Online Robot Introspection via Wrench-based Action Grammars

Robotic failure is all too common in unstructured robot tasks. Despite well-designed controllers, robots often fail due to unexpected events. How do robots measure unexpected events? Many do not. Most robots are driven by the sense-plan act paradigm, however more recently robots are undergoing a sense-plan-act-verify paradigm. In this work, we present a principled methodology to bootstrap online robot introspection for contact tasks. In effect, we are trying to enable the robot to answer the question: what did I do? Is my behavior as expected or not? To this end, we analyze noisy wrench data and postulate that the latter inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is generated by segmenting and encoding the data. When the wrench information represents a sequence of sub-tasks, we can think of the vocabulary forming a sentence (set of words with grammar rules) for a given sub-task; allowing the latter to be uniquely represented. The grammar, which can also include unexpected events, was classified in offline and online scenarios as well as for simulated and real robot experiments. Multiclass Support Vector Machines (SVMs) were used offline, while online probabilistic SVMs were are used to give temporal confidence to the introspection result. The contribution of our work is the presentation of a generalizable online semantic scheme that enables a robot to understand its high-level state whether nominal or abnormal. It is shown to work in offline and online scenarios for a particularly challenging contact task: snap assemblies. We perform the snap assembly in one-arm simulated and real one-arm experiments and a simulated two-arm experiment. This verification mechanism can be used by high-level planners or reasoning systems to enable intelligent failure recovery or determine the next most optima manipulation skill to be used.Comment: arXiv admin note: substantial text overlap with arXiv:1609.0494

Why do firms opt for Alternative-Format Financial Statements ? Some Evidence from France

Historically, the format of financial statements has varied from one country to another. Recently, due to the attractiveness of their capital markets, the strength of their accounting professions and the influence of their institutional investors, Anglo-American countries have seen the impact of their accounting practices on other nations increase steadily, even influencing the actual format of financial statements. Given that French accounting regulations allow a certain degree of choice in consolidated balance sheet format (‘by nature’ or ‘by term’) and income statement format (‘by nature’ or ‘by function’), this study examines a sample of 199 large French listed firms in an attempt to understand why some of these firms do not use the traditional French formats (‘by nature’ for the balance sheet and ‘by nature” for the income statement), instead preferring Anglo-American practices (‘by term’ format for the balance sheet and ‘by function’ format for the income statement). We first analyze the balance sheet and income statement formats separately using a logit model, then combine the two and enrich the research design with a generalized ordered logit model and a multinomial logit regression. Our results confirm that the major driving factor behind the adoption of one or two alternative formats is the firm’s degree of internationalization, not only financial (auditor type, foreign listing and the decision to apply alternative accounting standards) but also commercial (company size and the internationalization of sales).Disclosure; Determinants; Financial Statements; Alternative format; France; Logit; Generalized ordered logit; Multinomial logit

High-Resolution Road Vehicle Collision Prediction for the City of Montreal

Road accidents are an important issue of our modern societies, responsible for millions of deaths and injuries every year in the world. In Quebec only, in 2018, road accidents are responsible for 359 deaths and 33 thousands of injuries. In this paper, we show how one can leverage open datasets of a city like Montreal, Canada, to create high-resolution accident prediction models, using big data analytics. Compared to other studies in road accident prediction, we have a much higher prediction resolution, i.e., our models predict the occurrence of an accident within an hour, on road segments defined by intersections. Such models could be used in the context of road accident prevention, but also to identify key factors that can lead to a road accident, and consequently, help elaborate new policies. We tested various machine learning methods to deal with the severe class imbalance inherent to accident prediction problems. In particular, we implemented the Balanced Random Forest algorithm, a variant of the Random Forest machine learning algorithm in Apache Spark. Interestingly, we found that in our case, Balanced Random Forest does not perform significantly better than Random Forest. Experimental results show that 85% of road vehicle collisions are detected by our model with a false positive rate of 13%. The examples identified as positive are likely to correspond to high-risk situations. In addition, we identify the most important predictors of vehicle collisions for the area of Montreal: the count of accidents on the same road segment during previous years, the temperature, the day of the year, the hour and the visibility