Find Research Output

Research Output
  • All
  • Scholar Profiles
  • Research Units
  • Research Output
Department Publication Year Content Type Data Sources


School of Advanced Technology
Clear all

1.MidSens'09 - International Workshop on Middleware Tools, Services and Run-Time Support for Sensor Networks, Co-located with the 10th ACM/IFIP/USENIX International Middleware Conference Preface

Author:Michiels, Sam ; Hughes, Danny

Source:MidSens'09 - International Workshop on Middleware Tools, Services and Run-Time Support for Sensor Networks, Co-located with the 10th ACM/IFIP/USENIX International Middleware Conference,2009,Vol.

2.Modeling and Verification of NCL Circuits Using PAT

Author:Ma, JM;Man, KL;Lim, EG;Zhang, N;Lei, CU;Guan, SU;Jeong, TT;Seon, JK

Source:CEIS 2011,2011,Vol.15

Abstract:NULL Conventional Logic (NCL) is a Delay-Insensitive (DI) clockless paradigm and is suitable for implementing asynchronous circuits. Efficient methods of analysis are required to specify and verify such DI systems. Based on Delay Insensitive sequential Process (DISP) specification, this paper demonstrates the application of formal methods by applying Process Analysis Toolkit (PAT) to model and verify the behavior of NCL circuits. A few useful constructs are successfully modeled and verified by using PAT. The flexibility and simplicity of the coding, simulation and verification shows that PAT is effective and applicable for NCL circuit design and verification. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]

3.One-class kernel subspace ensemble for medical image classification (vol 2014, 17, 2014)

Author:Zhang, YG;Zhang, BL;Coenen, F;Xiao, JM;Lu, WJ


4.An exploration of techniques for off-screen content interaction in mobile devices

Author:Shen, T;Liang, HN;Liu, D;Man, KL


Abstract:This paper is an attempt to explore new ways for allowing interaction with mobile devices, such as smartphones and tablets. Mobile devices have become very popular in a relatively short time. Their popularity is mainly due to the portability and until very recently their ease-of-interaction through the touch-enabled screen when compared to older devices. Their portability comes with a trade-off: the small screen through which users can interact with their content. The small size screen can limit significantly the amount of content they can interact with, and also the manner in which this interaction is carried out. In this research, we explore the ways of extending the interaction space of these devices. We assess if, how well, and what mechanisms are possible for enabling users to interact with content located off the screen. In this paper, we report our approach and some early results of experiments we have conducted.

5.Subcellular phenotype images classification by MLP ensembles with random linear oracle

Author:Zhang, Bai-Ling ; Han, Guoxia

Source:5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011,2011,Vol.

Abstract:Subcellular localization is a key functional characteristic of proteins. An automatic, reliable and efficient prediction system for protein subcellular localization can be used for establishing knowledge of the spatial distribution of proteins within living cells and permits to screen systems for drug discovery or for early diagnosis of a disease. In this paper, we investigate an approach based on augmented image features by incorporating curvelet transform and neural network (MLP) ensemble for classification. A simple Random Subspace (RS) ensemble offers satisfactory performance, which contains a set of base MLP classifiers trained with subsets of attributes randomly drawn from the combined features of curvelet coefficients and original Subcellular Location Features (SLF). An MLP ensemble with Random Linear Oracle (RLO) can further improve the performance by replacing a base classifier with a "miniensemble", which consists of a pair of base classifiers and a fixed, randomly created oracle that selects between them. With the benchmarking 2D HeLa images, our experiments show the effectiveness of the proposed approach. The RS-MLP ensemble offers the classification rate 95%% while the RS-RLO ensemble gives 95.7%% accuracy, which compares sharply with the previously published benchmarking result 84%%. © 2011 IEEE.

