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Applying reinforcement learning in playing Robosoccer using the AIBO

- Mukherjee, Subhasis

  • Authors: Mukherjee, Subhasis
  • Date: 2010
  • Type: Text , Thesis , Masters
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  • Description: "Robosoccer is a popular test bed for AI programs around the world in which AIBO entertainments robots take part in the middle sized soccer event. These robots need a variety of skills to perform in a semi-real environment like this. The three key challenges are manoeuvrability, image recognition and decision making skills. This research is focussed on the decision making skills ... The work focuses on whether reinforcement learning as a form of semi supervised learning can effectively contribute to the goal keeper's decision making when a shot is taken." -
  • Description: Master of Computing (by research)

Structural image retrieval using automatic image annotation and region based inverted file

- Zhang, Dengsheng, Islam, Md, Lu, Guojun

  • Authors: Zhang, Dengsheng , Islam, Md , Lu, Guojun
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Journal of Visual Communication and Image Representation Vol. 24, no. 7 (2013), p. 1087-1098
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  • Description: Image retrieval has lagged far behind text retrieval despite more than two decades of intensive research effort. Most of the research on image retrieval in the last two decades are on content based image retrieval or image retrieval based on low level features. Recent research in this area focuses on semantic image retrieval using automatic image annotation. Most semantic image retrieval techniques in literature, however, treat an image as a bag of features/words while ignore the structural or spatial information in the image. In this paper, we propose a structural image retrieval method based on automatic image annotation and region based inverted file. In the proposed system, regions in an image are treated the same way as keywords in a structural text document, semantic concepts are learnt from image data to label image regions as keywords and weight is assigned to each keyword according to spatial position and relationship. As the result, images are indexed and retrieved in the same way as structural document retrieval. Specifically, images are broken down to regions which are represented using colour, texture and shape features. Region features are then quantized to create visual dictionaries which are similar to monolingual dictionaries like English or Chinese dictionaries. In the next step, a semantic dictionary similar to a bilingual dictionary like the English–Chinese dictionary is learnt to mapping image regions to semantic concepts. Finally, images are then indexed and retrieved using a novel region based inverted file data structure. Results show the proposed method has significant advantage over the widely used Bayesian annotation models.

Semantic image retrieval using region based inverted file

- Zhang, Dengsheng, Islam, Md, Lu, Guojun, Hou, Jin

  • Authors: Zhang, Dengsheng , Islam, Md , Lu, Guojun , Hou, Jin
  • Date: 2009
  • Type: Text , Journal article
  • Relation: Journal of Visual Communication and Image Representation Vol. 24, no. 7 (2009), p.242-249
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  • Description: Image retrieval has lagged far behind text retrieval despite more than two decades of intensive research effort. Most of the research on image retrieval in the last two decades are on content based image retrieval or image retrieval based on low level features. Recent research in this area focuses on semantic image retrieval using automatic image annotation. Most semantic image retrieval techniques in literature, however, treat an image as a bag of features/words while ignore the structural or spatial information in the image. In this paper, we propose a structural image retrieval method based on automatic image annotation and region based inverted file. In the proposed system, regions in an image are treated the same way as keywords in a structural text document, semantic concepts are learnt from image data to label image regions as keywords and weight is assigned to each keyword according to spatial position and relationship. As the result, images are indexed and retrieved in the same way as structural document retrieval. Specifically, images are broken down to regions which are represented using colour, texture and shape features. Region features are then quantized to create visual dictionaries which are similar to monolingual dictionaries like English or Chinese dictionaries. In the next step, a semantic dictionary similar to a bilingual dictionary like the English–Chinese dictionary is learnt to mapping image regions to semantic concepts. Finally, images are then indexed and retrieved using a novel region based inverted file data structure. Results show the proposed method has significant advantage over the widely used Bayesian annotation models.

