At Computing, we have four streams of HDR Seminars:
- HDR Seminars for Research Progress - Held by all HDR students at Computing (twice during their entire program) to report their research progress.
- HDR Seminars for Research Excellence - Held by HDR students to share the experience of having high-quality publications.
- HDR Seminars for Career Development - Held by external guests or former students to share their experience and successful stories.
- HDR Seminars for Supervision Enhancement - HDR supervisors introduce their experience of attracting/assessing MRes/PhD applicants and/or supervising MRes/PhD students.
Who to Contact?
If you wish to know more details about Seminar I&II or you have good research work to introduce at an HDR seminar, please contact HDR Director A/Prof. Yan Wang.
HDR Seminars for Research Progress
A PhD student at Computing needs to give two seminars during the entire program to report their research progress.
HDR Seminar I for Research Progress: This seminar should be given about 9 months (on full-time basis) since commencement
HDR Seminar II for Research Progress: This seminar should be given about 6 months (on full-time basis) before thesis submission
HDR Seminars for Research Excellence
Seminar 28 November 2018, 9 Wally's Walk (E6A) Room 357, 4:00pm - 5:00pm
Title: Towards Belief Contraction without Compactness
Speaker: Jandson S. Ribeiro
Abstract: In the AGM paradigm of belief change the background logic is taken to be classical, satisfying compactness among other properties. Compactness requires that any conclusion drawn from a set of propositions is implied by some finite subset of it. There are a number of interesting logics such as Computational Tree Logic (CTL, a temporal logic) which do not possess the compactness property, but are important from the belief change point of view. We have explored AGM style belief contraction in non-compact logics as a starting point, with the expectation that the resulting account will facilitate development of corresponding accounts of belief revision. We have shown that, when the background logic does not satisfy compactness, as long as the language in question is closed under classical negation and disjunction, AGM style belief contraction functions (with appropriate adjustments) can be constructed.
Bio: Jandson S. Ribeiro is a cotutelle PhD candidate in Artificial Intelligence at Macquarie University (MQ) and University at Sao Paulo (USP). He works under the joint supervision of A/Prof. Abhaya Nayak (MQ) and Prof. Renata Wassermann (USP). His research interests include Logic, Formal Methods, and Knowledge Representation and Reasoning. Before joining the PhD program Jandson completed BSc and MSc from the Federal University of Bahia (Brazil).
Publication: Towards belief contraction without compactness. In Principles of Knowledge Representation and Reasoning: Proceedings of the Sixteenth International Conference, KR 2018, Tempe, Arizona, 30 October - 2 November 2018.
Authors: Jandson S. Ribeiro, Abhaya Nayak and Renata Wassermann.
Award: Marco Cadoli Distinguished Student Paper Prize at KR 2018. As a result, this paper has been invited for fast-track review and publication by the AI Journal.
Seminar 11 October 2018 in 9 Wally's Walk (E6A) Room 357, 3.00pm - 4.00pm
Title: Towards an Adaptive System: Users’ Preferences and Responses to an Intelligent Virtual Advisor based on Individual Differences
Speaker: Hedieh Ranjbartabar
Abstract: Understanding individual differences in students could help Intelligent Virtual Agents (IVAs) to provide tailored educational and emotional support. Towards creating a student model that the agent can reason over and adapt to accordingly, we conducted a study to identify possible relationships and rules based on the students’ personality, emotional state and character preferences. The purpose of our virtual advisors was to “Reduce Study Stress”. The experiment with 73 participants, consisting of one within-subjects factor (virtual advisors with empathic and neutral dialogue) and one between-subjects factor (different order of receiving empathic and neutral advisors), formed two experimental and one control groups. We measured preferences, perceived helpfulness and study stress level. Groups using the IVAs reported significantly lower levels of study stress at the end of the study. Some differences were found in preferences for and responses to IVA behaviour based on participants’ gender, personality and levels of depression, anxiety and stress.
Bio: Hedieh Ranjbartabar is currently pursuing the PhD degree in Artificial Intelligence (AI) specialising in the area of Intelligent Virtual Agent (IVA) under supervision of Prof. Deborah Richards, in Department of computing, Macquarie University. She is co-supervised by Dr Cat Kutay from University of Technology Sydney and Dr Samuel Mascarenhas from University of Lisbon. With a bachelor degree in Software Engineering, she has an extensive experience working as an Analyst Programmer in Agricultural Bank in Tehran and Fujitsu in Sydney. She then gained her Master of IT in Software Engineering and Master of Research in AI from Macquarie University. She recently was awarded with the best paper nominee from ISD (Information System Development) conference 2018.
Award: Best Paper Nominee Award from 27th International Conference on Information System Development, Lund University, Sweden 22-24 August 2018.
Publication: RANJBARTABAR, H., RICHARDS, D., KUTAY, C. & MASCARENHAS, S. 2018. Towards an Adaptive System: Users’ Preferences and Responses to an Intelligent Virtual Advisor based on Individual Differences. Information Systems Development.
