Static analysis has been successfully used in many areas, from verifying mission-critical software to malware detection. Unfortunately, static analysis often produces false positives, which require significant manual effort to resolve. In this paper, we show how to overlay a probabilistic model, trained using domain knowledge, on top of static analysis results, in order to triage static analysis results. We apply this idea to analyzing mobile applications. Android application components can communicate with each other, both within single applications and between different applications. Unfortunately, techniques to statically infer Inter-Component Communication (ICC) yield many potential inter-component and interapplication links, most of which are false positives. At large scales, scrutinizing all potential links is simply not feasible. We therefore overlay a probabilistic model of ICC on top of static analysis results. Since computing the inter-component links is a prerequisite to inter-component analysis, we introduce a formalism for inferring ICC links based on set constraints. We design an efficient algorithm for performing link resolution. We compute all potential links in a corpus of 11,267 applications in 30 minutes and triage them using our probabilistic approach. We find that over 95.1% of all 636 million potential links are associated with probability values below 0.01 and are thus likely unfeasible links. Thus, it is possible to consider only a small subset of all links without significant loss of information. This work is the first significant step in making static inter-application analysis more tractable, even at large scales.
Quantifying the ability of a digital design concept to perform a function currently requires the use of costly and intensive solutions such as Computational Fluid Dynamics. To mitigate these challenges, the authors of this work propose a deep learning approach based on 3-Dimensional Convolutions that predicts Functional Quantities of digital design concepts. This work defines the term Functional Quantity to mean a quantitative measure of an artifact's ability to perform a function. Several research questions are derived from this work: i) Are learned 3D Convolutions able to accurately calculate these quantities, as measured by rank, magnitude and accuracy? ii) What do the latent features (that is, internal values in the model) discovered by this network mean? iii) Does this work perform better than other deep learning approaches at calculating Functional Quantities? In the case study, a proposed network design is tested for its ability to predict several functions (Sitting, Storing Liquid, Emitting Sound, Displaying Images, and Providing Conveyance) based on test form classes distinct from training class. This study evaluates several approaches to this problem based on a common architecture, with the best approach achieving F Scores of > 0.9 in 3 of the 5 functions identified. Testing trained models on novel input also yields accuracy as high as 98% for estimating rank of these functional quantities. This method is also employed to differentiate between decorative and functional head-wear, which yields an 84.4% accuracy and 0.786 precision.Dering MD-17-1178 1 3-D scanners, or RGB-D Sensors such as the Microsoft Kinect) has further enabled the capture, sharing, and production of such artifacts around the world [1, 2].However, just because a design "looks good" does not mean that it will achieve its intended functions or behaviors.While simulation models such as CFD use advanced numerical methods and algorithms to evaluate the feasibility of designs, they require extensive modeling expertise, advanced computing resources and are time consuming [3,4]. While some of these publicly-available digital design models are decorative in nature, many of these artifacts are meant to serve a specific purpose or function that may be implied or explicitly stated by the designer. Formally, a function is defined as a purpose intended for an object. This work proposes a method that uses as input, a model file representing a possible artifact. A neural network analyzes this artifact, and as output, predicts how well it will perform each intended function. For example, some objects can be used for sitting. This network will predict how much force it will be able to withstand, when sat upon. These trained predictive models will provide insight from their latent variables as well. A latent variable is a variable contained within the model whose value may describe the forms and patterns that have the most predictive power.Typically, design defines a product by its form, function, and behavior [5]. However, this work is limited to a stud...
In this paper, we present a method that uses a physics-based virtual environment to evaluate the feasibility of neural network-based generated designs. Deep learning models rely on large training data sets that are used for training. These training data sets are typically validated by human designers that have a conceptual understanding of the problem being solved. However, the requirement of human training data severely constrains the size and availability of training data for computer generated models due to the manual process of either creating or labeling such data sets. Furthermore, there may be misclassification errors that result from human labeling. To mitigate these challenges, we present a physics-based simulation environment that helps users discover correlations between the form of a generated design and the physical constraints that relate to its function. We hypothesize that training data that includes machine validated designs from a physics-based virtual environment will increase the probability of generative models creating functionally-feasible design concepts. A case study involving a generative model that is trained on over 70,000 human 2D boat sketches is used to test the hypothesis. Knowledge gained from testing this hypothesis will provide human designers with insights into the importance of training data in the resulting design solutions generated by deep neural networks.
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