The Engaged University: Providing a Platform for Research That Transforms Society

Despite a growing recognition that the solutions to current environmental problems will be developed through collaborations between scientists and stakeholders, substantial challenges stifle such cooperation and slow the transfer of knowledge. Challenges occur at several levels, including individual, disciplinary, and institutional. All of these have implications for scholars working at academic and research institutions. Fortunately, creative ideas and tested models exist that provide opportunities for conversation and serious consideration about how such institutions can facilitate the dialogue between scientists and society

Gesture Recognition Using Neural Networks Based on HW/SW Cosimulation Platform

Hardware/software (HW/SW) cosimulation integrates software simulation and hardware simulation simultaneously. Usually, HW/SW co-simulation platform is used to ease debugging and verification for very large-scale integration (VLSI) design. To accelerate the computation of the gesture recognition technique, an HW/SW implementation using field programmable gate array (FPGA) technology is presented in this paper. The major contributions of this work are: (1) a novel design of memory controller in the Verilog Hardware Description Language (Verilog HDL) to reduce memory consumption and load on the processor. (2) The testing part of the neural network algorithm is being hardwired to improve the speed and performance. The American Sign Language gesture recognition is chosen to verify the performance of the approach. Several experiments were carried out on four databases of the gestures (alphabet signs A to Z). (3) The major benefit of this design is that it takes only few milliseconds to recognize the hand gesture which makes it computationally more efficient.

A Research Platform for Artificial Neural Networks with Applications in Pediatric Epilepsy

This dissertation established a state-of-the-art programming tool for designing and training artificial neural networks (ANNs) and showed its applicability to brain research. The developed tool, called NeuralStudio, allows users without programming skills to conduct studies based on ANNs in a powerful and very user friendly interface. A series of unique features has been implemented in NeuralStudio, such as ROC analysis, cross-validation, network averaging, topology optimization, and optimization of the activation function’s slopes. It also included a Support Vector Machines module for comparison purposes. Once the tool was fully developed, it was applied to two studies in brain research. In the first study, the goal was to create and train an ANN to detect epileptic seizures from subdural EEG. This analysis involved extracting features from the spectral power in the gamma frequencies. In the second application, a unique method was devised to link EEG recordings to epileptic and non-epileptic subjects. The contribution of this method consisted of developing a descriptor matrix that can be used to represent any EEG file regarding its duration and the number of electrodes. The first study showed that the inter-electrode mean of the spectral power in the gamma frequencies and its duration above a specific threshold performs better than the other frequencies in seizure detection, exhibiting an accuracy of 95.90%, a sensitivity of 92.59%, and a specificity of 96.84%. The second study yielded that Hjorth’s parameter activity is sufficient to accurately relate EEG to epileptic and non-epileptic subjects. After testing, accuracy, sensitivity and specificity of the classifier were all above 0.9667. Statistical tests measured the superiority of activity at over 99.99 % certainty. It was demonstrated that 1) the spectral power in the gamma frequencies is highly effective in locating seizures from EEG and 2) activity can be used to link EEG recordings to epileptic and non-epileptic subjects. These two studies required high computational load and could be addressed thanks to NeuralStudio. From a medical perspective, both methods proved the merits of NeuralStudio in brain research applications. For its outstanding features, NeuralStudio has been recently awarded a patent (US patent No. 7502763).

Análise de usabilidade de livro digital na EaD: aplicação da avaliação heurística proposta por Pechansky

Nowadays the Distance Learning (DL) is in its fifth generation and New Information and Communication Technologies (NICT) mediate it. In this technological context it is then called Distance e-Learning (DeL). DeL has also in the e-books a potential interactive tool to the collaborative learning. So, this study was accomplished with the purpose of analyzing the e-books developed by SEDIS – Secretaria de Educação a Distância (Department of Distance Learning). This study was performed in two phases: Firstly it was done an exploratory study to check out the reading and the handling of digital e-learning material available to the student-users. These e-books are obtainable on SEDIS Moodle platform. Secondly, it was performed some analysis of an e-book sample by means of the heuristic evaluation. This research aims both to identify potential problems and give some suggestions to solve them. At last it is presented a final report with diagnosis and suggestions for a more applicable design in order to optimize the e-book by means of the distance e-learning usability guidelines

TS/Ph-3 autosampler platform, Taylor Slough

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

TS/Ph-5 autosampler platform, Taylor Slough

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

Laura downloading data on the autosampler platform at TS/Ph-2, Taylor Slough

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

TS/Ph-3 autosampler platform and macrophyte plots, Taylor Slough

This material is based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under Cooperative Agreements #DBI-0620409 and #DEB-9910514. This image is made available for non-commercial or educational use only.

