Automatic labelling of topic models learned from Twitter by summarisation

Latent topics derived by topic models such as Latent Dirichlet Allocation (LDA) are the result of hidden thematic structures which provide further insights into the data. The automatic labelling of such topics derived from social media poses however new challenges since topics may characterise novel events happening in the real world. Existing automatic topic labelling approaches which depend on external knowledge sources become less applicable here since relevant articles/concepts of the extracted topics may not exist in external sources. In this paper we propose to address the problem of automatic labelling of latent topics learned from Twitter as a summarisation problem. We introduce a framework which apply summarisation algorithms to generate topic labels. These algorithms are independent of external sources and only rely on the identification of dominant terms in documents related to the latent topic. We compare the efficiency of existing state of the art summarisation algorithms. Our results suggest that summarisation algorithms generate better topic labels which capture event-related context compared to the top-n terms returned by LDA. © 2014 Association for Computational Linguistics.

Spectral modification of type IA fibre Bragg gratings by high-power near-infrared lasers

We report the first experimental measurements on the spectral modification of type IA fibre Bragg gratings, incorporated in an optical network, which result from the use of high-power, near-infrared lasers. The fibre grating properties are modified in a controlled manner by exploiting the characteristics of the inherent 1400 nm absorption band of the optical fibre, which grows in strength during the type IA grating inscription. If the fibre network is illuminated with a high-power laser, having an emission wavelength coincident with the absorption band, the type IA centre wavelength and chirp can be modified. Furthermore, partial grating erasure is demonstrated. This has serious implications when using type IA gratings in an optical network, as their spectrum can be modified using purely optical methods (no external heating source acts on the fibre), and to their long-term stability as the grating is shown to decay. Conversely, suitably stabilized gratings can be spectrally tailored, for tuning fibre lasers or edge filter modification in sensing applications, by purely optical means. © 2006 IOP Publishing Ltd.

Dynamic complementarities in innovation strategies

Using a panel of Irish manufacturing plants over the period 1991-2008 we test for dynamic complementarities in the joint use of internal R&D and external knowledge sources. We find little evidence, either from considering successive cross-sectional waves of comparable surveys, or in terms of the strategy switch choices of specific plants, that there has been a systematic move towards the joint use of internal and external knowledge in innovation. We then test formally for the presence of complementarities in the joint use of internal R&D and external innovation linkages. In static terms we find no evidence of complementarity, but in dynamic terms find evidence that strategy switches by individual plants towards a more 'open' strategy are accompanied by increased innovation outputs.

Frontline employees' collaboration in industrial service innovation:routes of co-creation's effects on new service performance

From a Service-Dominant Logic (S-DL) perspective, employees constitute operant resources that firms can draw to enhance the outcomes of innovation efforts. While research acknowledges that frontline employees (FLEs) constitute, through service encounters, a key interface for the transfer of valuable external knowledge into the firm, the range of potential benefits derived from FLE-driven innovation deserves more investigation. Using a sample of knowledge intensive business services firms (KIBS), this study examines how the collaboration with FLEs along the new service development (NSD) process, namely FLE co-creation, impacts on service innovation performance following two routes of different effects. Partial least squares structural equation modeling (PLS-SEM) results indicate that FLE co-creation benefits the NS success among FLEs and firm’s customers, the constituents of the resources route. FLE co-creation also has a positive effect on the NSD speed, which in turn enhances the NS quality. NSD speed and NS quality integrate the operational route, which proves to be the most effective path to impact the NS market performance. Accordingly, KIBS managers must value their FLEs as essential partners to achieve successful innovation from an internal and external perspective, and develop the appropriate mechanisms to guarantee their effective involvement along the NSD process.