6.SimpleGAN Stabilizing generative adversarial networks with simple distributions

Author:Zhang, Shufei ; Qian, Zhuang ; Huang, Kaizhu ; Zhang, Rui ; Hussain, Amir

Source:IEEE International Conference on Data Mining Workshops, ICDMW,2019,Vol.2019-November

Abstract:Generative Adversarial Networks (GANs) are powerful generative models, but usually suffer from hard training and poor generation. Due to complex data and generation distributions in high dimensional space, it is difficult to measure the departure of two distributions, which is however vital for training successful GANs. Previous methods try to alleviate this problem by choosing reasonable divergence metrics. Unlike previous methods, in this paper, we propose a novel method called SimpleGAN to tackle this problem transform original complex distributions to simple ones in the low dimensional space while keeping information and then measure the departure of two simple distributions. This novel method offers a new direction to tackle the stability of GANs. Specifically, starting from maximization of the mutual information between variables in the original high dimensional space and low dimensional space, we eventually derive to optimize a much simplified version, i.e. the lower bound of the mutual information. For experiments, we implement our proposed method on different baselines i.e. traditional GAN, WGAN-GP and DCGAN for CIFAR-10 dataset. Our proposed method achieves obvious improvement over these baseline models. © 2019 IEEE.

7.SecIoT: a security framework for the Internet of Things

Author:Huang, X;Craig, P;Lin, HY;Yan, Z


Abstract:The 5th generation wireless system (5G) will support Internet of Things (IoT) by increasing the interconnectivity of electronic devices to support a variety of new and promising networked applications such as the home of the future, environmental monitoring networks, and infrastructure management systems. The potential benefits of the IoT are as profound as they are diverse. However, the benefits of the IoT come with some significant challenges. Not the least of these is that the increased interconnectivity integral to an IoT network increases its vulnerability to malevolent attacks. There is still no proven methodology for the design of security frameworks with device authentication and access control. This paper attempts to address this problem through the development of a prototype security framework with robust and transparent security protection. This includes an investigation into the security requirements of three different characteristic IoT scenarios (concretely, body IoT, home IoT, and hotel IoT), a design of new authentication mechanisms, and an access control subsystem with fine-grained roles and risk indicators. Our prototype security framework gives us an insight into some of the major difficulties of IoT security as well as providing some feasible solutions. Copyright (C) 2015 John Wiley & Sons, Ltd.

8.One-class kernel subspace ensemble for medical image classification

Author:Zhang, YG;Zhang, BL;Coenen, F;Xiao, JM;Lu, WJ


Abstract:Classification of medical images is an important issue in computer-assisted diagnosis. In this paper, a classification scheme based on a one-class kernel principle component analysis (KPCA) model ensemble has been proposed for the classification of medical images. The ensemble consists of one-class KPCA models trained using different image features from each image class, and a proposed product combining rule was used for combining the KPCA models to produce classification confidence scores for assigning an image to each class. The effectiveness of the proposed classification scheme was verified using a breast cancer biopsy image dataset and a 3D optical coherence tomography (OCT) retinal image set. The combination of different image features exploits the complementary strengths of these different feature extractors. The proposed classification scheme obtained promising results on the two medical image sets. The proposed method was also evaluated on the UCI breast cancer dataset (diagnostic), and a competitive result was obtained.

9.Customer churn prediction in the telecommunication sector using a rough set approach

