526 publications from this institution
Multi-level logic synthesis is a problem of immense practical significance, and is a key to developing circuits that optimize a number of parameters, such as depth, energy dissipation, reliability, etc. The problem can be defined as the task of taking a collection of components from which one wants to synthesize a circuit that optimizes a particular objective function. This problem is computationally hard, and there are very few automated approaches for its solution. To solve this problem we propose an algorithm, called Circuit-Decomposition Engine (CDE), that is based on learning decision trees, and uses a greedy approach for function learning. We empirically demonstrate that CDE, when given a library of different component types, can learn the function of Disjunctive Normal Form (DNF) Boolean representations and synthesize circuit structure using the input library. We compare the structure of the synthesized circuits with that of well-known circuits using a range of circuit similarity metrics.
Deep neural nets (DNNs) mostly tend to outperform other machine learning (ML) approaches when the training data is abundant, high-dimensional, sparse, or consisting of raw data (e.g., pixels). For datasets with other characteristics – for example, dense tabular numerical data – algorithms such as Gradient Boosting Machines and Random Forest often achieve comparable or better performance at a fraction of the time and resources. These differences suggest that combining these approaches has potential to yield superior performance. Existing attempts to combine DNNs with other ML approaches, which usually consist of feeding the output of the latter into the former, often do not produce positive results. We argue that this lack of improvement stems from the fact that the final classifications fail to provide the DNN with an understanding of the other algorithms’ decision-making process (i.e., its “logic”). In this study we present F-PENN, a novel approach for combining decision forests and DNNs. Instead of providing the final output of the forest (or its trees) to the DNN, we provide the paths traveled by each sample. This information, when fed to the neural net, yields significant improvement in performance. We demonstrate the effectiveness of our approach by conducting extensive evaluation on 56 datasets and comparing F-PENN to four leading baselines: DNNs, Gradient Boosted Decision Trees (GBDT), Random Forest and DeepFM. We show that F-PENN outperforms the baselines in 69%–89% of dataset and achieves an overall average error reduction of 16%–26%.
Data misuse may be performed by entities such as an organization's employees and business partners who are granted access to sensitive information and misuse their privileges. We assume that users can be either trusted or untrusted. The access of untrusted parties to data objects (e.g., client and patient records) should be monitored in an attempt to detect misuse. However, monitoring data objects is resource intensive and time-consuming and may also cause disturbance or inconvenience to the involved employees. Therefore, the monitored data objects should be carefully selected. In this article, we present two optimization problems carefully designed for selecting specific data objects for monitoring, such that the detection rate is maximized and the monitoring effort is minimized. In the first optimization problem, the goal is to select data objects for monitoring that are accessed by at most c trusted agents while ensuring access to at least k monitored objects by each untrusted agent (both c and k are integer variable). As opposed to the first optimization problem, the goal of the second optimization problem is to select monitored data objects that maximize the number of monitored data objects accessed by untrusted agents while ensuring that each trusted agent does not access more than d monitored data objects ( d is an integer variable as well). Two efficient heuristic algorithms for solving these optimization problems are proposed, and experiments were conducted simulating different scenarios to evaluate the algorithms’ performance. Moreover, we compared the heuristic algorithms’ performance to the optimal solution and conducted sensitivity analysis on the three parameters ( c , k , and d ) and on the ratio between the trusted and untrusted agents.
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