John A. Keane

Euro-Par 2001 Parallel Processing

A software component framework is one where an application designer programs by composing well understood and tested “components” rather than writing large volumes of not-very-reusable code. The software industry has been using component technology to build desktop applications for about ten years now. More recently this idea has been extended to application in distributed systems with frameworks like the Corba Component Model and Enterprise Java Beans. With the advent of Grid computing, high performance applications may be distributed over a wide area network of compute and data servers. Also “peerto-peer” applications exploit vast amounts of parallelism exploiting the resources of thousands of servers. In this talk we look at the problem of building a component technology for scientific applications. The common component architecture project seeks to build a framework that allows software components runing on a massively parallel computers to be linked together to form wide-area, high performance application services that may be accessed from desktop applications. This problem is far from being solved and the talk will describe progress to date and outline some of the difficult problems that remain to be solved. R. Sakellariou et al. (Eds.): Euro-Par 2001, LNCS 2150, pp. 5–5, 2001. c

Approximation Capabilities of Hierarchical Fuzzy Systems

Derived from practical application in location analysis and pricing, and based on the approach of hierarchical structure analysis of continuous functions, this paper investigates the approximation capabilities of hierarchical fuzzy systems. By first introducing the concept of the natural hierarchical structure, it is proved that continuous functions with natural hierarchical structure can be naturally and effectively approximated by hierarchical fuzzy systems to overcome the curse of dimensionality in both the number of rules and parameters. Then, based on Kolmogorovs theorem, it is shown that any continuous function can be represented as a superposition of functions with the natural hierarchical structure and can then be approximated by hierarchical fuzzy systems to achieve the universal approximation property. Further, the conditions under which the hierarchical fuzzy approximation is superior to the standard fuzzy approximation in overcoming the curse of dimensionality are analyzed

Text mining of cancer-related information: Review of current status and future directions

PURPOSE This paper reviews the research literature on text mining (TM) with the aim to find out (1) which cancer domains have been the subject of TM efforts, (2) which knowledge resources can support TM of cancer-related information and (3) to what extent systems that rely on knowledge and computational methods can convert text data into useful clinical information. These questions were used to determine the current state of the art in this particular strand of TM and suggest future directions in TM development to support cancer research. METHODS A review of the research on TM of cancer-related information was carried out. A literature search was conducted on the Medline database as well as IEEE Xplore and ACM digital libraries to address the interdisciplinary nature of such research. The search results were supplemented with the literature identified through Google Scholar. RESULTS A range of studies have proven the feasibility of TM for extracting structured information from clinical narratives such as those found in pathology or radiology reports. In this article, we provide a critical overview of the current state of the art for TM related to cancer. The review highlighted a strong bias towards symbolic methods, e.g. named entity recognition (NER) based on dictionary lookup and information extraction (IE) relying on pattern matching. The F-measure of NER ranges between 80% and 90%, while that of IE for simple tasks is in the high 90s. To further improve the performance, TM approaches need to deal effectively with idiosyncrasies of the clinical sublanguage such as non-standard abbreviations as well as a high degree of spelling and grammatical errors. This requires a shift from rule-based methods to machine learning following the success of similar trends in biological applications of TM. Machine learning approaches require large training datasets, but clinical narratives are not readily available for TM research due to privacy and confidentiality concerns. This issue remains the main bottleneck for progress in this area. In addition, there is a need for a comprehensive cancer ontology that would enable semantic representation of textual information found in narrative reports.

A Low-Cost Pedestrian-Detection System With a Single Optical Camera

The ultimate purpose of a pedestrian-detection system (PDS) is to reduce pedestrian-vehicle-related injury. Most such systems tend to adopt expensive sensors, such as infrared devices, in expectation of better performance. In comparison, a low-cost optical-camera-based system has much potential practical value, including a greater detection range, and can easily be trained to detect other objects. However, such low-cost systems are difficult to design (e.g., little original information can be collected, and the scene is very complex). To address these problems, an effective and reliable classifier is needed. The classifier should have a proper structure, its features need to be well selected, and a large number of high-quality samples are necessary for training. In this paper, we present a low-cost PDS which only uses a single optical camera. We design a cascade classifier to achieve an effective and reliable detection. First, our system scans two sequential frames at each zoom scale with a sliding window. Second, with each window, both appearance and motion features are extracted. A well-trained cascade classifier, combining statistical learning with a decomposed support-vector-machine classifier, then determines whether the window contains a human body. At the same time, to provide as much information as possible about the pedestrian, a small-scale weighted template tree trained by a coevolutionary algorithm is adopted to identify each pedestrians direction, and the distance of each from the vehicle is also provided using an estimation algorithm. During the training procedure, we select key features by using the AdaBoost algorithm and a large number of high-quality samples. Experimental results demonstrate that the system is suitable for pedestrian detection in city traffic: The detection speed is more than 10 ft/s, the detection rate reaches 80%, and the false positive rate is no more than 0.30/00.

Short-Term and Midterm Load Forecasting Using a Bilevel Optimization Model

During the last decade, neural networks have emerged as one of the most powerful and accurate nonlinear models for load forecasting. However, using neural networks requires users to have in-depth knowledge to determine the model structure and parameters, which limits their wide application. To overcome this weakness, this paper proposes an integrated approach which combines a self-organizing fuzzy neural network (SOFNN) learning method with a bilevel optimization method. SOFNNs can automatically determine both the model structure and parameters, while the bilevel optimization method automatically selects the best pre-training parameters to ensure that the best fuzzy neural networks be identified. Therefore, the proposed approach is able to automatically identify the best fuzzy neural network for a given forecasting task and is much easier to use in practice. The proposed approach is tested on real-load data from the Southern Power Network of Hebei Province, China, and on the EUNITE competition data. Results show the proposed approach improves existing load forecasting models.

