Wei Pang

Heriot-Watt University · Department of Computer Science

Many real-world problems can be naturally formulated as discrete multi-objective optimisation (DMOO) problems. We have proposed a novel Physarum-inspired competition algorithm (PCA) to tackle these DMOO problems. Our algorithm is based on hexagonal cellular automata (CA) as a representation of problem search space and reaction–diffusion systems tha...

In recent years, research on Physarum polycephalum has become more popular after Nakagaki (AIR 407: 6803-470, 2000) performed their famous experiment showing that Physarum was able to find the shortest route through a maze. Subsequent researches have confirmed the ability of Physarum-inspired algorithms to solve a wide range of real-world applicati...

Single image super-resolution (SISR) typically reconstructs higher-resolution (HR) images from the corresponding low-resolution (LR) images in the presence of natural HR images. SISR is highly important in generating high-quality images in forensic scenarios since it facilitates close investigation and examination of captured shoeprints. However, i...

The detection, identification and counting of bone marrow erythroid cells are vital for evaluating the health status and therapeutic schedules of patients with leukemia or hematopathy. However, traditional methods used in hospitals are still based on chemical reagent staining, manual detection and counting with the help of laboratory equipment. And...

Shoeprints are important information collected at the crime scene and are of great value for forensic analysis. Shoeprints collected in real-world scenarios are normally unclear, abrasive, and lack contextual and other kinds of missing information. In this research, we apply a novel deep learning technique called restorable inpainting to repair sho...

Support vector clustering (SVC) is a boundary-based algorithm, which has several advantages over other clustering methods, including identifying clusters of arbitrary shapes and numbers. Leveraged by the high generalization ability of the large margin distribution machine (LDM) and the optimal margin distribution clustering (ODMC), we propose a new...

Shoeprints contain valuable information for tracing evidence in forensic scenes, and they need to be generated into cleaned, sharp, and high-fidelity images. Most of the acquired shoeprints are found with low quality and/or in distorted forms. The high-fidelity shoeprint generation is of great significance in forensic science. A wide range of deep...

Recent years have seen a renewed interest in evolutionary computation applied to the automatic design of deep neural network architectures, i.e. Neural Architecture Search (NAS). The advantages of evolutionary approaches in NAS include their conceptual simplicity and their flexibility with regards to search space definition and/or optimization obje...

Physarum polycephalum (Physarum for short) is an example of plasmodial slime moulds that are classified as a fungus "Myxomycetes". In recent years, research on Physarum has become more popular after Nakagaki et al. (2000) performed his famous experiments showing that Physarum was able to find the shortest route through a maze. Physarum) may not hav...

Graph representation of structured data can facilitate the extraction of stereoscopic features, and it has demonstrated excellent ability when working with deep learning systems, the so-called Graph Neural Networks (GNNs). Choosing a promising architecture for constructing GNNs can be transferred to a hyperparameter optimisation problem, a very cha...

Density peak clustering (DPC) is a recently developed density-based clustering algorithm that achieves competitive performance in a non-iterative manner. DPC is capable of effectively handling clusters with single density peak (single center), i.e., based on DPC’s hypothesis, one and only one data point is chosen as the center of any cluster. Howev...

Inferring dynamic system models from observed time course data is very challenging compared to static system identification tasks. Dynamic system models are complicated to infer due to the underlying large search space and high computational cost for simulation and verification. In this research we aim to infer both the structure and parameters of...

In this paper, we present a survey on the progress of radiogenomics research, which predicts cancer genotypes from imaging phenotypes and investigates the associations between them. First, we present an overview of the popular technology modalities for obtaining diagnostic medical images. Second, we summarize recently used methodologies for radioge...

This article presents the modeling and solution of an extended type of split delivery vehicle routing problem (SDVRP). In SDVRP, the demands of customers need to be met by efficiently routing a given number of capacitated vehicles, wherein each customer may be served multiple times by more than one vehicle. Furthermore, in many real-world scenarios...

Clustering is an important unsupervised learning method in machine learning and data mining. Many existing clustering methods may still face the challenge in self-identifying clusters with varying shapes, sizes and densities. To devise a more generic clustering method that considers all the aforementioned properties of the natural clusters, we prop...

Numerous human actions such as “Phoning,” “PlayingGuitar,” and “RidingHorse” can be inferred by static cue-based approaches even if their motions in video are available considering one single still image may already sufficiently explain a particular action. In this research, we investigate human action recognition in still images and utilize deep e...

