40732361
Meeting Diverse User Needs
Any higher education department must provide a variety of information to several disparate audiences, recognising their diverse needs. Managing these challenges and changes demands flexibility and auditability. Technology can provide the mechanism to meet these information needs and educational challenges, and to manage changing social and political demands. In this paper we offer pointers abstracted and generalised from our experience for organizational change in similar environments. We explain the development of a large web-based information system designed to meet these challenges. Our goal has been not only to improve our site but also to embed it at the heart of the department’s culture. We describe the technical innovations that are required to manage this large-scale site and that permit the flexibility needed to respond to change. Finally, we suggest measures by which the success of enterprises of this kind may be measured. Evidence of changes in student and staff* expectations, behaviour, and study patterns is presented.
26305456
Tail Recursion without Space Leaks
The G-machine (Johnsson, 1987; Peyton Jones, 1987) is a compiled graph reduction machine for lazy functional languages. The G-machine compiler contains many optimizations to improve performance. One set of such optimizations is designed to improve the performance of tail recursive functions. Unfortunately, the abstract machine is subject to a space leak—objects are unnecessarily preserved by the garbage collector. This paper analyses why a particular form of space leak occurs in the G-machine, and presents some ideas for fixing this problem. This phenomena in other abstract machines is also examined briefly.
1946418
A Cyclic Distributed Garbage Collector for Network Objects
This paper presents an algorithm for distributed garbage collection and outlines its implementation within the Network Objects system. The algorithm is based on a reference listing scheme, which is augmented by partial tracing in order to collect distributed garbage cycles. Processes may be dynamically organised into groups, according to appropriate heuristics, to reclaim distributed garbage cycles. The algorithm places no overhead on local collectors and suspends local mutators only briefly. Partial tracing of the distributed graph involves only objects thought to be part of a garbage cycle: no collaboration with other processes is required. The algorithm offers considerable flexibility, allowing expediency and fault-tolerance to be traded against completeness.
610697
Marking EP Coursework Using Electronic Communication
SUMMARY This paper discusses experience of getting students undertaking EP coursework to submit their work electronically. This has a surprising number of advantages, beyond the obvious saving of paper, though there are disadvantages too.
583730
Cyclic Distributed Garbage Collection with Group Merger
This paper presents a new algorithm for distributed garbage collection and outlines its implementation within the Network Objects system. The algorithm is based on a reference listing scheme augmented by partial tracing in order to collect distributed garbage cycles. Our collector is designed to be flexible thereby allowing efficiency, expediency and fault-tolerance to be traded against completeness. Processes may be dynamically organised into groups, according to appropriate heuristics, in order to reclaim distributed garbage cycles. Unlike previous groupbased algorithms, multiple concurrent distributed garbage collections that span groups are supported: when two collections meet they may either merge, overlap or retreat. The algorithm places no overhead on local collectors and suspends local mutators only briefly. Partial tracing of the distributed graph involves only objects thought to be part of a garbage cycle: no collaboration with other processes is required.
6044805
GCspy: an adaptable heap visualisation framework
GCspy is an architectural framework for the collection, transmission, storage and replay of memory management behaviour. It makes new contributions to the understanding of the dynamic memory behaviour of programming languages (and especially object-oriented languages that make heavy demands on the performance of memory managers). GCspy's architecture allows easy incorporation into any memory management system: it is not limited to garbage-collected languages. It requires only small changes to the system in which it is incorporated but provides a simple to use yet powerful data-gathering API. GCspy scales to allow very large heaps to be visualised effectively and efficiently. It allows already-running, local or remote systems to be visualised and those systems to run at full speed outside the points at which data is gathered. GCspy's visualisation tool presents this information in a number of novel ways.Deep understanding of program behaviour is essential to the design of the next generation of garbage collectors and explicit allocators. Until now, no satisfactory tools have been available to assist the implementer in gaining an understanding of heap behaviour. GCspy has been demonstrated to be a practical solution to this dilemma. It has been used to analyse production Java virtual machines running applications of realistic sizes. Its use has revealed important insights into the interaction between application program and JVM and has led to the development of better garbage collectors.
19801110
A framework for superscalar microprocessor correctness statements

Abstract.Most verifications of superscalar, out-of-order microprocessors compare state-machine-based implementations and specifications, where the specification is based on the instruction-set architecture. The different efforts use a variety of correctness statements, implementations, and verification approaches. We present a framework for classifying correctness statements about safety properties of superscalar microprocessors. Our framework is independent of the implementation representation and verification approach, and is parameterized by the width of the processor. We characterize the relationships between the correctness statements of many different efforts and also illustrate how classical approaches to microprocessor verification fit within our framework.
1053009
Learning Dictionaries for Information Extraction by Multi-Level Bootstrapping
Information extraction systems usually require two dictionaries: a semantic lexicon and a dictionary of extraction patterns for the domain. We present a multilevel bootstrapping algorithm that generates both the semantic lexicon and extraction patterns simultaneously. As input, our technique requires only unannotated training texts and a handful of seed words for a category. We use a mutual bootstrapping technique to alternately select the best extraction pattern for the category and bootstrap its extractions into the semantic lexicon, which is the basis for selecting the next extraction pattern. To make this approach more robust, we add a second level of bootstrapping (metabootstrapping) that retains only the most reliable lexicon entries produced by mutual bootstrapping and then restarts the process. We evaluated this multilevel bootstrapping technique on a collection of corporate web pages and a corpus of terrorism news articles. The algorithm produced high-quality dictionaries for several semantic categories.
13080115
Building Minority Language Corpora by Learning to Generate Web Search Queries
The Web is a source of valuable information, but the process of collecting, organizing, and effectively utilizing the resources it contains is difficult. We describe CorpusBuilder, an approach for automatically generating Web search queries for collecting documents matching a minority concept. The concept used for this paper is that of text documents belonging to a minority natural language on the Web. Individual documents are automatically labeled as relevant or nonrelevant using a language filter, and the feedback is used to learn what query lengths and inclusion/exclusion term-selection methods are helpful for finding previously unseen documents in the target language. Our system learns to select good query terms using a variety of term scoring methods. Using odds ratio scores calculated over the documents acquired was one of the most consistently accurate query-generation methods. To reduce the number of estimated parameters, we parameterize the query length using a Gamma distribution and present empirical results with learning methods that vary the time horizon used when learning from the results of past queries. We find that our system performs well whether we initialize it with a whole document or with a handful of words elicited from a user. Experiments applying the same approach to multiple languages are also presented showing that our approach generalizes well across several languages regardless of the initial conditions.
5135848
Using temporal profiles of queries for precision prediction
A key missing component in information retrieval systems is self-diagnostic tests to establish whether the system can provide reasonable results for a given query on a document collection. If we can measure properties of a retrieved set of documents which allow us to predict average precision, we can automate the decision of whether to elicit relevance feedback, or modify the retrieval system in other ways. We use meta-data attached to documents in the form of time stamps to measure the distribution of documents retrieved in response to a query, over the time domain, to create a temporal profile for a query. We define some useful features over this temporal profile. We find that using these temporal features, together with the content of the documents retrieved, we can improve the prediction of average precision for a query.
16240719
Formal Verification of Out-of-Order Execution Using Incremental Flushing
We present a two-part approach for verifying out-of-order execution. First, the complexity of out-of-order issue and scheduling is handled by creating an in-order abstraction of the out-of-order execution core. Second, incremental flushing addresses the complexity difficulties encountered by automated abstraction functions on very deep pipelines. We illustrate the techniques on a model of a simple out-of-order processor core.