Predictive intelligence to the edge through approximate collaborative context reasoning

We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference

Clustering together to advance school improvement: working together in peer support with an external colleague

This research study explored how a group of rural primary schools, working together with the same school improvement partner (SIP), could positively affect the leadership of their schools through group strategic planning and the more efficient use of headteacher time and expertise. By using semi-structured interviews with headteachers and informal discussions with governors, the research investigated whether this method of collaborative working, with a single external professional facilitator, could enhance the leadership of the participating schools. The study concluded that the formation of such a collaborative group could have a positive impact on the leadership of the schools, the wellbeing of the headteachers themselves and the expertise of their governing bodies, when it was led by an external professional who had gained the respect and trust of all members of the group. Although the research specifically explored the role of a SIP held in common, its findings are transferable to any group of school leaders working together with a single external partner such as a national or local leader of education (NLE or LLE)

Export Performance in Small and Medium Enterprises in New South Wales: Sectoral and Regional Dimensions

This paper discusses the results from a survey of 146 value-adding exporters from regional New South Wales, Australia, the majority of whom were small and medium enterprises, using the Australian definition of having less than 200 employees. This study established that SME regional exporters were successful in gaining and maintaining sales in overseas markets in a variety of product areas. It thus raises the question of what factors lie behind this process. By identifying the causes of successful exporting in regional areas, policy-makers can design programs which best meet the needs of these firms and will encourage growth in their exports in the future.Export performance, small and medium-sized enterprises, sectoral and regional dimensions, Australia

Multi-view constrained clustering with an incomplete mapping between views

Multi-view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process. However, many applications provide only a partial mapping between the views, creating a challenge for current methods. To address this problem, we propose a multi-view algorithm based on constrained clustering that can operate with an incomplete mapping. Given a set of pairwise constraints in each view, our approach propagates these constraints using a local similarity measure to those instances that can be mapped to the other views, allowing the propagated constraints to be transferred across views via the partial mapping. It uses co-EM to iteratively estimate the propagation within each view based on the current clustering model, transfer the constraints across views, and then update the clustering model. By alternating the learning process between views, this approach produces a unified clustering model that is consistent with all views. We show that this approach significantly improves clustering performance over several other methods for transferring constraints and allows multi-view clustering to be reliably applied when given a limited mapping between the views. Our evaluation reveals that the propagated constraints have high precision with respect to the true clusters in the data, explaining their benefit to clustering performance in both single- and multi-view learning scenarios