Platform Privacy: The Missing Piece of Data Protection Legislation

After years of deliberation, the EU commission sped up the reform process of a common EU digital policy considerably in 2015 by launching the EU digital single market strategy. In particular, two core initiatives of the strategy were agreed upon: General Data Protection Regulation and the Network and Information Security (NIS) Directive law texts. A new initiative was additionally launched addressing the role of online platforms. This paper focuses on the platform privacy rationale behind the data protection legislation, primarily based on the proposal for a new EU wide General Data Protection Regulation. We analyse the legislation rationale from an Information System perspective to understand the role user data plays in creating platforms that we identify as “processing silos”. Generative digital infrastructure theories are used to explain the innovative mechanisms that are thought to govern the notion of digitalization and successful business models that are affected by digitalization. We foresee continued judicial data protection challenges with the now proposed Regulation as the adoption of the “Internet of Things” continues. The findings of this paper illustrate that many of the existing issues can be addressed through legislation from a platform perspective. We conclude by proposing three modifications to the governing rationale, which would not only improve platform privacy for the data subject, but also entrepreneurial efforts in developing intelligent service platforms. The first modification is aimed at improving service differentiation on platforms by lessening the ability of incumbent global actors to lock-in the user base to their service/platform. The second modification posits limiting the current unwanted tracking ability of syndicates, by separation of authentication and data store services from any processing entity. Thirdly, we propose a change in terms of how security and data protection policies are reviewed, suggesting a third party auditing procedure.

Monitoring sandy shores morphologies by DGPS: a practical tool to generate digital elevation models

The highly dynamic nature of some sandy shores with continuous morphological changes require the development of efficient and accurate methodological strategies for coastal hazard assessment and morphodynamic characterisation. During the past decades, the general methodological approach for the establishment of coastal monitoring programmes was based on photogrammetry or classical geodetic techniques. With the advent of new geodetic techniques, space-based and airborne-based, new methodologies were introduced in coastal monitoring programmes. This paper describes the development of a monitoring prototype that is based on the use of global positioning system (GPS). The prototype has a GPS multiantenna mounted on a fast surveying platform, a land vehicle appropriate for driving in the sand (four-wheel quad). This system was conceived to perform a network of shore profiles in sandy shores stretches (subaerial beach) that extend for several kilometres from which high-precision digital elevation models can be generated. An analysis of the accuracy and precision of some differential GPS kinematic methodologies is presented. The development of an adequate survey methodology is the first step in morphodynamic shore characterisation or in coastal hazard assessment. The sample method and the computational interpolation procedures are important steps for producing reliable three-dimensional surface maps that are real as possible. The quality of several interpolation methods used to generate grids was tested in areas where there were data gaps. The results obtained allow us to conclude that with the developed survey methodology, it is possible to Survey sandy shores stretches, under spatial scales of kilometers, with a vertical accuracy of greater than 0.10 m in the final digital elevation models.

Digital Media in Landscape Architecture Design Process

Thesis (Master's)--University of Washington, 2016-08

Digital rights management –systeemit ja kuluttajien suhtautuminen niihin peliteollisuudessa