A Text Mining Approach to the Prediction of Disease Status from Clinical Discharge Summaries

OBJECTIVE The authors present a system developed for the Challenge in Natural Language Processing for Clinical Data-the i2b2 obesity challenge, whose aim was to automatically identify the status of obesity and 15 related co-morbidities in patients using their clinical discharge summaries. The challenge consisted of two tasks, textual and intuitive. The textual task was to identify explicit references to the diseases, whereas the intuitive task focused on the prediction of the disease status when the evidence was not explicitly asserted. DESIGN The authors assembled a set of resources to lexically and semantically profile the diseases and their associated symptoms, treatments, etc. These features were explored in a hybrid text mining approach, which combined dictionary look-up, rule-based, and machine-learning methods. MEASUREMENTS The methods were applied on a set of 507 previously unseen discharge summaries, and the predictions were evaluated against a manually prepared gold standard. The overall ranking of the participating teams was primarily based on the macro-averaged F-measure. RESULTS The implemented method achieved the macro-averaged F-measure of 81% for the textual task (which was the highest achieved in the challenge) and 63% for the intuitive task (ranked 7(th) out of 28 teams-the highest was 66%). The micro-averaged F-measure showed an average accuracy of 97% for textual and 96% for intuitive annotations. CONCLUSIONS The performance achieved was in line with the agreement between human annotators, indicating the potential of text mining for accurate and efficient prediction of disease statuses from clinical discharge summaries.

A heuristic method to rectify intransitive judgments in pairwise comparison matrices

This paper investigates the effects of intransitive judgments on the consistency of pairwise comparison matrices. Statistical evidence regarding the occurrence of intransitive judgements in pairwise matrices of acceptable consistency is gathered by using a Monte–Carlo simulation, which confirms that relatively high percentage of comparison matrices, satisfying Saaty’s CR criterion are ordinally inconsistent. It is also shown that ordinal inconsistency does not necessarily decrease in the group aggregation process, in contrast with cardinal inconsistency. A heuristic algorithm is proposed to improve ordinal consistency by identifying and eliminating intransitivities in pairwise comparison matrices. The proposed algorithm generates near-optimal solutions and outperforms other tested approaches with respect to computation time.

Medication information extraction with linguistic pattern matching and semantic rules

OBJECTIVE This study presents a system developed for the 2009 i2b2 Challenge in Natural Language Processing for Clinical Data, whose aim was to automatically extract certain information about medications used by a patient from his/her medical report. The aim was to extract the following information for each medication: name, dosage, mode/route, frequency, duration and reason. DESIGN The system implements a rule-based methodology, which exploits typical morphological, lexical, syntactic and semantic features of the targeted information. These features were acquired from the training dataset and public resources such as the UMLS and relevant web pages. Information extracted by pattern matching was combined together using context-sensitive heuristic rules. MEASUREMENTS The system was applied to a set of 547 previously unseen discharge summaries, and the extracted information was evaluated against a manually prepared gold standard consisting of 251 documents. The overall ranking of the participating teams was obtained using the micro-averaged F-measure as the primary evaluation metric. RESULTS The implemented method achieved the micro-averaged F-measure of 81% (with 86% precision and 77% recall), which ranked this system third in the challenge. The significance tests revealed the systems performance to be not significantly different from that of the second ranked system. Relative to other systems, this system achieved the best F-measure for the extraction of duration (53%) and reason (46%). CONCLUSION Based on the F-measure, the performance achieved (81%) was in line with the initial agreement between human annotators (82%), indicating that such a system may greatly facilitate the process of extracting relevant information from medical records by providing a solid basis for a manual review process.

Using Web service technologies to create an information broker: an experience report

This paper reports on our experiences with using the emerging Web service technologies and tools to create a demonstration information broker system as part of our research into information management in a distributed environment. To provide a realistic context, we chose to study the use of information in the healthcare domain, and this context sets some challenging parameters and constraints for our research and for the demonstration system. In this paper, we both report on the extent to which existing Web service technologies have proved to be mature enough to meet these requirements, and also assess their current limitations.

Big Data Framework

We are constantly being told that we live in the Information Era - the Age of BIG data. It is clearly apparent that organizations need to employ data-driven decision making to gain competitive advantage. Processing, integrating and interacting with more data should make it better data, providing both more panoramic and more granular views to aid strategic decision making. This is made possible via Big Data exploiting affordable and usable Computational and Storage Resources. Many offerings are based on the Map-Reduce and Hadoop paradigms and most focus solely on the analytical side. Nonetheless, in many respects it remains unclear what Big Data actually is, current offerings appear as isolated silos that are difficult to integrate and/or make it difficult to better utilize existing data and systems. Paper addresses this lacunae by characterising the facets of Big Data and proposing a framework in which Big Data applications can be developed. The framework consists of three Stages and seven Layers to divide Big Data application into modular blocks. The aim is to enable organizations to better manage and architect a very large Big Data application to gain competitive advantage by allowing management to have a better handle on data processing.