Using machine learning to infer reasoning provenance from user interaction log data: based on the data/frame theory of sensemaking

The reconstruction of analysts’ reasoning processes (reasoning provenance) during complex sensemaking tasks can support reflection and decision making. One potential approach to such reconstruction is to automatically infer reasoning from low-level user interaction logs. We explore a novel method for doing this using machine learning. Two user studies were conducted in which participants performed similar intelligence analysis tasks. In one study, participants used a standard web browser and word processor; in the other, they used a system called INVISQUE (Interactive Visual Search and Query Environment). Interaction logs were manually coded for cognitive actions based on captured think-aloud protocol and posttask interviews based on Klein, Phillips, Rall, and Pelusos’s data/frame model of sensemaking as a conceptual framework. This analysis was then used to train an interaction frame mapper, which employed multiple machine learning models to learn relationships between the interaction logs and the codings. Our results show that, for one study at least, classification accuracy was significantly better than chance and compared reasonably to a reported manual provenance reconstruction method. We discuss our results in terms of variations in feature sets from the two studies and what this means for the development of the method for provenance capture and the evaluation of sensemaking systems

Game theory meets information security management

Part 1: Intrusion DetectionInternational audienceThis work addresses the challenge “how do we make better security decisions?” and it develops techniques to support human decision making and algorithms which enable well-founded cyber security decisions to be made. In this paper we propose a game theoretic model which optimally allocates cyber security resources such as administrators’ time across different tasks. We first model the interactions between an omnipresent attacker and a team of system administrators seen as the defender, and we have derived the mixed Nash Equilibria (NE) in such games. We have formulated general-sum games that represent our cyber security environment, and we have proven that the defender’s Nash strategy is also minimax. This result guarantees that independently from the attacker’s strategy the defender’s solution is optimal. We also propose Singular Value Decomposition (SVD) as an efficient technique to compute approximate equilibria in our games. By implementing and evaluating a minimax solver with SVD, we have thoroughly investigated the improvement that Nash defense introduces compared to other strategies chosen by common sense decision algorithms. Our key finding is that a particular NE, which we call weighted NE, provides the most effective defense strategy. In order to validate this model we have used real-life statistics from Hackmageddon, the Verizon 2013 Data Breach Investigation report, and the Ponemon report of 2011. We finally compare the game theoretic defense method with a method which implements a stochastic optimization algorithm

Differential exposure and reactivity to interpersonal stress predict sex differences in adolescent depression

This study tested the hypothesis that higher rates of depression in adolescent girls are explained by their greater exposure and reactivity to stress in the interpersonal domain in a large sample of 15-year-olds. Findings indicate that adolescent girls experienced higher levels of total and interpersonal episodic stress, whereas boys experienced higher levels of chronic stress (academic and close friendship domains). Higher rates of depression in girls were explained by their greater exposure to total stress, particularly interpersonal episodic stress. Adolescent girls were also more reactive (more likely to become depressed) to both total and interpersonal episodic stress. The findings suggest that girls experience higher levels of episodic stress and are more reactive to these stressors, increasing their likelihood of becoming depressed compared to boys. Results were discussed in terms of girls' greater interpersonal focus and implications for understanding sex differences in depression

Long-lived photoexcited states in polydiacetylenes with different molecular and supramolecular organization

With the aim of determining the importance of the molecular and supramolecular organization on the excited states of polydiacetylenes, we have studied the photoinduced absorption spectra of the red form of poly[1,6-bis(3,6-didodecyl-N-carbazolyl)-2,4-hexadiyne] (polyDCHD-S) and the results compared with those of the blue form of the same polymer. An interpretation of the data is given in terms of both the conjugation length and the interbackbone separation also in relation to the photoinduced absorption spectra of both blue and red forms of poly[1,6-bis(N-carbazolyl)-2,4-hexadiyne] (polyDCHD), which does not carry the alkyl substituents on the carbazolyl side groups. Information on the above properties is derived from the analysis of the absorption and Raman spectra of this class of polydiacetylenes

Using machine learning to infer reasoning provenance from user interaction log data: based on the data/frame theory of sensemaking

The reconstruction of analysts’ reasoning processes (reasoning provenance) during complex sensemaking tasks can support reflection and decision making. One potential approach to such reconstruction is to automatically infer reasoning from low-level user interaction logs. We explore a novel method for doing this using machine learning. Two user studies were conducted in which participants performed similar intelligence analysis tasks. In one study, participants used a standard web browser and word processor; in the other, they used a system called INVISQUE (Interactive Visual Search and Query Environment). Interaction logs were manually coded for cognitive actions based on captured think-aloud protocol and posttask interviews based on Klein, Phillips, Rall, and Pelusos’s data/frame model of sensemaking as a conceptual framework. This analysis was then used to train an interaction frame mapper, which employed multiple machine learning models to learn relationships between the interaction logs and the codings. Our results show that, for one study at least, classification accuracy was significantly better than chance and compared reasonably to a reported manual provenance reconstruction method. We discuss our results in terms of variations in feature sets from the two studies and what this means for the development of the method for provenance capture and the evaluation of sensemaking systems