Seminar 4 May 2018 in 9 Wally's Walk (E6A) Room 357, 2:00 - 3:00pm
Title: SAMD: Fine-Grained Application Sharing for Mobile Collaboration
Dr. Young Choon Lee
The collective use of ever connected and pervasive mobile devices has been increasingly sought for in mobile collaboration, such as multiplayer mobile gaming and distributed processing. The current model of mobile collaboration requires each device to install a particular, ‘full’ mobile app for a respective collaboration. Besides, collaboration functionalities are typically implemented at application level. In this talk, I will present Single Application Multiple Device (SAMD) as a platform-level mobile collaboration framework. A mobile app developed using SAMD is capable of fine-grained application sharing. In particular, SAMD enables devices, agreed to participate in collaboration, to get portions of the app on-the-fly and run them without the prior installation. To achieve this, we have developed three solutions as core functionalities of SAMD: 1) Controller packaging, 2) lookahead transfer and 3) code adaptation.We have implemented SAMD on Android as a proof-of-concept prototype. Our experimental results demonstrate SAMD can provide fine-grained sharing of latency-insensitive applications.
- Jaehoon Lee, Hochul Lee, Byoungjun Seo, Young Choon Lee, Hyuck Han and Sooyong Kang, SAMD: Fine-Grained Application Sharing for Mobile Collaboration, IEEE International Conference on Pervasive Computing and Communications (PerCom), 2018.
- Jaehoon Lee, Hochul Lee, Byoungjun Seo, Minkyung Chae, Young Choon Lee, Hyuck Han and Sooyong Kang, SAMD Apps: Install Once, Run Anywhere Instantly, IEEE International Conference on Pervasive Computing and Communications (PerCom), 2018. (invited for demo).
Young Choon Lee is currently a senior lecturer at Department of Computing, Macquarie University. His research focuses on distributed systems with particular interests of scheduling, resource management and sustainability. He was the recipient of Australia Research Council Discovery Early Career Researcher Award (DECRA), completed early 2017.
Previously Organised Seminars
- Seminar on 23 March 2018
- Seminar on 3 November 2017
- Seminar on 3 November 2017
- Seminar on 20 April 2017
- Seminars on 17 May 2016
- Seminar on 22 March 2016
- Seminar on 11 June 2015
- Seminar on 15 November, 2013
- Seminar on 22 November, 2012
- Seminar on 12 November, 2012
- Seminar on 1 November, 2012
- Seminar on 2 June, 2011
- Seminar on 19 May, 2011
- Seminar on 5 May, 2011
Seminar 23 March 2018 in 9 Wally's Walk (E6A) Room 357, 3:00 - 4:00pm
Title: Incremental Graph Pattern based Node Matching
Speaker: Guohao Sun
Graph Pattern based Node Matching (GPNM) is to find all the matches of the nodes in a data graph GD based on a given pattern graph GP. GPNM has become increasingly important in many applications, e.g., group finding and expert recommendation. In real scenarios, both GP and GD are updated frequently. However, the existing GPNM methods need to perform a new GPNM procedure from scratch to deliver the node matching results based on the updated GP and updated GD, which consumes much time. Therefore, there is a pressing need for a novel method to efficiently deliver the node matching results on the updated graphs.
In this talk, we will introduce our proposed novel INCremental GPNM method called INC-GPNM, where we first build an index to incrementally record the shortest path length range between different label types in GD, and then identify the affected parts of GD in GPNM including nodes and edges w.r.t. the updates of GP and GD. Moreover, based on the index structure and our novel search strategies, INC-GPNM can efficiently deliver node matching results taking the updates of GP and GD as input, and can greatly reduce the query processing time with improved time complexity. Extensive experiments on seven real-world social graphs demonstrate that our method greatly outperforms the state-of-the-art GPNM method in efficiency.
Guohao Sun is currently a PhD student in the Department of Computing, Macquarie University, Sydney, NSW, Australia. His Principal Supervisor is A/Prof. Yan Wang, the Associate Supervisor is Prof. Mehmet A. Orgun and the Adjunct Supervisor is A/Prof. Guanfeng Liu. His current research topic is graph pattern matching. He received the Master degree in Computer Science from Soochow University, China in 2015 and the Bachelor degree in Computer Science from Soochow University, China in 2013.
Guohao Sun, Guanfeng Liu, Yan Wang, Mehmet A. Orgun, and Xiaofang Zhou. "Incremental Graph Pattern based Node Matching", IEEE International Conference on Data Engineering (ICDE), 2018.
Seminar on 3 November 2017 in Building E6A 357
Time: 2pm - 3pm
Title: Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model
Speaker: Paria Jamshid Lou
Automatic speech recognition (ASR) has made a remarkable improvement in recent years, benefiting from availability of large training data and advances in deep learning. In spite of great progress in speech recognition technology, current recognition systems are still far from the ideal goal of generating enriched transcripts, where useful information such as the location of disfluencies is provided in addition to a sequence of words. Disfluencies are an integral part of spontaneous speech, so the output of ASR systems, no matter how perfect and error-less it is, will always contain speech disfluencies.
