorithms

Using microservices and event driven architecture for big data stream processing

Security Challenges in e-Assessment and Technical Solutions

Big data processing in the cloud - Challenges and platforms