Isolation-based anomaly detection

- Liu, Fei, Ting, Kaiming, Zhou, Zhi-Hua

  • Authors: Liu, Fei , Ting, Kaiming , Zhou, Zhi-Hua
  • Date: 2012
  • Type: Text , Journal article
  • Relation: ACM Transactions on Knowledge Discovery from Data Vol. 6, no. 1 (2012), p. 1-39
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  • Description: Anomalies are data points that are few and different. As a result of these properties, we show that, anomalies are susceptible to a mechanism called isolation. This article proposes a method called Isolation Forest (iForest), which detects anomalies purely based on the concept of isolation without employing any distance or density measure---fundamentally different from all existing methods. As a result, iForest is able to exploit subsampling (i) to achieve a low linear time-complexity and a small memory-requirement and (ii) to deal with the effects of swamping and masking effectively. Our empirical evaluation shows that iForest outperforms ORCA, one-class SVM, LOF and Random Forests in terms of AUC, processing time, and it is robust against masking and swamping effects. iForest also works well in high dimensional problems containing a large number of irrelevant attributes, and when anomalies are not available in training sample

A comparison of machine learning algorithms for multilabel classification of CAN

- Kelarev, Andrei, Stranieri, Andrew, Yearwood, John, Jelinek, Herbert

  • Authors: Kelarev, Andrei , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
  • Date: 2012
  • Type: Text , Journal article
  • Relation: Advances in Computer Science and Engineering Vol. 9, no. 1 (2012), p. 1-4
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  • Description: This article is devoted to the investigation and comparison of several important machine learning algorithms in their ability to obtain multilabel classifications of the stages of cardiac autonomic neuropathy (CAN). Data was collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments have achieved better results than those published previously in the literature for similar CAN identification tasks.

A review on automatic image annotation techniques

- Zhang, Dengsheng, Islam, Md, Lu, Guojun

  • Authors: Zhang, Dengsheng , Islam, Md , Lu, Guojun
  • Date: 2012
  • Type: Text , Journal article
  • Relation: Pattern Recognition Letters Vol. 45, no. 1 (2012), p. 346-362
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  • Description: Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and image semantics understandable by humans. As a result, research in this area has shifted to bridge the semantic gap between low level image features and high level semantics. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) which extracts semantic features using machine learning techniques. In this paper, we focus on this latest development in image retrieval and provide a comprehensive survey on automatic image annotation. We analyse key aspects of the various AIA methods, including both feature extraction and semantic learning methods. Major methods are discussed and illustrated in details. We report our findings and provide future research directions in the AIA area in the conclusions

Multi-source cyber-attacks detection using machine learning

- Taheri, Sona, Gondal, Iqbal, Bagirov, Adil, Harkness, Greg, Brown, Simon, Chi, Chihung

  • Authors: Taheri, Sona , Gondal, Iqbal , Bagirov, Adil , Harkness, Greg , Brown, Simon , Chi, Chihung
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1167-1172
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  • Description: The Internet of Things (IoT) has significantly increased the number of devices connected to the Internet ranging from sensors to multi-source data information. As the IoT continues to evolve with new technologies number of threats and attacks against IoT devices are on the increase. Analyzing and detecting these attacks originating from different sources needs machine learning models. These models provide proactive solutions for detecting attacks and their sources. In this paper, we propose to apply a supervised machine learning classification technique to identify cyber-attacks from each source. More precisely, we apply the incremental piecewise linear classifier that constructs boundary between sources/classes incrementally starting with one hyperplane and adding more hyperplanes at each iteration. The algorithm terminates when no further significant improvement of the separation of sources/classes is possible. The construction and usage of piecewise linear boundaries allows us to avoid any possible overfitting. We apply the incremental piecewise linear classifier on the multi-source real world cyber security data set to identify cyber-attacks and their sources.
  • Description: Proceedings of the IEEE International Conference on Industrial Technology

Secure passive keyless entry and start system using machine learning

- Ahmad, Usman, Song, Hong, Bilal, Awais, Alazab, Mamoun, Jolfaei, Alireza

Discovering regularities from traditional chinese medicine prescriptions via bipartite embedding model

- Ruan, Chunyang, Ma, Jiangang, Wang, Ye, Zhang, Yanchun, Yang, Yun

  • Authors: Ruan, Chunyang , Ma, Jiangang , Wang, Ye , Zhang, Yanchun , Yang, Yun
  • Date: 2019
  • Type: Text , Conference proceedings
  • Relation: International Joint Conferences on Artificial Intelligence (IJCAI-49); Macao, China; 10th-16th August 2019; published in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) p. 3346-3352
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  • Description: Regularities analysis for prescriptions is a significant task for traditional Chinese medicine (TCM), both in inheritance of clinical experience and in improvement of clinical quality. Recently, many methods have been proposed for regularities discovery, but this task is challenging due to the quantity, sparsity and free-style of prescriptions. In this paper, we address the specific problem of regularities discovery and propose a graph embedding based framework for regularities discovery for massive prescriptions. We model this task as a relation prediction in which the correlation of two herbs or of herb and symptom are incorporated to characterize the different relationships. Specifically, we first establish a heterogeneous network with herbs and symptoms as its nodes. We develop a bipartite embedding model termed HS2Vec to detect regularities, which explores multiple relations of herbherb, and herb-symptom based on the heterogeneous network. Experiments on four real-world datasets demonstrate that the proposed framework is very effective for regularities discovery.