In this talk, we present a model for disfluency detection in spontaneous speech transcripts called LSTM Noisy Channel Model. The model uses a Noisy Channel Model (NCM) to generate n-best candidate disfluency analyses and a Long Short-Term Memory (LSTM) language model to score the underlying fluent sentences of each analysis. The LSTM language model scores, along with other features, are used in a MaxEnt reranker to identify the most plausible analysis. We show that using an LSTM language model in the reranking process of noisy channel disfluency model improves the state-of-the-art in disfluency detection.
Paria Jamshid Lou is currently a PhD candidate in the Dept. of Computing, Macquarie University. She received her Master of Research in Computer Science from Macquarie University in 2017 and her Master of Science in Computational Linguistics form Sharif University of Technology, Iran. Her main research interests include speech-to-text models and speech emotion detection systems.
Paria Jamshid Lou and Mark Johnson. 2017. Disfluency detection using a noisy channel model and a deep neural language model. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Vancouver, Canada, ACL'17, pages 547–553. http://www.aclweb.org/anthology/P17-2087.
Seminar on 20 April 2017 in Building E6A 357
Title:Using Blockchain to Enhance Accountability of Cloud Services
Speaker: Jun Zou
Cloud computing has provided an attractive model for business service delivery. However, accountability aspects, mainly the monitoring of the execution of a service contract, liability assignment and dispute arbitration are still lacking. On the one hand, the traditional centralized monitoring and the trusted-third party (TTP) based arbitration solutions are not suitable for the distributed cloud environment. On the other hand, a decentralized solution also faces challenges in guaranteeing fairness, accuracy and sustainability. To address this issue, we firstly propose an innovative service contract management scheme that facilitates the monitoring of the execution of a service contract in a peer-to-peer environment, inspired by the concept of blockchain in Bitcoin. Secondly, on top of the scheme we present a novel dispute resolution protocol based on the Byzantine agreement and the commitment scheme. Thirdly, we identify the optimal settings of the key parameters of the protocol through a set of experiments and scenario analysis, aiming to strike the balance of fairness, accuracy, incentive maximization for the honest arbiters and cost minimization for the overall arbitration process. With our approach, service participants can be held accountable in a truly distributed environment without the presence of a central authority, which increases businesses confidence on adopting cloud services.
Jun Zou is currently a PhD student in the Department of Computing, Macquarie University, Sydney, NSW, Australia. His current research topic is on accountability in service computing. He received the BSc degree in computer science and engineering from South China University of Technology in 1988 and the MBA degree in business administration from Macquarie Graduate School of Management, Australia in 2007; He is the co-recipient of the Best Paper Award at IEEE ICEBE2006 and the Best Paper Award & Top 5 Picks Award at IEEE ICWS2016. He has been working in IT industry for over 29 years and currently is involved in research and development on cloud computing platforms and blockchain applications.
Best Student Paper Award & Top 5 Picks Award
Jun Zou, Yan Wang and Mehmet Orgun, A Dispute Arbitration Protocol Based on a Peer-to-Peer Service Contract Management Scheme, 23rd International Conference on Web Services (IEEE ICWS2016, research track, acceptance rate 14%, CORE rank A), June 27-July 2, 2016, San Francisco, USA
Seminar on 17 May, 2016 in Building E6A Rm357
Time: 2:00 PM – 3:00 PM
Time: 3:00 PM – 4:00 PM
Speaker: Robertus Nugroho
Title: Time-sensitive Topic Derivation in Twitter
Much research has been concerned with deriving topics from Twitter and applying the outcomes in a variety of real life applications such as emergency management, business advertisements and corporate/government
Robertus Nugroho is currently a PhD Student in the Department of Computing, Macquarie University, under the supervision of Prof. Jian Yang. He is also awarded a postgraduate studentship at CSIRO Data61 from 2014 to 2017. Robertus received a master degree in computing and information technology from The University of New South Wales in 2009. Recently, he received a Digital Productivity Staff Award from CSIRO Data61 for the significant contribution in support of scientific outcomes (December 2015). His current research interests include social media and big data analytics, machine learning, and web technology.
Award: Best Paper Award
R. Nugroho, W. Zhao, J. Yang, C. Paris, S. Nepal, Y. Mei: Time-sensitive topic derivation in twitter. In: Web Information Systems Engineering – WISE 2015: 16th International Conference, Miami, FL, USA, November 1-3,
Speaker: Farshid Anvari
Title: Effectiveness of Persona with Personality on Conceptual Design
User Centred Design (UCD) is a methodology used to develop applications that consider the goals of the users as a primary requirement. The use of personas, archetypical users, in UCD is well established in the software industry. Personas are used to facilitate the design of applications focusing on target users and to communicate with stakeholders. Personas may take various forms: personas, mash-up personas, incomplete personas and unspoken personas.
Holistic Personas, a persona enriched with personality traits, is deemed to better represent a user as personality provides a richer profile and affects the way users interact with technology. Our study explores the effects of the use of Holistic Personas on participants’ performance in creating conceptual designs.
Four Holistic Personas with different personalities were presented to 91 participants, in studies in Australia and Denmark and collectively they completed 218 design artefacts.
Our results indicate that the participants were able to identify the personality traits of the Holistic Personas and their ratings of the personalities matched closely with the intended personalities. The findings support that personas with personality traits can aid software engineers to produce requirements and conceptual designs tailored to the needs of specific personalities.
