Internet of Things (IoT) connects billions of objects and devices to furnish a real effective opportunity for the enterprises. Fourth industrial revolution must ensure efficient communication and l...

Utilising neutrosophic theory to solve transition difficulties of IoT-based enterprises

Teachers deal with plagiarism on a regular basis, so they try to prevent and detect plagiarism, a task that is complicated by the large size of some classes. Students who cheat often try to hide their plagiarism (obfuscate), and many different similarity detection engines (often called plagiarism detection tools) have been built to help teachers. This article focuses only on plagiarism detection and presents a detailed systematic review of the field of source-code plagiarism detection in academia. This review gives an overview of definitions of plagiarism, plagiarism detection tools, comparison metrics, obfuscation methods, datasets used for comparison, and algorithm types. Perspectives on the meaning of source-code plagiarism detection in academia are presented, together with categorisations of the available detection tools and analyses of their effectiveness. While writing the review, some interesting insights have been found about metrics and datasets for quantitative tool comparison and categorisation of detection algorithms. Also, existing obfuscation methods classifications have been expanded together with a new definition of “source-code plagiarism detection in academia.”

Source-code Similarity Detection and Detection Tools Used in Academia: A Systematic Review

Nowadays, the use of the Internet of Things (IoT) in diverse applications becomes very popular. Accordingly, a proliferation of objects with remote sensing, actuation, analysis, and sharing capabilities will be interconnected on top of heterogeneous communication networks. Their deployment contexts are continuously changed, which imply a change in their descriptions and characteristics. In addition, they are a fundamental source of a huge quantity of gathered data with different encoding formats. Accordingly, this data is badly expressed, understood and exploited by other systems and devices. From this regard, several challenges associated with standardization, interoperability, discovery, security, and description of IoT resources and their corresponding data have emerged. In this context, Semantic Web Technologies (SWT) seem a suitable and an efficient solution to relieve these challenges. Therefore, a Systematic Literature Review (SLR) methodology is performed to investiagte and analyze a set of the most recent and relevant approaches that deal with SWT in the IoT domain. These approaches are discussed and evaluated based on seven different research questions. Finally, future insights and research opportunities are suggested.

Semantic Web Technologies for the Internet of Things: Systematic Literature Review

Practical programming competencies are critical to the success in computer science (CS) education and go-to-market of fresh graduates. Acquiring the required level of skills is a long journey of discovery, trial and error, and optimization seeking through a broad range of programming activities that learners must perform themselves. It is not reasonable to consider that teachers could evaluate all attempts that the average learner should develop multiplied by the number of students enrolled in a course, much less in a timely, deep, and fair fashion. Unsurprisingly, exploring the formal structure of programs to automate the assessment of certain features has long been a hot topic among CS education practitioners. Assessing a program is considerably more complex than asserting its functional correctness, as the proliferation of tools and techniques in the literature over the past decades indicates. Program efficiency, behavior, and readability, among many other features, assessed either statically or dynamically, are now also relevant for automatic evaluation. The outcome of an evaluation evolved from the primordial Boolean values to information about errors and tips on how to advance, possibly taking into account similar solutions. This work surveys the state of the art in the automated assessment of CS assignments, focusing on the supported types of exercises, security measures adopted, testing techniques used, type of feedback produced, and the information they offer the teacher to understand and optimize learning. A new era of automated assessment, capitalizing on static analysis techniques and containerization, has been identified. Furthermore, this review presents several other findings from the conducted review, discusses the current challenges of the field, and proposes some future research directions.

Automated Assessment in Computer Science Education: A State-of-the-Art Review

Feature models have been used since the 90s to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of automated analysis of feature models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.

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Automated analysis of feature models: Quo vadis?

The main vision of the Internet of Things IoT is to enable seamless connection between physical devices and information systems to improve the lives of people. One of the main obstacles to achieve this vision is the current lack of IoT interoperability. In this article, the authors are giving an overview on how semantics is used in IoT interoperability related research. To do this, they performed a systematic literature review and extracted data from 105 selected primary studies dealing with semantics in IoT interoperability. The authors have analysed the maturity level of this research field and when and where the relevant works were published. This article answers five main research questions about the following issues: what are semantics used for; what types of ontologies exist which are used to give semantical descriptions; what are the main themes and suggestions for future work in these research articles; and what are other related areas.

Using Semantic Web for Internet of Things Interoperability: A Systematic Review

Plagiarism is a big concern in academia and it can be a problem in every course. Plagiarism occurs when someone present others work as their own. Students plagiarize in different areas: homework assignments, essays, projects, etc. In this work focus is on programming courses and plagiarism in programming assignments. While source-code plagiarism detection, is in some way very similar to text plagiarism detection, it is very different in other ways. So, a lot of research is done focusing on source-code plagiarism. Some questions that are researched in this field are: what is considered plagiarism in programming assignments, how to perform plagiarism detection in programming assignments, how to do it automatically, what tool(s) to use, how students cheat in programming courses, how they try to obfuscate cheating, and many other questions. This work is a review of important research papers in the field of source-code plagiarism detection in academia. This paper tries to answer some of the mentioned research questions and give indication to future work.

Review of source-code plagiarism detection in academia

In programming courses there are various ways in which students attempt to cheat. The most commonly used method is copying source code from other students and making minimal changes in it, like renaming variable names. Several tools like Sherlock, JPlag and Moss have been devised to detect source code plagiarism. However, for larger student assignments and projects that involve a lot of source code files these tools are not so effective. Also, issues may occur when source code is given to students in class so they can copy it. In such cases these tools do not provide satisfying results and reports. In this study, we present an improved process model for plagiarism detection when multiple student files exist and allowed source code is present. In the research in this paper we use the Sherlock detection tool, although the presented process model can be combined with any plagiarism detection engine. The proposed model is tested on assignments in three courses in two subsequent academic years.

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Process Model Improvement for Source Code Plagiarism Detection in Student Programming Assignments.

Extract, transform and load (ETL) is a process that makes it possible to extract data from operational data sources, to transform data in the way needed for data warehousing purposes and to load data into a data warehouse (DW). ETL process is the most important part when building the data warehouse. Because the ETL process is a very complex and time consuming, this paper presents a prototype of a web ETL tool that offers step-by-step guidance through the entire process to the end user. This ETL tool is designed as a web application so users can save time (and space) required for installation purposes.

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Matija Novak

Papers

Source-code Similarity Detection and Detection Tools Used in Academia: A Systematic Review

Using Semantic Web for Internet of Things Interoperability: A Systematic Review

Review of source-code plagiarism detection in academia

Process Model Improvement for Source Code Plagiarism Detection in Student Programming Assignments.

Prototype of a Web ETL Tool