Intelligent Data Analytics using Deep Learning for Data Science

Nowadays, data science stimulates the interest of academics and practitioners because it can assist in the extraction of significant insights from massive amounts of data. From the years 2018 through 2025, the Global Datasphere is expected to rise from 33 Zettabytes to 175 Zettabytes, according to the International Data Corporation. This dissertation proposes an intelligent data analytics framework that uses deep learning to tackle several difficulties when implementing a data science application. These difficulties include dealing with high inter-class similarity, the availability and quality of hand-labeled data, and designing a feasible approach for modeling significant correlations in features gathered from various data sources. The proposed intelligent data analytics framework employs a novel strategy for improving data representation learning by incorporating supplemental data from various sources and structures. First, the research presents a multi-source fusion approach that utilizes confident learning techniques to improve the data quality from many noisy sources. Meta-learning methods based on advanced techniques such as the mixture of experts and differential evolution combine the predictive capacity of individual learners with a gating mechanism, ensuring that only the most trustworthy features or predictions are integrated to train the model. Then, a Multi-Level Convolutional Fusion is presented to train a model on the correspondence between local-global deep feature interactions to identify easily confused samples of different classes. The convolutional fusion is further enhanced with the power of Graph Transformers, aggregating the relevant neighboring features in graph-based input data structures and achieving state-of-the-art performance on a large-scale building damage dataset. Finally, weakly-supervised strategies, noise regularization, and label propagation are proposed to train a model on sparse input labeled data, ensuring the model\u27s robustness to errors and supporting the automatic expansion of the training set. The suggested approaches outperformed competing strategies in effectively training a model on a large-scale dataset of 500k photos, with just about 7% of the images annotated by a human. The proposed framework\u27s capabilities have benefited various data science applications, including fluid dynamics, geometric morphometrics, building damage classification from satellite pictures, disaster scene description, and storm-surge visualization

On information filtering in social sensing

For decades, from the invention of Sensor Networks, people envisioned a global sensing platform with millions of sensors deployed globally. The platform has finally become real recently with the advent of multiple online social network services where humans act as sensors and the social networks act as sensor networks, a practice named Social Sensing. Social sensing was born with the advances of high-level semantics sensing (since humans are the “sensors” with texts or photos as the sensing data) and (almost) zero-cost real-time data infrastructure, which makes this new sensing paradigm very promising in multiple real-world applications including disaster response and global event discovery. However, its global scale results in a massive amount of data generated and collected in applications that far exceeds normal people’s cognitive capability of information consumption, thus we desire a system that can filter the massive sensing data and delivers only information and intelligence to the users with a human-consumable amount. In this thesis, I focus on designing an information filtering system for social sensing; specifically, I focus on three levels of information filtering. In the first level, we focus on untruthful information removal, also known as fact-finding, where the challenge lies in the unknown reliability of each individual social sensor (i.e. human) a prior. In the second level, we focus on event-level information summary, also known as event detection, where the challenge lies in de-multiplexing different event instances and fusing social events detected in multiple social networks that previous approaches do not perform well. In the third level, we focus on information-maximizing data delivery to social sensing users, especially on redundancy removal by diversifying the information feed, where the challenge lies in algorithm design that not only works well empirically but also has a theoretical performance guarantee. We address the above challenges by algorithm design and system implementation and real-world data evaluations verify the efficiency of our proposed solutions

Geoinformatics in Citizen Science

The book features contributions that report original research in the theoretical, technological, and social aspects of geoinformation methods, as applied to supporting citizen science. Specifically, the book focuses on the technological aspects of the field and their application toward the recruitment of volunteers and the collection, management, and analysis of geotagged information to support volunteer involvement in scientific projects. Internationally renowned research groups share research in three areas: First, the key methods of geoinformatics within citizen science initiatives to support scientists in discovering new knowledge in specific application domains or in performing relevant activities, such as reliable geodata filtering, management, analysis, synthesis, sharing, and visualization; second, the critical aspects of citizen science initiatives that call for emerging or novel approaches of geoinformatics to acquire and handle geoinformation; and third, novel geoinformatics research that could serve in support of citizen science

Senseable Spaces: from a theoretical perspective to the application in augmented environments

