Biblio

Hardware security has long been an important issue in the current IC design. In this paper, a hardware Trojan detection method based on frequency domain characteristics of power consumption is proposed. For some HTs, it is difficult to detect based on the time domain characteristics, these types of hardware Trojan can be analyzed in the frequency domain, and Mahalanobis distance is used to classify designs with or without HTs. The experimental results demonstrate that taking 10% distance as the criterion, the hardware Trojan detection results in the frequency domain have almost no failure cases in all the tested designs.

We present Falcon, an interactive, deterministic, and declarative data cleaning system, which uses SQL update queries as the language to repair data. Falcon does not rely on the existence of a set of pre-defined data quality rules. On the contrary, it encourages users to explore the data, identify possible problems, and make updates to fix them. Bootstrapped by one user update, Falcon guesses a set of possible sql update queries that can be used to repair the data. The main technical challenge addressed in this paper consists in finding a set of sql update queries that is minimal in size and at the same time fixes the largest number of errors in the data. We formalize this problem as a search in a lattice-shaped space. To guarantee that the chosen updates are semantically correct, Falcon navigates the lattice by interacting with users to gradually validate the set of sql update queries. Besides using traditional one-hop based traverse algorithms (e.g., BFS or DFS), we describe novel multi-hop search algorithms such that Falcon can dive over the lattice and conduct the search efficiently. Our novel search strategy is coupled with a number of optimization techniques to further prune the search space and efficiently maintain the lattice. We have conducted extensive experiments using both real-world and synthetic datasets to show that Falcon can effectively communicate with users in data repairing.

Many emerging applications, from domains such as healthcare and oil & gas, require several data processing systems for complex analytics. This demo paper showcases system, a framework that provides multi-platform task execution for such applications. It features a three-layer data processing abstraction and a new query optimization approach for multi-platform settings. We will demonstrate the strengths of system by using real-world scenarios from three different applications, namely, machine learning, data cleaning, and data fusion.