Data structure alignment

Abstract: Training word alignment models on large corpora is a very time-consuming processes. This paper describes two parallel implementations of GIZA++ that accelerate this word alignment process. One of the implementations runs on computer clusters, the other runs on multi-processor system using multi-threading technology. Results show a near-linear speed-up according to the number of CPUs used, and alignment quality is preserved.

Abstract: Objective : This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain–computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? Methods : We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials; second, its computational cost is very low; and third, it is unsupervised and does not need any label information from the new subject. Results : Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment. Conclusion : The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs. Significance : Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs.

Abstract: Memory deduplication, a well-known technique to reduce the memory footprint across virtual machines, is now also a default-on feature inside the Windows 8.1 and Windows 10 operating systems. Deduplication maps multiple identical copies of a physical page onto a single shared copy with copy-on-write semantics. As a result, a write to such a shared page triggers a page fault and is thus measurably slower than a write to a normal page. Prior work has shown that an attacker able to craft pages on the target system can use this timing difference as a simple single-bit side channel to discover that certain pages exist in the system. In this paper, we demonstrate that the deduplication side channel is much more powerful than previously assumed, potentially providing an attacker with a weird machine to read arbitrary data in the system. We first show that an attacker controlling the alignment and reuse of data in memory is able to perform byte-by-byte disclosure of sensitive data (such as randomized 64 bit pointers). Next, even without control over data alignment or reuse, we show that an attacker can still disclose high-entropy randomized pointers using a birthday attack. To show these primitives are practical, we present an end-to-end JavaScript-based attack against the new Microsoft Edge browser, in absence of software bugs and with all defenses turned on. Our attack combines our deduplication-based primitives with a reliable Rowhammer exploit to gain arbitrary memory read and write access in the browser. We conclude by extending our JavaScript-based attack to cross-process system-wide exploitation (using the popular nginx web server as an example) and discussing mitigation strategies.

Abstract: In this paper we describe a memory system designed for parallel array access. The system is based on the use of a prime nwnber of memories and a powerful combination of indexing hardware and data alignment switches. Particular emphasis is placed on the indexing equations and their implementation.