Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

NUDGE: IMPROVING DECISIONS ABOUT HEALTH, WEALTH, AND HAPPINESS by Richard H. Thaler and Cass R. Sunstein Penguin Books, 2009, 312 pp, ISBN 978-0-14-311526-7This book is best described formally as a general explanation of and advocacy for libertarian paternalism, a term coined by the authors in earlier publications. Informally, it is about how leaders, systems, organizations, and governments can nudge people to do the things the nudgers want and need done for the betterment of the nudgees, or of society. It is paternalism in the sense that "it is legitimate for choice architects to try to influence people's behavior in order to make their lives longer, healthier, and better", (p. 5) It is libertarian in that "people should be free to do what they like - and to opt out of undesirable arrangements if they want to do so", (p. 5) The built-in possibility of opting out or making a different choice preserves freedom of choice even though people's behavior has been influenced by the nature of the presentation of the information or by the structure of the decisionmaking system. I had never heard of libertarian paternalism before reading this book, and I now find it fascinating.Written for a general audience, this book contains mostly social and behavioral science theory and models, but there is considerable discussion of structure and process that has roots in mathematical and quantitative modeling. One of the main applications of this social system is economic choice in investing, selecting and purchasing products and services, systems of taxes, banking (mortgages, borrowing, savings), and retirement systems. Other quantitative social choice systems discussed include environmental effects, health care plans, gambling, and organ donations. Softer issues that are also subject to a nudge-based approach are marriage, education, eating, drinking, smoking, influence, spread of information, and politics. There is something in this book for everyone.The basis for this libertarian paternalism concept is in the social theory called "science of choice", the study of the design and implementation of influence systems on various kinds of people. The terms Econs and Humans, are used to refer to people with either considerable or little rational decision-making talent, respectively. The various libertarian paternalism concepts and systems presented are tested and compared in light of these two types of people. Two foundational issues that this book has in common with another book, Network of Echoes: Imitation, Innovation and Invisible Leaders, that was also reviewed for this issue of the Journal are that 1 ) there are two modes of thinking (or components of the brain) - an automatic (intuitive) process and a reflective (rational) process and 2) the need for conformity and the desire for imitation are powerful forces in human behavior. …

Nudge: Improving Decisions about Health, Wealth, and Happiness

Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software system's self (internal causes, e.g., failure) or context (external events, e.g., increasing requests from users). Such a system is required to monitor itself and its context, detect significant changes, decide how to react, and act to execute such decisions. These processes depend on adaptation properties (called self-a properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is, how, what, when and where, towards providing a unified view of this emerging area. Moreover, as adaptive systems are encountered in many disciplines, it is imperative to learn from the theories and models developed in these other areas. This survey article presents a landscape of research in self-adaptive software by highlighting relevant disciplines and some prominent research projects. This landscape helps to identify the underlying research gaps and elaborates on the corresponding challenges.

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Self-adaptive software: Landscape and research challenges

The majority of modern-day companies store commercially sensitive and valuable information assets in digital form. It is essential for the Chief Information Security Officer (CISO) within an organisation to ensure that such information is adequately protected. External standards exist to advise CISOs on how to secure information, but these are essentially “one-size-fits-all”. Furthermore they do not consider the humanbehavioural aspects that determine the impact of security controls upon employees, or how security controls can be best deployed to manage insecure employee behaviour. CISOs require more information than they are currently provided with to justify their information security management decisions. Here we present a knowledge base and accompanying user interface. The knowledge base represents key structural components of the ISO27002 security standard, formally relating them to one another. This empowers CISOs to understand how different security measures impact upon each other. It also considers how human-behavioural factors can be associated with these concepts. The accompanying user interface provides a means to present formalised information security concepts to CISOs. This paper describes the development of the knowledge base and user interface, highlighting and discussing key challenges and how they were resolved. © 2009 University of Newcastle upon Tyne. Printed and published by the University of Newcastle upon Tyne, Computing Science, Claremont Tower, Claremont Road, Newcastle upon Tyne, NE1 7RU, England. Bibliographical details STEPANOVA, D., PARKIN, S. E., VAN MOORSEL, A. A Knowledge Base for Justified Information Security Decision-Making [By] D. Stepanova, S. E. Parkin, A. van Moorsel. Newcastle upon Tyne: University of Newcastle upon Tyne: Computing Science, 2009. (University of Newcastle upon Tyne, Computing Science, Technical Report Series, No. CS-TR-1137)

A Knowledge Base for Justified Information Security Decision-Making

We introduce the architecture, object model, components, and protocols of a management overlay for federated service management, called Web Services Management Network (WSMN). WSMN targets management of web services that interact across administrative domains, and therefore typically involves multiple stakeholders (examples are business-to-business, service provider interconnections, help desks). The architecture is based on (implicit) SLAs to formalize relations across domains. It relies on a network of communicating service intermediaries, each such intermediary being a proxy positioned between the service and the outside world. WSMN also exchanges control information to agree on what to monitor, where to monitor, and whom to provide visibility.

/pdf/web-services-management-network-30gbfk072b.pdf

Web Services Management Network

IP Traceback systems facilitate tracing of IP packets back to their origin, despite possibly forged or overwritten source address data. A common shortcoming of existing proposals is that they identify source network, but not the source host. Our work extends the traceback process to allow tracing of (switched) Ethernet frames. We build on SPIE (which operates at IP routers) to design and implement 'switch-SPIE'. Traffic logging is deployed in a 'switch-DGA' tap-box at each switch. The (switched) Ethernet traffic visibility issue is resolved with port mirroring, and the MAC address table establishes causality between source MAC address and source switch port. Our solution works for any network topology, as opposed to earlier layer 2 extensions to IP Traceback. We provide an implementation and experimental evaluation to establish the efficacy of our approach, with respect to processing overhead and memory use.

Logging based IP Traceback in switched ethernets

On the one hand, an access control mechanism must make a conclusive decision for a given access request. On the other hand, such a mechanism usually relies on one or several decision making processes, which can return partial decisions, inconclusive ones, or conflicting ones. In some cases, this information might not be sufficient to automatically make a conclusive decision, and the access control mechanism might have to involve a human expert to make the final decision. In this paper, we formalise these decision making processes as quantitative access control systems, which associate each decision with a measure, indicating for instance the level of confidence of the system in the decision. We then propose to explore how nudging, i.e., how modifying the context of the decision making process for that human expert, can be used in this context. We thus formalise when such a delegation is required, when nudging is applicable, and illustrate some examples from the MINDSPACE framework in the context of access control.

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Nudging for Quantitative Access Control Systems

Workflows capture complex operational processes and include security constraints limiting which users can perform which tasks. An improper security policy may prevent certain tasks being assigned and may force a policy violation. Deciding whether a valid user-task assignment exists for a given policy is known to be extremely complex, especially when considering user unavailability (known as the resiliency problem). Therefore tools are required that allow automatic evaluation of workflow resiliency. Modelling well defined workflows is fairly straightforward, however user availability can be modelled in multiple ways for the same workflow. Correct choice of model is a complex yet necessary concern as it has a major impact on the calculated resiliency. We describe a number of user availability models and their encoding in the model checker PRISM, used to evaluate resiliency. We also show how model choice can affect resiliency computation in terms of its value, memory and CPU time.

/pdf/modelling-user-availability-in-workflow-resiliency-analysis-500koysar0.pdf

Aad van Moorsel

Papers

A Knowledge Base for Justified Information Security Decision-Making

Web Services Management Network

Logging based IP Traceback in switched ethernets

Nudging for Quantitative Access Control Systems

Modelling user availability in workflow resiliency analysis