9 research outputs found

    Scientific Advances in STEM: From Professor to Students

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    This book collects the publications of the special Topic Scientific advances in STEM: from Professor to students. The aim is to contribute to the advancement of the Science and Engineering fields and their impact on the industrial sector, which requires a multidisciplinary approach. University generates and transmits knowledge to serve society. Social demands continuously evolve, mainly because of cultural, scientific, and technological development. Researchers must contextualize the subjects they investigate to their application to the local industry and community organizations, frequently using a multidisciplinary point of view, to enhance the progress in a wide variety of fields (aeronautics, automotive, biomedical, electrical and renewable energy, communications, environmental, electronic components, etc.). Most investigations in the fields of science and engineering require the work of multidisciplinary teams, representing a stockpile of research projects in different stages (final year projects, master’s or doctoral studies). In this context, this Topic offers a framework for integrating interdisciplinary research, drawing together experimental and theoretical contributions in a wide variety of fields

    On I/O Performance and Cost Efficiency of Cloud Storage: A Client\u27s Perspective

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    Cloud storage has gained increasing popularity in the past few years. In cloud storage, data are stored in the service provider’s data centers; users access data via the network and pay the fees based on the service usage. For such a new storage model, our prior wisdom and optimization schemes on conventional storage may not remain valid nor applicable to the emerging cloud storage. In this dissertation, we focus on understanding and optimizing the I/O performance and cost efficiency of cloud storage from a client’s perspective. We first conduct a comprehensive study to gain insight into the I/O performance behaviors of cloud storage from the client side. Through extensive experiments, we have obtained several critical findings and useful implications for system optimization. We then design a client cache framework, called Pacaca, to further improve end-to-end performance of cloud storage. Pacaca seamlessly integrates parallelized prefetching and cost-aware caching by utilizing the parallelism potential and object correlations of cloud storage. In addition to improving system performance, we have also made efforts to reduce the monetary cost of using cloud storage services by proposing a latency- and cost-aware client caching scheme, called GDS-LC, which can achieve two optimization goals for using cloud storage services: low access latency and low monetary cost. Our experimental results show that our proposed client-side solutions significantly outperform traditional methods. Our study contributes to inspiring the community to reconsider system optimization methods in the cloud environment, especially for the purpose of integrating cloud storage into the current storage stack as a primary storage layer

    Securing Wireless Communications of the Internet of Things from the Physical Layer, An Overview

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    The security of the Internet of Things (IoT) is receiving considerable interest as the low power constraints and complexity features of many IoT devices are limiting the use of conventional cryptographic techniques. This article provides an overview of recent research efforts on alternative approaches for securing IoT wireless communications at the physical layer, specifically the key topics of key generation and physical layer encryption. These schemes can be implemented and are lightweight, and thus offer practical solutions for providing effective IoT wireless security. Future research to make IoT-based physical layer security more robust and pervasive is also covered

    VideoTag: Encouraging the Effective Tagging of Internet Videos Through Tagging Games

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    A thesis submitted in partial fulfillment of the requirements of the University of Wolverhampton for the degree of Doctor of PhilosophyAbstract The tags and descriptions entered by video owners in video sharing sites are typically inadequate for retrieval purposes, yet the majority of video search still uses this text. This problem is escalating due to the ease with which users can self-publish videos, generating masses that are poorly labelled and poorly described. This thesis investigates how users tag videos and whether video tagging games can solve this problem by generating useful sets of tags. A preliminary study investigated tags in two social video sharing sites, YouTube and Viddler. YouTube contained many irrelevant tags because the system does not encourage users to tag their videos and does not promote tags as useful. In contrast, using tags as the sole means of categorisation in Viddler motivated users to enter a higher proportion of relevant tags. Poor tags were found in both systems, however, highlighting the need to improve video tagging. In order to give users incentives to tag videos, the VideoTag project in this thesis developed two tagging games, Golden Tag and Top Tag, and one non-game tagging system, Simply Tag, and conducted two experiments with them. In the first experiment VideoTag was a portal to play video tagging games whereas in the second experiment it was a portal to curate collections of special interest videos. Users preferred to tag videos using games, generating tags that were relevant to the videos and that covered a range of tag types that were descriptive of the video content at a predominately specific, objective level. Users were motivated by interest in the content rather than by game elements, and content had an effect on the tag types used. In each experiment, users predominately tagged videos using objective language, with a tendency to use specific rather than basic tags. There was a significant difference between the types of tags entered in the games and in Simply Tag, with more basic, objective vocabulary entered into the games and more specific, objective language entered into the non-game system. Subjective tags were rare but were more frequent in Simply Tag. Gameplay also had an influence on the types of tags entered; Top Tag generated more basic tags and Golden Tag generated more specific and subjective tags. Users were not attracted to use VideoTag by the games alone. Game mechanics had little impact on motivations to use the system. VideoTag used YouTube videos, but could not upload the tags to YouTube and so users could see no benefit for the tags they entered, reducing participation. Specific interest content was more of a motivator for use than games or tagging and that this warrants further research. In the current game-saturated climate, gamification of a video tagging system may therefore be most successful for collections of videos that already have a committed user base.University of Wolverhampto

