890 research outputs found
Modern computing: Vision and challenges
Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments
In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident.
In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion.
This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture.
Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data.
As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis
Digital Traces of the Mind::Using Smartphones to Capture Signals of Well-Being in Individuals
General context and questions Adolescents and young adults typically use their smartphone several hours a day. Although there are concerns about how such behaviour might affect their well-being, the popularity of these powerful devices also opens novel opportunities for monitoring well-being in daily life. If successful, monitoring well-being in daily life provides novel opportunities to develop future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). Taking an interdisciplinary approach with insights from communication, computational, and psychological science, this dissertation investigated the relation between smartphone app use and well-being and developed machine learning models to estimate an individual’s well-being based on how they interact with their smartphone. To elucidate the relation between smartphone trace data and well-being and to contribute to the development of technologies for monitoring well-being in future clinical practice, this dissertation addressed two overarching questions:RQ1: Can we find empirical support for theoretically motivated relations between smartphone trace data and well-being in individuals? RQ2: Can we use smartphone trace data to monitor well-being in individuals?Aims The first aim of this dissertation was to quantify the relation between the collected smartphone trace data and momentary well-being at the sample level, but also for each individual, following recent conceptual insights and empirical findings in psychological, communication, and computational science. A strength of this personalized (or idiographic) approach is that it allows us to capture how individuals might differ in how smartphone app use is related to their well-being. Considering such interindividual differences is important to determine if some individuals might potentially benefit from spending more time on their smartphone apps whereas others do not or even experience adverse effects. The second aim of this dissertation was to develop models for monitoring well-being in daily life. The present work pursued this transdisciplinary aim by taking a machine learning approach and evaluating to what extent we might estimate an individual’s well-being based on their smartphone trace data. If such traces can be used for this purpose by helping to pinpoint when individuals are unwell, they might be a useful data source for developing future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). With this aim, the dissertation follows current developments in psychoinformatics and psychiatry, where much research resources are invested in using smartphone traces and similar data (obtained with smartphone sensors and wearables) to develop technologies for detecting whether an individual is currently unwell or will be in the future. Data collection and analysis This work combined novel data collection techniques (digital phenotyping and experience sampling methodology) for measuring smartphone use and well-being in the daily lives of 247 student participants. For a period up to four months, a dedicated application installed on participants’ smartphones collected smartphone trace data. In the same time period, participants completed a brief smartphone-based well-being survey five times a day (for 30 days in the first month and 30 days in the fourth month; up to 300 assessments in total). At each measurement, this survey comprised questions about the participants’ momentary level of procrastination, stress, and fatigue, while sleep duration was measured in the morning. Taking a time-series and machine learning approach to analysing these data, I provide the following contributions: Chapter 2 investigates the person-specific relation between passively logged usage of different application types and momentary subjective procrastination, Chapter 3 develops machine learning methodology to estimate sleep duration using smartphone trace data, Chapter 4 combines machine learning and explainable artificial intelligence to discover smartphone-tracked digital markers of momentary subjective stress, Chapter 5 uses a personalized machine learning approach to evaluate if smartphone trace data contains behavioral signs of fatigue. Collectively, these empirical studies provide preliminary answers to the overarching questions of this dissertation.Summary of results With respect to the theoretically motivated relations between smartphone trace data and wellbeing (RQ1), we found that different patterns in smartphone trace data, from time spent on social network, messenger, video, and game applications to smartphone-tracked sleep proxies, are related to well-being in individuals. The strength and nature of this relation depends on the individual and app usage pattern under consideration. The relation between smartphone app use patterns and well-being is limited in most individuals, but relatively strong in a minority. Whereas some individuals might benefit from using specific app types, others might experience decreases in well-being when spending more time on these apps. With respect to the question whether we might use smartphone trace data to monitor well-being in individuals (RQ2), we found that smartphone trace data might be useful for this purpose in some individuals and to some extent. They appear most relevant in the context of sleep monitoring (Chapter 3) and have the potential to be included as one of several data sources for monitoring momentary procrastination (Chapter 2), stress (Chapter 4), and fatigue (Chapter 5) in daily life. Outlook Future interdisciplinary research is needed to investigate whether the relationship between smartphone use and well-being depends on the nature of the activities performed on these devices, the content they present, and the context in which they are used. Answering these questions is essential to unravel the complex puzzle of developing technologies for monitoring well-being in daily life.<br/
Tiny Machine Learning Environment: Enabling Intelligence on Constrained Devices
Running machine learning algorithms (ML) on constrained devices at the extreme edge of the network is problematic due to the computational overhead of ML algorithms, available resources on the embedded platform, and application budget (i.e., real-time requirements, power constraints, etc.). This required the development of specific solutions and development tools for what is now referred to as TinyML. In this dissertation, we focus on improving the deployment and performance of TinyML applications, taking into consideration the aforementioned challenges, especially memory requirements.
