Personalization platform for multimodal ubiquitous computing applications

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaWe currently live surrounded by a myriad of computing devices running multiple applications. In general, the user experience on each of those scenarios is not adapted to each user’s specific needs, without personalization and integration across scenarios. Moreover, developers usually do not have the right tools to handle that in a standard and generic way. As such, a personalization platform may provide those tools. This kind of platform should be readily available to be used by any developer. Therefore, it must be developed to be available over the Internet. With the advances in IT infrastructure, it is now possible to develop reliable and scalable services running on abstract and virtualized platforms. Those are some of the advantages of cloud computing, which offers a model of utility computing where customers are able to dynamically allocate the resources they need and are charged accordingly. This work focuses on the creation of a cloud-based personalization platform built on a previously developed generic user modeling framework. It provides user profiling and context-awareness tools to third-party developers. A public display-based application was also developed. It provides useful information to students, teachers and others in a university campus as they are detected by Bluetooth scanning. It uses the personalization platform as the basis to select the most relevant information in each situation, while a mobile application was developed to be used as an input mechanism. A user study was conducted to assess the usefulness of the application and to validate some design choices. The results were mostly positive

Reaaliaikaiset ennustukset verkkopalveluissa

In this Master's Theses a real-time analytics pipeline is built to serve predictions to users based on the usage and the operational data of a Web service. The data of the service is analyzed and a predictive model is built using statistical learning methods. The pipeline is set up to serve the predictions real-time using components from Amazon Cloud Services. The aim is to show the user a prediction of how long will it take until she/he gets a verdict on her/his application from the service. As additional goals, the aim is to study the dataset and its possibilities and research the suitability of the Amazon Machine Learning service in real-time predictions in Web context. The features for the predictive model are selected by exploring the dataset and using the Amazon Machine Learning service to evaluate the features. The Amazon Machine Learning service is also used to build a predictive machine learning model. The real-time analytics pipeline is built using Amazon components and following the Lambda Architecture guidelines. The best model performed better than the baseline model, though only moderately. The data lacked some vital information for the prediction target such as information about the personnel. Implementing the pipeline with Amazon components was considered straightforward. The Lambda Architecture worked well for the problem. It was found out that the Amazon Machine Learning service is easy to use but its machine learning capabilities and user interface are limited. It was highlighted that it is essential to explore and learn the dataset before building or designing the pipeline, as the pipeline design depends heavily from the data and from the use case.Tässä diplomityössä on rakennettu reaaliaikainen analytiikkajärjestelmä, jolla näytetään ennustuksia käyttäjille eräässä verkkopalvelussa, perustuen verkkopalvelun käyttödataan ja operatiiviseen dataan. Verkkopalvelun dataa analysoidaan ja sen perusteella rakennetaan tilastollisiin menetelmiin pohjaava ennustava koneoppimismalli. Analytiikkajärjestelmä rakennetaan käyttäen komponentteja Amazonin pilvipalvelusta. Tarkoituksena on näyttää käyttäjälle ennustus siitä kauanko kestää, että hän saa vastauksen verkkopalveluun jättämäänsä hakemukseen. Tämän lisäksi tavoitteena on muodostaa ymmärrys verkkopalvelun datasta ja sen mahdollisuuksista, sekä tutkia soveltuuko Amazonin koneoppimispalvelu reaaliaikaisten ennustuksien näyttämiseen verkkoympäristössä. Ennustavan mallin ominaisuudet valittiin tarkastelemalla dataa ja evaluoimalla ominaisuudet Amazonin koneoppimispalvelun avulla. Amazonin koneoppimispalvelua käytettiin myös ennustavan koneoppimismallin rakentamiseen. Reaaliaikainen analytiikkajärjestelmä rakennettiin käyttäen komponentteja Amazonin pilvipalveluista ja seuraten Lambda-arkkitehtuurin suunnitteluperiaatteita. Paras rakennetuista koneoppimismalleista oli parempi kuin pohjamalli, joskaan ei mitenkään merkittävästi. Datasta puuttui joitain ennustettavan arvon kannalta tärkeitä tekijöitä kuten tietoa hakemuksia käsittelevästä henkilökunnasta. Analytiikkajärjestelmän rakentaminen Amazoniin osoittautui kuitenkin helpoksi. Amazonin koneoppimispalvelu todettiin helppokäyttöiseksi, vaikkakin se todettiin koneoppimisominaisuuksiltaan melko yksinkertaiseksi, sekä käyttöliittymän osalta rajoittuneeksi. Työssä korostetaan, että on tärkeää tutkia dataa ennen kuin rakentaa analytiikkajärjestelmän, sillä järjestelmän rakenne riippuu suuresti siitä, minkälaista data on ja mikä on sen sekä datan käyttötarkoitus

