Dr David Elizondo

Professor David Elizondo

Job: Professor in Intelligent Transport

Faculty: Computing, Engineering and Media

School/department: School of Computer Science and Informatics

Research group(s): The 羞羞视频 Interdisciplinary Group in Intelligent Transport Systems (DIGITS)

Address: 羞羞视频, The Gateway, Leicester, LE1 9BH, United Kingdom

T: +44 (0)116 207 8471

E: Elizondo@dmu.ac.uk

W: /digits

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Personal profile

Dr. David Elizondo is a Principal Lecturer in the Department of Computer Technology at 羞羞视频. After completing his BA in Computer Science from Knox College , Galesbourg, Illinois, USA, he worked as a software engineer/lab manager for a latinoamerican agronomical research and teaching institute based in Costa Rica ( CATIE ). This institute, through a Swiss project, sponsored him to do a MS in Artificial Intelligence at the Department of Artificial Intelligence and Cognitive Computing of the University of Georgia, Athens, Georgia, USA. After this he obtained a PhD in computer science from the University of Strasbourg , France in cooperation with the Swiss Dalle Molle Institute for Perceptual Artificial Intelligence (IDIAP). He then worked for Neuvoice, formerly Neural Systems, a spin off company of the University of Plymouth , UK. As a senior researcher he worked in the development of an intelligent monitoring system for the petroleum industry. This system was based on neural network techniques. Later, he worked as a software architect for ACTERNA, an international company which supplies software/hardware solutions to telecom companies. He was part of the team developing QMS, a quality of service management system for leased lines. In parallel to this work, he was a part time lecturer at the University of Plymouth where he taught database, and data structures and algorithms.

Research group affiliations

The 羞羞视频 Interdisciplinary Group in Intelligent Transport Systems (DIGITS)

I am also an active member of the following research groups:
(1) The Cyber Security Centre
(2) The Centre for Computational Intelligence (CCI).

I am the research leader of the CCI Neural Network subgroup, which is particularly well known internationally for the research work conducted in the area of Constructive Neural Networks and Linear Separability as evidenced by my on-going list of high quality publications in these two fields of research.

