Source-code plagiarism : an academic perspective

In computing courses, students are often required to complete tutorial and laboratory exercises asking them to produce source-code. Academics may require students to submit source-code produced as part of such exercises in order to monitor their students’ understanding of the material taught on that module, and submitted source-code may be checked for similarities in order to identify instances of plagiarism. In exercises that require students to work individually, source-code plagiarism can occur between students or students may plagiarise by copying material from a book or from other sources. We have conducted a survey of UK academics who teach programming on computing courses, in order to establish what is understood to constitute source-code plagiarism in an undergraduate context. In this report, we analyse the responses received from 59 academics. This report presents a detailed description of what can constitute source-code plagiarism from the perspective of academics who teach programming on computing courses, based on the responses to the survey

Source-code plagiarism : a UK academic perspective

In computing courses, students are often required to complete tutorial and laboratory exercises asking them to produce source-code. Academics may require students to submit source-code produced as part of such exercises in order to monitor their students' understanding of the material taught on that module, and submitted source-code may be checked for similarities in order to identify instances of plagiarism. In exercises that require students to work individually, source-code plagiarism can occur between students or students may plagiarise by copying material from a book or from other sources. We have conducted a survey of UK academics who teach programming on computing courses, in order to establish what is understood to constitute source-code plagiarism in an undergraduate context. In this report, we analyse the responses received from 59 academics. This report presents a detailed description of what can constitute source-code plagiarism from the perspective of academics who teach programming on computing courses, based on the responses to the survey

Post-Truth as a Feature of Hypermodern Times

In this paper I will defend the idea of the success of post-truth as one of the main features of hypermodernity. In order to understand such a claim, I will start by defining “post-truth” and showing the key differences that separate it from simple manipulation or lies. I will explain how post-truth characterizes a whole new way of understanding the difference between truth and falsity: a new attitude of indifference to the sharp distinction that moderns and ancients had placed between these two notions. I will contend that this new attitude had been announced by the work of at least three recent philosophers: Harry Frankfurt, Gianni Vattimo and Mario Perniola. They give different names to “post-truth”, though, and attribute it to different causes (from anti-intellectualism to the new media and to sheer carelessness). After that, I will explore how two key aspects of hypermodernity (according to Gilles Lipovetsky), i.e. hyperindividualism and hyperconsumption, cohere with this spread of post-truth. Finally, I will summarily refer to some political and geopolitical events that corroborate the relevance of post-truth in our hypermodern world

Why Are People's Decisions Sometimes Worse with Computer Support?

In many applications of computerised decision support, a recognised source of undesired outcomes is operators' apparent over-reliance on automation. For instance, an operator may fail to react to a potentially dangerous situation because a computer fails to generate an alarm. However, the very use of terms like "over-reliance" betrays possible misunderstandings of these phenomena and their causes, which may lead to ineffective corrective action (e.g. training or procedures that do not counteract all the causes of the apparently "over-reliant" behaviour). We review relevant literature in the area of "automation bias" and describe the diverse mechanisms that may be involved in human errors when using computer support. We discuss these mechanisms, with reference to errors of omission when using "alerting systems", with the help of examples of novel counterintuitive findings we obtained from a case study in a health care application, as well as other examples from the literature

Knowledge Extraction from Natural Language Requirements into a Semantic Relation Graph

Knowledge extraction and representation aims to identify information and to transform it into a machine-readable format. Knowledge representations support Information Retrieval tasks such as searching for single statements, documents, or metadata. Requirements specifications of complex systems such as automotive software systems are usually divided into different subsystem specifications. Nevertheless, there are semantic relations between individual documents of the separated subsystems, which have to be considered in further processes (e.g. dependencies). If requirements engineers or other developers are not aware of these relations, this can lead to inconsistencies or malfunctions of the overall system. Therefore, there is a strong need for tool support in order to detects semantic relations in a set of large natural language requirements specifications. In this work we present a knowledge extraction approach based on an explicit knowledge representation of the content of natural language requirements as a semantic relation graph. Our approach is fully automated and includes an NLP pipeline to transform unrestricted natural language requirements into a graph. We split the natural language into different parts and relate them to each other based on their semantic relation. In addition to semantic relations, other relationships can also be included in the graph. We envision to use a semantic search algorithm like spreading activation to allow users to search different semantic relations in the graph

Danger is My Middle Name: Experimenting with SSL Vulnerabilities in Android Apps

This paper presents a measurement study of information leakage and SSL vulnerabilities in popular Android apps. We perform static and dynamic analysis on 100 apps, downloaded at least 10M times, that request full network access. Our experiments show that, although prior work has drawn a lot of attention to SSL implementations on mobile platforms, several popular apps (32/100) accept all certificates and all hostnames, and four actually transmit sensitive data unencrypted. We set up an experimental testbed simulating man-in-the-middle attacks and find that many apps (up to 91% when the adversary has a certificate installed on the victim's device) are vulnerable, allowing the attacker to access sensitive information, including credentials, files, personal details, and credit card numbers. Finally, we provide a few recommendations to app developers and highlight several open research problems.Comment: A preliminary version of this paper appears in the Proceedings of ACM WiSec 2015. This is the full versio