Data clustering is a well-researched area in data mining and machine learning. The clustering algorithms that can handle both numeric and categorical variables have been extensively researched in the recent years. However, the clustering algorithms have a major limitation that converge to a local optima. Therefore, to address this problem this pape...

In this paper we propose DeepSwarm, a novel neural architecture search (NAS) method based on Swarm Intelligence principles. At its core DeepSwarm uses Ant Colony Optimization (ACO) to generate ant population which uses the pheromone information to collectively search for the best neural architecture. Furthermore, by using local and global pheromone...

Support Vector Regression (SVR) and its variants are widely used regression algorithms, and they have demonstrated high generalization ability. This research proposes a new SVR-based regressor: v-minimum absolute deviation distribution regression (v-MADR) machine. Instead of merely minimizing structural risk, as with v-SVR, v-MADR aims to achieve b...

Current Neural Architecture Search techniques can suffer from a few shortcomings, including high computational cost, excessive bias from the search space, conceptual complexity or uncertain empirical benefits over random search. In this paper, we present ImmuNeCS, an attempt at addressing these issues with a method that offers a simple, flexible, a...

Self-healing materials with an inherent repair mechanism have been widely studied. However, the self-healing efficiencies of most materials can only be measured by laboratory-based experiments, which can be time consuming and expensive. Inspired by modern machine learning approaches, we are interested in predicting the self-healing efficiency of ne...

Many real-world problems can be naturally formulated as discrete multi-objective optimization (DMOO) problems. In this research we propose a novel bio-inspired Physarum competition algorithm (PCA) to tackle DMOO problems by modelling the Physarum discrete motility over a hexagonal cellular automaton. Our algorithm is based on the chemo-attraction f...

Inferring models of dynamic systems from their time series data is a challenging task for optimization algorithms due to its potentially expensive computational cost and underlying large search space. In this study we aim to infer both the structure and parameters of a dynamic system model simultaneously by Particle Swarm Optimization (PSO), enhanc...

The most common issues for many clustering algorithms include the slow convergence, requirement for pre-specification of a number of parameters, and the lack of robustness when dealing with anomalies. Recently, the density peak clustering (DPC) algorithm was proposed to discover the centers of clusters by finding the density peaks in a dataset base...

Simultaneously inferring both the structure and parameters of Ordinary Differential Equations (ODEs) for a complex dynamic system is more practical in many systems identification problems, but it remains challenging due to the complexity of the underlying search space. In this research, we propose a novel algorithm based on Particle Swarm Optimizat...

Gravitation Field Algorithm (GFA) is a novel optimization algorithm derived from the Solar Nebular Disk Model (SNDM) in astronomy and inspired by the formation process of planets. Although it has achieved good performance when solving many unconstrained optimization problems, which demonstrated its promising application potential in many real-world...

An important issue in statistical modeling is to determine the complexity of the model based on the scale of data so as to effectively mitigate the model's overfitting problems without big data. We adopt a data-driven approach to automatically determine the number of components of the model. In order to better extract robust features, we propose a...

Wireless sensor-actor networks (WSANs) are a core component of Internet of Things (IOT), and are useful for environments that are difficult and/or dangerous for sensors to be deployed deterministically. After random deployment, the sensors are required to disperse autonomously without central control to maximize the coverage and re-establish the co...

Swarm intelligence algorithms (SIAs) have demonstrated excellent performance when solving optimization problems including many real-world problems. However, because of their expensive computational cost for some complex problems, SIAs need to be accelerated effectively for better performance. This paper presents a high-performance general framework...

As the important elements of the Internet of Things system, wireless sensor network (WSN) has gradually become popular in many application fields. However, due to the openness of WSN, attackers can easily eavesdrop, intercept, and rebroadcast data packets. WSN has also faced many other security issues. Intrusion detection system (IDS) plays a pivot...

Fireworks algorithm (FWA) is a novel swarm intelligence algorithm recently proposed for solving complex optimization problems. Because of its powerful global optimization ability to solve classification problems, we first present an optimization classification model in this paper. In this model, a linear equation set is constructed according to cla...