Deployment and operational aspects of rural broadband wireless access networks

Broadband speeds, Internet literacy and digital technologies have been steadily evolving over the last decade. Broadband infrastructure has become a key asset in today’s society, enabling innovation, driving economic efficiency and stimulating cultural inclusion. However, populations living in remote and rural communities are unable to take advantage of these trends. Globally, a significant part of the world population is still deprived of basic access to the Internet. Broadband Wireless Access (BWA) networks are regarded as a viable solution for providing Internet access to populations living in rural regions. In recent years, Wireless Internet Service Providers (WISPs) and community organizations around the world proved that rural BWA networks can be an effective strategy and a profitable business. This research began by deploying a BWA network testbed, which also provides Internet access to several remote communities in the harsh environment of the Scottish Highlands and Islands. The experience of deploying and operating this network pointed out three unresolved research challenges that need to be addressed to ease the path towards widespread deployment of rural BWA networks, thereby bridging the rural-urban broadband divide. Below, our research contributions are outlined with respect to these challenges. Firstly, an effective planning paradigm for deploying BWA networks is proposed: incremental planning. Incremental planning allows to anticipate return of investment and to overcome the limited network infrastructure (e.g., backhaul fibre links) in rural areas. I have developed a software tool called IncrEase and underlying network planning algorithms to consider a varied set of operational metrics to guide the operator in identifying the regions that would benefit the most from a network upgrade, automatically suggesting the best long-term strategy to the network administrator. Second, we recognize that rural and community networks present additional issues for network management. As the Internet uplink is often the most expensive part of the operational expenses for such deployments, it is desirable to minimize overhead for network management. Also, unreliable connectivity between the network operation centre and the network being managed can render traditional centralized management approaches ineffective. Finally, the number of skilled personnel available to maintain such networks is limited. I have developed a distributed network management platform called Stix for BWA networks, to make it easy to manage such networks for rural/community deployments and WISPs alike while keeping the network management infrastructure scalable and flexible. Our approach is based on the notions of goal-oriented and in-network management: administrators graphically specify network management activities as workflows, which are run in the network on a distributed set of agents that cooperate in executing those workflows and storing management information. The Stix system was implemented on low-cost and small form-factor embedded boards and shown to have a low memory footprint. Third, the research focus moves to the problem of assessing broadband coverage and quality in a given geographic region. The outcome is BSense, a flexible framework that combines data provided by ISPs with measurements gathered by distributed software agents. The result is a census (presented as maps and tables) of the coverage and quality of broadband connections available in the region of interest. Such information can be exploited by ISPs to drive their growth, and by regulators and policy makers to get the true picture of broadband availability in the region and make informed decisions. In exchange for installing the multi-platform measurement software (that runs in the background) on their computers, users can get statistics about their Internet connection and those in their neighbourhood. Finally, the lessons learned through this research are summarised. The outcome is a set of suggestions about how the deployment and operation of rural BWA networks, including our own testbed, can be made more efficient by using the proper tools. The software systems presented in this thesis have been evaluated in lab settings and in real networks, and are available as open-source software

Building the Hyperconnected Society- Internet of Things Research and Innovation Value Chains, Ecosystems and Markets

This book aims to provide a broad overview of various topics of Internet of Things (IoT), ranging from research, innovation and development priorities to enabling technologies, nanoelectronics, cyber-physical systems, architecture, interoperability and industrial applications. All this is happening in a global context, building towards intelligent, interconnected decision making as an essential driver for new growth and co-competition across a wider set of markets. It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC – Internet of Things European Research Cluster from research to technological innovation, validation and deployment.The book builds on the ideas put forward by the European Research Cluster on the Internet of Things Strategic Research and Innovation Agenda, and presents global views and state of the art results on the challenges facing the research, innovation, development and deployment of IoT in future years. The concept of IoT could disrupt consumer and industrial product markets generating new revenues and serving as a growth driver for semiconductor, networking equipment, and service provider end-markets globally. This will create new application and product end-markets, change the value chain of companies that creates the IoT technology and deploy it in various end sectors, while impacting the business models of semiconductor, software, device, communication and service provider stakeholders. The proliferation of intelligent devices at the edge of the network with the introduction of embedded software and app-driven hardware into manufactured devices, and the ability, through embedded software/hardware developments, to monetize those device functions and features by offering novel solutions, could generate completely new types of revenue streams. Intelligent and IoT devices leverage software, software licensing, entitlement management, and Internet connectivity in ways that address many of the societal challenges that we will face in the next decade