Publications and outputs

dc.title: A didactic overview of Transformer applications: model variations and user guidelines dc.contributor.author: Cabrera-Bermejo, M. I.; Germ谩n, M.; de la Rosa, D.; Jesus, M. J. Del; Rivera, A. J.; Elizondo, David; Charte, F.; Godoy, M. D. P茅rez dc.description.abstract: Transformers models were originally designed for the processing of textual data. In the last years they have been extended to handle different modalities of data including image and video, audio, tabular, and even multimodal data. Adapting the vanilla Transformer architecture is necessary to optimize the performance for each data type. The vast amount of new architectures that have emerged makes it difficult to detect and understand the differences with the original Transformer. This paper provides an overview of Transformer applications for various input modalities, and recommendations to guide the development and use of models. dc.description: open access article
dc.title: DESReg: Dynamic Ensemble Selection library for Regression tasks dc.contributor.author: P茅rez-Godoy, Mar铆a D.; Molina, Marta; Mart铆nez, Francisco; Elizondo, David; Charte, Francisco; Rivera, Antonio J. dc.description.abstract: Nowadays, regression is a very demanded predictive task to solve a wide range of problems belonging to different research and society areas. Examples of applications include industry, economic, medical and energy fields. Ensemble methodology works by merging the output obtained from a set of base methods (learners), achieving successful results in both classification and regression tasks. Traditional ensembles use the output of the whole set of base methods, in a static way, to obtain the result of the ensemble. However, latest studies show that dynamic selection of learners or even dynamic aggregation of their outputs produce better results. Methodologies that integrate these techniques are called dynamic ensembles or dynamic ensemble selection. Although the literature and tools to work with dynamic ensembles for classification tasks is abundant, for regression tasks these resources are scarcer. This paper aims to mitigate these shortcomings by presenting a library for the design, development and execution of dynamic ensembles for regression problems. Specifically, the Python software package DESReg is presented. This library allows us to access to the latest dynamic ensemble techniques in the field, standing out for its high configurability, its support for extending it with user-defined functions or its parallel computation capabilities. dc.description: open access article
dc.title: Aggregation of Convolutional Neural Network Estimations of Homographies by Color Transformations of the Inputs dc.contributor.author: Molina-Cabello, Miguel A.; Elizondo, David; Luque-Baena, Rafael Marcos; Lopez-Rubio, Ezequiel dc.description.abstract: The standard approach to the estimation of homographies consists in the application of the RANSAC algorithm to a set of tentative matches. More recent strategies based on deep learning, namely convolutional architectures, have become available. In this work, a new algorithm for the estimation of homographies is developed. It is rooted in a convolutional neural network for homography estimation, which is provided with a range of versions of the input pair of pictures. Such versions are generated by perturbation of the color levels of the input images. Each generated pair of images yields a distinct estimation of the homography, and then the estimations are combined together to obtain a final, more robust estimation. Experiments have been designed and carried out to test the validity of our approach, including qualitative and quantitative performance measures. In particular, it is demonstrated that our approach consistently outperforms the baseline approach consisting of using the output of the homography estimation deep network for the original input pair of images. dc.description: open access article
dc.title: Foreground detection by ensembles of random polygonal tilings dc.contributor.author: Molina-Cabello, Miguel A.; Elizondo, David; Luque-Baena, Rafael M.; L贸pez-Rubio, Ezequiel dc.description.abstract: In this work a novel region-based approach for the detection of foreground in video sequences is presented. The model consists of an ensemble of layers or tilings, where each tiling represents, by means of randomly chosen parallelogram regions, the background of the scene. Currently, the image size of video surveillance cameras far exceeds one megapixel (more than 1024 脳 768), and pixel-based proposals are poorly suited for near real-time ratios. Therefore, the analysis by pixel is replaced by an analysis by region, improving the final resolution by overlapping regions or parallelograms with different shapes and sizes. Thus, for each frame, each region estimates the probability of belonging to the foreground or background, to finally compute the consensus foreground mask among all the tilings. With this proposal, it is possible to detect the foreground in high resolution sequences, a process that is not feasible using pixel-level techniques. Several experiments have been carried out by employing a wide range of videos. A qualitative and quantitative comparison with the state-of-the-art algorithms is performed by using a well-known video dataset benchmark. The results show the feasibility of our proposal compared with higher resolution methods.
dc.title: Enhancing Object Segmentation via聽Few-Shot Learning with聽Limited Annotated Data dc.contributor.author: Garc铆a-Aguilar, Iv谩n; Jafri, Syed Ali Haider; Elizondo, David; Calder贸n, Saul; Greenfield, Sarah; Luque-Baena, Rafael M. dc.description.abstract: Significant advancements in machine learning in recent years have revolutionized multiple sectors. The Segment-Anything Model (SAM) is a notable example of state-of-the-art image segmentation. Despite claims of zero-shot generalization, SAM exhibits limitations in specific scenarios like medical mammography images. SAM generates three segmentation masks per image to address this and recommends selecting the one with the highest confidence score. However, this is not always the optimal choice. This paper introduces a system that extends SAM鈥檚 segmentation capabilities by automatically selecting the correct mask, leveraging few-shot learning methods and an Out-of-Distribution threshold strategy. Several backbones were subjected to experimentation, highlighting the relationship between the support set size and the model鈥檚 accuracy.
dc.title: Characterising Payload Entropy in Packet Flows鈥擝aseline Entropy Analysis for Network Anomaly Detection dc.contributor.author: Kenyon, Anthony; Deka, Lipika; Elizondo, David dc.description.abstract: The accurate and timely detection of cyber threats is critical to keeping our online economy and data safe. A key technique in early detection is the classification of unusual patterns of network behaviour, often hidden as low-frequency events within complex time-series packet flows. One of the ways in which such anomalies can be detected is to analyse the information entropy of the payload within individual packets, since changes in entropy can often indicate suspicious activity鈥攕uch as whether session encryption has been compromised, or whether a plaintext channel has been co-opted as a covert channel. To decide whether activity is anomalous, we need to compare real-time entropy values with baseline values, and while the analysis of entropy in packet data is not particularly new, to the best of our knowledge, there are no published baselines for payload entropy across commonly used network services. We offer two contributions: (1) we analyse several large packet datasets to establish baseline payload information entropy values for standard network services, and (2) we present an efficient method for engineering entropy metrics from packet flows from real-time and offline packet data. Such entropy metrics can be included within feature subsets, thus making the feature set richer for subsequent analysis and machine learning applications dc.description: open access article