Mobile wireless sensor networks (Mobile-WSN) are useful in harsh environments due to the presence of obstacles and/or dangerous for sensors to be deployed deterministically. In this paper, we proposed a Physarum inspired autonomous, model for dynamic deployment of sensor nodes where multiple Physarum (as representation of sensors) will compete over...

The performance of density based clustering algorithms may be greatly influenced by the chosen parameter values, and achieving optimal or near optimal results very much depends on empirical knowledge obtained from previous experiments. To address this limitation, we propose a novel density based clustering algorithm called the Density Propagation b...

Missing data research is hindered by a lack in imputation evaluation techniques. Imputation has the potential to increase the impact and validity of studies from different sectors (research, public and private). By creating robust evaluation software, more researchers may be willing to use and justify using imputation methods. This paper aims to en...

We propose a novel support vector regression approach called \(\varepsilon \)-Distance Weighted Support Vector Regression (\(\varepsilon \)-DWSVR). \(\varepsilon \)-DWSVR specifically addresses a challenging issue in support vector regression: how to deal with the situation when the distribution of the internal data in the \(\varepsilon \)-tube is...

We investigate a novel way of robust face image feature extraction by adopting the methods based on Unsupervised Linear Subspace Learning to extract a small number of good features. Firstly, the face image is divided into blocks with the specified size, and then we propose and extract pooled Histogram of Oriented Gradient (pHOG) over each block. Se...

The Artificial Bee Colony (ABC) algorithm is an optimization algorithm inspired by the foraging behavior of bee swarms. Similar to some evolutionary algorithms, there is a main limitation in ABC, i.e., in many problems, ABC is good at exploration but poor at exploitation. Thus, in order to overcome this limitation and improve the performance of ABC...

We are interested in the relationship between the team composition and the outcome in the filmmaking process. We studied the “diversity” of the group of actors and directors and how it is related to the movie rank given by the audience. The “diversity” is considered as the representation of the degree of variety based on the possibilities of collab...

A computational pipeline is developed to accurately predict urine excretory proteins and the possible origins of the proteins. The novel contributions of this study include: (i) a new method for predicting if a cellular protein is urine excretory based on unique features of proteins known to be urine excretory; and (ii) a novel method for identifyi...

Support Vector Clustering (SVC) has become a significant boundary-based clustering algorithm. In this paper we propose a novel SVC algorithm named “Partitioning Clustering Based on Support Vector Ranking (PC-SVR)”, which is aimed at improving the traditional SVC, which suffers the drawback of high computational cost during the process of cluster pa...

During the past decade, a vast number of GPS devices have produced massive amounts of data containing both time and spatial information. This poses a great challenge for traditional spatial databases. With the development of distributed cloud computing, many high-performance cloud platforms have been built, which can be used to process such spatio-...

The Dendritic Cell Algorithm (DCA) and its improved version: Deterministic Dendritic Cell Algorithm (dDCA) are essentially binary classification algorithms based on the behavior of Dendritic Cells (DCs) in the immune system. Both DCA and dDCA collect and process the data in form of signals, and produce output signal. The signals are divided in two...

In this paper, we proposed a link density clustering (LDC) method for overlapping community detection based on density peaks. We firstly use an extended cosine link distance metric to reflect the relationship of links. Then we introduce a clustering algorithm with fast search for solving the link clustering (LC) problem by density peaks with box pl...

‘Big data’ is an area of growing research interest within sociology and numerous other disciplines. The rapid development of social media platforms and other web resources offer a vast and readily accessible repository of data associated with participants’ activities, attitudes and personal information on a scale and depth that would have previousl...

Link community gradually unfolds its capacity in complex network research. In this paper, a novel link similarity measure on line graphs is proposed. This measure can be adapted to different types of networks with an adjustable parameter. We prove its value converges to a limit on line graphs with the relationship of the nonneighbor links taken int...

For many machine learning and pattern recognition tasks, most of the data are high dimensional, such as human action videos. Vectorization of action video is one common approach to dealing with the high dimensionality issue. However, the vectorization process requires a huge amount of memory, and it is also very time consuming. Recently

In this paper, we present a semi-automatic system (Sherlock) for quiz generation using Linked Data and textual descriptions of RDF resources. Sherlock is distinguished from existing quiz generation systems in its ability to control the difficulty level of the generated quizzes. We cast the problem of perceiving the level of knowledge difficulty as...