A server side solution for detecting webInject : A machine learning approach

- Moniruzzaman, Md, Bagirov, Adil, Gondal, Iqbal, Brown, Simon

A new loss function for robust classification

- Zhao, Lei, Mammadov, Musa, Yearwood, John

  • Authors: Zhao, Lei , Mammadov, Musa , Yearwood, John
  • Date: 2014
  • Type: Text , Journal article
  • Relation: Intelligent Data Analysis Vol. 18, no. 4 (2014), p. 697-715
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  • Description: Loss function plays an important role in data classification. Manyloss functions have been proposed and applied to differentclassification problems. This paper proposes a new so called thesmoothed 0-1 loss function, that could be considered as anapproximation of the classical 0-1 loss function. Due to thenon-convexity property of the proposed loss function, globaloptimization methods are required to solve the correspondingoptimization problems. Together with the proposed loss function, wecompare the performance of several existing loss functions in theclassification of noisy data sets. In this comparison, differentoptimization problems are considered in regards to the convexity andsmoothness of different loss functions. The experimental resultsshow that the proposed smoothed 0-1 loss function works better ondata sets with noisy labels, noisy features, and outliers. © 2014 - IOS Press and the authors. All rights reserved.

Function similarity using family context

- Black, Paul, Gondal, Iqbal, Vamplew, Peter, Lakhotia, Arun

  • Authors: Black, Paul , Gondal, Iqbal , Vamplew, Peter , Lakhotia, Arun
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Electronics Vol. 9, no. 7 (Jul 2020), p. 20
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  • Description: Finding changed and similar functions between a pair of binaries is an important problem in malware attribution and for the identification of new malware capabilities. This paper presents a new technique called Function Similarity using Family Context (FSFC) for this problem. FSFC trains a Support Vector Machine (SVM) model using pairs of similar functions from two program variants. This method improves upon previous research called Cross Version Contextual Function Similarity (CVCFS) e epresenting a function using features extracted not just from the function itself, but also, from other functions with which it has a caller and callee relationship. We present the results of an initial experiment that shows that the use of additional features from the context of a function significantly decreases the false positive rate, obviating the need for a separate pass for cleaning false positives. The more surprising and unexpected finding is that the SVM model produced by FSFC can abstract function similarity features from one pair of program variants to find similar functions in an unrelated pair of program variants. If validated by a larger study, this new property leads to the possibility of creating generic similar function classifiers that can be packaged and distributed in reverse engineering tools such as IDA Pro and Ghidra.
  • Description: This research was performed in the Internet Commerce Security Lab (ICSL), which is a joint venture with research partners Westpac, IBM, and Federation University Australia.

An application of high-dimensional statistics to predictive modeling of grade variability

- Hinz, Juri, Grigoryev, Igor, Novikov, Alexander

  • Authors: Hinz, Juri , Grigoryev, Igor , Novikov, Alexander
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Geosciences (Switzerland) Vol. 10, no. 4 (2020), p.
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  • Description: The economic viability of a mining project depends on its efficient exploration, which requires a prediction of worthwhile ore in a mine deposit. In this work, we apply the so-called LASSO methodology to estimate mineral concentration within unexplored areas. Our methodology outperforms traditional techniques not only in terms of logical consistency, but potentially also in costs reduction. Our approach is illustrated by a full source code listing and a detailed discussion of the advantages and limitations of our approach. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Hybrid intrusion detection system based on the stacking ensemble of C5 decision tree classifier and one class support vector machine

- Khraisat, Ansam, Gondal, Iqbal, Vamplew, Peter, Kamruzzaman, Joarder, Alazab, Ammar

