Meetings and Meeting Modeling in Smart Environments

In this paper we survey our research on smart meeting rooms and its relevance for augmented reality meeting support and virtual reality generation of meetings in real time or off-line. The research reported here forms part of the European 5th and 6th framework programme projects multi-modal meeting manager (M4) and augmented multi-party interaction (AMI). Both projects aim at building a smart meeting environment that is able to collect multimodal captures of the activities and discussions in a meeting room, with the aim to use this information as input to tools that allow real-time support, browsing, retrieval and summarization of meetings. Our aim is to research (semantic) representations of what takes place during meetings in order to allow generation, e.g. in virtual reality, of meeting activities (discussions, presentations, voting, etc.). Being able to do so also allows us to look at tools that provide support during a meeting and at tools that allow those not able to be physically present during a meeting to take part in a virtual way. This may lead to situations where the differences between real meeting participants, human-controlled virtual participants and (semi-) autonomous virtual participants disappear

Soundscape Indices: New Features for Classifying Beehive Audio Samples

As the study of honey bee health has gained attention in the biology community, researchers have looked for new, non-invasive methods to monitor the health status of the colony. Since the beehive sound alters when the colony is exposed to stressors, analysis of the acoustic response of the colony has been used as a method to identify the type of stressor, whether it is chemical, pest, or disease. So far, two feature sets have been successfully used for this kind of analysis, being these low-level signal features and Mel Frequency Cepstral Coefficients (MFCC). Here we propose using soundscape indices, developed initially to delineate acoustic diversity in ecosystems, as an alternative to now used features. In our study, we examine the beehive acoustic response to trichloromethane laced-air and blank air and compare the performance of all three feature sets to discern the colony's sound between the hive being exposed to the chemical and not. Our results show that sound indices overperform the alternative features sets on this task. Based on these findings, we consider sound indices to be a valid set of features for beehive sound analysis and present our results to call the attention of the community on this fact

Comparison of Some Prediction Models and their Relevance in the Clinical Research

In healthcare research, predictive modeling is commonly utilized to forecast risk variables and enhance treatment procedures for improved patient outcomes. Enormous quantities of data are being created as a result of recent advances in research, clinical trials, next-generation genomic sequencing, biomarkers, and transcriptional and translational studies. Understanding how to handle and comprehend scientific data to offer better treatment for patients is critical. Currently, multiple prediction models are being utilized to investigate patient outcomes. However, it is critical to recognize the limitations of these models in the research design and their unique benefits and drawbacks. In this overview, we will look at linear regression, logistic regression, decision trees, and artificial neural network prediction models, as well as their advantages and disadvantages. The two most perilous requirements for building any predictive healthcare model are feature selection and model validation. Typically, feature selection is done by a review of the literature and expert opinion on that subject. Model validation is also an essential component of every prediction model. It characteristically relates to the predictive model's performance and accuracy. It is strongly recommended that all clinical parameters should be thoroughly examined before using any prediction model

A Comparison Analysis of BLE-Based Algorithms for Localization in Industrial Environments

Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters

A comparison analysis of ble-based algorithms for localization in industrial environments

Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters

Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound

In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in children’s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy.In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in children’s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy

Face Recognition and the Emergence of Smart Photography

This article examines face recognition as a key instance of the emergence of smart photography. Smart photography, drawing on Artificial Intelligence (AI) and Ambient Intelligence (AmI) manifests a ‘habit of mind’ (Karen Barad), or a way of thinking that is humanist in so far as it is predicated on human and machine autonomy, and representationalist in its quest for unmediated objects-in-themselves. Faces are among the objects that smart photography seeks (autonomously) to represent. By examining two of the principal algorithms of face recognition technology, the article will show how ways of seeing allied to ways of thinking that are also, ultimately, discriminatory and essentialist, materialise through software. Finally, if the ‘smart’ in smart photography means learning to discriminate between classes of faces that are fixed, essentialised and ultimately elusive (the stereotypical face of terror is both gendered and racialised) then how could smart be made smarter? This is a question of politics rather than progress