New integrated multilevel converter for switched reluctance motor drives in plug-in hybrid electric vehicles with flexible energy conversion

This paper presents an integrated multilevel converter of switched reluctance motors (SRMs) fed by a modular front-end circuit for plug-in hybrid electric vehicle (PHEV) applications. Several operating modes can be achieved by changing the on-off states of the switches in the front-end circuit. In generator driving mode, the battery bank is employed to elevate the phase voltage for fast excitation and demagnetization. In battery driving mode, the converter is reconfigured as a four-level converter, and the capacitor is used as an additional charge capacitor to produce multilevel voltage outputs, which enhances the torque capability. The operating modes of the proposed drive are explained and the phase current and voltage are analyzed in details. The battery charging is naturally achieved by the demagnetization current in motoring mode and by the regenerative current in braking mode. Moreover, the battery can be charged by the external AC source or generator through the proposed converter when the vehicle is in standstill condition. The SRM-based PHEV can operate at different speeds by coordinating the power flow between the generator and battery. Simulation in MATLAB/Simulink and experiments on a three-phase 12/8 SRM confirm the effectiveness of the proposed converter topology.

Open innovation in the performing arts. Examples from contemporary dance and theatre production

Scholarship on open innovation examines the different shades of opening up the innovation process of firms, where the most important feature is sourcing in knowledge. In this paper I examine the implications of adapting open innovation frames to a field where it was not investigated before: performing arts (contemporary dance and theatre). I draw on case studies and demonstrate that open innovation strategies are viable for artistic production. Independent companies purposefully mining out external knowledge in production, and commercializing on the spillovers of their body of knowledge, put themselves on the shelf of firms adopting and adapting to open innovation.

Resonance Energy Transfer-Based Molecular Switch Designed Using a Systematic Design Process Based on Monte Carlo Methods and Markov Chains

A RET network consists of a network of photo-active molecules called chromophores that can participate in inter-molecular energy transfer called resonance energy transfer (RET). RET networks are used in a variety of applications including cryptographic devices, storage systems, light harvesting complexes, biological sensors, and molecular rulers. In this dissertation, we focus on creating a RET device called closed-diffusive exciton valve (C-DEV) in which the input to output transfer function is controlled by an external energy source, similar to a semiconductor transistor like the MOSFET. Due to their biocompatibility, molecular devices like the C-DEVs can be used to introduce computing power in biological, organic, and aqueous environments such as living cells. Furthermore, the underlying physics in RET devices are stochastic in nature, making them suitable for stochastic computing in which true random distribution generation is critical.

In order to determine a valid configuration of chromophores for the C-DEV, we developed a systematic process based on user-guided design space pruning techniques and built-in simulation tools. We show that our C-DEV is 15x better than C-DEVs designed using ad hoc methods that rely on limited data from prior experiments. We also show ways in which the C-DEV can be improved further and how different varieties of C-DEVs can be combined to form more complex logic circuits. Moreover, the systematic design process can be used to search for valid chromophore network configurations for a variety of RET applications.

We also describe a feasibility study for a technique used to control the orientation of chromophores attached to DNA. Being able to control the orientation can expand the design space for RET networks because it provides another parameter to tune their collective behavior. While results showed limited control over orientation, the analysis required the development of a mathematical model that can be used to determine the distribution of dipoles in a given sample of chromophore constructs. The model can be used to evaluate the feasibility of other potential orientation control techniques.

