Climate change adaptation planning under uncertainty in Ho Chi Minh City, Vietnam: a case study on institutional vulnerability, adaptive capacity and climate change governance

Climate change is expected to have wide-ranging impacts on urban areas and creates additional challenges for sustainable development. Urban areas are inextricably linked with climate change, as they are major contributors to it, while also being particularly vulnerable to its impacts. Climate change presents a new challenge to urban areas, not only because of the expected rises in temperature and sea-level, but also the current context of failure to fully address the institutional barriers preventing action to prepare for climate change, or feedbacks between urban systems and agents. Despite the importance of climate change, there are few cities in developing countries that are attempting to address these issues systematically as part of their governance and planning processes. While there is a growing literature on the risks and vulnerabilities related to climate change, as yet there is limited research on the development of institutional responses, the dissemination of relevant knowledge and evaluation of tools for practical planning responses by decision makers at the city level. This thesis questions the dominant assumptions about the capacity of institutions and potential of adaptive planning. It argues that achieving a balance between climate change impacts and local government decision-making capacity is a vital for successful adaptation to the impacts of climate change. Urban spatial planning and wider environmental planning not only play a major role in reducing/mitigating risks but also have a key role in adapting to uncertainty in over future risk. The research focuses on a single province - the biggest city in Vietnam - Ho Chi Minh City - as the principal case study to explore this argument, by examining the linkages between urban planning systems, the structures of governance, and climate change adaptation planning. In conclusion it proposes a specific framework to offer insights into some of the more practical considerations, and the approach emphasises the importance of vertical and horizontal coordination in governance and urban planning.

Is Our Students Learning - QuestionMark: Extending E-Assessment Systems for Adapative Marking and Feedback

Our key contribution is a flexible, automated marking system that adds desirable functionality to existing E-Assessment systems. In our approach, any given E-Assessment system is relegated to a data-collection mechanism, whereas marking and the generation and distribution of personalised per-student feedback is handled separately by our own system. This allows content-rich Microsoft Word feedback documents to be generated and distributed to every student simultaneously according to a per-assessment schedule.

The feedback is adaptive in that it corresponds to the answers given by the student and provides guidance on where they may have gone wrong. It is not limited to simple multiple choice which are the most prescriptive question type offered by most E-Assessment Systems and as such most straightforward to mark consistently and provide individual per-alternative feedback strings. It is also better equipped to handle the use of mathematical symbols and images within the feedback documents which is more flexible than existing E-Assessment systems, which can only handle simple text strings.

As well as MCQs the system reliably and robustly handles Multiple Response, Text Matching and Numeric style questions in a more flexible manner than Questionmark: Perception and other E-Assessment Systems. It can also reliably handle multi-part questions where the response to an earlier question influences the answer to a later one and can adjust both scoring and feedback appropriately.

New question formats can be added at any time provided a corresponding marking method conforming to certain templates can also be programmed. Indeed, any question type for which a programmatic method of marking can be devised may be supported by our system. Furthermore, since the student’s response to each is question is marked programmatically, our system can be set to allow for minor deviations from the correct answer, and if appropriate award partial marks.

Methods, Models & Systems for Dealing with Some Business, Management & Engineering Problems

This keynote presentation will report some of our research work and experience on the development and applications of relevant methods, models, systems and simulation techniques in support of different types and various levels of decision making for business, management and engineering. In particular, the following topics will be covered. Modelling, multi-agent-based simulation and analysis of the allocation management of carbon dioxide emission permits in China (Nanfeng Liu & Shuliang Li Agent-based simulation of the dynamic evolution of enterprise carbon assets (Yin Zeng & Shuliang Li) A framework & system for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps: a big data perspective (Jin Xu, Zheng Li, Shuliang Li & Yanyan Zhang) Open innovation: intelligent model, social media & complex adaptive system simulation (Shuliang Li & Jim Zheng Li) A framework, model and software prototype for modelling and simulation for deshopping behaviour and how companies respond (Shawkat Rahman & Shuliang Li) Integrating multiple agents, simulation, knowledge bases and fuzzy logic for international marketing decision making (Shuliang Li & Jim Zheng Li) A Web-based hybrid intelligent system for combined conventional, digital, mobile, social media and mobile marketing strategy formulation (Shuliang Li & Jim Zheng Li) A hybrid intelligent model for Web & social media dynamics, and evolutionary and adaptive branding (Shuliang Li) A hybrid paradigm for modelling, simulation and analysis of brand virality in social media (Shuliang Li & Jim Zheng Li) Network configuration management: attack paradigms and architectures for computer network survivability (Tero Karvinen & Shuliang Li)

