Zero-padding Network Coding and Compressed Sensing for Optimized Packets Transmission

Ubiquitous Internet of Things (IoT) is destined to connect everybody and everything on a never-before-seen scale. Such networks, however, have to tackle the inherent issues created by the presence of very heterogeneous data transmissions over the same shared network. This very diverse communication, in turn, produces network packets of various sizes ranging from very small sensory readings to comparatively humongous video frames. Such a massive amount of data itself, as in the case of sensory networks, is also continuously captured at varying rates and contributes to increasing the load on the network itself, which could hinder transmission efficiency. However, they also open up possibilities to exploit various correlations in the transmitted data due to their sheer number. Reductions based on this also enable the networks to keep up with the new wave of big data-driven communications by simply investing in the promotion of select techniques that efficiently utilize the resources of the communication systems. One of the solutions to tackle the erroneous transmission of data employs linear coding techniques, which are ill-equipped to handle the processing of packets with differing sizes. Random Linear Network Coding (RLNC), for instance, generates unreasonable amounts of padding overhead to compensate for the different message lengths, thereby suppressing the pervasive benefits of the coding itself. We propose a set of approaches that overcome such issues, while also reducing the decoding delays at the same time. Specifically, we introduce and elaborate on the concept of macro-symbols and the design of different coding schemes. Due to the heterogeneity of the packet sizes, our progressive shortening scheme is the first RLNC-based approach that generates and recodes unequal-sized coded packets. Another of our solutions is deterministic shifting that reduces the overall number of transmitted packets. Moreover, the RaSOR scheme employs coding using XORing operations on shifted packets, without the need for coding coefficients, thus favoring linear encoding and decoding complexities. Another facet of IoT applications can be found in sensory data known to be highly correlated, where compressed sensing is a potential approach to reduce the overall transmissions. In such scenarios, network coding can also help. Our proposed joint compressed sensing and real network coding design fully exploit the correlations in cluster-based wireless sensor networks, such as the ones advocated by Industry 4.0. This design focused on performing one-step decoding to reduce the computational complexities and delays of the reconstruction process at the receiver and investigates the effectiveness of combined compressed sensing and network coding

Processing Structured Hypermedia : A Matter of Style

With the introduction of the World Wide Web in the early nineties, hypermedia has become the uniform interface to the wide variety of information sources available over the Internet. The full potential of the Web, however, can only be realized by building on the strengths of its underlying research fields. This book describes the areas of hypertext, multimedia, electronic publishing and the World Wide Web and points out fundamental similarities and differences in approaches towards the processing of information. It gives an overview of the dominant models and tools developed in these fields and describes the key interrelationships and mutual incompatibilities. In addition to a formal specification of a selection of these models, the book discusses the impact of the models described on the software architectures that have been developed for processing hypermedia documents. Two example hypermedia architectures are described in more detail: the DejaVu object-oriented hypermedia framework, developed at the VU, and CWI's Berlage environment for time-based hypermedia document transformations

Uncertainty, interpretability and dataset limitations in Deep Learning

[eng] Deep Learning (DL) has gained traction in the last years thanks to the exponential increase in compute power. New techniques and methods are published at a daily basis, and records are being set across multiple disciplines. Undeniably, DL has brought a revolution to the machine learning field and to our lives. However, not everything has been resolved and some considerations must be taken into account. For instance, obtaining uncertainty measures and bounds is still an open problem. Models should be able to capture and express the confidence they have in their decisions, and Artificial Neural Networks (ANN) are known to lack in this regard. Be it through out of distribution samples, adversarial attacks, or simply unrelated or nonsensical inputs, ANN models demonstrate an unfounded and incorrect tendency to still output high probabilities. Likewise, interpretability remains an unresolved question. Some fields not only need but rely on being able to provide human interpretations of the thought process of models. ANNs, and specially deep models trained with DL, are hard to reason about. Last but not least, there is a tendency that indicates that models are getting deeper and more complex. At the same time, to cope with the increasing number of parameters, datasets are required to be of higher quality and, usually, larger. Not all research, and even less real world applications, can keep with the increasing demands. Therefore, taking into account the previous issues, the main aim of this thesis is to provide methods and frameworks to tackle each of them. These approaches should be applicable to any suitable field and dataset, and are employed with real world datasets as proof of concept. First, we propose a method that provides interpretability with respect to the results through uncertainty measures. The model in question is capable of reasoning about the uncertainty inherent in data and leverages that information to progressively refine its outputs. In particular, the method is applied to land cover segmentation, a classification task that aims to assign a type of land to each pixel in satellite images. The dataset and application serve to prove that the final uncertainty bound enables the end-user to reason about the possible errors in the segmentation result. Second, Recurrent Neural Networks are used as a method to create robust models towards lacking datasets, both in terms of size and class balance. We apply them to two different fields, road extraction in satellite images and Wireless Capsule Endoscopy (WCE). The former demonstrates that contextual information in the temporal axis of data can be used to create models that achieve comparable results to state-of-the-art while being less complex. The latter, in turn, proves that contextual information for polyp detection can be crucial to obtain models that generalize better and obtain higher performance. Last, we propose two methods to leverage unlabeled data in the model creation process. Often datasets are easier to obtain than to label, which results in many wasted opportunities with traditional classification approaches. Our approaches based on self-supervised learning result in a novel contrastive loss that is capable of extracting meaningful information out of pseudo-labeled data. Applying both methods to WCE data proves that the extracted inherent knowledge creates models that perform better in extremely unbalanced datasets and with lack of data. To summarize, this thesis demonstrates potential solutions to obtain uncertainty bounds, provide reasonable explanations of the outputs, and to combat lack of data or unbalanced datasets. Overall, the presented methods have a positive impact on the DL field and could have a real and tangible effect for the society.[cat] És innegable que el Deep Learning ha causat una revolució en molts aspectes no solament de l’aprenentatge automàtic però també de les nostres vides diàries. Tot i així, encara queden aspectes a millorar. Les xarxes neuronals tenen problemes per estimar la seva confiança en les prediccions, i sovint reporten probabilitats altes en casos que no tenen relació amb el model o que directament no tenen sentit. De la mateixa forma, interpretar els resultats d’un model profund i complex resulta una tasca extremadament complicada. Aquests mateixos models, cada cop amb més paràmetres i més potents, requereixen també de dades més ben etiquetades i més completes. Tenint en compte aquestes limitacions, l’objectiu principal és el de buscar mètodes i algoritmes per trobar-ne solució. Primerament, es proposa la creació d’un mètode capaç d’obtenir incertesa en imatges satèl·lit i d’utilitzar-la per crear models més robustos i resultats interpretables. En segon lloc, s’utilitzen Recurrent Neural Networks (RNN) per combatre la falta de dades mitjançant l’obtenció d’informació contextual de dades temporals. Aquestes s’apliquen per l’extracció de carreteres d’imatges satèl·lit i per la classificació de pòlips en imatges obtingudes amb Wireless Capsule Endoscopy (WCE). Finalment, es plantegen dos mètodes per tractar amb la falta de dades etiquetades i desbalancejos en les classes amb l’ús de Self-supervised Learning (SSL). Seqüències no etiquetades d’imatges d’intestins s’incorporen en el models en una fase prèvia a la classificació tradicional. Aquesta tesi demostra que les solucions proposades per obtenir mesures d’incertesa són efectives per donar explicacions raonables i interpretables sobre els resultats. Igualment, es prova que el context en dades de caràcter temporal, obtingut amb RNNs, serveix per obtenir models més simples que poden arribar a solucionar els problemes derivats de la falta de dades. Per últim, es mostra que SSL serveix per combatre de forma efectiva els problemes de generalització degut a dades no balancejades en diversos dominis de WCE. Concloem que aquesta tesi presenta mètodes amb un impacte real en diversos aspectes de DL a la vegada que demostra la capacitat de tenir un impacte positiu en la societat

