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This study explored how artificial intelligence (AI) could assist patent examiners as part of the prior art search process. The proof-of-concept allowed experimentation with different AI techniques to suggest search terms, retrieve most relevant documents, rank them and visualise their content. The study suggested that AI is less effective in formulating search queries but can reduce the time and cost of the process of sifting through a large number of patents. The study highlighted the importance of the humanin-the-loop approach and the need for better tools for human-centred decision and performance support in prior art searching.

Energy efficiency is often identified as one of the key reasons for migrating to Cloud environments. It is stated that a data center hosting the Cloud environment is likely to achieve greater energy efficiency (at a reduced cost) compared to a local deployment. With increasing energy prices, it is also estimated that a large percentage of operational costs within a Cloud environment can be attributed to energy. In this work, we investigate and measure energy consumption of a number of virtual machines running the Hadoop system, over an OpenNebula Cloud. Our workload is based on sentiment analysis undertaken over Twitter messages. Our objective is to understand the tradeoff between energy efficiency and performance for such a workload. From our results we generalize and speculate on how such an analysis could be used as a basis to establish a Service Level Agreement (SLA) with a Cloud provider-especially where there is likely to be a high level of variability (both in performance and energy use) over multiple runs of the same application (at different times). Among the service level objectives that might be included in a SLA, Quality of Service (QoS) related metrics (i.e., latency) are one of the most challenging to support. This work provides some insight on the relationship between power consumption and QoS related metrics, describing how a combined consideration of these two metrics could be supported for a particular workload. Power consumption characterization of Hadoop Clouds (with a social media use case).Study of the QoS related to power consumption (in terms of processing time).Experimentation on two different Cloud infrastructures (single node-multi node).OpenNebula based private Cloud environments.

The growing number of people using social media to communicate with others and document their personal opinion and action is creating a significant stream of data that provides the opportunity for social scientists to conduct online forms of research, providing an insight into online social formations. This paper investigates the possibility of forecasting spikes in social tension – defined by the UK police service as “any incident that would tend to show that the normal relationship between individuals or groups has seriously deteriorated” – through social media. A number of different computational methods were trialed to detect spikes in tension using a human coded sample of data collected from Twitter, relating to an accusation of racial abuse during a Premier League football match. Conversation analysis combined with syntactic and lexicon-based text mining rules; sentiment analysis; and machine learning methods was tested as a possible approach. Results indicate that a combination of conversation analysis methods and text mining outperforms a number of machine learning approaches and a sentiment analysis tool at classifying tension levels in individual tweets.

Background Exposure to uncontrollable stress reduces baseline plasma neuropeptide-Y levels in animals. We previously reported that baseline plasma neuropeptide-Y levels, as well as neuropeptide-Y responses to yohimbine, were lower in combat veterans with posttraumatic stress disorder, but we were unable to determine whether this was attributable to posttraumatic stress disorder or trauma exposure. The current report addresses this issue. Methods A) Baseline plasma neuropeptide-Y levels were measured in 8 healthy combat veterans compared to 18 combat veterans with posttraumatic stress disorder and 8 healthy nontraumatized subjects; and B) Baseline plasma neuropeptide-Y levels, trauma exposure, and posttraumatic stress disorder symptoms were assessed in 41 active military personnel. Results Plasma neuropeptide-Y was negatively associated with trauma exposure but not posttraumatic stress disorder symptoms in active duty personnel. Baseline neuropeptide-Y was reduced in combat veterans with and without posttraumatic stress disorder. Conclusions Trauma exposure rather than posttraumatic stress disorder is associated with reduced baseline plasma neuropeptide-Y levels. Future studies must determine if neuropeptide-Y reactivity differentiates trauma-exposed individuals with and without posttraumatic stress disorder.