A multi-input deep learning model for C/C++ source code attribution

Code stylometry is applying analysis techniques to a collection of source code or binaries to determine variations in style. The variations extracted are often used to identify the author of the text or to differentiate one piece from another. In this research, we were able to create a multi-input deep learning model that could accurately categorize and group code from multiple projects. The deep learning model took as input word-based tokenization for code comments, character-based tokenization for the source code text, and the metadata features described by A. Caliskan-Islam et al. Using these three inputs, we were able to achieve 90% validation accuracy with a loss value of 0.1203 using 12 projects consisting of 5,877 files. Finally, we analyzed the Bitcoin source code using our data model showing a high probability match to the OpenSSL project

From prediction error to incentive salience: mesolimbic computation of reward motivation

Reward contains separable psychological components of learning, incentive motivation and pleasure. Most computational models have focused only on the learning component of reward, but the motivational component is equally important in reward circuitry, and even more directly controls behavior. Modeling the motivational component requires recognition of additional control factors besides learning. Here I discuss how mesocorticolimbic mechanisms generate the motivation component of incentive salience. Incentive salience takes Pavlovian learning and memory as one input and as an equally important input takes neurobiological state factors (e.g. drug states, appetite states, satiety states) that can vary independently of learning. Neurobiological state changes can produce unlearned fluctuations or even reversals in the ability of a previously learned reward cue to trigger motivation. Such fluctuations in cue‐triggered motivation can dramatically depart from all previously learned values about the associated reward outcome. Thus, one consequence of the difference between incentive salience and learning can be to decouple cue‐triggered motivation of the moment from previously learned values of how good the associated reward has been in the past. Another consequence can be to produce irrationally strong motivation urges that are not justified by any memories of previous reward values (and without distorting associative predictions of future reward value). Such irrationally strong motivation may be especially problematic in addiction. To understand these phenomena, future models of mesocorticolimbic reward function should address the neurobiological state factors that participate to control generation of incentive salience. Reward contains separable psychological components of learning, incentive motivation and pleasure. Most computational models have focused only on the learning component of reward, but the motivational component is equally important in reward circuitry, and even more directly controls behavior.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90564/1/j.1460-9568.2012.07990.x.pd

On Priority Assignment for Controller Area Network when some Message Identifiers are Fixed

International audienceController Area Network (CAN) is widely used in automotive applications. With CAN, the network utilisation that may be obtained while ensuring that all messages meet their deadlines is strongly dependent on the policy used for priority (message identifier) assignment. This paper addresses the problem of priority assignment when some message identifiers are fixed. There are two variants of this problem: P1 where the gaps between fixed identifiers are large enough to accommodate the freely assignable messages and P2 when the gaps are too small. For problem P1, we provide algorithms that give optimal and robust priority orderings based on an adaptation of existing techniques. Problem P2 is more difficult to solve. We show via a counter example that the algorithms derived for P1 and others recently published can fail to find a schedulable priority ordering when the gaps are small, even though one exists. We derive an optimal and robust solution to this problem with respect to a simple form of schedulability analysis which assumes the same upper bound on the length of all messages