All Outputs (116)

Tuning a Simulated Annealing metaheuristic for cross-domain search (2017)
Conference Proceeding

Simulated Annealing is a well known local search metaheuristic used for solving computationally hard optimization problems. Cross-domain search poses a higher level issue where a single solution method is used with minor, preferably no modification f... Read More about Tuning a Simulated Annealing metaheuristic for cross-domain search.

Information core optimization using Evolutionary Algorithm with Elite Population in recommender systems (2017)
Conference Proceeding

Recommender system (RS) plays an important role in helping users find the information they are interested in and providing accurate personality recommendation. It has been found that among all the users, there are some user groups called “core users”... Read More about Information core optimization using Evolutionary Algorithm with Elite Population in recommender systems.

A first attempt on global evolutionary undersampling for imbalanced big data (2017)
Conference Proceeding

The design of efficient big data learning models has become a common need in a great number of applications. The massive amounts of available data may hinder the use of traditional data mining techniques, especially when evolutionary algorithms are i... Read More about A first attempt on global evolutionary undersampling for imbalanced big data.

Change detection in SAR images based on the salient map guidance and an accelerated genetic algorithm (2017)
Conference Proceeding

This paper proposes a change detection algorithm in synthetic aperture radar (SAR) images based on the salient image guidance and an accelerated genetic algorithm (S-aGA). The difference image is first generated by logarithm ratio operator based on t... Read More about Change detection in SAR images based on the salient map guidance and an accelerated genetic algorithm.

Diversity-based adaptive genetic algorithm for a Workforce Scheduling and Routing Problem (2017)
Conference Proceeding

The Workforce Scheduling and Routing Problem refers to the assignment of personnel to visits across various geographical locations. Solving this problem demands tackling numerous scheduling and routing constraints while aiming to minimise total opera... Read More about Diversity-based adaptive genetic algorithm for a Workforce Scheduling and Routing Problem.

Multi-start methods for the capacitated clustering problem (2017)
Conference Proceeding

In this work, we investigate the adaptation of the Greedy Randomized Adaptive Search Procedure (GRASP) and Iterated Greedy methodologies to the Capacitated Clustering Problem (CCP). In particular, we focus on the effect of the balance between randomi... Read More about Multi-start methods for the capacitated clustering problem.

The continuity of monadic stream functions (2017)
Conference Proceeding

© 2017 IEEE. Brouwer's continuity principle states that all functions from infinite sequences of naturals to naturals are continuous, that is, for every sequence the result depends only on a finite initial segment. It is an intuitionistic axiom that... Read More about The continuity of monadic stream functions.

The Functional Dendritic Cell Algorithm: A formal specification with Haskell (2017)
Conference Proceeding

The Dendritic Cell Algorithm (DCA) has been described in a number of different ways, sometimes resulting in incorrect implementations. We believe this is due to previous, imprecise attempts to describe the algorithm. The main contribution of this pap... Read More about The Functional Dendritic Cell Algorithm: A formal specification with Haskell.

Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data (2017)
Conference Proceeding

Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are neurodevelopmental conditions which impact on a significant number of children and adults. Currently, the diagnosis of such disorders is done by experts who employ... Read More about Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data.

Small Sample Deep Learning for Newborn Gestational Age Estimation (2017)
Conference Proceeding

A baby’s gestational age determines whether or not they are preterm, which helps clinicians decide on suitable post-natal treatment. The most accurate dating methods use Ultrasound Scan (USS) machines, but these machines are expensive, require traine... Read More about Small Sample Deep Learning for Newborn Gestational Age Estimation.

Young people's policy recommendations on algorithm fairness (2017)
Conference Proceeding

This paper explores the policy recommendations made by young people regarding algorithm fairness. It describes a piece of ongoing research developed to bring children and young people to the front line of the debate regarding children's digital right... Read More about Young people's policy recommendations on algorithm fairness.

CRNN: A Joint Neural Network for Redundancy Detection (2017)
Conference Proceeding

This paper proposes a novel framework for detecting redundancy in supervised sentence categorisation. Unlike traditional singleton neural network, our model incorporates character-aware convolutional neural network (Char-CNN) with character-aware rec... Read More about CRNN: A Joint Neural Network for Redundancy Detection.

Enabling hand-crafted visual markers at scale (2017)
Conference Proceeding

As locative media and augmented reality spread into the everyday world so it becomes important to create aesthetic visual markers at scale. We explore a designer-centred approach in which skilled designers handcraft seed designs that are automaticall... Read More about Enabling hand-crafted visual markers at scale.

Accountable Internet of things?: outline of the IoT Databox model (2017)
Conference Proceeding

This paper outlines the IoT Databox model as a means of making the Internet of Things (IoT) accountable to individuals. Accountability is a key to building consumer trust and mandated in data protection legislation. We briefly outline the ‘external’... Read More about Accountable Internet of things?: outline of the IoT Databox model.

Touchomatic: Interpersonal touch gaming in the wild (2017)
Conference Proceeding

Direct touch between people is a key element of social behaviour. Recently a number of researchers have explored games which sense aspects of such interpersonal touch to control interaction with a multiplayer computer game. In this paper, we describe... Read More about Touchomatic: Interpersonal touch gaming in the wild.

The Rough Mile: Testing a Framework of Immersive Practice (2017)
Conference Proceeding

We present our case study on gifting digital music, The Rough Mile, as an example of a Framework of Immersive Practice, intended for researchers and practitioners in HCI and interaction design. Although immersion is a frequently used term in the HCI... Read More about The Rough Mile: Testing a Framework of Immersive Practice.

Learning heuristic selection using a time delay neural network for open vehicle routing (2017)
Conference Proceeding

A selection hyper-heuristic is a search method that controls a prefixed set of low-level heuristics for solving a given computationally difficult problem. This study investigates a learning-via demonstrations approach generating a selection hyper-heu... Read More about Learning heuristic selection using a time delay neural network for open vehicle routing.

Exploring the landscape of the space of heuristics for local search in SAT (2017)
Conference Proceeding

Local search is a powerful technique on many combinatorial optimisation problems. However, the effectiveness of local search methods will often depend strongly on the details of the heuristics used within them. There are many potential heuristics, an... Read More about Exploring the landscape of the space of heuristics for local search in SAT.

A modified indicator-based evolutionary algorithm (mIBEA) (2017)
Conference Proceeding

Multi-objective evolutionary algorithms (MOEAs) based on the concept of Pareto-dominance have been successfully applied to many real-world optimisation problems. Recently, research interest has shifted towards indicator-based methods to guide the sea... Read More about A modified indicator-based evolutionary algorithm (mIBEA).

Fusing deep learned and hand-crafted features of appearance, shape, and dynamics for automatic pain estimation (2017)
Conference Proceeding

Automatic continuous time, continuous value assessment of a patient's pain from face video is highly sought after by the medical profession. Despite the recent advances in deep learning that attain impressive results in many domains, pain estimation... Read More about Fusing deep learned and hand-crafted features of appearance, shape, and dynamics for automatic pain estimation.