In this paper, we present a semi-automatic system (Sherlock) for quiz generation using linked data and textual descriptions of RDF resources. Sherlock is distinguished from existing quiz generation systems in its generic framework for domain-independent quiz generation as well as in the ability of controlling the difficulty level of the generated q...

Object tracking has always been a hot research topic in the field of computer vision; its purpose is to track objects with specific characteristics or representation and estimate the information of objects such as their locations, sizes, and rotation angles in the current frame. Object tracking in complex scenes will usually encounter various sorts...

Most of the initialization approaches are dedicated to the partitional clustering algorithms which process categorical or numerical data only. However, in real-world applications, data objects with both numeric and categorical features are ubiquitous. The coexistence of both categorical and numerical attributes make the initialization methods desig...

Computational modelling of biochemical systems based on top-down and bottom-up approaches has been well studied over the last decade. In this research, after illustrating how to generate atomic components by a set of given reactants and two user pre-defined component patterns, we propose an integrative top-down and bottom-up modelling approach for...

Cancer is a systemic disease involving dysregulated biological processes of cell proliferation, metabolism, and apoptosis. It is known that some types of cancer have longer life span, and they are even curable if they are diagnosed and treated properly in the early stage. So it is essential to find biomarkers to detect these cancers in their early...

Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-mod...

Both qualitative and quantitative model learning frameworks for biochemical systems have been studied in computational systems biology. In this research, after introducing two forms of pre-defined component patterns to represent biochemical models, we propose an integrative qualitative and quantitative modelling framework for inferring biochemical...

In this paper we demonstrate how Morven, a computational framework which can perform qualitative, semi-quantitative, and quantitative simulation of dynamical systems using the same model formalism, is applied to study the osmotic stress response pathway in yeast. First the Morven framework itself is briefly introduced in terms of the model formalis...

Tensor analysis is widely used for face recognition and action recognition. In this paper, a mode-driven discriminant analysis (MDA) in tensor subspace is proposed for visual recognition. For training, we treat each sample as an N-order tensor, of which the first N-1 modes capture the spatial information of images while the N-th mode captures the s...

The k-prototypes algorithms are well known for their efficiency to cluster mixed numeric and categorical data. In kprototypes type algorithms the initial cluster centers are often determined in a random manner. It is acknowledged that the initial placement of cluster centers has a direct impact on the performance of the k-prototypes algorithms. How...

Online and virtual spaces comprise a myriad of ad-hoc networks and online communities. Such communities are composed of smart devices, agents, systems and people who seek to interact in one way or another. We argue that the task of detecting anomalies in such settings is non-trivial. The complexity is further compounded since there is no clear cut...

Essential proteins play a crucial role in cellular survival and development process. Experimentally, essential proteins are identified by gene knockouts or RNA interference, which are expensive and often fatal to the target organisms. Regarding this, an alternative yet important approach to essential protein identification is through computational...

Collaborative filtering algorithms haven been widely used in recommender systems. However, they often suffer from the data sparsity and cold start problems. With the increasing popularity of social media, these problems may be solved by using social-based recommendation. Social-based recommendation, as an emerging research area, uses social informa...

In this paper, we investigate the social-based recommendation algorithms on heterogeneous social networks and proposed Hete-CF, a social collaborative filtering algorithm using heterogeneous relations. Distinct from the exiting methods, Hete-CF can effectively utilise multiple types of relations in a heterogeneous social network. More importantly,...

In this paper, we explore the application of Opt-AiNet, an immune network approach for search and optimisation problems, to learning qualitative models in the form of qualitative differential equations. The Opt-AiNet algorithm is adapted to qualitative model learning problems, resulting in the proposed system QML-AiNet. The potential of QML-AiNet t...

In this paper we continue the research on learning qualitative differential equation (QDE) models of biological pathways building on previous work. In particular, we adapt opt-AiNet, an immune-inspired network approach, to effectively search the qualitative model space. To improve the performance of opt-AiNet on the discrete search space, the hyper...

In this research we focus on dealing with fuzzy multivariate relations and how we could perform fuzzy qualitative simulation with models containing such relations. To achieve this, we extended Morven, a fuzzy qualitative reasoning framework, and proposed novel types of constraints for the framework. We first introduced fuzzy multivariate function (...

In this paper, a novel qualitative differential equation model learning (QML) framework named QML-Morven is presented. QML-Morven employs both symbolic and evolutionary approaches as its learning strategies to deal with models of different complexity. Based on this framework, a series of experiments were designed and carried out to: (1) investigate...