Grazie all’ enorme diffusione di dispositivi senzienti nella vita di tutti i giorni, nell’ ultimo decennio abbiamo assistito ad un cambio definitivo nel modo in cui gli utenti interagiscono con lo spazio circostante. Viene coniato il termine Spazio Sensibile, per descrivere quegli spazi in grado di fornire servizi contestuali agli utenti, misurando e analizzando le dinamiche che in esso avvengono, e di reagire conseguentemente a questo continuo flusso di dati bidirezionale. La ricerca è stata condotta abbracciando diversi domini di applicazione, le cui singole esigenze hanno reso necessario testare il concetto di Spazi Sensibili in diverse declinazioni, mantenendo al centro della ricerca l’utente, con la duplice accezione di end-user e manager. Molteplici sono i contributi rispetto allo stato dell’ arte. Il concetto di Spazio Sensibile è stato calato nel settore dei Beni Culturali, degli Spazi Pubblici, delle Geosciences e del Retail. I casi studio nei musei e nella archeologia dimostrano come l’ utilizzo della Realtà Aumentata possa essere sfruttata di fronte a un dipinto o in outdoor per la visualizzazione di modelli complessi, In ambito urbano, il monitoraggio di dati generati dagli utenti ha consentito di capire le dinamiche di un evento di massa, durante il quale le stesse persone fruivano di servizi contestuali. Una innovativa applicazione di Realtà Aumentata è stata come servizio per facilitare l’ ispezione di fasce tampone lungo i fiumi, standardizzando flussi di dati e modelli provenienti da un Sistema Informativo Territoriale. Infine, un robusto sistema di indoor localization è stato istallato in ambiente retail, per scopi classificazione dei percorsi e per determinare le potenzialità di un punto vendita. La tesi è inoltre una dimostrazione di come Space Sensing e Geomatica siano discipline complementari: la geomatica consente di acquisire e misurare dati geo spaziali e spazio temporali a diversa scala, lo Space Sensing utilizza questi dati per fornire servizi all’ utente precisi e contestuali.Given the tremendous growth of ubiquitous services in our daily lives, during the last few decades we have witnessed a definitive change in the way users' experience their surroundings. At the current state of art, devices are able to sense the environment and users’ location, enabling them to experience improved digital services, creating synergistic loop between the use of the technology, and the use of the space itself. We coined the term Senseable Space, to define the kinds of spaces able to provide users with contextual services, to measure and analyse their dynamics and to react accordingly, in a seamless exchange of information. Following the paradigm of Senseable Spaces as the main thread, we selected a set of experiences carried out in different fields; central to this investigation there is of course the user, placed in the dual roles of end-user and manager. The main contribution of this thesis lies in the definition of this new paradigm, realized in the following domains: Cultural Heritage, Public Open Spaces, Geosciences and Retail. For the Cultural Heritage panorama, different pilot projects have been constructed from creating museum based installations to developing mobile applications for archaeological settings. Dealing with urban areas, app-based services are designed to facilitate the route finding in a urban park and to provide contextual information in a city festival. We also outlined a novel application to facilitate the on-site inspection by risk managers thanks to the use of Augmented Reality services. Finally, a robust indoor localization system has been developed, designed to ease customer profiling in the retail sector. The thesis also demonstrates how Space Sensing and Geomatics are complementary to one another, given the assumption that the branches of Geomatics cover all the different scales of data collection, whilst Space Sensing gives one the possibility to provide the services at the correct location, at the correct time

Senseable Spaces: from a theoretical perspective to the application in augmented environments

openGrazie all’ enorme diffusione di dispositivi senzienti nella vita di tutti i giorni, nell’ ultimo decennio abbiamo assistito ad un cambio definitivo nel modo in cui gli utenti interagiscono con lo spazio circostante. Viene coniato il termine Spazio Sensibile, per descrivere quegli spazi in grado di fornire servizi contestuali agli utenti, misurando e analizzando le dinamiche che in esso avvengono, e di reagire conseguentemente a questo continuo flusso di dati bidirezionale. La ricerca è stata condotta abbracciando diversi domini di applicazione, le cui singole esigenze hanno reso necessario testare il concetto di Spazi Sensibili in diverse declinazioni, mantenendo al centro della ricerca l’utente, con la duplice accezione di end-user e manager. Molteplici sono i contributi rispetto allo stato dell’ arte. Il concetto di Spazio Sensibile è stato calato nel settore dei Beni Culturali, degli Spazi Pubblici, delle Geosciences e del Retail. I casi studio nei musei e nella archeologia dimostrano come l’ utilizzo della Realtà Aumentata possa essere sfruttata di fronte a un dipinto o in outdoor per la visualizzazione di modelli complessi, In ambito urbano, il monitoraggio di dati generati dagli utenti ha consentito di capire le dinamiche di un evento di massa, durante il quale le stesse persone fruivano di servizi contestuali. Una innovativa applicazione di Realtà Aumentata è stata come servizio per facilitare l’ ispezione di fasce tampone lungo i fiumi, standardizzando flussi di dati e modelli provenienti da un Sistema Informativo Territoriale. Infine, un robusto sistema di indoor localization è stato istallato in ambiente retail, per scopi classificazione dei percorsi e per determinare le potenzialità di un punto vendita. La tesi è inoltre una dimostrazione di come Space Sensing e Geomatica siano discipline complementari: la geomatica consente di acquisire e misurare dati geo spaziali e spazio temporali a diversa scala, lo Space Sensing utilizza questi dati per fornire servizi all’ utente precisi e contestuali.Given the tremendous growth of ubiquitous services in our daily lives, during the last few decades we have witnessed a definitive change in the way users' experience their surroundings. At the current state of art, devices are able to sense the environment and users’ location, enabling them to experience improved digital services, creating synergistic loop between the use of the technology, and the use of the space itself. We coined the term Senseable Space, to define the kinds of spaces able to provide users with contextual services, to measure and analyse their dynamics and to react accordingly, in a seamless exchange of information. Following the paradigm of Senseable Spaces as the main thread, we selected a set of experiences carried out in different fields; central to this investigation there is of course the user, placed in the dual roles of end-user and manager. The main contribution of this thesis lies in the definition of this new paradigm, realized in the following domains: Cultural Heritage, Public Open Spaces, Geosciences and Retail. For the Cultural Heritage panorama, different pilot projects have been constructed from creating museum based installations to developing mobile applications for archaeological settings. Dealing with urban areas, app-based services are designed to facilitate the route finding in a urban park and to provide contextual information in a city festival. We also outlined a novel application to facilitate the on-site inspection by risk managers thanks to the use of Augmented Reality services. Finally, a robust indoor localization system has been developed, designed to ease customer profiling in the retail sector. The thesis also demonstrates how Space Sensing and Geomatics are complementary to one another, given the assumption that the branches of Geomatics cover all the different scales of data collection, whilst Space Sensing gives one the possibility to provide the services at the correct location, at the correct time.INGEGNERIA DELL'INFORMAZIONEembargoed_20181001Pierdicca, RobertoPierdicca, Robert