New Foods, New Consumers: Innovation in Food Product Development

BACKGROUND: Today, new lifestyles, higher incomes and consumer awareness are creating consumer demand for a year-round supply of high-quality, diverse and innovative food products. However, when it comes to innovation, the food sector is less changeable when compared to other sectors, such as high technology. Still, in the past decades much and important developments have been achieved in several areas related to foods and the food industry. METHODS: A systematic review of scientific literature was conducted on Science Direct. The topics investigated were: aspects related to innovation in food development (such as the transfer of innovation, open innovation, collaborative innovation and consumer perception and its role in the developing process); the innovation in the food industry (particularly regarding the processing technologies and packaging, which are two prominent areas of innovation in this sector nowadays); the innovation in the cooking sector (particularly in regards to the molecular gastronomy and science based cooking). RESULTS: A total of 146 articles were included in the review and the aspects focused allowed confirming that innovation has been recognized as a key driver of economic growth. Within the framework of ‘open innovation’, a number of key issues related to the acquisition of external knowledge in food technology must be taken into consideration. Food product development is highly dependent on the consumer perception and acceptance, and hence it is of utmost importance to include the consumer in the development process to minimize failure probabilities. The sectors of the food industry where important developments and innovation are registered include the processing technologies and the packaging systems, where the latest progresses have produced very significant outcomes. CONCLUSION: The present work allowed verifying the latest improvements and trend towards food product development from two perspectives, the product itself and the industrial processing. This sector is undoubtedly a major key for the success and competitiveness nowadays in the food industry.

EMMA 2--a MAGE-compliant system for the collaborative analysis and integration of microarray data

Background: Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems. Results: The EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services. Conclusion: Our model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays.

The role of context in image annotation and recommendation

With the rise of smart phones, lifelogging devices (e.g. Google Glass) and popularity of image sharing websites (e.g. Flickr), users are capturing and sharing every aspect of their life online producing a wealth of visual content. Of these uploaded images, the majority are poorly annotated or exist in complete semantic isolation making the process of building retrieval systems difficult as one must firstly understand the meaning of an image in order to retrieve it. To alleviate this problem, many image sharing websites offer manual annotation tools which allow the user to “tag” their photos, however, these techniques are laborious and as a result have been poorly adopted; Sigurbjörnsson and van Zwol (2008) showed that 64% of images uploaded to Flickr are annotated with < 4 tags. Due to this, an entire body of research has focused on the automatic annotation of images (Hanbury, 2008; Smeulders et al., 2000; Zhang et al., 2012a) where one attempts to bridge the semantic gap between an image’s appearance and meaning e.g. the objects present. Despite two decades of research the semantic gap still largely exists and as a result automatic annotation models often offer unsatisfactory performance for industrial implementation. Further, these techniques can only annotate what they see, thus ignoring the “bigger picture” surrounding an image (e.g. its location, the event, the people present etc). Much work has therefore focused on building photo tag recommendation (PTR) methods which aid the user in the annotation process by suggesting tags related to those already present. These works have mainly focused on computing relationships between tags based on historical images e.g. that NY and timessquare co-exist in many images and are therefore highly correlated. However, tags are inherently noisy, sparse and ill-defined often resulting in poor PTR accuracy e.g. does NY refer to New York or New Year? This thesis proposes the exploitation of an image’s context which, unlike textual evidences, is always present, in order to alleviate this ambiguity in the tag recommendation process. Specifically we exploit the “what, who, where, when and how” of the image capture process in order to complement textual evidences in various photo tag recommendation and retrieval scenarios. In part II, we combine text, content-based (e.g. # of faces present) and contextual (e.g. day-of-the-week taken) signals for tag recommendation purposes, achieving up to a 75% improvement to precision@5 in comparison to a text-only TF-IDF baseline. We then consider external knowledge sources (i.e. Wikipedia & Twitter) as an alternative to (slower moving) Flickr in order to build recommendation models on, showing that similar accuracy could be achieved on these faster moving, yet entirely textual, datasets. In part II, we also highlight the merits of diversifying tag recommendation lists before discussing at length various problems with existing automatic image annotation and photo tag recommendation evaluation collections. In part III, we propose three new image retrieval scenarios, namely “visual event summarisation”, “image popularity prediction” and “lifelog summarisation”. In the first scenario, we attempt to produce a rank of relevant and diverse images for various news events by (i) removing irrelevant images such memes and visual duplicates (ii) before semantically clustering images based on the tweets in which they were originally posted. Using this approach, we were able to achieve over 50% precision for images in the top 5 ranks. In the second retrieval scenario, we show that by combining contextual and content-based features from images, we are able to predict if it will become “popular” (or not) with 74% accuracy, using an SVM classifier. Finally, in chapter 9 we employ blur detection and perceptual-hash clustering in order to remove noisy images from lifelogs, before combining visual and geo-temporal signals in order to capture a user’s “key moments” within their day. We believe that the results of this thesis show an important step towards building effective image retrieval models when there lacks sufficient textual content (i.e. a cold start).