Resource management in cable access networks

Een kabelnetwerk is tegenwoordig niet meer alleen een medium waarover analoge TV-signalen vanuit een centraal punt, kopstation genaamd, naar de aangesloten huizen worden gestuurd. Sinds enkele jaren is het mogelijk om thuis data digitaal te versturen en te ontvangen. Deze data gaat via een kabelmodem thuis en het kopstation, dat in verbinding staat met andere netwerken. Op deze wijze zijn kabelnetwerken onderdeel geworden van het wereldwijde Internet en kunnen computers thuis hier mee verbonden worden. Door aan zo’n kopstation een digitaal videosysteem met duizenden films te koppelen, ontstaat er de mogelijkheid een video-op-verzoek dienst aan te bieden: Via de computer of zelfs de TV thuis kunnen films worden besteld en direct bekeken, of worden opgeslagen in de computer. Om dit te bewerkstelligen is meer nodig dan alleen een netwerk: Voor de transmissie van video data dient er zorg voor te worden gedragen dat deze zonder hinderende interrupties kan geschieden, omdat dergelijke gebeurtenissen door de gebruiker direct te zien zijn in de vorm van een stilstaand of zwart beeld. Verder is ook de reactiesnelheid van het systeem van belang voor het ondersteunen van operaties door de gebruiker, zoals het bestellen van een film, maar ook het vooruit- of terugspoelen, pauzeren, enzovoorts. Binnen deze context beschrijven en analyseren we in dit proefschrift zes problemen. Vier daarvan houden verband met de transmissie van data over het kabelnetwerk en de overige twee houden verband met het opslaan van video data op een harde schijf. In twee van de vier problemen uit de eerste categorie analyseren we de vertraging die data ondervindt wanneer die vanuit een modem wordt gestuurd naar het kopstation. Deze vertraging bepaalt met name de reactiesnelheid van het systeem. Karakteristiek voor dataverkeer in deze richting is dat pakketten van verschillende modems tegelijkertijd mogen worden verstuurd en daardoor verloren gaan. Met name de vereiste hertransmissies zorgen voor vertraging. Meer concreet beschouwen we een variant op het bekende ALOHA protocol, waarbij we uitgaan van een kanaalmodel dat afwijkt van het conventionele model. Het afwijkende model is van toepassing wanneer een modem een eerste contact probeert te leggen met het kopstation na te zijn opgestart. Met name na een stroomuitval, wanneer een groot aantal modems tegelijkertijd opnieuw opstart, kunnen de vertragingen aanzienlijk zijn. Daarnaast beschouwen we modems tijdens normale operatie en analyseren wij de verbetering in vertraging wanneer pakketten die vanuit ´e´en modem moeten worden verstuurd, worden verpakt in een groter pakket. In beide studies worden wiskundige resultaten vergeleken met simulaties die re¨ele situaties nabootsen. In de andere twee van de vier problemen richten wij ons op de transmissie van video data in de andere richting, namelijk van het kopstation naar de modems. Hierbij spelen stringente tijdsrestricties een voorname rol, zoals hierboven reeds is beschreven. Meer specifiek presenteren we een planningsalgoritme dat pakketten voor een aantal gebruikers op een kanaal zodanig na elkaar verstuurt dat de variatie in de vertraging die de verschillende pakketten ondervinden, minimaal is. Op deze wijze wordt zo goed mogelijk een continue stroom van data gerealiseerd die van belang is voor het probleemloos kunnen bekijken van een film. Daarnaast analyseren we een bestaand algoritme om een film via een aantal kanalen periodiek naar de aangesloten huizen te versturen. In dit geval ligt de nadruk op de wachttijd die een gebruiker ondervindt na het bestellen van een film. In deze analyse onderbouwen we een in het algoritme gebruikte heuristiek en brengen hierin verdere verbeteringen aan. Daarnaast bewijzen we dat het algoritme asymptotisch optimaal is, iets dat reeds langer werd aangenomen, maar nooit rigoreus bewezen was. Bij de laatste twee problemen, die verband houden met het opslaan van video data op een harde schijf, analyseren we hoe deze data zodanig kan worden opgeslagen dat die er nadien efficient van kan worden teruggelezen. In het ene probleem beschouwen we een bestaand planningsalgoritme om pakketten van verschillende videostromen naar een harde schijf te schrijven en passen dit aan om ervoor te zorgen dat het teruglezen van de stroom met bijvoorbeeld een andere pakketgrootte mogelijk wordt zonder daarbij de schijf onnodig te belasten. In het andere probleem analyseren we hoe we effectief gebruik kunnen maken van het gegeven dat data aan de buitenkant van de schijf sneller gelezen kan worden dan aan de binnenkant. We bewijzen dat het probleem van het zo efficient mogelijk opslaan van een gegeven aantal video files NPlastig is en presenteren een eenvoudige heuristiek die, hoewel voor bijzondere instanties een bewijsbaar slechte prestatie levert, in de praktijk in het algemeen goede prestaties levert. Hierbij maken we met name gebruik van het verschil in populariteit van de verschillende films