Tämän kandidaatintutkimuksen tarkoituksena on löytää vastaus siihen, miten vahva voi olla DRM-systeemi, ennen kuin kuluttajat eivät enää hyväksy sitä. DRM-systeemejä on monen tasoisia, mutta ne eivät ole soveltuvia sellaisenaan kaikille eri alustoille. Peliteollisuuden digitaalisten käyttöoikeuksien hallintajärjestelmillä on omanlaisensa lainalaisuudet kuin esimerkiksi musiikkiteollisuudella. Lisäksi on olemassa tietty tämän hetkinen hyväksytty DRM:n taso, josta voi olla vaarallista poiketa. Tutkimus on luonteeltaan laadullinen tutkimus. Työssä on sovellettu sekä diskurssi- että sisällönanalyysin oppeja. Tutkimuksen aineistona on erilaisten viestiketjujen tekstit, joiden pohjalta pyritään löytämään vastaus tutkimuskysymykseen. Ketjut on jaettu eri vahvuisiksi sen perusteella, miten vahva on DRM:ää koskeva uutinen, jonka pohjalta viestiketju on syntynyt. Koska aineisto on puhuttua kieltä ja sillä on aina oma merkityksensä kontekstissaan, ovat valitut menetelmät soveltuvia analysoimaan aineistoa. Eri ketjujen analyysien tuloksien pohjalta voidaan sanoa, että DRM ei voi olla sitä tasoa suurempi kuin mikä on sen hetkinen vallitseva taso. Jos tästä tasosta poiketaan pikkaisenkin, voi se aiheuttaa suurta närästystä kuluttajien keskuudessa, jopa siihen saakka, että yritys menettää tuloja. Sen hetkiseen tasoon on päästy erinäisten kokeilujen kautta, joista kuluttajat ovat kärsineet, joten he eivät suosiolla hyväksy yhtään sen suurempaa tasoa kuin mikä vallitsee sillä hetkellä. Jos yritys näkee, että tasoa on pakko tiukentaa, täytyy tiukennus tehdä pikkuhiljaa ja naamioida se lisäominaisuuksilla. Kuluttajat ovat tietoisia omista oikeuksistaan, eivätkä he helpolla halua luopua niistä yhtään sen enempää kuin on tarpeellista.

Towards Proactive Context-aware Computing and Systems

A primary goal of context-aware systems is delivering the right information at the right place and right time to users in order to enable them to make effective decisions and improve their quality of life. There are three key requirements for achieving this goal: determining what information is relevant, personalizing it based on the users’ context (location, preferences, behavioral history etc.), and delivering it to them in a timely manner without an explicit request from them. These requirements create a paradigm that we term as “Proactive Context-aware Computing”. Most of the existing context-aware systems fulfill only a subset of these requirements. Many of these systems focus only on personalization of the requested information based on users’ current context. Moreover, they are often designed for specific domains. In addition, most of the existing systems are reactive - the users request for some information and the system delivers it to them. These systems are not proactive i.e. they cannot anticipate users’ intent and behavior and act proactively without an explicit request from them. In order to overcome these limitations, we need to conduct a deeper analysis and enhance our understanding of context-aware systems that are generic, universal, proactive and applicable to a wide variety of domains. To support this dissertation, we explore several directions. Clearly the most significant sources of information about users today are smartphones. A large amount of users’ context can be acquired through them and they can be used as an effective means to deliver information to users. In addition, social media such as Facebook, Flickr and Foursquare provide a rich and powerful platform to mine users’ interests, preferences and behavioral history. We employ the ubiquity of smartphones and the wealth of information available from social media to address the challenge of building proactive context-aware systems. We have implemented and evaluated a few approaches, including some as part of the Rover framework, to achieve the paradigm of Proactive Context-aware Computing. Rover is a context-aware research platform which has been evolving for the last 6 years. Since location is one of the most important context for users, we have developed ‘Locus’, an indoor localization, tracking and navigation system for multi-story buildings. Other important dimensions of users’ context include the activities that they are engaged in. To this end, we have developed ‘SenseMe’, a system that leverages the smartphone and its multiple sensors in order to perform multidimensional context and activity recognition for users. As part of the ‘SenseMe’ project, we also conducted an exploratory study of privacy, trust, risks and other concerns of users with smart phone based personal sensing systems and applications. To determine what information would be relevant to users’ situations, we have developed ‘TellMe’ - a system that employs a new, flexible and scalable approach based on Natural Language Processing techniques to perform bootstrapped discovery and ranking of relevant information in context-aware systems. In order to personalize the relevant information, we have also developed an algorithm and system for mining a broad range of users’ preferences from their social network profiles and activities. For recommending new information to the users based on their past behavior and context history (such as visited locations, activities and time), we have developed a recommender system and approach for performing multi-dimensional collaborative recommendations using tensor factorization. For timely delivery of personalized and relevant information, it is essential to anticipate and predict users’ behavior. To this end, we have developed a unified infrastructure, within the Rover framework, and implemented several novel approaches and algorithms that employ various contextual features and state of the art machine learning techniques for building diverse behavioral models of users. Examples of generated models include classifying users’ semantic places and mobility states, predicting their availability for accepting calls on smartphones and inferring their device charging behavior. Finally, to enable proactivity in context-aware systems, we have also developed a planning framework based on HTN planning. Together, these works provide a major push in the direction of proactive context-aware computing.