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS

Proceedings of the EAA Joint Symposium on Auralization and Ambisonics 2014

In consideration of the remarkable intensity of research in the field of Virtual Acoustics, including different areas such as sound field analysis and synthesis, spatial audio technologies, and room acoustical modeling and auralization, it seemed about time to organize a second international symposium following the model of the first EAA Auralization Symposium initiated in 2009 by the acoustics group of the former Helsinki University of Technology (now Aalto University). Additionally, research communities which are focused on different approaches to sound field synthesis such as Ambisonics or Wave Field Synthesis have, in the meantime, moved closer together by using increasingly consistent theoretical frameworks. Finally, the quality of virtual acoustic environments is often considered as a result of all processing stages mentioned above, increasing the need for discussions on consistent strategies for evaluation. Thus, it seemed appropriate to integrate two of the most relevant communities, i.e. to combine the 2nd International Auralization Symposium with the 5th International Symposium on Ambisonics and Spherical Acoustics. The Symposia on Ambisonics, initiated in 2009 by the Institute of Electronic Music and Acoustics of the University of Music and Performing Arts in Graz, were traditionally dedicated to problems of spherical sound field analysis and re-synthesis, strategies for the exchange of ambisonics-encoded audio material, and – more than other conferences in this area – the artistic application of spatial audio systems. This publication contains the official conference proceedings. It includes 29 manuscripts which have passed a 3-stage peer-review with a board of about 70 international reviewers involved in the process. Each contribution has already been published individually with a unique DOI on the DepositOnce digital repository of TU Berlin. Some conference contributions have been recommended for resubmission to Acta Acustica united with Acustica, to possibly appear in a Special Issue on Virtual Acoustics in late 2014. These are not published in this collection.European Acoustics Associatio

An FPGA implementation of an investigative many-core processor, Fynbos : in support of a Fortran autoparallelising software pipeline

Includes bibliographical references.In light of the power, memory, ILP, and utilisation walls facing the computing industry, this work examines the hypothetical many-core approach to finding greater compute performance and efficiency. In order to achieve greater efficiency in an environment in which Moore’s law continues but TDP has been capped, a means of deriving performance from dark and dim silicon is needed. The many-core hypothesis is one approach to exploiting these available transistors efficiently. As understood in this work, it involves trading in hardware control complexity for hundreds to thousands of parallel simple processing elements, and operating at a clock speed sufficiently low as to allow the efficiency gains of near threshold voltage operation. Performance is there- fore dependant on exploiting a new degree of fine-grained parallelism such as is currently only found in GPGPUs, but in a manner that is not as restrictive in application domain range. While removing the complex control hardware of traditional CPUs provides space for more arithmetic hardware, a basic level of control is still required. For a number of reasons this work chooses to replace this control largely with static scheduling. This pushes the burden of control primarily to the software and specifically the compiler, rather not to the programmer or to an application specific means of control simplification. An existing legacy tool chain capable of autoparallelising sequential Fortran code to the degree of parallelism necessary for many-core exists. This work implements a many-core architecture to match it. Prototyping the design on an FPGA, it is possible to examine the real world performance of the compiler-architecture system to a greater degree than simulation only would allow. Comparing theoretical peak performance and real performance in a case study application, the system is found to be more efficient than any other reviewed, but to also significantly under perform relative to current competing architectures. This failing is apportioned to taking the need for simple hardware too far, and an inability to implement static scheduling mitigating tactics due to lack of support for such in the compiler