Discrete solitons in coupled active lasing cavities

We examine the existence and stability of discrete spatial solitons in coupled nonlinear lasing cavities (waveguide resonators), addressing the case of active defocusing media, where the gain exceeds damping in the low-amplitude limit. A new family of stable localized structures is found: these are bright and gray cavity solitons representing the connections between homogeneous and inhomogeneous states. Solitons of this type can be controlled by discrete diffraction and are stable when the bistability of homogenous states is absent. © 2012 Optical Society of America.

The challenges of developing an online tool to measure the quality of the passenger experience in a PanEuropean context

METPEX is a 3 year, FP7 project which aims to develop a PanEuropean tool to measure the quality of the passenger's experience of multimodal transport. Initial work has led to the development of a comprehensive set of variables relating to different passenger groups, forms of transport and journey stages. This paper addresses the main challenges in transforming the variables into usable, accessible computer based tools allowing for the real time collection of information, across multiple journey stages in different EU countries. Non-computer based measurement instruments will be used to gather information from those who may not have or be familiar with mobile technology. Smartphone-based measurement instruments will also be used, hosted in two applications. The mobile applications need to be easy to use, configurable and adaptable according to the context of use. They should also be inherently interesting and rewarding for the participant, whilst allowing for the collection of high quality, valid and reliable data from all journey types and stages (from planning, through to entry into and egress from different transport modes, travel on public and personal vehicles and support of active forms of transport (e.g. cycling and walking). During all phases of the data collection and processing, the privacy of the participant is highly regarded and is ensured. © 2014 Springer International Publishing.

A knowledge driven approach towards the validation of externally acquired traceability datasets in supply chain business processes

The sharing of near real-time traceability knowledge in supply chains plays a central role in coordinating business operations and is a key driver for their success. However before traceability datasets received from external partners can be integrated with datasets generated internally within an organisation, they need to be validated against information recorded for the physical goods received as well as against bespoke rules defined to ensure uniformity, consistency and completeness within the supply chain. In this paper, we present a knowledge driven framework for the runtime validation of critical constraints on incoming traceability datasets encapuslated as EPCIS event-based linked pedigrees. Our constraints are defined using SPARQL queries and SPIN rules. We present a novel validation architecture based on the integration of Apache Storm framework for real time, distributed computation with popular Semantic Web/Linked data libraries and exemplify our methodology on an abstraction of the pharmaceutical supply chain.

EPCIS Event-Based traceability in pharmaceutical supply chains via automated generation of linked pedigrees

In this paper we show how event processing over semantically annotated streams of events can be exploited, for implementing tracing and tracking of products in supply chains through the automated generation of linked pedigrees. In our abstraction, events are encoded as spatially and temporally oriented named graphs, while linked pedigrees as RDF datasets are their specific compositions. We propose an algorithm that operates over streams of RDF annotated EPCIS events to generate linked pedigrees. We exemplify our approach using the pharmaceuticals supply chain and show how counterfeit detection is an implicit part of our pedigree generation. Our evaluation results show that for fast moving supply chains, smaller window sizes on event streams provide significantly higher efficiency in the generation of pedigrees as well as enable early counterfeit detection.

A rule-based approach to implicit emotion detection in text

Most research in the area of emotion detection in written text focused on detecting explicit expressions of emotions in text. In this paper, we present a rule-based pipeline approach for detecting implicit emotions in written text without emotion-bearing words based on the OCC Model. We have evaluated our approach on three different datasets with five emotion categories. Our results show that the proposed approach outperforms the lexicon matching method consistently across all the three datasets by a large margin of 17–30% in F-measure and gives competitive performance compared to a supervised classifier. In particular, when dealing with formal text which follows grammatical rules strictly, our approach gives an average F-measure of 82.7% on “Happy”, “Angry-Disgust” and “Sad”, even outperforming the supervised baseline by nearly 17% in F-measure. Our preliminary results show the feasibility of the approach for the task of implicit emotion detection in written text.

Comparing sentence-level features for authorship analysis in Portuguese

In this paper we compare the robustness of several types of stylistic markers to help discriminate authorship at sentence level. We train a SVM-based classifier using each set of features separately and perform sentence-level authorship analysis over corpus of editorials published in a Portuguese quality newspaper. Results show that features based on POS information, punctuation and word / sentence length contribute to a more robust sentence-level authorship analysis. © Springer-Verlag Berlin Heidelberg 2010.

Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.