Title : On Sufficient and Necessary Conditions in Bounded CTL
| Presenter | Renyan Feng |
| Abstract | Computation Tree Logic (CTL) is one of the central for- malisms in formal verification. As a specification language, it is used to express a property that the system at hand is ex- pected to satisfy. From both the verification and the system design points of view, some information content of such prop- erty might become irrelevant for the system due to various reasons e.g., it might become obsolete by time, or perhaps in- feasible due to practical difficulties. Then, the problem arises on how to subtract such piece of information without altering the relevant system behaviour or violating the existing spec- ifications. Moreover, in such a scenario, two crucial notions are informative: the strongest necessary condition (SNC) and the weakest sufficient condition (WSC) of a given property. To address such a scenario in a principled way, we introduce a forgetting-based approach in CTL and show that it can be used to compute SNC and WSC of a property under a given model. We study its theoretical properties and also show that our notion of forgetting satisfies existing essential postulates. Furthermore, we analyse the computational complexity of ba- sic tasks, including various results for the relevant fragment CTLAF. |
Title : Towards Serendipity and Explainability in Research Paper Recommendation
| Presenter | Xueli Pan |
| Abstract | The growing number of scientific published papers has resulted in information overload, which also raises the problem that researchers have difficulty in finding papers relevant to their interests. Recommendation system is a good solution to address this problem. Most of the previous works have focused on the accuracy of recommendations. However, several works argue that there are important aspects other than accuracy, such as serendipity and explainability. Knowledge Graphs contain a large number of semantic associations and may provide great values in revealing interesting connections between different terms/concepts/topics extracted from research articles. Therefore, my work is focused on how to leverage Knowledge Graphs to represent researcher profiles as well as candidate papers to be recommended so as to generate recommendations that will not only surprise users but also provide users with explanations on why these articles are recommended. |
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| Presenter | Zoom Link to the presentations |
| Abstract | Topic: WAI meeting Time: May 18, 2020 04:00 PM Amsterdam Join Zoom Meeting https://vu-live.zoom.us/j/93238249565 Meeting ID: 932 3824 9565 Password: 116319 |