Author:Amin, A;Anwar, S;Adnan, A;Nawaz, M;Alawfi, K;Hussain, A;Huang, KZ


Abstract:Customer churn is a critical and challenging problem affecting business and industry, in particular, the rapidly growing, highly competitive telecommunication sector. It is of substantial interest to both academic researchers and industrial practitioners, interested in forecasting the behavior of customers in order to differentiate the churn from non-churn customers. The primary motivation is the dire need of businesses to retain existing customers, coupled with the high cost associated with acquiring new ones. A review of the field has revealed a lack of efficient, rule-based Customer Churn Prediction (CCP) approaches in the telecommunication sector. This study proposes an intelligent rule-based decision-making technique, based on rough set theory (RST), to extract important decision rules related to customer churn and non-churn. The proposed approach effectively performs classification of churn from non-churn customers, along with prediction of those customers who will churn or may possibly churn in the near future. Extensive simulation experiments are carried out to evaluate the performance of our proposed RST based CCP approach using four rule-generation mechanisms, namely, the Exhaustive Algorithm (EA), Genetic Algorithm (GA); Covering Algorithm (CA) and the LEM2 algorithm (LA). Empirical results show that RST based on GA is the most efficient technique for extracting implicit knowledge in the form of decision rules from the publicly available; benchmark telecom dataset. Further, coniparative results demonstrate that our proposed approach offers a globally optimal solution for CCP in the telecom sector, when benchmarked against several state-of-the-art methods. Finally, we show how attribute-level analysis can pave the way for developing a successful customer retention policy that could form an indispensable part of strategic decision making and planning process in the telecom sector.

10.A novel classifier ensemble method with sparsity and diversity

Author:Yin, XC;Huang, KZ;Hao, HW;Iqbal, K;Wang, ZB


Abstract:We consider the classifier ensemble problem in this paper. Due to its superior performance to individual classifiers, class ensemble has been intensively studied in the literature. Generally speaking, there are two prevalent research directions on this, i.e., to diversely generate classifier components, and to sparsely combine multiple classifiers. While most current approaches are emphasized on either sparsity or diversity only, we investigate the classifier ensemble by learning both sparsity and diversity simultaneously. We manage to formulate the classifier ensemble problem with the sparsity or/and diversity learning in a general framework. In particular, the classifier ensemble with sparsity and diversity can be represented as a mathematical optimization problem. We then propose a heuristic algorithm, capable of obtaining ensemble classifiers with consideration of both sparsity and diversity. We exploit the genetic algorithm, and optimize sparsity and diversity for classifier selection and combination heuristically and iteratively. As one major contribution, we introduce the concept of the diversity contribution ability so as to select proper classifier components and evolve classifier weights eventually. Finally, we compare our proposed novel method with other conventional classifier ensemble methods such as Bagging, least squares combination, sparsity learning, and AdaBoost, extensively on UCI benchmark data sets and the Pascal Large Scale Learning Challenge 2008 webspam data. The experimental results confirm that our approach leads to better performance in many aspects. (C) 2014 Elsevier B.V. All rights reserved.

11.Model Checking Denial-of-Service Attack against IEEE 802.15.6 Protocol

Author:Liu, HN;Wang, YM;Zheng, K;Huang, X


Abstract:With the prevalence of Internet of Things, numerous system vulnerabilities can be explored, and the hacker may launch a series of DoS attacks by exploiting the vulnerabilities of the system. In this paper, a simulation experiment is designed to implement DoS attacks on a system that utilizes the IEEE 802.15.6 password association protocol to accomplish the information transmission. During the process, authors introduce the probabilistic model checking technique with the tool of PRISM. This method is used for the analysis of the factors affecting the success rate of DoS attacks. The study is expected to enrich the analysis of the factors influencing the success rate of DoS attacks.

12.Multi-GHz Microstrip Transmission Lines Realised by Screen Printing on Flexible Substrates

Author:Shi, YZ;Jiang, ZZ;Lam, S;Leach, M;Wang, JC;Lim, EG


Abstract:This paper reports experimental work on 50 SI microstrip transmission lines implemented by screen-printing low-cost silver paste onto thin flexible polyethylene terephthalate (PET) substrates of varying thickness. The microstrip line designs are based on PET substrates with thicknesses of 1.4 mm, 0.7 mm and 0.5 mm, leading to conductive track widths of 3.8 mm, 1.7 mm and 1.2 mm respectively for a 50 SI line; these designs were then realised. The S-parameter measurements show that the insertion loss of the microstrip transmission lines on each substrate can be as low as 0.2 dB/cm, 0.17 dB/cm, and 0.14 dB/cm up to a frequency of 5 GHz in spite of the average quality of the silver paste used. The experimental results also show that the screen-printed transmission lines still work quite well in bent condition and wearable electronics application at GHz is possible.