  • Authors: Khraisat, Ansam , Gondal, Iqbal , Vamplew, Peter , Kamruzzaman, Joarder , Alazab, Ammar
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Electronics (Switzerland) Vol. 9, no. 1 (2020), p.
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  • Description: Cyberttacks are becoming increasingly sophisticated, necessitating the efficient intrusion detection mechanisms to monitor computer resources and generate reports on anomalous or suspicious activities. Many Intrusion Detection Systems (IDSs) use a single classifier for identifying intrusions. Single classifier IDSs are unable to achieve high accuracy and low false alarm rates due to polymorphic, metamorphic, and zero-day behaviors of malware. In this paper, a Hybrid IDS (HIDS) is proposed by combining the C5 decision tree classifier and One Class Support Vector Machine (OC-SVM). HIDS combines the strengths of SIDS) and Anomaly-based Intrusion Detection System (AIDS). The SIDS was developed based on the C5.0 Decision tree classifier and AIDS was developed based on the one-class Support Vector Machine (SVM). This framework aims to identify both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the benchmark datasets, namely, Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) and Australian Defence Force Academy (ADFA) datasets. Studies show that the performance of HIDS is enhanced, compared to SIDS and AIDS in terms of detection rate and low false-alarm rates. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Survey of intrusion detection systems : techniques, datasets and challenges

- Khraisat, Ansam, Iqbal, Gondal, Vamplew, Peter, Kamruzzaman, Joarder

Rapid health data repository allocation using predictive machine learning

- Uddin, Ashraf, Stranieri, Andrew, Gondal, Iqbal, Balasubramanian, Venki

  • Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Health Informatics Journal Vol. 26, no. 4 (2020), p. 3009-3036
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  • Description: Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used. © The Author(s) 2020.

Predicting the condensate viscosity near the wellbore by ELM and ANFIS-PSO strategies

- Mousazadeh, Fatemeh, Naeem, Mohammad, Daneshfar, Reza, Soulgani, Bahram, Naseri, Maryam

  • Authors: Mousazadeh, Fatemeh , Naeem, Mohammad , Daneshfar, Reza , Soulgani, Bahram , Naseri, Maryam
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Journal of Petroleum Science and Engineering Vol. 204, no. (2021), p.
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  • Description: By lowering the pressure beneath the dew point as the result of production in gas condensate (GC) reservoirs, liquid droplets are formed in the borehole zone. Accurate calculation of pressure decline and optimization operations in these reservoirs need to know and predict the specific properties such as liquid viscosity. Empirical models have already been developed to predict this parameter. Due to the peculiar behavior of fluids beneath the dew point pressure (DPP), the prediction of liquid viscosity associates with an error. With the development of machine learning (ML) approaches, studies on fluid properties like other sciences have entered a new phase. In this study, extreme learning machine (ELM) and adaptive neuro-fuzzy inference system with particle swarm optimization (ANFIS-PSO) methods applied to this end. Therefore, a large data bank of reservoir and fluid properties including reservoir temperature and pressure, specific gravity (SG) of gas, API gravity, and gas to oil ratio (Rs) were used. The results showed that R-squared and RMSE for ANFIS-PSO are 0.762 and 0.15, respectively, while these values are 0.941 and 0.06 for ELM which shows that the last model has a better performance in estimating output values. Also, the range of reliable data is determined, and further, a sensitivity analysis was done, which showed that the greatest impact on the viscosity was from SG, and API gravity has the least effect on it. This model can be used as a reference for calculating condensate viscosity and also by expanding the range of datasets, it can be applied in the commercial software. © 2021 Elsevier B.V.

