Title: Supporting Cyber Resilience with Semantic Wiki

Authors: Riku Nykänen and Tommi Kärkkäinen (University of Jyväskylä)

Abstract: Cyber resilient organizations, their functions and computing infrastructures, should be tolerant towards rapid and unexpected changes in the environment. Information security is an organization-wide common mission; whose success strongly depends on efficient knowledge sharing. For this purpose, semantic wikis have proved their strength as a flexible collaboration and knowledge sharing platforms. However, there has not been notable academic research on how semantic wikis could be used as information security management platform in organizations for improved cyber resilience. In this paper, we propose to use semantic wiki as an agile information security management platform. More precisely, the wiki contents are based on the structured model of the NIST Special Publication 800-53 information security control catalogue that is extended in the research with the additional properties that support the information security management and especially the security control implementation. We present common uses cases to manage the information security in organizations and how the use cases can be implemented using the semantic wiki platform. As organizations seek cyber resilience, where focus is in the availability of cyber related assets and services, we extend the control selection with option to focus on availability. The results of the study show that a semantic wiki based information security management and collaboration platform can provide a cost-efficient solution for improved cyber resilience, especially for small and medium sized organizations that struggle to develop information security with the limited resources.

This contribution to OpenSym 2016 will be made available as part of the OpenSym 2016 proceedings on or after August 17, 2016.

Title: Enabling team collaboration with task management tools

Authors: Dimitra Chasanidou, Brian Elvesæter, and Arne-Jørgen Berre (SINTEF ICT)

Abstract: Project and task management tools aim to support remote or face-to-face collaboration. Despite the growing needs for these tools, little is known about how they are utilized in practice. This paper presents the results of an exploratory study using UpWave, a task management tool, and the ways that it enables team collaboration. The group interviewees utilize UpWave for their collaborations and report on its features in terms of use, best practices, motivations and rewards for users to encourage their collaboration. This paper concludes that project and task management tools offer new possibilities for collaborations; it also makes suggestions for using such tools in teams. This study’s future work will include a mixed-methods approach to gain a greater understanding of the tools’ effects in various collaboration settings.

This contribution to OpenSym 2016 will be made available as part of the OpenSym 2016 proceedings on or after August 17, 2016.

Title: Predicting the quality of user contributions via LSTMs

Authors: Rakshit Agrawal and Luca de Alfaro (University of California, Santa Cruz)

Abstract: In many collaborative systems it is useful to automatically estimate the quality of new contributions; the estimates can be used for instance to flag contributions for review. To predict the quality of a contribution by a user, it is useful to take into account both the characteristics of the revision itself, and the past history of contributions by that user. In several approaches, the user’s history is first summarized into a number of features, such as number of contributions, user reputation, time from previous revision, and so forth. These features are then passed along with features of the current revision to a machine-learning classifier, which outputs a prediction for the user contribution. The summarization step is used because the usual machine learning models, such as neural nets, SVMs, etc. rely on a fixed number of input features.We show in this paper that this manual selection of summarization features can be avoided by adopting machine-learning approaches that are able to cope with temporal sequences of input.

In particular, we show that Long-Short Term Memory (LSTM) neural nets are able to process directly the variable length history of a user’s activity in the system, and produce an output that is highly predictive of the quality of the next contribution by the user. Our approach does not eliminatethe process of feature selection, which is present in all machine learning. Rather, it eliminates the need for deciding which features from a user’s past are most useful for predicting the future: we can simply pass to the machine-learning apparatus all the past, and let it come up with an estimate for the quality of the next contribution.

We present models combining LSTM and NN for predicting revision quality and show that the prediction accuracy attained is far superior to the one obtained using the NN alone. More interestingly, we also show that the prediction attained is superior to the one obtained using user reputation as a feature summarizing the quality of a user’s past work. This can be explained by noting that the primary function of user reputation is to provide an incentive towards performing useful contributions, rather than to be a feature optimized for prediction of future contribution quality.

We also show that the LSTM output changes in a natural way in response to user behavior, increasing when the user performs a sequence of good quality contributions,and decreasing when the user performs a sequence of low-quality work. The LSTM output for a user could thus be usefully shown to other users, alongside the user’s reputation and other information.

This contribution to OpenSym 2016 will be made available as part of the OpenSym 2016 proceedings on or after August 17, 2016.