13.Accelerating Infinite Ensemble of Clustering by Pivot Features

Author:Jin, XB;Xie, GS;Huang, KZ;Hussain, A


Abstract:The infinite ensemble clustering (IEC) incorporates both ensemble clustering and representation learning by fusing infinite basic partitions and shows appealing performance in the unsupervised context. However, it needs to solve the linear equation system with the high time complexity in proportion to O(d(3)) where d is the concatenated dimension of many clustering results. Inspired by the cognitive characteristic of human memory that can pay attention to the pivot features in a more compressed data space, we propose an acceleration version of IEC (AIEC) by extracting the pivot features and learning the multiple mappings to reconstruct them, where the linear equation system can be solved with the time complexity O(dr(2)) (r << d). Experimental results on the standard datasets including image and text ones show that our algorithm AIEC improves the running time of IEC greatly but achieves the comparable clustering performance.

14.A blockchain based data management system for energy trade

Author:Chen, Mengjie ; Li, Yuexuan ; Xu, Zhuocheng ; Huang, Xin ; Wang, Wei

Source:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),2018,Vol.11373 LNCS

Abstract:A new type of energy trade called the distributed energy resource has emerged in recent years, which can bring several benefits to people. However, trust issue also appeared among governments, users and energy companies. To solve the problem, smart contract and Ethereum are used to develop a system for distributed energy trade. A smart contract is a computer protocol intended to digitally facilitate, verify, or enforce the negotiation or performance of a contract. Ethereum is one of the decentralized platforms that run smart contracts. With the usage of smart contract and Ethereum, the system is reliable and it avoids the risk of using centralized energy management system. Therefore, the purpose of this project is to develop a blockchain based data management IoT system for energy chain transaction by Ethereum and test the smart contract by communicating sequential process (CSP) which is a formal language for describing patterns of interaction in concurrent systems. © 2018, Springer Nature Switzerland AG.

15.Long Short-Term Attention

Author:Zhong, GQ;Lin, X;Chen, K;Li, QY;Huang, KZ


Abstract:Attention is an important cognition process of humans, which helps humans concentrate on critical information during their perception and learning. However, although many machine learning models can remember information of data, they have no the attention mechanism. For example, the long short-term memory (LSTM) network is able to remember sequential information, but it cannot pay special attention to part of the sequences. In this paper, we present a novel model called long short-term attention (LSTA), which seamlessly integrates the attention mechanism into the inner cell of LSTM. More than processing long short term dependencies, LSTA can focus on important information of the sequences with the attention mechanism. Extensive experiments demonstrate that LSTA outperforms LSTM and related models on the sequence learning tasks.

16.Analyzing Healthcare Big Data for Patient Satisfaction

Author:Wan, KY;Alagar, V


Abstract:Healthcare Big Data (HBD) is more complex than Big Data (BD) arising from any other critical sector because a variety of data sources and procedures are followed in traditional hospital settings and in healthcare network (e-Health). In order to achieve their primary goal, which is to enhance patient experience while sustaining dependable care within financial viability and respect for government regulations, the HBD should be analyzed to determine patent satisfaction level. In general, there exists no accepted method yet in measuring patient satisfaction. The traditional approach for evaluating hospital-based healthcare is through a statistical analysis of responses of clients to a survey, often conducted by a third party. Such methods are often infected with incomplete information, inaccurate hypothesis, and error-prone analysis. Analyzing data generated through automated healthcare networks for assessing the effectiveness of service provision and patient satisfaction are more challenging. It is in this context that we discuss in this paper factors that contribute to patient satisfaction, and propose an algorithmic method to assess it from HBD analysis.