Graph construction plays a vital role in improving the performance of graph-based dimension reduction (DR) algorithms. In this paper, we propose a novel graph construction method, and we name the graph constructed from such method as samples’ inner structure based graph (SISG). Instead of determining the -nearest neighbors of each sample by calcula...

Semi-supervised clustering on information networks combines both the labeled and unlabeled data sets with an aim to improve the clustering performance. However, the existing semi-supervised clustering methods are all designed for homogeneous networks and do not deal with heterogeneous ones. In this work, we propose a semi-supervised clustering appr...

In this paper, we proposed a new building recognition method named subregion’s multiscale gist feature (SM-gist) extraction and corresponding columns information based dimensionality reduction (CCI-DR). Our proposed building recognition method is presented as a two-stage model: in the first stage, a building image is divided into 4 × 5 subregions,...

In many applications, data objects are described by both numeric and categorical features. The k-prototype algorithm is one of the most important algorithms for clustering this type of data. However, this method performs hard partition, which may lead to misclassification for the data objects in the boundaries of regions, and the dissimilarity meas...

Modelling of biochemical pathways in a computational way has received considerable attention over the last decade from biochemistry, computing sciences, and mathematics. In this paper we present an approach to evolutionarily stepwise constructing models of biochemical pathways by a qualitative model learning methodology. Given a set of reactants in...

To better equip with a non-expert to carry out the diagnosis operations, a new method for intelligent fault identification of rotating machinery based on the empirical mode decomposition (EMD), dimensionless parameters, fault decision table (FDT), MLEM2 ...

In this paper we continue our previous research on kernel subset analysis for Qualitative Model Learning (QML).We focus on investigating the kernel subsets and learning precision of QML when the number of the training data is relatively large, which makes the corresponding kernel subset experiments very computationally expensive to perform. We use...

Pathogenic microbes exist in dynamic niches and have evolved robust adaptive responses to promote survival in their hosts. The major fungal pathogens of humans, Candida albicans and Candida glabrata, are exposed to a range of environmental stresses in their hosts including osmotic, oxidative and nitrosative stresses. Significant efforts have been d...

Canonical correlations analysis (CCA) is often used for feature extraction and dimensionality reduction. However, the image vectorization of CCA breaks the spatial structure of the original image, and the excessive dimensions of vectors often cause the curse of dimensionality problem. In this paper, we propose a novel feature extraction method base...

This letter addresses the problem of analyzing spatio–temporal patterns for action recognition. In this letter we organize the whole training set in a single tensor, with each mode indicating one factor which influences the result of recognition, e.g., various view points. A novel method is proposed for tensor decomposition by discriminant analysis...

In this report, inspired by non-constructive simulation developed in the qualitative reasoning field, we present a non-constructive interval simulation algorithm for the simulation of dynamic systems. To perform this kind of simulation, we first recast two integration methods, which were originally used in traditional numerical simulation, and made...

In this paper, a special-purpose qualitative model learning (QML) system using an immune-inspired algorithm is proposed to qualitatively reconstruct biological pathways. We choose a real-world application, the detoxification pathway of Methylglyoxal (MG), as a case study. First a converter is implemented to convert possible pathways to qualitative...

A software system has been developed for modern maritime container transportation. This system contains six modules: the demand forecasting, the stowage planning, the shipping line optimization, the slot pricing and allocation, the container distribution, and the contribution analysis. The first three modules will be presented in this paper. The sy...

In this paper, an Evolutionary Qualitative Model Learning Framework (EQML) is proposed and tested by learning the qualitative metabolic models under the condition of incomplete knowledge. JMorven, a fuzzy qualitative reasoning engine, is slightly modified and integrated into the framework as a sub-module to represent and verify the candidate models...

In this paper we continue the research on applying immune-inspired algorithms as search strategies to Qualitative Model Learning (QML). A new search strategy based on opt-AiNet is proposed, and this results in the development of a novel QML system called QML-AiNet. The performance of QML-AiNet is compared with previous work using the CLONALG approa...

Over the last two decades, qualitative reasoning (QR) has become an important domain in Artificial Intelligence. QDE (Qualitative Differential Equation) model learning (QML), as a branch of QR, has also received an increasing amount of attention; many systems have been proposed to solve various significant problems in this field. QML has been appli...