Personnel engaged in developing applications using UCD techniques need to have special abilities and training to design products that meet the needs of users. Thirty-three participants completed a spatial ability test, answered personality trait questionnaires and performed a design activity using one of the Holistic Personas. Our assessment of design artefacts indicate that participants who score high in imagination personality factor and spatial ability tests are talented designers in the use of personas with personality within UCD methodologies. The implication of our study is that the talented designers can be identified and utilised more productively.
Farshid Anvari holds a Master of Philosophy (MPhil), Macquarie University (2016), a Graduate Diploma of Information Technology, University of Southern Queensland (1998), a Graduate Diploma of Science, University of Tasmania (1996), and a Bachelor of Engineering, Swinburne University of Technology (1984). His MPhil supervisors were Prof. Deborah Richards and A/Prof. Michael Hitchens.
He has over 15 years of experience in software engineering. He was working at Australian Biosecurity Intelligence Network (ABIN) CSIRO, University of New South Wales (UNSW) and Special Broadcasting Service (SBS). At ABIN he developed an application for scientists to disseminate tools. At UNSW he architected solutions and led the development team for implementation of Healthy Me: an online multi-research application, supporting consumers in informed decision making about their health. At SBS he designed and implemented the automated digital channel system, SBS Essential, which was highly commended in the Commonwealth Broadcasting Association awards for cost-effective engineering. He also has over 12 years of experience in power engineering, specialising in hydro system modelling and reliability studies. His research interests include cognitive load, user centred design, persona ontology and architecting systems that are economical, robust and reliable.
Mr. Anvari is a member of the Australian Computer Society and the Association for Computing Machinery.
Anvari, F., Richards, D., Hitchens, M. and Babar, M. A. (2015) Effectiveness of Persona with Personality Traits on Conceptual Design. In Proceedings of the ICSE 37th International Conference on Software Engineering (CORE2014 rank A*), Florence, Italy, May 2015. pp. 263-272.
Seminar on 22 March, 2016
Speaker: Kinzang Chhogyal
Title: Probabilistic Belief Contraction Using Argumentation
The epistemic or belief state of an intelligent agent is generally in a state of flux with new beliefs being added and old beliefs being removed. The field of belief change deals with the modelling of different types of changes an agent’s belief state may undergo. Contraction is one of the major belief change operations where a sentence loses its status of a belief and is demoted to a non-belief, i.e. it is neither believed nor disbelieved. When a belief state is represented as a probability function P, the resulting belief state after contracting a belief h from the original belief state P can be given by the mixture of two states: the original state P, and the resultant state P' of revising P by the negation of h. However, it is not clear exactly what these proportions should be and there is no work in literature addressing this. The primary aim of this work is to propose a plausible solution to this problem. We begin by first classifying different belief states according to their stability, and then exploit the quantitative nature of probabilities and combine it with the basic ideas of argumentation theory to determine the mixture proportions. We, therefore, propose a novel approach to the problem of probabilistic belief contraction using argumentation.
Kinzang Chhogyal recently submitted his PhD thesis in Computer Science/Artificial Intelligence to Griffith University in Brisbane. Much of the work in his dissertation was undertaken while he was a visiting student at Macquarie University and where he worked under the supervision of Assoc. Prof. Abhaya Nayak. His current research interests include knowledge representation and reasoning, belief change and cognitive science.
Chhogyal, Kinzang, A. C. Nayak, Zhiqiang Zhuang, and Abdul Sattar. "Probabilistic belief contraction using argumentation." In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI, pp. 25-31. 2015.
Seminar on 11 June, 2015
Speaker: Xiaoming Zheng
Title: Trust Prediction with Propagation and Similarity Regularization
Online social networks have been used for a variety of rich activities in recent years, such as investigating potential employees and seeking recommendations of high quality services and service providers. In such activities, trust is one of the most critical factors for the decision-making of users. In the literature, the state-of-the-art trust prediction approaches focus on either dispositional trust tendency and propagated trust of the pair-wise trust relationships along a path or the similarity of trust rating values. However, there are other influential factors that should be taken into account, such as the similarity of the trust rating distributions. In addition, tendency, propagated trust and similarity are of different types, as either personal properties or interpersonal properties. But the difference has been neglected in existing models. Therefore, in trust prediction, it is necessary to take all the above factors into consideration in modelling, and process them separately and differently.
In this talk, we introduce a new trust prediction model based on trust decomposition and matrix factorization, considering all the above influential factors and differentiating both personal and interpersonal properties. In this model, we first decompose trust into trust tendency and tendency-reduced trust. Then, based on tendency-reduced trust ratings, matrix factorization with a regularization term is leveraged to predict the tendency-reduced values of missing trust ratings, incorporating both propagated trust and the similarity of users’ rating habits. In the end, the missing trust ratings are composed with predicted tendency-reduced values and trust tendency values. Experiments conducted on a real-world dataset illustrate significant improvement delivered by our approach in trust prediction accuracy over the state-of-the-art approaches.