Biopsychosocial Data Analytics and Modeling

- Santhanagopalan, Meena

  • Authors: Santhanagopalan, Meena
  • Date: 2021
  • Type: Text , Thesis , PhD
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  • Description: Sustained customisation of digital health intervention (DHI) programs, in the context of community health engagement, requires strong integration of multi-sourced interdisciplinary biopsychosocial health data. The biopsychosocial model is built upon the idea that biological, psychological and social processes are integrally and interactively involved in physical health and illness. One of the longstanding challenges of dealing with healthcare data is the wide variety of data generated from different sources and the increasing need to learn actionable insights that drive performance improvement. The growth of information and communication technology has led to the increased use of DHI programs. These programs use an observational methodology that helps researchers to study the everyday behaviour of participants during the course of the program by analysing data generated from digital tools such as wearables, online surveys and ecological momentary assessment (EMA). Combined with data reported from biological and psychological tests, this provides rich and unique biopsychosocial data. There is a strong need to review and apply novel approaches to combining biopsychosocial data from a methodological perspective. Although some studies have used data analytics in research on clinical trial data generated from digital interventions, data analytics on biopsychosocial data generated from DHI programs is limited. The study in this thesis develops and implements innovative approaches for analysing the existing unique and rich biopsychosocial data generated from the wellness study, a DHI program conducted by the School of Science, Psychology and Sport at Federation University. The characteristics of variety, value and veracity that usually describe big data are also relevant to the biopsychosocial data handled in this thesis. These historical, retrospective real-life biopsychosocial data provide fertile ground for research through the use of data analytics to discover patterns hidden in the data and to obtain new knowledge. This thesis presents the studies carried out on three aspects of biopsychosocial research. First, we present the salient traits of the three components - biological, psychological and social - of biopsychosocial research. Next, we investigate the challenges of pre-processing biopsychosocial data, placing special emphasis on the time-series data generated from wearable sensor devices. Finally, we present the application of statistical and machine learning (ML) tools to integrate variables from the biopsychosocial disciplines to build a predictive model. The first chapter presents the salient features of the biopsychosocial data for each discipline. The second chapter presents the challenges of pre-processing biopsychosocial data, focusing on the time-series data generated from wearable sensor devices. The third chapter uses statistical and ML tools to integrate variables from the biopsychosocial disciplines to build a predictive model. Among its other important analyses and results, the key contributions of the research described in this thesis include the following: 1. using gamma distribution to model neurocognitive reaction time data that presents interesting properties (skewness and kurtosis for the data distribution) 2. using novel ‘peak heart-rate’ count metric to quantify ‘biological’ stress 3. using the ML approach to evaluate DHIs 4. using a recurrent neural network (RNN) and long short-term memory (LSTM) data prediction model to predict Difficulties in Emotion Regulation Scale (DERS) and primary emotion (PE) using wearable sensor data.
  • Description: Doctor of Philosophy

Cyberbullying detection on social networks using machine learning approaches

- Islam, Md Manowarul, Uddin, Md Ashraf, Islam, Linta, Akter, Arnisha, Sharmin, Selina, Acharjee, Uzzal

  • Authors: Islam, Md Manowarul , Uddin, Md Ashraf , Islam, Linta , Akter, Arnisha , Sharmin, Selina , Acharjee, Uzzal
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020
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  • Description: The use of social media has grown exponentially over time with the growth of the Internet and has become the most influential networking platform in the 21st century. However, the enhancement of social connectivity often creates negative impacts on society that contribute to a couple of bad phenomena such as online abuse, harassment cyberbullying, cybercrime and online trolling. Cyberbullying frequently leads to serious mental and physical distress, particularly for women and children, and even sometimes force them to attempt suicide. Online harassment attracts attention due to its strong negative social impact. Many incidents have recently occurred worldwide due to online harassment, such as sharing private chats, rumours, and sexual remarks. Therefore, the identification of bullying text or message on social media has gained a growing amount of attention among researchers. The purpose of this research is to design and develop an effective technique to detect online abusive and bullying messages by merging natural language processing and machine learning. Two distinct freatures, namely Bag-of Words (BoW) and term frequency-inverse text frequency (TFIDF), are used to analyse the accuracy level of four distinct machine learning algorithms. © 2020 IEEE.

A critical review of intrusion detection systems in the internet of things : techniques, deployment strategy, validation strategy, attacks, public datasets and challenges

- Khraisat, Ansam, Alazab, Ammar

  • Authors: Khraisat, Ansam , Alazab, Ammar
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Cybersecurity Vol. 4, no. 1 (2021), p.
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  • Description: The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack on the end nodes. To this end, Numerous IoT intrusion detection Systems (IDS) have been proposed in the literature to tackle attacks on the IoT ecosystem, which can be broadly classified based on detection technique, validation strategy, and deployment strategy. This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques, deployment Strategy, validation strategy and datasets that are commonly applied for building IDS. We also review how existing IoT IDS detect intrusive attacks and secure communications on the IoT. It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure. These purposes help IoT security researchers by uniting, contrasting, and compiling scattered research efforts. Consequently, we provide a unique IoT IDS taxonomy, which sheds light on IoT IDS techniques, their advantages and disadvantages, IoT attacks that exploit IoT communication systems, corresponding advanced IDS and detection capabilities to detect IoT attacks. © 2021, The Author(s).