dc.title: Natural Language Processing Tools and Workflows for Improving Research Processes dc.contributor.author: Khan, Noel; Elizondo, David; Deka, Lipika; Molina-Cabello, Miguel A. dc.description.abstract: The modern research process involves refining a set of keywords until sufficiently pertinent results are obtained from acceptable sources. References and citations from the most relevant results can then be traced to related works. This process iteratively develops a set of keywords to find the most relevant literature. However, because a keyword-based search essentially samples a corpus, it may be inadequate for capturing a broad or exhaustive understanding of a topic. Further, a keyword-based search is dependent upon the underlying storage and retrieval technology and is essentially a syntactical search rather than a semantic search. To overcome such limitations, this paper explores the use of well-known natural language processing (NLP) techniques to support a semantic search and identifies where specific NLP techniques can be employed and what their primary benefits are, thus enhancing the opportunities to further improve the research process. The proposed NLP methods were tested through different workflows on different datasets and each workflow was designed to exploit latent relationships within the data to refine the keywords. The results of these tests demonstrated an improvement in the identified literature when compared to the literature extracted from the end-user-given keywords. For example, one of the defined workflows reduced the number of search results by two orders of magnitude but contained a larger percentage of pertinent results. dc.description: open access article
dc.title: Out-of-Distribution Detection with Memory-Augmented Variational Autoencoder dc.contributor.author: Ataeiasad, Faezeh; Elizondo, David; Ram铆rez, Sa煤l Calder贸n; Greenfield, Sarah; Deka, Lipika dc.description.abstract: This paper proposes a novel method capable of both detecting OOD data and generating in-distribution data samples. To achieve this, a VAE model is adopted and augmented with a memory module, providing capacities for identifying OOD data and synthesising new in-distribution samples. The proposed VAE is trained on normal data and the memory stores prototypical patterns of the normal data distribution. At test time, the input is encoded by the VAE encoder; this encoding is used as a query to retrieve related memory items, which are then integrated with the input encoding and passed to the decoder for reconstruction. Normal samples reconstruct well and yield low reconstruction errors, while OOD inputs produce high reconstruction errors as their encodings get replaced by retrieved normal patterns. Prior works use memory modules for OOD detection with autoencoders, but this method leverages a VAE architecture to enable generation abilities. Experiments conducted with CIFAR-10 and MNIST datasets show that the memory-augmented VAE consistently outperforms the baseline, particularly where OOD data resembles normal patterns. This notable improvement is due to the enhanced latent space representation provided by the VAE. Overall, the memory-equipped VAE framework excels in identifying OOD and generating creative examples effectively. dc.description: open access article
dc.title: Oil spill classification using an autoencoder and hyperspectral technology dc.contributor.author: Carrasco-Garcia, Maria Gema; Inmaculada Rodr铆guez-Garc铆a, M.; Ruiz-Aguilar, Juan Jesus; Deka, Lipika; Elizondo, David; Turias-Dom铆nguez, Ignacio J. dc.description.abstract: Hyperspectral technology has been playing a leading role in monitoring oil spills in marine environments, an issue of international concern. In the case of monitoring oil spills in local areas, hyperspectral technology of small dimensions becomes the ideal solution. This research explores the use of encoded hyperspectral signatures to develop automated classifiers capable of discriminating between polluted and clean water, and even distinguishing between various types of oil. The overall objective is to leverage these classifiers to be able to improve the performance of conventional systems that rely solely on hyperspectral imagery. The acquisition of the hyperspectral signatures of water and hydrocarbons was carried out with a spectroradiometer. The range of the spectroradiometer used in this study covers the ranges between [350-1000] (visible near-infrared) and [1000-2500] (short-wavelength infrared). This gives detailed information with regards to the targets of interest. Different neural autoencoders (AEs) have been developed to reduce inputs into different dimensions, from 1 to 15. Each of these encoded sets was used to train decision tree (DT) classifiers. The results are very promising, as they show that AEs performance encoded data with correlation coefficients above 0.95. The classifiers trained with the different sets provide accuracies close to 1. dc.description: open access article This work has been conducted in collaboration with the University of Cadiz, when Maria Gema Carrasco-Garcia, a PhD student at the University of Cadiz came as a visiting student to work with Dr Lipika Deka and Professor David Elizondo. The funding has come from University of Cadiz, Spain and the projects of our collaborators.
dc.title: Characterising Payload Entropy in Packet Flows dc.contributor.author: Kenyon, Anthony; Deka, Lipika; Elizondo, David dc.description.abstract: Accurate and timely detection of cyber threats is critical to keeping our online economy and data safe. A key technique in early detection is the classification of unusual patterns of network behaviour, often hidden as low-frequency events within complex time-series packet flows. One of the ways in which such anomalies can be detected is to analyse the information entropy of the payload within individual packets, since changes in entropy can often indicate suspicious activity - such as whether session encryption has been compromised, or whether a plaintext channel has been co-opted as a covert channel. To decide whether activity is anomalous we need to compare real-time entropy values with baseline values, and while the analysis of entropy in packet data is not particularly new, to the best of our knowledge there are no published baselines for payload entropy across common network services. We offer two contributions: 1) We analyse several large packet datasets to establish baseline payload information entropy values for common network services, 2) We describe an efficient method for engineering entropy metrics when performing flow recovery from live or offline packet data, which can be expressed within feature subsets for subsequent analysis and machine learning applications.

