High-Entropy Visual Identification for Touch Screen Devices

We exhibit a system for improving the quality of user-derived keying material on touch-screen devices. We allow a device to recover previously generated, highly entropic data suitable for use as (part of) a strong secret key from a user’s act of identifying to the device. Our system uses visual cryptography [22], using no additional electronics and no memorization on the part of the user. Instead, we require the use of a transparency overlaid on the touch-screen. Our scheme is similar to the identification scheme of [23] but tailored for constrained, touch-screen displays

CHERI Concentrate: Practical Compressed Capabilities

We present CHERI Concentrate, a new fat-pointer compression scheme applied to CHERI, the most developed capability-pointer system at present. Capability fat-pointers are a primary candidate for enforcing fine-grained and non-bypassable security properties in future computer systems, although increased pointer size can severely affect performance. Thus, several proposals for capability compression have been suggested but these did not support legacy instruction sets, ignored features critical to the existing software base, and also introduced design inefficiencies to RISC-style processor pipelines. CHERI Concentrate improves on the state-of-the-art region-encoding efficiency, solves important pipeline problems, and eases semantic restrictions of compressed encoding, allowing it to protect a full legacy software stack. We analyze and extend logic from the open-source CHERI prototype processor design on FPGA to demonstrate encoding efficiency, minimize delay of pointer arithmetic, and eliminate additional load-to-use delay. To verify correctness of our proposed high-performance logic, we present a HOL4 machine-checked proof of the decode and pointer-modify operations. Finally, we measure a 50%-75% reduction in L2 misses for many compiled C-language benchmarks running under a commodity operating system using compressed 128-bit and 64-bit formats, demonstrating both compatibility with and increased performance over the uncompressed, 256-bit format

Dyna 2: Towards a General Weighted Logic Language

We investigate the design of an expressive, purely-declarative, weighted logic programming language, Dyna. Dyna is a decade-plus effort in pushing the boundaries of declarative programming and “executable mathematics;” it instantiates an unusual point in the design space, as it is both Turing-complete (unlike Datalog) and devoid of a specified execution order (unlike Prolog). That is, it is designed to be, at once, both highly expressive and rich in opportunities for automated optimization. This thesis contains two major thrusts. We first consider both the denotational (§2.1.2 and §3.1.4) and operational aspects (§2.2 to §2.5, §3.2 to §3.6, and §4) of Dyna. In particular, for operational semantics, we introduce (§2.2) and extend (through §2.5) our EarthBound solver for finite circuits; §3 considers the generalization to logic programs proper. We then turn our attention to the static analysis of this language, considering mechanisms for reasoning both about abstract notions of well-formedness of programs (§5.2) as well as more mundane concerns of realizability of programs in actual computation (§5.3 and §5.4). Along the way we endeavour to place our work in the context of the larger field of logic programming languages and present our current thoughts on future avenues of exploration

Dyna 2: Towards a General Weighted Logic Language

We investigate the design of an expressive, purely-declarative, weighted logic programming language, Dyna. Dyna is a decade-plus effort in pushing the boundaries of declarative programming and “executable mathematics;” it instantiates an unusual point in the design space, as it is both Turing-complete (unlike Datalog) and devoid of a specified execution order (unlike Prolog). That is, it is designed to be, at once, both highly expressive and rich in opportunities for automated optimization. This thesis contains two major thrusts. We first consider both the denotational (§2.1.2 and §3.1.4) and operational aspects (§2.2 to §2.5, §3.2 to §3.6, and §4) of Dyna. In particular, for operational semantics, we introduce (§2.2) and extend (through §2.5) our EarthBound solver for finite circuits; §3 considers the generalization to logic programs proper. We then turn our attention to the static analysis of this language, considering mechanisms for reasoning both about abstract notions of well-formedness of programs (§5.2) as well as more mundane concerns of realizability of programs in actual computation (§5.3 and §5.4). Along the way we endeavour to place our work in the context of the larger field of logic programming languages and present our current thoughts on future avenues of exploration

A Flexible Solver for Finite Arithmetic Circuits

Arithmetic circuits arise in the context of weighted logic programming languages, such as Datalog with aggregation, or Dyna. A weighted logic program defines a generalized arithmetic circuit— the weighted version of a proof forest, with nodes having arbitrary rather than boolean values. In this paper, we focus on finite circuits. We present a flexible algorithm for efficiently querying node values as they change under updates to the circuit’s inputs. Unlike traditional algorithms, ours is agnostic about which nodes are tabled (materialized), and can vary smoothly between the traditional strategies of forward and backward chaining. Our algorithm is designed to admit future generalizations, including cyclic and infinite circuits and propagation of delta updates

A Modality Lexicon and its use in Automatic Tagging

This paper describes our resource-building results for an eight-week JHU Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically-Informed Machine Translation. Specifically, we describe the construction of a modality annotation scheme, a modality lexicon, and two automated modality taggers that were built using the lexicon and annotation scheme. Our annotation scheme is based on identifying three components of modality: a trigger, a target and a holder. We describe how our modality lexicon was produced semi-automatically, expanding from an initial hand-selected list of modality trigger words and phrases. The resulting expanded modality lexicon is being made publicly available. We demonstrate that one tagger—a structure-based tagger—results in precision around 86 % (depending on genre) for tagging of a standard LDC data set. In a machine translation application, using the structure-based tagger to annotate English modalities on an English-Urdu training corpus improved the translation quality score for Urdu by 0.3 Bleu points in the face of sparse training data.