Incremental learning of context free grammars based on bottom-up parsing and search

This paper describes approaches for machine learning of context free grammars (CFGs) from positive and negative sample strings, which are implemented in Synapse system. The grammatical inference consists of a rule generation by “inductive CYK algorithm,” mechanisms for incremental learning, and search. Inductive CYK algorithm generates minimum production rules required for parsing positive samples, when the bottom-up parsing by CYK algorithm does not succeed. The incremental learning is used not only for synthesizing grammars by giving the system positive strings in the order of their length but also for learning grammars from other similar grammars. Synapse can synthesize fundamental ambiguous and unambiguous CFGs including nontrivial grammars such as the set of strings not of the form ww with w∈{a,b}⁺.     

Fast incremental learning methods inspired by biological learning behavior

Model-based learning systems such as neural networks usually “forget” learned skills due to incremental learning of new instances. This is because the modification of a parameter interferes with old memories. Therefore, to avoid forgetting, incremental learning processes in these learning systems must include relearning of old instances. The relearning process, however, is time-consuming. We present two types of incremental learning method designed to achieve quick adaptation with low resources. One approach is to use a sleep phase to provide time for learning. The other one involves a “meta-learning module” that acquires learning skills through experience. The system carries out “reactive modification” of parameters not only to memorize new instances, but also to avoid forgetting old memories using a meta-learning module.This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

快照系统SsMirror的设计与实现

快照是保证数据可用性的一种重要方法。磁盘存储子系统层次快照系统SsMirror支持全量快照和增量快照,该文介绍了它的设计思想和系统模块构成。SsMirror采用写前拷贝处理写请求和I/O后台拷贝技术,保证了系统的高效实现。     

一种基于Dwarf的快速有效增量更新算法

数据立方计算是代价非常大的操作,并且被广泛研究。受空问的限制,存储一个完全实例化的数据立方是不可行的。最近提出的一种语义压缩数据立方一Dwarf,通过消除前缀冗余和后缀冗余把一个完全实例化的数据立方压缩存储到一个很小的空问。然而,当数据源发生变化时,它的更新过程是很复杂的。本文通过研究Dwarf在更新过程中汇总结点的变化特性,提出了一种基于Dwarf的新的增量更新算法,既能完全实例化数据立方又不需要重新计算,大大提高了数据立方的更新效率。实验进一步证明了该算法的效率和有效性,尤其适合数据仓库中的高维数据集。     

一种增量式的社区发现算法研究

传统社区发现算法基本上属于静态的分析算法,其计算复杂性使其难以适应目前网络结构的频繁变化.为了改善静态算法的这一局限性,通过对Radiechi静态算法进行扩展,提出一种增量式的社区发现算法,并将其应用于MSN Space链接结构分析上.该算法能在网络结构变化频繁时进行增量式计算并保证社区发现的实时性.实验结果表明,该增量式算法在处理网络结构变化时的效率相对传统算法有显著提高,尤其对小规模频繁变化的网络有很强的适应力.     

基于磁带分级存储的文件备份系统设计与实现

随着磁盘价格的不断下降,越来越多的容灾备份系统采用将本地数据经过网络传输到服务器磁盘进行备份,但是大数据时代的来,瞄,导致存储压力越来越大。然而在庞大的数据中,有很大一部分数据活跃率并不高。设计并实现了一种基于磁带分级存储的文件备份与恢复系统,采用索引的方式记录并组织备份文件,实现增量备份;该系统将服务器端拆分为两部分,首先将文件数据备份到服务器磁盘,再根据相关时间保留策略,将活跃率较低的数据归档到脏带中。保证了大数据时代服务器磁盘的高效利用。     

An adaptive genetic-based signature learning system for intrusion detection

Rule-based intrusion detection systems generally rely on hand crafted signatures developed by domain experts. This could lead to a delay in updating the signature bases and potentially compromising the security of protected systems. In this paper, we present a biologically-inspired computational approach to dynamically and adaptively learn signatures for network intrusion detection using a supervised learning classifier system. The classifier is an online and incremental parallel production rule-based system.A signature extraction system is developed that adaptively extracts signatures to the knowledge base as they are discovered by the classifier. The signature extraction algorithm is augmented by introducing new generalisation operators that minimise overlap and conflict between signatures. Mechanisms are provided to adapt main algorithm parameters to deal with online noisy and imbalanced class data. Our approach is hybrid in that signatures for both intrusive and normal behaviours are learnt.The performance of the developed systems is evaluated with a publicly available intrusion detection dataset and results are presented that show the effectiveness of the proposed system.     

Parallel context-sensitive compilation

The searching power of massively parallel associative computers is an under used and under investigated capability that can be used to facilitate software development. This paper describes the development of a context sensitive compiler for pattern-matching languages using that searching power. The described compiler was implemented on the STARAN parallel computer and the compiled OPS5 programs were also executed on the STARAN obtaining an estimated throughput of 6000 rules per second. The described compilation of production rules into equivalent procedural rules is completely data parallel, with the degree of parallelism depending on the number of tokens in the program being compiled. During any one step of the context-sensitive analysis, the entire program is processed in constant time.     

基于Rough集的规则学习研究

Rough Sets方法是一种处理不确定或模糊知识的重要工具,本文在对Rough Sets理论进行深入研究的基础上,提出了一种基于Rough Sets的自增量学习算法,该算法利用简化的差异矩阵和置信度,能较好地进行确定性规则和非确定性规则的学习。     

Reducing the overhead of dynamic compilation

The execution model for mobile, dynamically‐linked, object‐oriented programs has evolved from fast interpretation to a mix of interpreted and dynamically compiled execution. The primary motivation for dynamic compilation is that compiled code executes significantly faster than interpreted code. However, dynamic compilation, which is performed while the application is running, introduces execution delay. In this paper we present two dynamic compilation techniques that enable high performance execution while reducing the effect of this compilation overhead. These techniques can be classified as (1) decreasing the amount of compilation performed, and (2) overlapping compilation with execution. We first present and evaluate lazy compilation, an approach used in most dynamic compilation systems in which individual methods are compiled on‐demand upon their first invocation. This is in contrast to eager compilation, in which all methods in a class are compiled when a new class is loaded. In this work, we describe our experience with eager compilation, as well as the implementation and transition to lazy compilation. We empirically detail the effectiveness of this decision. Our experimental results using the SpecJVM Java benchmarks and the Jalapeño JVM show that, compared to eager compilation, lazy compilation results in 57% fewer methods being compiled and reductions in total time of 14 to 26%. Total time in this context is compilation plus execution time. Next, we present profile‐driven, background compilation, a technique that augments lazy compilation by using idle cycles in multiprocessor systems to overlap compilation with application execution. With this approach, compilation occurs on a thread separate from that of application threads so as to reduce intermittent, and possibly substantial, delay in execution. Profile information is used to prioritize methods as candidates for background compilation. Methods are compiled according to this priority scheme so that performance‐critical methods are invoked using optimized code as soon as possible. Our results indicate that background compilation can achieve the performance of off‐line compiled applications and masks almost all compilation overhead. We show significant reductions in total time of 14 to 71% over lazy compilation. Copyright © 2001 John Wiley & Sons, Ltd.