    Improved self-management of datacenter systems applying machine learning

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    Autonomic Computing is a Computer Science and Technologies research area, originated during mid 2000's. It focuses on optimization and improvement of complex distributed computing systems through self-control and self-management. As distributed computing systems grow in complexity, like multi-datacenter systems in cloud computing, the system operators and architects need more help to understand, design and optimize manually these systems, even more when these systems are distributed along the world and belong to different entities and authorities. Self-management lets these distributed computing systems improve their resource and energy management, a very important issue when resources have a cost, by obtaining, running or maintaining them. Here we propose to improve Autonomic Computing techniques for resource management by applying modeling and prediction methods from Machine Learning and Artificial Intelligence. Machine Learning methods can find accurate models from system behaviors and often intelligible explanations to them, also predict and infer system states and values. These models obtained from automatic learning have the advantage of being easily updated to workload or configuration changes by re-taking examples and re-training the predictors. So employing automatic modeling and predictive abilities, we can find new methods for making "intelligent" decisions and discovering new information and knowledge from systems. This thesis departs from the state of the art, where management is based on administrators expertise, well known data, ad-hoc studied algorithms and models, and elements to be studied from computing machine point of view; to a novel state of the art where management is driven by models learned from the same system, providing useful feedback, making up for incomplete, missing or uncertain data, from a global network of datacenters point of view. - First of all, we cover the scenario where the decision maker works knowing all pieces of information from the system: how much will each job consume, how is and will be the desired quality of service, what are the deadlines for the workload, etc. All of this focusing on each component and policy of each element involved in executing these jobs. -Then we focus on the scenario where instead of fixed oracles that provide us information from an expert formula or set of conditions, machine learning is used to create these oracles. Here we look at components and specific details while some part of the information is not known and must be learned and predicted. - We reduce the problem of optimizing resource allocations and requirements for virtualized web-services to a mathematical problem, indicating each factor, variable and element involved, also all the constraints the scheduling process must attend to. The scheduling problem can be modeled as a Mixed Integer Linear Program. Here we face an scenario of a full datacenter, further we introduce some information prediction. - We complement the model by expanding the predicted elements, studying the main resources (this is CPU, Memory and IO) that can suffer from noise, inaccuracy or unavailability. Once learning predictors for certain components let the decision making improve, the system can become more ¿expert-knowledge independent¿ and research can focus on an scenario where all the elements provide noisy, uncertainty or private information. Also we introduce to the management optimization new factors as for each datacenter context and costs may change, turning the model as "multi-datacenter" - Finally, we review of the cost of placing datacenters depending on green energy sources, and distribute the load according to green energy availability

    Enhancing AQM to combat wireless losses

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    In order to maintain a small, stable backlog at the router buffer, active queue management (AQM) algorithms drop packets probabilistically at the onset of congestion, leading to backoffs by Transmission Control Protocol (TCP) flows. However, wireless losses may be misinterpreted as congestive losses and induce spurious backoffs. In this paper, we raise the basic question: Can AQM maintain a stable, small backlog under wireless losses? We find that the representative AQM, random early detection (RED), fails to maintain a stable backlog under time-varying wireless losses. We find that the key to resolving the problem is to robustly track the backlog to a preset reference level, and apply the control-theoretic vehicle, internal model principle, to realize such tracking. We further devise the integral controller (IC) as an embodiment of the principle. Our simulation results show that IC is robust against time-varying wireless losses under various network scenarios. © 2012 IEEE.published_or_final_versio

    Self-Evaluation Applied Mathematics 2003-2008 University of Twente

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    This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008
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