Biography: Xiaoming Zheng is currently a PhD candidate in the Department of Computing, Macquarie University. He received his B.Eng degree in Automation from North University of China, P.R. China in 2008, and M.Eng degree in Control Theory and Control Engineering from Shanghai University, P.R. China in 2011. His research interests include trust prediction in online social networks and EEG pattern recognition in spontaneous BCI. At MQ, his supervisors are Prof. Mehmet A. Orgun and A/Prof. Yan Wang.
Publication: X. Zheng, Y. Wang, M. A. Orgun, Y. Zhong and G. Liu, Trust Prediction with Propagation and Similarity Regularization, AAAI2014, July 27-31, 2014, Quebec City, Quebec, Canada
Seminar on 15 November, 2013
Speaker: Byungho Min
Title: Antivirus security: naked during updates
The security of modern computer systems heavily depends on security tools, especially on antivirus software solutions. In the anti-malware research community, development of techniques for evading detection by antivirus software is an active research area. This has led to malware that can bypass or subvert antivirus software. The common strategies deployed include the use of obfuscated code and staged malware whose first instance (usually installer such as dropper and downloader) is not detected by the antivirus software. Increasingly, most of the modern malware are staged ones in order for them to be not detected by antivirus solutions at the early stage of intrusion. The installers then determine the method for further intrusion including antivirus bypassing techniques. Some malware target boot and/or shutdown time when antivirus software may be inactive so that they can perform their malicious activities. However, there can be another time frame where antivirus solutions may be inactive, namely, during the time of update. All antivirus software share a unique characteristic that they must be updated at a very high frequency to provide up-to-date protection of their system. In this paper, we suggest a novel attack vector that targets antivirus updates and show practical examples of how a system and antivirus software itself can be compromised during the update of antivirus software. Local privilege escalation using this vulnerability is also described. We have investigated this design vulnerability with several of the major antivirus software products such as Avira, AVG, McAfee, Microsoft, and Symantec and found that they are vulnerable to this new attack vector. The paper also discusses possible solutions that can be used to mitigate the attack in the existing versions of the antivirus software as well as in the future ones.
Biography: Byungho Min is currently a PhD candidate in the Department of Computing, Macquarie University. He received his B.Sc and M.Sc in Computer Science and Engineering from Seoul National University (SNU), Republic of Korea. He worked in the Ministry of Defence, Republic of Korea as an IT security expert for 4+ years. His research interests include Smart Grid and SCADA security, malware analysis and offensive techniques. His supervisors are Prof. Vijay Varadharajan, A/Prof Michael Hitchens and Dr Udaya Tupakula.
Publication: Byungho Min, Vijay Varadharajan, Udaya Tupakula and Michael Hitchens: Antivirus security: naked during updates. Software: Practice and Experience. (2013, Accepted)
Seminar on 22 November, 2012
Speaker: Haibin Zhang
Title: Efficient Contextual Transaction Trust Computation in E‐Commerce Environments
In e-commerce environments, trust is a dominating factor in seller selection. Most existing trust evaluation studies compute a single trust value to reflect the “general” or “global” trust level of a seller provider without any information of transaction context taken into account. As a result, a buyer may be easily deceived by a malicious seller in a forthcoming transaction. For example, with the notorious “value imbalance problem”, i.e., a malicious seller can build up a high trust level by selling cheap products and then starts to deceive buyers in selling expensive products.
To detect this problem, which is actually one type of the transaction context imbalance problem, and avoid massive monetary losses of buyers, the trust results (referred to as Contextual Transaction Trust, CTT for short) should be transaction context based and have the capacity to outline the seller’s reputation profile indicating the trust level in a specific product or a product category, a price range, a time period or any necessary combination of them. However, it requires the pre-computation of aggregates over large-scale ratings and transaction data with necessary combinations of three dimensions (i.e. product category, price and time), so as to deliver real-time responses to a buyer’s query.
Towards solving this challenging problem, we propose three methods, i.e., aR-tree based approach, aP-tree based approach and a hybrid structure of aP+-tree and aB+-tree to fast indexing rating aggregates for CTT computation. The experimental results illustrate the performance of these methods in responding to CTT queries, respectively. To the best of our knowledge, this is the first solution in the literature to the computation of CTT in e-commerce environments.
Biography: Haibin Zhang is currently a PhD candidate in the Department of Computing, Macquarie University, Australia. He received his BEng degree in computer science and technology from the Harbin Engineering University (HEU), P.R. China in 2007, and MEng degree in computer science and technology from the Harbin Institute of Technology (HIT), P. R. China in 2010. His current research focuses on context-aware trust computation in e-commerce applications. At MQ, his supervisors are Assoc/Prof. Yan Wang and Prof. Mehmet A. Orgun.
