Triggering information by context

With the increased availability of personal computers with attached sensors to capture their environment, there is a big opportunity for context-aware applications; these automatically provide information and/or take actions according to the user's present context, as detected by sensors. When wel l designed, these applications provide an opportunity to tailor the provision of information closely to the user's current needs. A sub-set of context-a ware applications are discrete applications, where discrete pieces of i nformation are attached to individual contexts, to be triggered when the user enters those contexts. The advantage of discrete applications is that authori ng them can be solely a creative process rather than a programming process: it can be a task akin to creating simple web pages. This paper looks at a general system that can be used in any discrete context- aware application. It propounds a general triggering rule, and investigates how this rule applies in practical applications

Time-triggering versus event-triggering control over communication channels

Time-triggered and event-triggered control strategies for stabilization of an unstable plant over a rate-limited communication channel subject to unknown, bounded delay are studied and compared. Event triggering carries implicit information, revealing the state of the plant. However, the delay in the communication channel causes information loss, as it makes the state information out of date. There is a critical delay value, when the loss of information due to the communication delay perfectly compensates the implicit information carried by the triggering events. This occurs when the maximum delay equals the inverse of the entropy rate of the plant. In this context, extensions of our previous results for event triggering strategies are presented for vector systems and are compared with the data-rate theorem for time-triggered control, that is extended here to a setting with unknown delay.Comment: To appear in the 56th IEEE Conference on Decision and Control (CDC), Melbourne, Australia. arXiv admin note: text overlap with arXiv:1609.0959

Hierarchical Gated Recurrent Neural Tensor Network for Answer Triggering

In this paper, we focus on the problem of answer triggering ad-dressed by Yang et al. (2015), which is a critical component for a real-world question answering system. We employ a hierarchical gated recurrent neural tensor (HGRNT) model to capture both the context information and the deep in-teractions between the candidate answers and the question. Our result on F val-ue achieves 42.6%, which surpasses the baseline by over 10 %

Coordinating criminal justice: a Qualitative Comparative Analysis of inter-organisational information sharing of four EU Member States

Qualitative-comparative analysis of four cases of inter-organisational information sharing in criminal justice chains demonstrates the causal asymmetry between successful and unsuccessful inter-organisational information sharing. While unsuccessful information sharing requires poor project management, successful information sharing also requires compatible technologies which are implemented either by means of a small-scale, bottom-up approach to standardization or a top-down, centralised architecture. By triggering the radical restructuring of information-sharing workflows, good project management and compatible technologies set in motion underlying mechanisms that generate successful inter-organisational information sharing. Implications are discussed by highlighting the role of coordination by technological feedback in a context of increasing digitization

Chronic infection: punctuated interpenetration and pathogen virulence

We apply an information dynamics formalism to the Levens and Lewontin vision of biological interpenetration between a 'cognitive condensation' including immune function embedded in social and cultural structure on the one hand, and an established, highly adaptive, parasite population on the other. We iterate the argument, beginning with direct interaction between cognitive condensation and pathogen, then extend the analysis to second order 'mutator' mechanisms inherent both to immune function and to certain forms of rapid pathogen antigenic variability. The methodology, based on the Large Deviations Program of applied probability, produces synergistic cognitive/adaptive 'learning plateaus' that represent stages of chronic infection, and, for human populations, is able to encompass the fundamental biological reality of culture omitted by other approaches. We conclude that, for 'evolution machine' pathogens like HIV and malaria, simplistic magic bullet 'medical' drug, vaccine, or behavior modification interventions which do not address the critical context of overall living and working conditions may constitute selection pressures triggering adaptations in life history strategy resulting in marked increase of pathogen virulenc

Map, Trigger, Score, Procedure: machine-listening paradigms in live-electronics

Since the advent of real-time computer music environments, composers have increasingly incorporated DSP analysis, synthesis, and processing algorithms in their creative practices. Those processes became part of interactive systems that use real-time computational tools in musical compositions that explore diverse techniques to generate, spatialize, and process instrumental/vocal sounds. Parallel to the development of these tools and the expansion of DSP methods, new techniques focused on sound/musical information extraction became part of the tools available for music composition. In this context, this article discusses the creative use of Machine Listening and Musical Information Retrieval techniques applied in the composition of live-electronics works. By pointing out some practical applications and creative approaches, we aim to circumscribe, in a general way, the strategies for employing Machine Listening and Music Information Retrieval techniques observed in a set of live-electronics pieces, categorizing four compositional approaches, namely: mapping, triggering, scoring, and procedural paradigms of application of machine listening techniques in the context of live-electronics music compositions

Memory-full context-aware predictive mobility management in dual connectivity 5G networks

Network densification with small cell deployment is being considered as one of the dominant themes in the fifth generation (5G) cellular system. Despite the capacity gains, such deployment scenarios raise several challenges from mobility management perspective. The small cell size, which implies a small cell residence time, will increase the handover (HO) rate dramatically. Consequently, the HO latency will become a critical consideration in the 5G era. The latter requires an intelligent, fast and light-weight HO procedure with minimal signalling overhead. In this direction, we propose a memory-full context-aware HO scheme with mobility prediction to achieve the aforementioned objectives. We consider a dual connectivity radio access network architecture with logical separation between control and data planes because it offers relaxed constraints in implementing the predictive approaches. The proposed scheme predicts future HO events along with the expected HO time by combining radio frequency performance to physical proximity along with the user context in terms of speed, direction and HO history. To minimise the processing and the storage requirements whilst improving the prediction performance, a user-specific prediction triggering threshold is proposed. The prediction outcome is utilised to perform advance HO signalling whilst suspending the periodic transmission of measurement reports. Analytical and simulation results show that the proposed scheme provides promising gains over the conventional approach