Interorganizational Innovation: The Role of Suppliers in Enhancing Buyer Innovation

This dissertation explores the effect of innovative knowledge transfer across supply chain partners. My research seeks to understand the manner by which a firm is able to benefit from the innovative capabilities of its supply chain partners and utilize the external knowledge they hold to increase its own levels of innovation. Specifically, I make use of patent data as a proxy for firm-level innovation and develop both independent and dependent variables from the data contained within the patent filings. I further examine the means by which key dyadic and portfolio supply chain relationship characteristics moderate the relationship between supplier innovation and buyer innovation. I investigate factors such as the degree of transactional reciprocity between the buyer and supplier, the similarity of the firms’ knowledge bases, and specific chain characteristics (e.g., geographic propinquity) to provide greater understanding of the means by which the transfer of innovative knowledge across firms in a supply chain can be enhanced or inhibited. This dissertation spans three essays to provide insights into the role that supply chain relationships play in affecting a focal firm’s level of innovation. While innovation has been at the core of a wide body of research, very little empirical work exists that considers the role of vertical buyer-supplier relationships on a firm’s ability to develop new and novel innovations. I begin by considering the fundamental unit of analysis within a supply chain, the buyer-supplier dyad. After developing initial insights based on the interactions between singular buyers and suppliers, essay two extends the analysis to consider the full spectrum of a buyer’s supply base by aggregating the individual buyer-supplier dyad level data into firm-supply network level data. Through this broader level of analysis, I am able to examine how the relational characteristics between a buyer firm and its supply base affect its ability to leverage the full portfolio of its suppliers’ innovative knowledge. Finally, in essay three I further extend the analysis to explore the means by which a buyer firm can use its suppliers to enhance its ability to access distant knowledge held by other organizations that the buyer is only connected to indirectly through its suppliers.

Energy harvesting for monitoring bridges over their operational life

The use of energy harvesting materials for large infrastructure is a promising and growing field. In this regard, the use of such harvesters for the purpose of structural health monitoring of bridges has been proposed in recent times as one of the feasible options since the deployment of them can remove the necessity of an external power source. This paper addresses the performance issue of such monitors over the life-cycle of a bridge as it deteriorates and the live load on the structure increases. In this regard, a Lead Zirconate Titanate (PZT) material is considered as the energy harvesting material and a comparison is carried out over the operational life of a reinforced concrete bridge. The evolution of annual average daily traffic (AADT) is taken into consideration, as is the degradation of the structure over time, due to the effects of corrosion. Evolution of such harvested energy is estimated over the life-cycle of the bridge and the sensitivity of harvested energy is investigated for varying rates of degradation and changes in AADT. The study allows for designing and understanding the potential of energy harvesters as a health monitor for bridges. This paper also illustrates how the natural growth of traffic on a bridge over time can accentuate the identification of damage, which is desirable for an ageing structure. The paper also assesses the impact and effects of deployment of harvesters in a bridge as a part of its design process, considering performance over the entire life-cycle versus a deployment at a certain age of the structure.

