Declassification of Faceted Values in JavaScript

This research addresses the issues with protecting sensitive information at the language level using information flow control mechanisms (IFC). Most of the IFC mechanisms face the challenge of releasing sensitive information in a restricted or limited manner. This research uses faceted values, an IFC mechanism that has shown promising flexibility for downgrading the confidential information in a secure manner, also called declassification. In this project, we introduce the concept of first-class labels to simplify the declassification of faceted values. To validate the utility of our approach we show how the combination of faceted values and first-class labels can build various declassification mechanisms

Policy-agnostic programming on the client-side

Browser security has become a major concern especially due to web pages becoming more complex. These web applications handle a lot of information, including sensitive data that may be vulnerable to attacks like data exfiltration, cross-site scripting (XSS), etc. Most modern browsers have security mechanisms in place to prevent such attacks but they still fall short in preventing more advanced attacks like evolved variants of data exfiltration. Moreover, there is no standard that is followed to implement security into the browser. A lot of research has been done in the field of information flow security that could prove to be helpful in solving the problem of securing the client-side. Policy- agnostic programming is a programming paradigm that aims to make implementation of information flow security in real world systems more flexible. In this paper, we explore the use of policy-agnostic programming on the client-side and how it will help prevent common client-side attacks. We verify our results through a client-side salary management application. We show a possible attack and how our solution would prevent such an attack

Unified Analysis of Collapsible and Ordered Pushdown Automata via Term Rewriting

We model collapsible and ordered pushdown systems with term rewriting, by encoding higher-order stacks and multiple stacks into trees. We show a uniform inverse preservation of recognizability result for the resulting class of term rewriting systems, which is obtained by extending the classic saturation-based approach. This result subsumes and unifies similar analyses on collapsible and ordered pushdown systems. Despite the rich literature on inverse preservation of recognizability for term rewrite systems, our result does not seem to follow from any previous study.Comment: in Proc. of FRE

Partially-commutative context-free languages

The paper is about a class of languages that extends context-free languages (CFL) and is stable under shuffle. Specifically, we investigate the class of partially-commutative context-free languages (PCCFL), where non-terminal symbols are commutative according to a binary independence relation, very much like in trace theory. The class has been recently proposed as a robust class subsuming CFL and commutative CFL. This paper surveys properties of PCCFL. We identify a natural corresponding automaton model: stateless multi-pushdown automata. We show stability of the class under natural operations, including homomorphic images and shuffle. Finally, we relate expressiveness of PCCFL to two other relevant classes: CFL extended with shuffle and trace-closures of CFL. Among technical contributions of the paper are pumping lemmas, as an elegant completion of known pumping properties of regular languages, CFL and commutative CFL.Comment: In Proceedings EXPRESS/SOS 2012, arXiv:1208.244

Developmental Stages of Perception and Language Acquisition in a Perceptually Grounded Robot

The objective of this research is to develop a system for language learning based on a minimum of pre-wired language-specific functionality, that is compatible with observations of perceptual and language capabilities in the human developmental trajectory. In the proposed system, meaning (in terms of descriptions of events and spatial relations) is extracted from video images based on detection of position, motion, physical contact and their parameters. Mapping of sentence form to meaning is performed by learning grammatical constructions that are retrieved from a construction inventory based on the constellation of closed class items uniquely identifying the target sentence structure. The resulting system displays robust acquisition behavior that reproduces certain observations from developmental studies, with very modest “innate” language specificity

Evolino for recurrent support vector machines

Traditional Support Vector Machines (SVMs) need pre-wired finite time windows to predict and classify time series. They do not have an internal state necessary to deal with sequences involving arbitrary long-term dependencies. Here we introduce a new class of recurrent, truly sequential SVM-like devices with internal adaptive states, trained by a novel method called EVOlution of systems with KErnel-based outputs (Evoke), an instance of the recent Evolino class of methods. Evoke evolves recurrent neural networks to detect and represent temporal dependencies while using quadratic programming/support vector regression to produce precise outputs. Evoke is the first SVM-based mechanism learning to classify a context-sensitive language. It also outperforms recent state-of-the-art gradient-based recurrent neural networks (RNNs) on various time series prediction tasks.Comment: 10 pages, 2 figure