In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
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Question: The theory of actual causality, defined by Halpern and Pearl, and its quantitative measure – the degree of responsibility – was shown to be extremely useful in various areas of computer science due to a good match between the results it produces and our intuition. In this paper, I describe the applications of causality to formal verification, namely, explanation of counter-examples, refinement of coverage metrics, and symbolic trajectory evaluation. I also briefly discuss recent applications of causality to legal reasoning.

Answer: Causality and Responsibility for Formal Verification and Beyond


Question: The First-Order Variable Elimination (FOVE) algorithm allows exact inference to be applied directly to probabilistic relational models, and has proven to be vastly superior to the application of standard inference methods on a grounded propositional model. Still, FOVE operators can be applied under restricted conditions, often forcing one to resort to propositional inference. This paper aims to extend the applicability of FOVE by providing two new model conversion operators: the first and the primary is joint formula conversion and the second is just-different counting conversion. These new operations allow efficient inference methods to be applied directly on relational models, where no existing efficient method could be applied hitherto. In addition, aided by these capabilities, we show how to adapt FOVE to provide exact solutions to Maximum Expected Utility (MEU) queries over relational models for decision under uncertainty. Experimental evaluations show our algorithms to provide significant speedup over the alternatives.

Answer: Extended Lifted Inference with Joint Formulas


Question: Device-free (DF) localization is an emerging technology that allows the detection and tracking of entities that do not carry any devices not participate actively in the localization process. Typically, DF systems require a large number of transmitters and receivers to achieve acceptable accuracy, which is not available in many scenarios such as homes and small businesses. In this paper, we introduce MonoStream as an accurate single-stream DF localization system that leverages the rich Channel State Information (CSI) as well as MIMO information from the physical layer to provide accurate DF localization with only one stream. To boost its accuracy and attain low computational requirements, MonoStream models the DF localization problem as an object recognition problem and uses a novel set of CSI-context features and techniques with proven accuracy and efficiency. Experimental evaluation in two typical testbeds, with a side-by-side comparison with the state-of-the-art, shows that MonoStream can achieve an accuracy of 0.95m with at least 26% enhancement in median distance error using a single stream only. This enhancement in accuracy comes with an efficient execution of less than 23ms per location update on a typical laptop. This highlights the potential of MonoStream usage for real-time DF tracking applications.

Answer:
MonoStream: A Minimal-Hardware High Accuracy Device-free WLAN Localization System