Toward the automated assessment of entity-relationship diagrams

The need to interpret imprecise diagrams (those with malformed, missing or extraneous features) occurs in the automated assessment of diagrams. We outline our proposal for an architecture to enable the interpretation of imprecise diagrams. We discuss our preliminary work on an assessment tool, developed within this architecture, for automatically grading answers to a computer architecture examination question. Early indications are that performance is similar to that of human markers. We will be using Entity-Relationship Diagrams (ERDs) as the primary application area for our investigation of automated assessment. This paper will detail our reasons for choosing this area and outline the work ahead

A Review on the Applications of Crowdsourcing in Human Pathology

The advent of the digital pathology has introduced new avenues of diagnostic medicine. Among them, crowdsourcing has attracted researchers' attention in the recent years, allowing them to engage thousands of untrained individuals in research and diagnosis. While there exist several articles in this regard, prior works have not collectively documented them. We, therefore, aim to review the applications of crowdsourcing in human pathology in a semi-systematic manner. We firstly, introduce a novel method to do a systematic search of the literature. Utilizing this method, we, then, collect hundreds of articles and screen them against a pre-defined set of criteria. Furthermore, we crowdsource part of the screening process, to examine another potential application of crowdsourcing. Finally, we review the selected articles and characterize the prior uses of crowdsourcing in pathology

Reinforcement learning for efficient network penetration testing

Penetration testing (also known as pentesting or PT) is a common practice for actively assessing the defenses of a computer network by planning and executing all possible attacks to discover and exploit existing vulnerabilities. Current penetration testing methods are increasingly becoming non-standard, composite and resource-consuming despite the use of evolving tools. In this paper, we propose and evaluate an AI-based pentesting system which makes use of machine learning techniques, namely reinforcement learning (RL) to learn and reproduce average and complex pentesting activities. The proposed system is named Intelligent Automated Penetration Testing System (IAPTS) consisting of a module that integrates with industrial PT frameworks to enable them to capture information, learn from experience, and reproduce tests in future similar testing cases. IAPTS aims to save human resources while producing much-enhanced results in terms of time consumption, reliability and frequency of testing. IAPTS takes the approach of modeling PT environments and tasks as a partially observed Markov decision process (POMDP) problem which is solved by POMDP-solver. Although the scope of this paper is limited to network infrastructures PT planning and not the entire practice, the obtained results support the hypothesis that RL can enhance PT beyond the capabilities of any human PT expert in terms of time consumed, covered attacking vectors, accuracy and reliability of the outputs. In addition, this work tackles the complex problem of expertise capturing and re-use by allowing the IAPTS learning module to store and re-use PT policies in the same way that a human PT expert would learn but in a more efficient way

Integrating groupware technology into the learning environment

This paper presents the hard lessons learned from the introduction of groupware technology within a final‐year software engineering module. The module began in 1997 and is now in its fourth year. The paper provides a detailed account of our successes and failures in each year, and describes what the authors now feel is a successful model for integrating groupware into the learning environment. The paper is important because it provides a longitudinal study of the use of groupware within a learning environment and an insight into the key success factors associated with the use of groupware. Success factors relate not only to the technology but also to social factors such as group facilitation and social protocols, to factors associated with monitoring and assessment, and to factors related to the skills development associated with being a member of a global team

Integrated process of images and acceleration measurements for damage detection

The use of mobile robots and UAV to catch unthinkable images together with on-site global automated acceleration measurements easy achievable by wireless sensors, able of remote data transfer, have strongly enhanced the capability of defect and damage evaluation in bridges. A sequential procedure is, here, proposed for damage monitoring and bridge condition assessment based on both: digital image processing for survey and defect evaluation and structural identification based on acceleration measurements. A steel bridge has been simultaneously inspected by UAV to acquire images using visible light, or infrared radiation, and monitored through a wireless sensor network (WSN) measuring structural vibrations. First, image processing has been used to construct a geometrical model and to quantify corrosion extension. Then, the consistent structural model has been updated based on the modal quantities identified using the acceleration measurements acquired by the deployed WSN. © 2017 The Authors. Published by Elsevier Ltd

Using patterns in the automatic marking of ER-Diagrams

This paper illustrates how the notion of pattern can be used in the automatic analysis and synthesis of diagrams, applied particularly to the automatic marking of ER-diagrams. The paper describes how diagram patterns fit into a general framework for diagram interpretation and provides examples of how patterns can be exploited in other fields. Diagram patterns are defined and specified within the area of ER-diagrams. The paper also shows how patterns are being exploited in a revision tool for understanding ER-diagrams

Issues of quality assurance in the management of plagiarism in blended learning environments

Increasing access to and availability of electronic resources presents students with a rich library of opportunities for independent study. But students also find themselves in the confusing territory of how they should best use these resources within their assessment activities. Likewise, teaching institutions are faced with the problems of plagiarism and collusion, and the challenges of educating, deterring, detecting, and dealing with breaches of policy in a fair and consistent way across all disciplines. This paper examines issues of quality assurance in the management of plagiarism by discussing the following questions: – How can effective automated plagiarism detection services be introduced and managed across the institution? – What teaching and assessment practices can be adopted to deter plagiarism? – What part should collusion and plagiarism detection tools play in educating and deterring students? – What are appropriate penalties for plagiarism and collusion and how can these be applied consistently across disciplines? Drawing together three distinct strands of research, in both distance and campus based institutions, the authors discuss how practice and policy have evolved in recent years in an attempt to reduce the incidence of plagiarism and collusion. The paper will illustrate this evolution by reporting on recent developments in assessment strategy, detection tools, and policy within two UK HE Institutions: The UK Open University and Manchester Metropolitan University

Automatic assessment of sequence diagrams

In previous work we showed how student-produced entity-relationship diagrams (ERDs) could be automatically marked with good accuracy when compared with human markers. In this paper we report how effective the same techniques are when applied to syntactically similar UML sequence diagrams and discuss some issues that arise which did not occur with ERDs. We have found that, on a corpus of 100 student-drawn sequence diagrams, the automatic marking technique is more reliable that human markers. In addition, an analysis of this corpus revealed significant syntax errors in student-drawn sequence diagrams. We used the information obtained from the analysis to build a tool that not only detects syntax errors but also provides feedback in diagrammatic form. The tool has been extended to incorporate the automatic marker to provide a revision tool for learning how to model with sequence diagrams