Static and Dynamic Affordance Learning in Vision-based Direct Perception for Autonomous Driving

The recent development in autonomous driving involves high-level computer vision and detailed road scene understanding. Today, most autonomous vehicles are using the mediated perception approach for path planning and control, which highly rely on high-definition 3D maps and real-time sensors. Recent research efforts aim to substitute the massive HD maps with coarse road attributes. In this thesis, We follow the direct perception-based method to train a deep neural network for affordance learning in autonomous driving. The goal and the main contributions of this thesis are in two folds. Firstly, to develop the affordance learning model based on freely available Google Street View panoramas and Open Street Map road vector attributes. Driving scene understanding can be achieved by learning affordances from the images captured by car-mounted cameras. Such scene understanding by learning affordances may be useful for corroborating base-maps such as HD maps so that the required data storage space is minimized and available for processing in real-time. We compare capability in road attribute identification between human volunteers and the trained model by experimental evaluation. The results indicate that this method could act as a cheaper way for training data collection in autonomous driving. The cross-validation results also indicate the effectiveness of the trained model. Secondly, We propose a scalable and affordable data collection framework named I2MAP (image-to-map annotation proximity algorithm) for autonomous driving systems. We built an automated labeling pipeline with both vehicle dynamics and static road attributes. The data collected and annotated under our framework is suitable for direct perception and end-to-end imitation learning. Our benchmark consists of 40,000 images with more than 40 affordance labels under various day time and weather even with very challenging heavy snow. We train and evaluate a ConvNet based traffic flow prediction model for driver warning and suggestion under low visibility condition

LOCALIZATION OF EVENTS USING UNDERDEVELOPED MICROBLOGGING DATA

Event localization is the task of finding the location of an event. Events are defined as significant one-time occurrences that show notable deviation from expected or normal behavior. Event localization has been studied in many domains including medical data, internet-of-things (IoT), sensor data, and microblogging/social media domain. In this dissertation, we focus on event localization in the microblogging domain. The data in the microblogging presents a unique challenge in that it is underdeveloped. Underdeveloped data has low reliability and sporadic delivery slate. Since, microblogging data is underdeveloped it provides subjective and incomplete information, which is unsuitable for event localization. We propose enrichment methods for underdeveloped data that would make the data more suitable for event localization. Our enrichment methods include disaggregation, semantic filtering, and data generation using top-down and bottom-up approaches. Once the data is enriched, we identify event signatures that are specific to an event. We find both explicit and latent event signatures within the enriched data. Using these signatures an event can be efficiently localized. We use generated data and data collected from Twitter to test our enrichment methods and implement event localization strategies

DRONE DELIVERY OF CBNRECy – DEW WEAPONS Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD)

Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD) is our sixth textbook in a series covering the world of UASs and UUVs. Our textbook takes on a whole new purview for UAS / CUAS/ UUV (drones) – how they can be used to deploy Weapons of Mass Destruction and Deception against CBRNE and civilian targets of opportunity. We are concerned with the future use of these inexpensive devices and their availability to maleficent actors. Our work suggests that UASs in air and underwater UUVs will be the future of military and civilian terrorist operations. UAS / UUVs can deliver a huge punch for a low investment and minimize human casualties.https://newprairiepress.org/ebooks/1046/thumbnail.jp