Development and evaluation of neural network freeway incident detection models using field data

This paper discusses a multi-layer feedforward (MLF) neural network incident detection model that was developed and evaluated using field data. In contrast to published neural network incident detection models which relied on simulated or limited field data for model development and testing, the model described in this paper was trained and tested on a real-world data set of 100 incidents. The model uses speed, flow and occupancy data measured at dual stations, averaged across all lanes and only from time interval t. The off-line performance of the model is reported under both incident and non-incident conditions. The incident detection performance of the model is reported based on a validation-test data set of 40 incidents that were independent of the 60 incidents used for training. The false alarm rates of the model are evaluated based on non-incident data that were collected from a freeway section which was video-taped for a period of 33 days. A comparative evaluation between the neural network model and the incident detection model in operation on Melbourne's freeways is also presented. The results of the comparative performance evaluation clearly demonstrate the substantial improvement in incident detection performance obtained by the neural network model. The paper also presents additional results that demonstrate how improvements in model performance can be achieved using variable decision thresholds. Finally, the model's fault-tolerance under conditions of corrupt or missing data is investigated and the impact of loop detector failure/malfunction on the performance of the trained model is evaluated and discussed. The results presented in this paper provide a comprehensive evaluation of the developed model and confirm that neural network models can provide fast and reliable incident detection on freeways. (C) 1997 Elsevier Science Ltd. All rights reserved.

Privacy and security enhanced offline oblivious transfer for massive data distribution

Unauthorized accesses to digital contents are serious threats to international security and informatics. We propose an offline oblivious data distribution framework that preserves the sender's security and the receiver's privacy using tamper-proof smart cards. This framework provides persistent content protections from digital piracy and promises private content consumption.

How does social security affect economic growth? Evidence from cross-country data

This paper investigates how social security interacts with growth and growth determinants (savings, human capital investment, and fertility). Our empirical investigation finds that the estimated coefficient on social security is significantly negative in the fertility equation, insignificant in the saving equation, and significantly positive in the growth and education equations. By contrast, the estimated coefficient on growth is insignificant in the social security equation. The results suggest that social security may indeed be conducive to growth through tipping the trade-off between the number and quality of children toward the latter.

Capillary network models for transport in packed beds: Considerations of pore aspect ratio

The conventional analysis for the estimation of the tortuosity factor for transport in porous media is modified here to account for the effect of pore aspect ratio. Structural models of the porous medium are also constructed for calculating the aspect ratio as a function of porosity. Comparison of the model predictions with the extensive data of Currie (1960) for the effective diffusivity of hydrogen in packed beds shows good agreement with a network model of randomly oriented intersecting pores for porosities upto about 50 percent, which is the region of practical interest. The predictions based on this network model are also found to be in better agreement with the data of Currie than earlier expressions developed for unconsolidated and grainy media.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.

Knowledge discovery and data mining in biological databases

The new technologies for Knowledge Discovery from Databases (KDD) and data mining promise to bring new insights into a voluminous growing amount of biological data. KDD technology is complementary to laboratory experimentation and helps speed up biological research. This article contains an introduction to KDD, a review of data mining tools, and their biological applications. We discuss the domain concepts related to biological data and databases, as well as current KDD and data mining developments in biology.

An object-oriented neural network approach to short-term traffic forecasting

This paper discusses an object-oriented neural network model that was developed for predicting short-term traffic conditions on a section of the Pacific Highway between Brisbane and the Gold Coast in Queensland, Australia. The feasibility of this approach is demonstrated through a time-lag recurrent network (TLRN) which was developed for predicting speed data up to 15 minutes into the future. The results obtained indicate that the TLRN is capable of predicting speed up to 5 minutes into the future with a high degree of accuracy (90-94%). Similar models, which were developed for predicting freeway travel times on the same facility, were successful in predicting travel times up to 15 minutes into the future with a similar degree of accuracy (93-95%). These results represent substantial improvements on conventional model performance and clearly demonstrate the feasibility of using the object-oriented approach for short-term traffic prediction. (C) 2001 Elsevier Science B.V. All rights reserved.

Functional MR Imaging of visuo-spatial network in Autism Spectrum Disorder

Individuals with Autism Spectrum Disorder (ASD) are generally thought to have impaired attentional and executive function upon which all their cognitive and behaviour functions are based. Mental Rotation is a recognized visuo-spatial task, involving spatial working memory, known to involve activation in the fronto-parietal networks. To elucidate the functioning of fronto-parietal networks in ASD, the aim of this study was to use fMRI techniques with a mental rotation task, to characterize the underlying functional neural system. Sixteen male participants (seven highfunctioning autism or Asperger's syndrome; nine ageand performance IQ-matched controls) underwent fMRI. Participants were presented with 18 baseline and 18 rotation trials, with stimuli rotated 3- dimensionaUy (45°-180°). Data were acquired on a 3- Tesla scanner. The most widely accepted area reported to be involved in processing of visuo-spatial information. Posterior Parietal Cortex, was found to be activated in both groups, however, the ASD group showed decreased activation in cortical and subcortical frontal structures that are highly interconnected, including lateral and medial Brodmann area 6, frontal eye fields, caudate, dorsolateral prefrontal cortex and anterior cingulate. The suggested connectivity between these regions indicates that one or more circuits are impaired as a result of the disorder. In future it is hoped that we are able to identify the possible point of origin of this dysfunction, or indeed if the entire network is dysfunctional.

Probabilistic modelling of CO2 corrosion laboratory data using neural networks

The present paper addresses two major concerns that were identified when developing neural network based prediction models and which can limit their wider applicability in the industry. The first problem is that it appears neural network models are not readily available to a corrosion engineer. Therefore the first part of this paper describes a neural network model of CO2 corrosion which was created using a standard commercial software package and simple modelling strategies. It was found that such a model was able to capture practically all of the trends noticed in the experimental data with acceptable accuracy. This exercise has proven that a corrosion engineer could readily develop a neural network model such as the one described below for any problem at hand, given that sufficient experimental data exist. This applies even in the cases when the understanding of the underlying processes is poor. The second problem arises from cases when all the required inputs for a model are not known or can be estimated with a limited degree of accuracy. It seems advantageous to have models that can take as input a range rather than a single value. One such model, based on the so-called Monte Carlo approach, is presented. A number of comparisons are shown which have illustrated how a corrosion engineer might use this approach to rapidly test the sensitivity of a model to the uncertainities associated with the input parameters. (C) 2001 Elsevier Science Ltd. All rights reserved.

Securing the commercial internet: Lessons learned in developing a postgraduate course in information security management

This paper describes the inception, planning and first delivery of a security course as part of a postgraduate ecommerce program. The course is reviewed in terms of existing literature on security courses, the common body of knowledge established for security professionals and the job market into which students will graduate. The course described in this paper is a core subject for the e-commerce program. This program was established in 1999 and the first batch of students graduated in 2001. The program is offered at both postgraduate and undergraduate level. The work described here relates to the postgraduate offering. Students on this program are graduates of diverse disciplines and do not have a common e-commerce or business background.