Award: Best Paper Award
Publication: Haibin Zhang, Yan Wang and Xiuzhen Zhang, Efficient Contextual Transaction Trust Computation in E-Commerce Environments, 11th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom-2012) (acceptance rate: 100/358 = 28%), 25-27 June 2012, Liverpool, UK
Speaker: Lan Zhou
Title: Enforcing Role-based Access Control for Secure Data Storage in the Cloud
With the rapid developments occurring in cloud computing and services, there has been a growing trend to use the cloud for large-scale data storage. This has raised the important security issue of how to control and prevent unauthorised access to data stored in the cloud. One well-known access control model is the role-based access control (RBAC), which provides flexible controls and management by having two mappings, users to roles and roles to privileges on data objects. Controlling the access in cloud systems is different from traditional access control systems, where access control enforcement is carried out by a trusted authority which is usually the service provider. In a cloud system, as data can be stored in distributed data centres, there may not be a single central authority which controls all the data centres. We propose a role-base encryption (RBE) scheme which integrates the cryptographic techniques with RBAC. Our scheme allows the owner of data to store it in an encrypted form in the cloud and to grant access to that data for users with specific roles. The scheme specifies a set of roles to which the users are assigned, with each role having a set of permissions. The data owner can encrypt the data and store it in the cloud in such a way that only users with specific roles can decrypt the data. Anyone else, including the cloud providers themselves, will not be able to decrypt the data.
Biography: Lan Zhou is currently a PhD student in Department of Computing in Macquarie University. His research interests include role-based access control, secure cloud storage, secure credential systems, and trust management system. Lan Zhou obtained his Masters Research Degree in Computer Science at the University of Wollongong, and Bachelor Degree at Huazhong University of Science and Technology, China. His PhD supervisors are Prof. Vijay Varadharajan and Assoc/Prof. Michael Hitchen.
Award: The Computer Journal Wilkes Award for 2011
Publication: Lan Zhou, Vijay Varadharajan and Michael Hitchens: Enforcing Role-Based Access Control for Secure Data Storage in the Cloud.Computing. J. 54(10): 1675-1687 (2011)
Seminar on 12 November, 2012
Speaker: Dr Raghav Ramachandran
Title: Probabilistic Belief Contraction
Probabilities have often been used to represent a rational agent’s knowledge/belief state. Any sentence with probability 1 is considered to be a ‘belief of the agent’. Probabilistic belief contraction refers to the problem of reducing the probability of a sentence from 1 to strictly below 1. Probabilistic belief contraction has been a much neglected topic in the field of probabilistic reasoning. This is due to the difficulty in establishing a reasonable reversal of the effect of Bayesian conditionalization on a probabilistic distribution. The uncertainty associated with a probability function is measured in terms of entropy measures. The application of ‘measures of uncertainty’ or entropy measures to perform probabilistic belief contraction was suggested by E. Olsson which lead to an account of probabilistic belief contraction proposed by A. Ramer based on the principle of maximum entropy. The principle of maximum entropy chooses that probability distribution which assumes the least, among those that satisfy the necessary conditions. The account of probabilistic contraction proposed by Ramer, however, results in unnecessary and excessive changes to the initial probability distribution. This solution needs to be improved. We study this problem using both the Shannon entropy measure as well as the Hartley entropy measure, with an aim to avoid excessive loss of beliefs.
Biography: Dr. Raghav Ramachandran graduated with a PhD from the Department of Computing, Macquarie University in 2012. His PhD thesis titled ‘Three Studies in Belief Removal’ was supervised by Dr. Nayak and Prof. Orgun. He also has a Master degree in Mathematics from the Indian Institute of Technology Madras. He is currently a research associate at the Computing Department, Macquarie University. He has been a visiting research fellow at the School of Information and Communication Technology, Griffith University, Queensland. His research are focused in the areas of probabilistic reasoning, belief revision and description logics. His PhD supervisors were Dr. Anhaya Nayak and Prof. Mehmet A. Orgun.
Publication: Raghav Ramachandran, Arthur Ramer and Abhaya Nayak: Probabilistic Belief Contraction, Minds and Machines 22 (4), pp. 325-351, 2012. Springer
Seminar on 1 November, 2012
Speaker: Dr Hadi Mashinchi
Title: Verification of A Smart Incontinence Monitoring System and The Use of Machine Learning for Analysing Sensor Data
This paper proposes a method of analysing and using data collected from a sensor embedded in a continence pad which is a component in the SIM™ wireless incontinence monitoring system. In contrast to systems described in the literature, SIM™ does not only detect urinary events but estimates volume. An optimized volume estimation model is developed using data remotely collected by SIM™ through the application of a machine learning algorithm. For verification of the model, a new objective function is introduced based on an understanding of the requirements of caregivers in nursing homes. The emulation results reveal that SIM™ makes recommendations which can prevent a resident being kept in a soaked pad with a performance of greater than 90%.
Biography: Hadi Mashinchi received his PhD degree in computer science from Macquarie University, Sydney, Australia in 2012. He is currently working as data mining specialist in Sydney. His research interests are hybrid optimization, global continuous optimization, fuzzy regression, and the application of data mining in engineering applications, such as campaign analytics, sensor data and etc. He has organized a few special sessions and served on the review boards of journals and international conferences. His PhD supervisors were Prof. Mehmet A. Orgun and Dr. Anhaya Nayak.