Analys av NIR hårdvaruuppsättning samt metod för fotografering av individer i fordon

Abstract Purpose – To study the possibility of using a NIR hardware solution to photograph individuals in a private vehicle as well as an analysis of its images. Method – A study of existing theories around the NIR-method’s performance in selected conditions and individual tests were performed to examine if the literature statements were valid for this study. Two empirical tests have been carried out, the first was carried out at Kapsch test track and the other outside the test track in a single stationary test. An interview which formed the basis for the assessment on the quality of empirical data with a focus on computer-based detection of the number of individuals in the vehicle. Findings – The results have demonstrated the potential of the NIR method’s performance in a fully automated detection system for the number of individuals inside the vehicle. Empirical data indicates that the method can depict individuals inside vehicles of sufficiently high quality, but it is greatly affected by reflections, weather and light conditions. Implications – Result supports the assumption that the NIR method using an external light source can be used to image the interior through a varying number of weather and lightning conditions. The study originated until the results suggested that a NIR-based hardware setup can create images with high enough quality for the human eye to be able to detect the number of individuals inside the vehicle.If the overall performance into account, it suggests that the main problem with the use of the hardware set is to maintain the quality of the whole sample and that the crucial variables for the method’s performance is the influence of light and reflection conditions. Limitations – The major limitations have been that we limited ourselves to a subjective analysis of the selection and assessment on the image features for computer-based detection of the number of individuals in the vehicle. We were limited to two tests, one in tough conditions where only the driver was in the vehicle and the second stationary test, where the focus was on the number of people in vehicles and light sources impact on the result.

Development of an innovative non rule-based energy management strategy for a Hybrid Electric Vehicle, structured for predictive driving controls based on external information knowledge

Recently, the interest of the automotive market for hybrid vehicles has increased due to the more restrictive pollutants emissions legislation and to the necessity of decreasing the fossil fuel consumption, since such solution allows a consistent improvement of the vehicle global efficiency. The term hybridization regards the energy flow in the powertrain of a vehicle: a standard vehicle has, usually, only one energy source and one energy tank; instead, a hybrid vehicle has at least two energy sources. In most cases, the prime mover is an internal combustion engine (ICE) while the auxiliary energy source can be mechanical, electrical, pneumatic or hydraulic. It is expected from the control unit of a hybrid vehicle the use of the ICE in high efficiency working zones and to shut it down when it is more convenient, while using the EMG at partial loads and as a fast torque response during transients. However, the battery state of charge may represent a limitation for such a strategy. That’s the reason why, in most cases, energy management strategies are based on the State Of Charge, or SOC, control. Several studies have been conducted on this topic and many different approaches have been illustrated. The purpose of this dissertation is to develop an online (usable on-board) control strategy in which the operating modes are defined using an instantaneous optimization method that minimizes the equivalent fuel consumption of a hybrid electric vehicle. The equivalent fuel consumption is calculated by taking into account the total energy used by the hybrid powertrain during the propulsion phases. The first section presents the hybrid vehicles characteristics. The second chapter describes the global model, with a particular focus on the energy management strategies usable for the supervisory control of such a powertrain. The third chapter shows the performance of the implemented controller on a NEDC cycle compared with the one obtained with the original control strategy.

ScriptLattes: an open-source knowledge extraction system from the Lattes platform

The Lattes platform is the major scientific information system maintained by the National Council for Scientific and Technological Development (CNPq). This platform allows to manage the curricular information of researchers and institutions working in Brazil based on the so called Lattes Curriculum. However, the public information is individually available for each researcher, not providing the automatic creation of reports of several scientific productions for research groups. It is thus difficult to extract and to summarize useful knowledge for medium to large size groups of researchers. This paper describes the design, implementation and experiences with scriptLattes: an open-source system to create academic reports of groups based on curricula of the Lattes Database. The scriptLattes system is composed by the following modules: (a) data selection, (b) data preprocessing, (c) redundancy treatment, (d) collaboration graph generation among group members, (e) research map generation based on geographical information, and (f) automatic report creation of bibliographical, technical and artistic production, and academic supervisions. The system has been extensively tested for a large variety of research groups of Brazilian institutions, and the generated reports have shown an alternative to easily extract knowledge from data in the context of Lattes platform. The source code, usage instructions and examples are available at http://scriptlattes.sourceforge.net/.