Building the Hyperconnected Society- Internet of Things Research and Innovation Value Chains, Ecosystems and Markets

This book aims to provide a broad overview of various topics of Internet of Things (IoT), ranging from research, innovation and development priorities to enabling technologies, nanoelectronics, cyber-physical systems, architecture, interoperability and industrial applications. All this is happening in a global context, building towards intelligent, interconnected decision making as an essential driver for new growth and co-competition across a wider set of markets. It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC – Internet of Things European Research Cluster from research to technological innovation, validation and deployment.The book builds on the ideas put forward by the European Research Cluster on the Internet of Things Strategic Research and Innovation Agenda, and presents global views and state of the art results on the challenges facing the research, innovation, development and deployment of IoT in future years. The concept of IoT could disrupt consumer and industrial product markets generating new revenues and serving as a growth driver for semiconductor, networking equipment, and service provider end-markets globally. This will create new application and product end-markets, change the value chain of companies that creates the IoT technology and deploy it in various end sectors, while impacting the business models of semiconductor, software, device, communication and service provider stakeholders. The proliferation of intelligent devices at the edge of the network with the introduction of embedded software and app-driven hardware into manufactured devices, and the ability, through embedded software/hardware developments, to monetize those device functions and features by offering novel solutions, could generate completely new types of revenue streams. Intelligent and IoT devices leverage software, software licensing, entitlement management, and Internet connectivity in ways that address many of the societal challenges that we will face in the next decade

Bioinformatic tools to alleviate the annotation bottleneck within precision oncology

In the era of advanced ability to perform complex genomic sequencing, precision oncology has been adopted as the ideal paradigm for optimization of outcomes for patients with cancer. However, despite technological advances in all aspects of the massively parallel sequencing pipeline, the application of precision oncology to every clinical workflow has been unattainable. Suboptimal adoption of custom medicine within oncology is attributable to the annotation bottleneck, which currently demands inordinate manual and computational requirements for completion. Alleviation of the annotation bottleneck requires co-development of bioinformatic strategies and analysis knowledgebanks to automate variant identification and variant annotation for clinical utility. The body of work presented here provides validated methods to alleviate the annotation bottleneck within the precision oncology pipeline. The introduction describes the specific aspects of the massively parallel sequencing pipeline that require development. Subsequently, we present three tools (DeepSVR, a Manual Review Standard Operating Procedure, and OpenCAP) that were developed to improve upon existing methods for variant identification and annotation. DeepSVR provides a machine learning approach to improve automated somatic variant calling by reducing false positives associated with sequencing pipelines that are observable by manual reviewers. The Manual Review Standard Operating Procedure provides a systemic and standardized approach for manual review of aligned sequencing reads for sequencing data with paired tumor and normal samples. Finally, the Open-sourced CIViC Annotation Pipeline (OpenCAP) serves as a software to create rationally designed clinical capture panels that are linked to clinical relevance summaries to improve library preparation and clinical annotation. The combined utility of these three tools for alleviation of the analysis bottleneck are demonstrated using a clinical example. Specifically, we developed a targeted clinical capture panel (MyeloSeq) to evaluate recurrent mutations observed in myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). The MyeloSeq sequencing pipeline incorporated many of the tools described above for variant identification and annotation and provides a succinct output report for physician consumption. When surveying physicians who utilize the MyeloSeq panel, we observed that over 44% of physicians changed their treatment protocol based on the MyeloSeq results. This included 39 new therapeutics prescribes, 4 definitive diagnoses, and 13 changes in treatment plan (stem-cell transplant versus chemotherapy) based on prognostic indicators. This example demonstrates that the developed tools help alleviate the analysis bottleneck within precision oncology and will improve physician’s ability to integrate precision medicine into clinical workflow

Enabling and Understanding Failure of Engineering Structures Using the Technique of Cohesive Elements

In this paper, we describe a cohesive zone model for the prediction of failure of engineering solids and/or structures. A damage evolution law is incorporated into a three-dimensional, exponential cohesive law to account for material degradation under the influence of cyclic loading. This cohesive zone model is implemented in the finite element software ABAQUS through a user defined subroutine. The irreversibility of the cohesive zone model is first verified and subsequently applied for studying cyclic crack growth in specimens experiencing different modes of fracture and/or failure. The crack growth behavior to include both crack initiation and crack propagation becomes a natural outcome of the numerical simulation. Numerical examples suggest that the irreversible cohesive zone model can serve as an efficient tool to predict fatigue crack growth. Key issues such as crack path deviation, convergence and mesh dependency are also discussed

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

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