Publication: M. H. Mashinchi, M. A. Orgun, M. Mashinchi, W. Pedrycz: A Tabu-Harmony Search-Based Approach to Fuzzy Linear Regression. IEEE Transactions on Fuzzy Systems 19(3): 432-448 (2011)
Joint Honours and HDR Seminar on 2 June, 2011
Speaker: Abeed Sarker
Title: Improved Reconstruction of Flutter Shutter Images for Motion Blur Reduction
Relative motion between a camera and its subject introduces motion blur in captured images. Reconstruction of unblurred images is ill-posed due to the loss of spatial high frequencies. The flutter shutter preserves high frequencies by rapidly opening and closing the shutter during exposure, providing greatly improved reconstruction. We address two open problems in the reconstruction of unblurred images from flutter shutter images. Firstly, we propose a noise reduction technique that reduces reconstruction noise while preserving image detail. Secondly, we propose a semi-automatic technique for estimating the Point Spread Function of the motion blur. Together these techniques provide substantial improvement in reconstruction of flutter shutter images.
Biography: Abeed Sarker is now a PhD candidate in the Department of Computing, Macquarie University (enrolled in April 2010). He received his Bachelor of Information Systems (HONS) degree from Macquarie University in 2009 . His Honours research focused on digital image processing under the supervision of Dr. Leonard Hamey. His current research focuses on automatic text summarization for the medical domain and his supervisors are Dr. Diego Molla-Aliod (principal supervisor) and Dr. Cecile Paris (co-supervisor).
Award: Best Paper Award
Publication: Abeed Sarker, Leonard G. C. Hamey, “Improved Reconstruction of Flutter Shutter Images for Motion Blur Reduction”, 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA, 37 papers were selected for oral presentation from 194 submissions), 2010, pp.417-422
Seminar on 19 May, 2011
Speaker: Guanfeng Liu
Title: A Heuristic Algorithm for Trust-Oriented Service Provider Selection in Complex Social Networks
In a service-oriented online social network consisting of service providers and consumers, a service consumer can search trustworthy service providers via the social network. This requires the evaluation of the trustworthiness of a service provider along a certain social trust path from the service consumer to the service provider. However, there are usually many social trust paths between participants in social networks. Thus, a challenging problem is which social trust path is the optimal one that can yield the most trustworthy evaluation result. In this paper, we first present a novel complex social network structure and a new concept, Quality of Trust (QoT). We then model the optimal social trust path selection with multiple end-to-end QoT constraints as a Multi-Constrained Optimal Path (MCOP) selection problem, which is NP-Complete. For solving this challenging problem, we propose an efficient heuristic algorithm, H-OSTP. The results of our experiments conducted on a large real dataset of online social networks illustrate that our proposed algorithm significantly outperforms existing approaches.
Biography: Guanfeng Liu is a now PhD candidate in the Department of Computing, Macquarie University, who was enrolled in Feb. 2009. He received his Bachelor of Engineering degree in Computer Science and Technology from Qingdao University of Science and Technology, P. R. China in 2005, and the Master of Engineering degree in Computer Software and Theory from Qingdao University, P. R. China in 2008. His current research focuses on the trust management and evaluation in online social networks. At Macquarie, Guanfeng’s supervisors are Dr. Yan Wang and Prof. Mehmet Orgun.
Award: Best Paper Award
Publication: Guanfeng Liu, Yan Wang, Mehmet A. Orgun and Ee-Peng Lim, A Heuristic Algorithm for Trust-Oriented Service Provider Selection in Complex Social Networks. 2010 IEEE International Conference on Services Computing (SCC 2010, ERA rank A) (research track, 29 papers accepted out of 165 submissions, acceptance rate=17.6%), pp. 130-137, Miami, Florida, USA, July 05-10, 2010
Seminar on 5 May, 2011
Speaker: Huiyuan Zheng
Title: QoS Analysis for Web Service Composition
The quality of service (QoS) is a major concern in the design and management of Web service compositions. Existing QoS calculation methods cannot deal with service compositions with complex structures and are time consuming when it comes to probability distribution modelled QoS. A systematic approach is proposed to estimate QoS for Web service compositions. Four types of basic composition patterns in a service composition are discussed. A set of formulae are developed to calculate the QoS probability distributions for the basic composition patterns. In particular, QoS solutions are provided for unstructured conditional and loop patterns. By using a directed graph to model a Web service composition, patterns are able to be identified recursively by a depth-first search (DFS) method so that complex structures in service compositions can be dealt with. Experimental results show that comparing with existing approaches, the proposed QoS estimation approach greatly improves the efficiency in probabilistic QoS estimation and can be used in large scale service compositions.
Biography: Ms. Huiyuan Zheng is a PhD candidate in Department of Computing, Macquarie University. She received a Bachelor of Engineering degree in Communication from Beihang University (China). Her research interests are in the areas of Service Oriented Computing and performance analysis of distributed systems. Her supervisor is Prof. Jian Yang.
Award: Best Student Paper Award
Publication: Huiyuan Zheng, Weiliang Zhao, Jian Yang and Athman Bouguettaya, QoS Analysis for Web Service Composition. IEEE International Conference on Services Computing (SCC 2009, ERA rank A), Bangalore, India, September 23-26, 2009, 235-242, (research track, 35 papers accepted out of 189 submissions, acceptance rate=18.5%)
HDR Seminars for Career Development
Title: Observations and Experiences from Distributed Systems Research
In this talk, I will share my observations and experiences from distributed systems research from the perspective of Early Career Researcher (ECR). I will focus on discussing 'quality vs quantity' in terms of research performance.