Stochastic Modeling of Reversible Biochemical Reaction-Diffusion Systems and High-Resolution Shock-Capturing Methods for Fluid Interfaces

Thesis (Ph.D.)--University of Washington, 2016-08

Adaptive Crowd Algorithms for Open-Ended Problems

Thesis (Ph.D.)--University of Washington, 2016-06

An appraisal of adaptive management planning and implementation in ecological restoration: case studies from the San Francisco Bay Delta, USA

Adaptive management has been defined and redefined in the context of natural resource management, yet there are few examples of its successful application in ecological restoration. Although the 2009 Delta Reform Act now legally requires adaptive management for all restoration efforts in the Sacramento-San Joaquin Delta, in California, USA, projects in this region still encounter problems with implementation. We used a comparative case study analysis to examine adaptive management planning and implementation both in and around the Delta, assessing not only why adaptive management is not yet well implemented, but also what changes can be made to facilitate the adaptive management approach without sacrificing scientific rigor. Adaptive management seems to be directly and indirectly affected by a variety of challenges and convoluted by ambiguity in both planning documents and practitioner’s interpretations of the concept. Addressing these challenges and ambiguities at the project level may facilitate the adaptive management process and help make it more accessible to practitioners.

Detecting Danger: Applying a Novel Immunological Concept to Intrusion Detection Systems'

INTRODUCTION In recent years computer systems have become increasingly complex and consequently the challenge of protecting these systems has become increasingly difficult. Various techniques have been implemented to counteract the misuse of computer systems in the form of firewalls, antivirus software and intrusion detection systems. The complexity of networks and dynamic nature of computer systems leaves current methods with significant room for improvement. Computer scientists have recently drawn inspiration from mechanisms found in biological systems and, in the context of computer security, have focused on the human immune system (HIS). The human immune system provides an example of a robust, distributed system that provides a high level of protection from constant attacks. By examining the precise mechanisms of the human immune system, it is hoped the paradigm will improve the performance of real intrusion detection systems. This paper presents an introduction to recent developments in the field of immunology. It discusses the incorporation of a novel immunological paradigm, Danger Theory, and how this concept is inspiring artificial immune systems (AIS). Applications within the context of computer security are outlined drawing direct reference to the underlying principles of Danger Theory and finally, the current state of intrusion detection systems is discussed and improvements suggested.

'Investigating Artificial Immune Systems For Job Shop Rescheduling In Changing Environments'

ABSTRACT Artificial immune system can be used to generate schedules in changing environments and it has been proven to be more robust than schedules developed using a genetic algorithm. Good schedules can be produced especially when the number of the antigens is increased. However, an increase in the range of the antigens had somehow affected the fitness of the immune system. In this research, we are trying to improve the result of the system by rescheduling the same problem using the same method while at the same time maintaining the robustness of the schedules.

'Movie Recommendation Systems Using An Artificial Immune System'

We apply the Artificial Immune System (AIS)technology to the collaborative Filtering (CF)technology when we build the movie recommendation system. Two different affinity measure algorithms of AIS, Kendall tau and Weighted Kappa, are used to calculate the correlation coefficients for this movie recommendation system. From the testing we think that Weighted Kappa is more suitable than Kendall tau for movie problems.

'Artificial Immune Systems Tutorial'

The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self-cells or non-self cells. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical messengers for communication. There are two major branches of the immune system. The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years. A novel computational intelligence technique, inspired by immunology, has emerged, called Artificial Immune Systems. Several concepts from the immune have been extracted and applied for solution to real world science and engineering problems. In this tutorial, we briefly describe the immune system metaphors that are relevant to existing Artificial Immune Systems methods. We will then show illustrative real-world problems suitable for Artificial Immune Systems and give a step-by-step algorithm walkthrough for one such problem. A comparison of the Artificial Immune Systems to other well-known algorithms, areas for future work, tips & tricks and a list of resources will round this tutorial off. It should be noted that as Artificial Immune Systems is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from time to time and from those examples given here.