Research interests/expertise

My research interests include both work in the theory and application of Neural Networks. Application areas include transport related problems that led to the development of DIGITS (iTRAQ project).

Areas of teaching

Artificial Neural Networks and Prolog programming.

Qualifications

French Qualification: University Full Professor Qualification by the Conseille National des Universites (CNU). Artificial Neural Networks, Theory and Applications - 2008.
French Qualification: Senior Lecturer/Principal Lecturer (Maitre de Conferences) Qualification by the Conseille National des Universites (CNU) - 2003.
PhD in Computer Science from the University Louis Pasteur, Strasbourg, France and IDIAP, Martigny, Switzerland. The Recursive Deterministic Perceptron and some Strategies for Topology Reduction on Neural Networks -1998.
DEA in Computer Science from the University of Montpellier, Montpellier, France, Application of Neural Networks to a control process in a dynamic environment - 1993.
Master of Science in Artificial Intelligence from the University of Georgia, Athens, Georgia, USA, Neural Network Models to Predict Solar Radiation and Plant Phenology - 1992.
Bachelor of Science in Computer Science from Knox College, Galesburg, Illinois, USA - 1986.

Courses taught

Artificial Neural Networks and Prolog programming.

Membership of external committees

Workshop Organizer for The British Computer Society Specialist Group on Artificial Intelligence (SGAI) International Conference in Artificial Intelligence for 2010.
UK Computational Intelligence workshop (UKCI).
IEEE International Conference in Artificial Neural Networks (2004,2005, 2006,2007, 2008, 2009).

Membership of professional associations and societies

IEEE Senior Member.