17.Guided Policy Search for Sequential Multitask Learning

Author:Xiong, FZ;Sun, B;Yang, X;Qiao, H;Huang, KZ;Hussain, A;Liu, ZY


Abstract:Policy search in reinforcement learning (RL) is a practical approach to interact directly with environments in parameter spaces, that often deal with dilemmas of local optima and real-time sample collection. A promising algorithm, known as guided policy search (GPS), is capable of handling the challenge of training samples using trajectory-centric methods. It can also provide asymptotic local convergence guarantees. However, in its current form, the GPS algorithm cannot operate in sequential multitask learning scenarios. This is due to its batch-style training requirement, where all training samples are collectively provided at the start of the learning process. The algorithm's adaptation is thus hindered for real-time applications, where training samples or tasks can arrive randomly. In this paper, the GPS approach is reformulated, by adapting a recently proposed, lifelong-learning method, and elastic weight consolidation. Specifically, Fisher information is incorporated to impart knowledge from previously learned tasks. The proposed algorithm, termed sequential multitask learning-GPS, is able to operate in sequential multitask learning settings and ensuring continuous policy learning, without catastrophic forgetting. Pendulum and robotic manipulation experiments demonstrate the new algorithms efficacy to learn control policies for handling sequentially arriving training samples, delivering comparable performance to the traditional, and batch-based GPS algorithm. In conclusion, the proposed algorithm is posited as a new benchmark for the real-time RL and robotics research community.

18.Fast Iterative Semi-Blind Receiver for URLLC in Short-Frame Full-Duplex Systems With CFO

Author:Liu, YJ;Zhu, X;Lim, EG;Jiang, YF;Huang, Y


Abstract:We propose an iterative semi-blind (ISB) receiver structure to enable ultra-reliable low-latency communications in short-frame full-duplex (FD) systems with carrier frequency offset (CFO). To the best of our knowledge, this is the first paper to propose an integral solution to channel estimation and CFO estimation for short-frame FD systems by utilizing a single pilot. By deriving an equivalent system model with the CFO included implicitly, a subspace-based blind channel estimation is proposed at the initial stage, followed by CFO estimation and channel ambiguities elimination. Then, the refinement of channel and the CFO estimates is conducted iteratively. The integer and fractional parts of CFO in the full range are estimated as a whole and in closed-form at each iteration. The proposed ISB receiver significantly outperforms the previous methods in terms of frame error rate, mean square errors of channel estimation and CFO estimation and output signal-to-interference-and-noise ratio, while at a halved spectral overhead. Cramer-Rao lower bounds are derived to verify the effectiveness of the proposed ISB receiver structure. It also demonstrates high-computational efficiency as well as the fast convergence speed.

19.Understanding Quantum Protocols Using Information Visualization

Author:Huang, X;Wang, QL


Abstract:There are many cases where people need to be aware of the security status of their network in order to be able to respond effectively to security risks. Most security mechanisms are however too complex and most users find it difficult to understand the system and respond effectively. There is therefore a need to design understandable security mechanisms. In this paper, we focus on the learnability of complex security protocols which is improved using symmetrical information visualization. From preliminary results, we can see that visualization and symmetry are useful tools for usable security. They are aesthetically pleasing for the user and can improve the understanding of protocols that would otherwise be difficult to comprehend for normal users.

20.Determine the Permittivity of the Plastic Materials

Author:Lim, EG;Wang, Z;Leach, MP;Gray, D;Man, KL;Zhang, N


Abstract:Microwave dielectric measurements are difficult to make, each method involves a compromise between accuracy, experimental simplicity, and complexity of the analysis. It is with these ideas in mind that we present a technique in which a waveguide is completely filled with the dielectric of interest to determine the dielectric constant for plastic materials (e.g. Rexolite, Lacqrene and PTFE). Good agreements between measured and manufacturer material specifications have been obtained.
Total 375 results found
Copyright 2006-2020 © Xi'an Jiaotong-Liverpool University 苏ICP备07016150号-1 京公网安备 11010102002019号