Open problems in the security of learning

Machine learning has become a valuable tool for detecting and preventing malicious activity. However, as more applications employ machine learning techniques in adversarial decision-making situations, increasingly powerful attacks become possible against machine learning systems. In this paper, we present three broad research directions towards the end of developing truly secure learning. First, we suggest that finding bounds on adversarial influence is important to understand the limits of what an attacker can and cannot do to a learning system. Second, we investigate the value of adversarial capabilities-the success of an attack depends largely on what types of information and influence the attacker has. Finally, we propose directions in technologies for secure learning and suggest lines of investigation into secure techniques for learning in adversarial environments. We intend this paper to foster discussion about the security of machine learning, and we believe that the research directions we propose represent the most important directions to pursue in the quest for secure learning.

Overcoming the complexity of diagnostic problems due to sensor network architecture

In fault detection and diagnostics, limitations coming from the sensor network architecture are one of the main challenges in evaluating a system’s health status. Usually the design of the sensor network architecture is not solely based on diagnostic purposes, other factors like controls, financial constraints, and practical limitations are also involved. As a result, it quite common to have one sensor (or one set of sensors) monitoring the behaviour of two or more components. This can significantly extend the complexity of diagnostic problems. In this paper a systematic approach is presented to deal with such complexities. It is shown how the problem can be formulated as a Bayesian network based diagnostic mechanism with latent variables. The developed approach is also applied to the problem of fault diagnosis in HVAC systems, an application area with considerable modeling and measurement constraints.

HRM’s impact on innovative work behavior

The importance of innovation to the long-term survival and growth of an organization has been well recognized and acknowledged, and HRM practitioners face a critical challenge to design and implement practices that ensure the behaviors and attitudes necessary for sustained innovation. In this paper, we present the findings from an exploratory study that establish initial indications of links between work design, training and dcvelopment, employee engagement, and innovative work behaviors, setting the agenda for lurther investigation of how spccific HR practices of work design and training and development can supp0l1 employee engagement, and facilitate sustained innovation.

Floods, earthquakes and insurance coverage : issues, problems and solutions

The recent floods in Queensland and elsewhere in Australia as well as the recent earthquakes in New Zealand have again given rise to very significant uninsured losses. This article looks at the issue of cover protection against catastrophes such as floods and earthquakes affecting home buildings and contents insurance and the standard cover provisions of the Insurance Contracts Act 1984 (Cth). It points also to the possibility of a national scheme to cover natural disasters including floods.

Rating counselor - client behavior in online counseling: Development and preliminary psychometric properties of the Counseling Progress and Depth Rating Instrument

Although there are widely accepted and utilized models and frameworks for nondirective counseling (NDC), there is little in the way of tools or instruments designed to assist in determining whether or not a specific episode of counseling is consistent with the stated model or framework. The Counseling Progress and Depth Rating Instrument (CPDRI) was developed to evaluate counselor integrity in the use of Egan's skilled helper model in online counseling. The instrument was found to have sound internal consistency, good interrater reliability, and good face and convergent validity. The CPDRI is, therefore, proposed as a useful tool to facilitate investigation of the degree to which counselors adhere to and apply a widely used approach to NDC