JavaScript is widely used for writing client-side web applications and is getting increasingly popular for writing mobile applications. However, unlike C, C++, and Java, there are not that many tools available for analysis and testing of JavaScript applications. In this paper, we present a simple yet powerful framework, called Jalangi, for writing heavy-weight dynamic analyses. Our framework incorporates two key techniques: 1) selective record-replay, a technique which enables to record and to faithfully replay a user-selected part of the program, and 2) shadow values and shadow execution, which enables easy implementation of heavy-weight dynamic analyses. Our implementation makes no special assumption about JavaScript, which makes it applicable to real-world JavaScript programs running on multiple platforms. We have implemented concolic testing, an analysis to track origins of nulls and undefined, a simple form of taint analysis, an analysis to detect likely type inconsistencies, and an object allocation profiler in Jalangi. Our evaluation of Jalangi on the SunSpider benchmark suite and on five web applications shows that Jalangi has an average slowdown of 26X during recording and 30X slowdown during replay and analysis. The slowdowns are comparable with slowdowns reported for similar tools, such as PIN and Valgrind for x86 binaries. We believe that the techniques proposed in this paper are applicable to other dynamic languages.

We describe a tool framework, called Jalangi, for dynamic analysis and concolic testing of JavaScript programs. The framework is written in JavaScript and allows implementation of various heavy-weight dynamic analyses for JavaScript. Jalangi incorporates two key techniques: 1) selective record-replay, a technique which enables to record and to faithfully replay a user-selected part of the program, and 2) shadow values and shadow execution, which enables easy implementation of heavy-weight dynamic analyses such as concolic testing and taint tracking. Jalangi works through source-code instrumentation which makes it portable across platforms. Jalangi is available at https://github.com/SRA-SiliconValley/jalangi under Apache 2.0 license. Our evaluation of Jalangi on the SunSpider benchmark suite and on five web applications shows that Jalangi has an average slowdown of 26X during recording and 30X slowdown during replay and analysis. The slowdowns are comparable with slowdowns reported for similar tools, such as PIN and Valgrind for x86 binaries.

Object recognition is a key problem in the field of computer vision. However, highly accurate object recognition systems are also computationally intensive, which limits their applicability. In this paper, we focus on a state-of-the-art object recognition system. We identify key computations of the system, examine efficient algorithms for parallelizing key computations, and develop a parallel object recognition system. The time taken by the training procedure on 127 images, with an average size of 0.15 M pixels, is reduced from 2332 seconds to 20 seconds. Similarly, the classification time of one 0.15 M pixel image is reduced from 331 seconds to 2.78 seconds. This efficient implementation of the object recognition system now makes it practical to train hundreds of images within minutes, and makes it possible to analyze image databases with hundreds or thousands of images in minutes, which was previously not possible.

Graph algorithms are widely used in image processing techniques. With technology advancements, image sizes are increasing, and the contents inside images are becoming more complex, resulting in increased runtimes for graph algorithms on these images. Breadth First Search (BFS) is a fundamental graph traversal approach. A key to parallelizing graph algorithms used in image processing is to parallelize the BFS graph traversal operation. In this paper, we propose using highly parallelizable structured grid computations to realize the BFS graph traversal operations. This mapping enables efficient implementation of the BFS graph traversal operations on highly parallel manycore platforms. By using such a mapping, we were able to achieve performance gains of 2× to 33× depending on image complexity.

In any distributed networked environment, security systems for network access and communication are necessary to allow legitimate hosts on the network to access the network services while denying non-registered hosts. Ideally, such systems should ensure both confidentiality and integrity of messages exchanged over the network. We discuss the deficiencies with the registration and authentication services provided by Global System for Mobile Communication (GSM), Cellular Digital Packet Data (CDPD), and IS-41. Next, we present the design of the Mutual Authentication, Confidentiality, and Key MANagement (MACKMAN) system to provide a more secure registration and authentication service for mobile computing and wireless communication. The capabilities provided by MACKMAN include registration of legitimate hosts with the network, mutual authentication, and data confidentiality and integrity in a mobile and wireless environment. Data confidentiality and integrity are provided by using the public key Elliptic Curve RSA (ECRSA) cryptosystem in conjunction with a hierarchy of certification authorities for key distribution and management.