Young Choon Lee is currently a senior lecturer at Department of Computing, Macquarie University. His research focuses on distributed systems with particular interests of scheduling, resource management and sustainability. He was the recipient of Australia Research Council Discovery Early Career Researcher Award (DECRA), completed early 2017.
Seminars on Friday, 12.05.2017 in Building E6A Rm357
Talk title: Every Cloud Has a Silver Lining
Speaker: Dr. Lina Yao
How do we get through the journey of PhD with constant challenges as a HDR student, and how do we embark our career as an early career academic with facing the unknown? I would like to explore these questions by sharing the specific examples from my real-life experiences. The truth is no matter how difficult or challenging our situations are, it will definitely lead to better selves to come.
Dr. Lina Yao is currently a Lecturer in the School of Computer Science and Engineering at UNSW. Her research interest lies in Internet of Things, Information filtering and recommending, and human activity recognition. She is the recipient of Australia Research Council Discovery Early Career Researcher Award and Inaugural Vice Chancellor's Women's Research Excellence Award at the University of Adelaide
|Seminar at |
Title: The $10,000+ wedding: How to transform academic skills into business success
Speaker: Dr Aries Tao
Join WPPI Master Aries Tao of Clover Image, in a spirited presentation on applying research skills in the real business world. With typical sales at over $10,000 per couple, this would be a fun and informative discussion packed with Aries’ experience and inspirations. It is the secret that bridges between your academic skills and the business development. It’s about living happy life every day, doing the research you love and also succeeding in the business world.
Bio data of the speaker:
Graduated with a doctorate degree in computing from Macquarie University in 2009, Aries Tao did not further his career in research, but established his photography studio “Cover Image”. Benefited by the key skills he developed during his Ph.D. candidature, he soon grew to be one world renowned photographer with 100+ international awards. Aries won “World Top20 wedding photographers” titles three times by ISPWP 2012-2014. He also won 2 gold awards, 1st place, 2nd place and 3rd place in WPPI 2014-2017. He is the first Chinese-Australian awarded as “WPPI Master Photographer”. Because of his contribution in wedding photography industry, he is elected as part of the judging panel in ISPWP and AsiaWPA competition. With his success in the academic world, Clover Image became the most prestigious pre-wedding photography studio based in Australia. His work was published in Channel 7, SBS, Portrait Magazine China, MSN, etc. Besides wedding photography, Aries is also active in commercial photography. His customers include NAB, Westpac, ANZ, etc.
ISPWP: International Society of Professional Wedding Photographers, www.ispwp.com
WPPI: Wedding & Portrait Photographers International, www.wppiawards.com
AsiaWPA: Asia Wedding Photographers Association, www.asiawpa.com
HDR Seminars for Supervision Enhancement
23 March 2018 in 9 Wally's Walk (E6A) Room 357, 2:00 - 3:00pm
Title: How to Attract Top HDR Students: An Influence Maximization Approach
Speaker: Amin Beheshti
Influence Maximization is the problem of finding a small subset of nodes in a (social) network that could maximize the spread of influence. This talk outlines my approach, leveraging Influence Maximization techniques to attract potential PhD students. I will also talk about the screening phase: how to assess the suitability and to select top candidates from the selected students. This approach enabled me to attract over 25 good HDR students (in 4 months) and select 6 top HDR students as well as 2 visiting fellows, already joined Data Analytics Group @Department of Computing, MQ.
Dr. Amin Beheshti is a Lecturer in Data Science (Macquarie University) and Adjunct Lecturer in Computer Science (UNSW Sydney). Amin is also leading the Data Analytics Research Group and acting as the Director of Industry and External Relations, Department of Computing, Macquarie University. Amin did his PhD and Postdoc in Computer Science and Engineering at UNSW and holds a Master and Bachelor in Computer Science both with First Class Honors. Before starting his PhD in 2009, he has been working over 7 years in industry as solution architect, R&D Team Lead and IT Project Manager. He has been extensively contributed to research projects; where he was the R&D Team Lead and Key Researcher in the Case Walls & Data Curation Foundry Project (2015-2017 @UNSW); and the key researcher in the Big Data for Intelligence project (2012-2014 @UNSW). He is the leading author of "Process Analytics" Book, co-authored with high profile researchers in UNSW and IBM research, recently published by Springer.
Previously Organised Seminars:
Seminar on 3 November 2017 in 9 Wally's Walk (E6A) Rm 357
Time: 3pm - 4pm
Title: How to Attract and Support Scholarship Winning HDR Applications
Speaker: Len Hamey
That unsolicited email inquiry arrives – a potential PhD student! But how to turn the inquiry into an application? And will they win that all-important scholarship? This talk outlines my approach, developed over 3 years, for responding to inquiries, evaluating scholarship potential, and supporting the student through the application process.
This talk outlines an approach for responding to HDR inquiries, evaluating scholarship potential, and supporting the student through the application process.