Empowering Human Services Organizations to Embrace Evidence-informed Practice: International Best Practices

Human service organizations are increasingly using knowledge as a mechanism for implementing change. Knowledge emerging from many sources that may include academic publications, grey literature, and service user and practitioner wisdom contributes toward informing best practice. The question is: how do we harness this knowledge to make practice more effective? This paper synthesizes the lessons learned from eight international organizations that have made a commitment to knowledge mobilization as an important priority in their mission and operation. The paper provides a conceptual model, tools and resources to help human services organizations create strategies for building, enhancing or sustaining their knowledge mobilization efforts. The paper describes a flexible blueprint for human service organizations to leverage knowledge mobilization efforts at all levels of service delivery.

Performance and Fault Tolerance of Preconditioned Iterative Solvers on Low-Power ARM Architectures

As the complexity of computing systems grows, reliability and energy are two crucial challenges asking for holistic solutions. In this paper, we investigate the interplay among concurrency, power dissipation, energy consumption and voltage-frequency scaling for a key numerical kernel for the solution of sparse linear systems. Concretely, we leverage a task-parallel implementation of the Conjugate Gradient method, equipped with an state-of-the-art pre-conditioner embedded in the ILUPACK software, and target a low-power multi core processor from ARM.In addition, we perform a theoretical analysis on the impact of a technique like Near Threshold Voltage Computing (NTVC) from the points of view of increased hardware concurrency and error rate.

High accuracy android malware detection using ensemble learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.

Government as Institutional Entrepreneur: Extending Working Life in the UK and Japan

Through the lens of Institutional Entrepreneurship, this paper discusses how governments use the levers of power afforded through business and welfare systems to affect change in the organisational management of older workers. It does so using national stakeholder interviews in two contrasting economies: the United Kingdom and Japan. Both governments have taken a ‘light-touch’ approach to work and retirement. However, the highly institutionalised Japanese system affords the government greater leverage than that of the liberal UK system in changing employer practices at the workplace level.

A Compositional and Interpretable Semantic Space

Vector Space Models (VSMs) of Semantics are useful tools for exploring the semantics of single words, and the composition of words to make phrasal meaning. While many methods can estimate the meaning (i.e. vector) of a phrase, few do so in an interpretable way. We introduce a new method (CNNSE) that allows word and phrase vectors to adapt to the notion of composition. Our method learns a VSM that is both tailored to support a chosen semantic composition operation, and whose resulting features have an intuitive interpretation. Interpretability allows for the exploration of phrasal semantics, which we leverage to analyze performance on a behavioral task.

Using Computational Significance and Resilience in System Software Stacks.:Keynote Talk

This talk explores how the runtime system and operating system can leverage metrics that express the significance and resilience of application components in order to reduce the energy footprint of parallel applications. We will explore in particular how software can tolerate and indeed exploit higher error rates in future processors and memory technologies that may operate outside their safe margins.