Optimising plant bed performance through adaptive research with the Australian sweetpotato industry

A key driver of Australian sweetpotato productivity improvements and consumer demand has been industry adoption of disease-free planting material systems. On a farm isolated from main Australian sweetpotato areas, virus-free germplasm is annually multiplied, with subsequent 'pathogen-tested' (PT) sweetpotato roots shipped to commercial Australian sweetpotato growers. They in turn plant their PT roots into specially designated plant beds, commencing in late winter. From these beds, they cut sprouts as the basis for their commercial fields. Along with other intense agronomic practices, this system enables Australian producers to achieve worldRSQUOs highest commercial yields (per hectare) of premium sweetpotatoes. Their industry organisation, ASPG (Australian Sweetpotato Growers Inc.), has identified productivity of mother plant beds as a key driver of crop performance. Growers and scientists are currently collaborating to investigate issues such as catastrophic plant beds losses; optimisation of irrigation and nutrient addition; rapidity and uniformity of initial plant bed harvests; optimal plant bed harvest techniques; virus re-infection of plant beds; and practical longevity of plant beds. A survey of 50 sweetpotato growers in Queensland and New South Wales identified a substantial diversity in current plant bed systems, apparently influenced by growing district, scale of operation, time of planting, and machinery/labour availability. Growers identified key areas for plant bed research as: optimising the size and grading specifications of PT roots supplied for the plant beds; change in sprout density, vigour and performance through sequential cuttings of the plant bed; optimal height above ground level to cut sprouts to maximise commercial crop and plant bed performance; and use of structures and soil amendments in plant bed systems. Our ongoing multi-disciplinary research program integrates detailed agronomic experiments, grower adaptive learning sites, product quality and consumer research, to enhance industry capacity for inspired innovation and commercial, sustainable practice change.

Integrating Innate and Adaptive Immunity for Intrusion Detection

Network Intrusion Detection Systems (NIDS) monitor a net- work with the aim of discerning malicious from benign activity on that network. While a wide range of approaches have met varying levels of success, most IDS’s rely on having access to a database of known attack signatures which are written by security experts. Nowadays, in order to solve problems with false positive alerts, correlation algorithms are used to add additional structure to sequences of IDS alerts. However, such techniques are of no help in discovering novel attacks or variations of known attacks, something the human immune system (HIS) is capable of doing in its own specialised domain. This paper presents a novel immune algorithm for application to an intrusion detection problem. The goal is to discover packets containing novel variations of attacks covered by an existing signature base.

'Artificial Immune Systems Tutorial'

The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self-cells or non-self cells. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical messengers for communication. There are two major branches of the immune system. The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time. This remarkable information processing biological system has caught the attention of computer science in recent years. A novel computational intelligence technique, inspired by immunology, has emerged, called Artificial Immune Systems. Several concepts from the immune have been extracted and applied for solution to real world science and engineering problems. In this tutorial, we briefly describe the immune system metaphors that are relevant to existing Artificial Immune Systems methods. We will then show illustrative real-world problems suitable for Artificial Immune Systems and give a step-by-step algorithm walkthrough for one such problem. A comparison of the Artificial Immune Systems to other well-known algorithms, areas for future work, tips & tricks and a list of resources will round this tutorial off. It should be noted that as Artificial Immune Systems is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from time to time and from those examples given here.

'Investigating Artificial Immune Systems For Job Shop Rescheduling In Changing Environments'

ABSTRACT Artificial immune system can be used to generate schedules in changing environments and it has been proven to be more robust than schedules developed using a genetic algorithm. Good schedules can be produced especially when the number of the antigens is increased. However, an increase in the range of the antigens had somehow affected the fitness of the immune system. In this research, we are trying to improve the result of the system by rescheduling the same problem using the same method while at the same time maintaining the robustness of the schedules.