Explaining violation traces with finite state natural language generation models

An essential element of any verification technique is that of identifying and communicating to the user, system behaviour which leads to a deviation from the expected behaviour. Such behaviours are typically made available as long traces of system actions which would benefit from a natural language explanation of the trace and especially in the context of business logic level specifications. In this paper we present a natural language generation model which can be used to explain such traces. A key idea is that the explanation language is a CNL that is, formally speaking, regular language susceptible transformations that can be expressed with finite state machinery. At the same time it admits various forms of abstraction and simplification which contribute to the naturalness of explanations that are communicated to the user.peer-reviewe

On Link Estimation in Dense RPL Deployments

The Internet of Things vision foresees billions of devices to connect the physical world to the digital world. Sensing applications such as structural health monitoring, surveillance or smart buildings employ multi-hop wireless networks with high density to attain sufficient area coverage. Such applications need networking stacks and routing protocols that can scale with network size and density while remaining energy-efficient and lightweight. To this end, the IETF RoLL working group has designed the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). This paper discusses the problems of link quality estimation and neighbor management policies when it comes to handling high densities. We implement and evaluate different neighbor management policies and link probing techniques in Contiki’s RPL implementation. We report on our experience with a 100-node testbed with average 40-degree density. We show the sensitivity of high density routing with respect to cache sizes and routing metric initialization. Finally, we devise guidelines for design and implementation of density-scalable routing protocols

Fault prediction in aircraft engines using Self-Organizing Maps

Aircraft engines are designed to be used during several tens of years. Their maintenance is a challenging and costly task, for obvious security reasons. The goal is to ensure a proper operation of the engines, in all conditions, with a zero probability of failure, while taking into account aging. The fact that the same engine is sometimes used on several aircrafts has to be taken into account too. The maintenance can be improved if an efficient procedure for the prediction of failures is implemented. The primary source of information on the health of the engines comes from measurement during flights. Several variables such as the core speed, the oil pressure and quantity, the fan speed, etc. are measured, together with environmental variables such as the outside temperature, altitude, aircraft speed, etc. In this paper, we describe the design of a procedure aiming at visualizing successive data measured on aircraft engines. The data are multi-dimensional measurements on the engines, which are projected on a self-organizing map in order to allow us to follow the trajectories of these data over time. The trajectories consist in a succession of points on the map, each of them corresponding to the two-dimensional projection of the multi-dimensional vector of engine measurements. Analyzing the trajectories aims at visualizing any deviation from a normal behavior, making it possible to anticipate an operation failure.Comment: Communication pr\'esent\'ee au 7th International Workshop WSOM 09, St Augustine, Floride, USA, June 200

Towards a possibility-theoretic approach to uncertainty in medical data interpretation for text generation

Many real-world applications that reason about events obtained from raw data must deal with the problem of temporal uncertainty, which arises due to error or inaccuracy in data. Uncertainty also compromises reasoning where relationships between events need to be inferred. This paper discusses an approach to dealing with uncertainty in temporal and causal relations using Possibility Theory, focusing on a family of medical decision support systems that aim to generate textual summaries from raw patient data in a Neonatal Intensive Care Unit. We describe a framework to capture temporal uncertainty and to express it in generated texts by mean of linguistic modifiers. These modifiers have been chosen based on a human experiment testing the association between subjective certainty about a proposition and the participants’ way of verbalising it.peer-reviewe

Mining local staircase patterns in noisy data

Most traditional biclustering algorithms identify biclusters with no or little overlap. In this paper, we introduce the problem of identifying staircases of biclusters. Such staircases may be indicative for causal relationships between columns and can not easily be identified by existing biclustering algorithms. Our formalization relies on a scoring function based on the Minimum Description Length principle. Furthermore, we propose a first algorithm for identifying staircase biclusters, based on a combination of local search and constraint programming. Experiments show that the approach is promising

Uniqueness, spatial mixing, and approximation for ferromagnetic 2-spin systems

For anti-ferromagnetic 2-spin systems, a beautiful connection has been established, namely that the following three notions align perfectly: The uniqueness of Gibbs measures in infinite regular trees, the decay of correlations (also known as spatial mixing), and the approximability of the partition function. The uniqueness condition implies spatial mixing, and an FPTAS for the partition function exists based on spatial mixing. On the other hand, non-uniqueness implies some long range correlation, based on which NP-hardness reductions are built. These connections for ferromagnetic 2-spin systems are much less clear, despite their similarities to anti-ferromagnetic systems. The celebrated Jerrum-Sinclair Markov chain [8] works even if spatial mixing fails. Also, for a fixed degree the uniqueness condition is non-monotone with respect to the external field, which seems to have no meaningful interpretation in terms of computational complexity. However, it is still intriguing whether there are some relationship underneath the apparent disparities among them. We provide some answers to this question. Let β,γbe the (0, 0) and (1, 1) edge interactions respectively (βγ > 1), and λ the external field for spin "0". For graphs with degree bound Δ ≤ Δc + 1 where Δc = √ βγ+1 √ βγ-1 , regardless of the field (even inconsistent fields are allowed), correlation decay always holds and FPTAS exists. If all fields satisfy λ λint c 0, where λint c 0 = (γ/β) b-cc+2 2 , then approximating the partition function is #BIS-hard. Interestingly, unless λc is an integer, neither λc nor λint c is the tight bound in each own respect. We provide examples where correlation decay continues to hold in a small interval beyond λc, and irregular trees in which spatial mixing fails for some λ < λint c

A case study on graphically modelling and detecting knowledge mobility risks

As the world continues to increasingly depend on a knowledge economy, companies are realising that their most valuable asset is knowledge held by their employees. This asset is hard to track, manage and retain especially in a situation where employees are free to job-hop for better pay after providing a few weeks’ notice to their employers. In previous work we have defined the concept of knowledge risk, and presented a graph-based approach for detecting it. In this paper, we present the results of a case study which employs knowledge graphs in the context of four software development teams.peer-reviewe