Conference attendance

Organiser and chairman of the following special conference sessions:

IEEE-WCCI-2012, Brisbane, Australia. Special session on Computational Intelligence for Privacy. (
IEEE-WCCI-2010, Barcelona, Spain. Special session on Computational Intelligence for Privacy, Security, Forensics. (
IEEE-ICANN-2008 Prague, Czech Republic. Special session on Constructive Neural Network Algorithms (http://www.icann2008.org/ssession.php). Contacted by Springer to produce a book of extended versions of these papers. The book will be published by January 2009.). Contacted by Springer to produce a book of extended versions of these papers. The book will be published by January 2009.
IEEE-ICANN-2005 Warsaw, Poland. Special session on Knowledge Extraction (

National Conference Chairman

Programme Chair Workshop on Computational Intelligence (UKCI), 羞羞视频, Leicester, Sept 10-12 2008 (.

Consultancy work

Large International Banana producer Company. Banana hand cut optimization using Artificial Intelligence Techniques.

Current research students

2010-2013 John North. Associating Cause and Effect: Applying Computational Intelligence to Post-Incident Security Data. 羞羞视频, Symantec.

2010-2014 Harold Kimball. Adaptive Security for Mobile Devices.

2013-2016 Simon Witheridge. Integrated Traffic Management and Air Quality Control.

Externally funded research grants information

“TITLE”, SPONSOR	ROLE	AMOUNT	PERIOD
“Banana Hand cut optimization using Computational Intelligence Techniques”, Chiquita Brands International Inc., USA.	PI	£12000	June 2010
“Travel Grant, WCCI-2010, Barcelona, Spain”, Royal Academy of Engineering.	PI	£600	2010
“Dynamic Traffic Management and Passenger Guidance to Meet the Carbon Challenge”, Transport iNet HECF.	PI	£45K	2009−2010
“Travel Grant, IJCNN-2009, Atlanta, Georgia”, Royal Academy of Engineering.	PI	£800	2009
“Travel Grant, ICANN-2008, Prague, Czek Republic”, Royal Academy of Engineering.	PI	£800	2008
“Travel Grant, ICANN-2007, Porto Portugal”, Royal Academy of Engineering.	PI	£800	2007
“Design of constructive methods on neural computing systems and its application to data mining in oncology”, Spanish Research Council.	CI	£225K	2008−2012
“New strategies in the design of neurocomputing systems. Application to the process of oncology data”, Spanish Research Council.	CI	£90K	2008−2010
“Integrated Traffic Management and Air Quality Control Using Downstream Space Services”, European Space Agency.	PI	e500K (£160K for 羞羞视频)	2011
“Innovation Fellowship with the School of Pharmacy”, EMDA, UK.	PI	£15K	2011

Internally funded research project information

“TITLE”, SPONSOR	ROLE	AMOUNT	PERIOD
“Associating Cause and Effect: Applying Computational Intelligence to Post-Incident Security Data”, 羞羞视频 Research Scholarship, 羞羞视频, UK, Symantec, UK.	PI	£50K	2011−2014
“Intelligent Transport Systems: Integrated Traffic Management Control”, 羞羞视频 Research Scholarship, 羞羞视频, UK.	CI	£50K	2012−2015
“De Montfort Interest Group in Transport Systems (DIGITS)”, 羞羞视频 RIF.	CI	£10K	Jan−Apr 2012

Professional esteem indicators

Associate editor for the IEEE Transactions on Neural Networks and Learning Systems Journal (2.95 Impact Factor and in position 12 out of 111 according to the impact factor in the area of Artificial Intelligence)
Reviewer of European FP7 research projects (2009)
Referee for the Swiss National Science Foundation (2010)
Industrial Liaison for the IEEE Computational Intelligence Society (CSI), UKRI Chapter
Workshop Organizer for The British Computer Society Specialist Group on Artificial Intelligence (SGAI)
International Conference in Artificial Intelligence for 2010
Senior Member of the IEEE
Industrial Liaison for the IEEE Computational Intelligence Society (CSI), UKRI Chapter.