When inspecting information visualizations under time critical settings, such as emergency response or monitoring the heart rate in a surgery room, the user only has a small amount of time to view the visualization "at a glance". In these settings, it is important to provide a quantitative measure of the visualization to understand whether or not the visualization is too "complex" to accurately judge at a glance. This paper proposes Pixel Approximate Entropy (PAE), which adapts the approximate entropy statistical measure commonly used to quantify regularity and unpredictability in time-series data, as a measure of visual complexity for line charts. We show that PAE is correlated with user-perceived chart complexity, and that increased chart PAE correlates with reduced judgement accuracy. 'We also find that the correlation between PAE values and participants' judgment increases when the user has less time to examine the line charts.

Verifying real-world programs often requires inferring loop invariants with nonlinear constraints. This is especially true in programs that perform many numerical operations, such as control systems for avionics or industrial plants. Recently, data-driven methods for loop invariant inference have shown promise, especially on linear loop invariants. However, applying data-driven inference to nonlinear loop invariants is challenging due to the large numbers of and large magnitudes of high-order terms, the potential for overfitting on a small number of samples, and the large space of possible nonlinear inequality bounds.In this paper, we introduce a new neural architecture for general SMT learning, the Gated Continuous Logic Network (G-CLN), and apply it to nonlinear loop invariant learning. G-CLNs extend the Continuous Logic Network (CLN) architecture with gating units and dropout, which allow the model to robustly learn general invariants over large numbers of terms. To address overfitting that arises from finite program sampling, we introduce fractional sampling-a sound relaxation of loop semantics to continuous functions that facilitates unbounded sampling on the real domain. We additionally design a new CLN activation function, the Piecewise Biased Quadratic Unit (PBQU), for naturally learning tight inequality bounds.We incorporate these methods into a nonlinear loop invariant inference system that can learn general nonlinear loop invariants. We evaluate our system on a benchmark of * Equal contribution

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The insider threat is one of the most serious security problems faced by modern organizations. High profile cases demonstrate the serious consequences of successful attacks. The problem has been studied for many years leading to a number of technologies and products that have been widely deployed in government and commercial enterprises. A fundamental question is how well do these systems work? How may they be tested and how computationally expensive a widely deployed monitoring infrastructure cost? Measuring real systems that are dynamic in nature, encounter unknown configuration bugs and have sensitivities to the vagaries of human nature and adversarial behavior require a formal means to continuously test and evaluate deployed detection systems. We present a framework to deploy in situ simulated user bots (SUBs) that can emulate the actions of real users. By creating a user account and running a host in the enterprise network, a SUB can be introduced into an enterprise system that runs at a realistic pace and does not interfere with normal operations. Infusing malicious behavior into the SUB should be detected by the insider threat monitoring infrastructure. The SUB framework can be controlled to explore the limits of deployed systems and to test the effectiveness of insider evasion tactics, especially low and slow behaviors. We demonstrate our framework by generating user data to test the detection of malicious users and our ability to produce variable ground truths through intrusion detection testing using several commonly used machine learning techniques.

Program verification offers a framework for ensuring program correctness and therefore systematically eliminating different classes of bugs. Inferring loop invariants is one of the main challenges behind automated verification of real-world programs which often contain many loops. In this paper, we present Continuous Logic Network (CLN), a novel neural architecture for automatically learning loop invariants directly from program execution traces. Unlike existing neural networks, CLNs can learn precise and explicit representations of formulas in Satisfiability Modulo Theories (SMT) for loop invariants from program execution traces. We develop a new sound and complete semantic mapping for assigning SMT formulas to continuous truth values that allows CLNs to be trained efficiently. We use CLNs to implement a new inference system for loop invariants, CLN2INV, that significantly outperforms existing approaches on the popular Code2Inv dataset. CLN2INV is the first tool to solve all 124 theoretically solvable problems in the Code2Inv dataset. Moreover, CLN2INV takes only 1.1 seconds on average for each problem, which is 40× faster than existing approaches. We further demonstrate that CLN2INV can even learn 12 significantly more complex loop invariants than the ones required for the Code2Inv dataset. * Co-student leads listed in alphabetical order; each contributed equally.