This dissertation contributed to the construction of the Edge Learning Machine environment (ELM), a platform-independent open-source framework that provides three main TinyML services, namely shallow ML, self-supervised ML, and binary deep learning on constrained devices. In this context, this work includes the following steps, which are reflected in the thesis structure. First, we present the performance analysis of state-of-the-art shallow ML algorithms including dense neural networks, implemented on mainstream microcontrollers. The comprehensive analysis in terms of algorithms, hardware platforms, datasets, preprocessing techniques, and configurations shows similar performance results compared to a desktop machine and highlights the impact of these factors on overall performance. Second, despite the assumption that TinyML only permits models inference provided by the scarcity of resources, we have gone a step further and enabled self-supervised on-device training on microcontrollers and tiny IoT devices by developing the Autonomous Edge Pipeline (AEP) system. AEP achieves comparable accuracy compared to the typical TinyML paradigm, i.e., models trained on resource-abundant devices and then deployed on microcontrollers. Next, we present the development of a memory allocation strategy for convolutional neural networks (CNNs) layers, that optimizes memory requirements. This approach reduces the memory footprint without affecting accuracy nor latency. Moreover, e-skin systems share the main requirements of the TinyML fields: enabling intelligence with low memory, low power consumption, and low latency. Therefore, we designed an efficient Tiny CNN architecture for e-skin applications. The architecture leverages the memory allocation strategy presented earlier and provides better performance than existing solutions. A major contribution of the thesis is given by CBin-NN, a library of functions for implementing extremely efficient binary neural networks on constrained devices. The library outperforms state of the art NN deployment solutions by drastically reducing memory footprint and inference latency. All the solutions proposed in this thesis have been implemented on representative devices and tested in relevant applications, of which results are reported and discussed. The ELM framework is open source, and this work is clearly becoming a useful, versatile toolkit for the IoT and TinyML research and development community
Horizontally distributed inference of deep neural networks for AI-enabled IoT
Motivated by the pervasiveness of artificial intelligence (AI) and the Internet of Things (IoT) in the current “smart everything” scenario, this article provides a comprehensive overview of the most recent research at the intersection of both domains, focusing on the design and development of specific mechanisms for enabling a collaborative inference across edge devices towards the in situ execution of highly complex state-of-the-art deep neural networks (DNNs), despite the resource-constrained nature of such infrastructures. In particular, the review discusses the most salient approaches conceived along those lines, elaborating on the specificities of the partitioning schemes and the parallelism paradigms explored, providing an organized and schematic discussion of the underlying workflows and associated communication patterns, as well as the architectural aspects of the DNNs that have driven the design of such techniques, while also highlighting both the primary challenges encountered at the design and operational levels and the specific adjustments or enhancements explored in response to them.Agencia Estatal de Investigación | Ref. DPI2017-87494-RMinisterio de Ciencia e Innovación | Ref. PDC2021-121644-I00Xunta de Galicia | Ref. ED431C 2022/03-GR
Improving low latency applications for reconfigurable devices
This thesis seeks to improve low latency application performance via architectural improvements in reconfigurable devices. This is achieved by improving resource utilisation and access, and by exploiting the different environments within which reconfigurable devices are deployed.
Our first contribution leverages devices deployed at the network level to enable the low latency processing of financial market data feeds. Financial exchanges transmit messages via two identical data feeds to reduce the chance of message loss. We present an approach to arbitrate these redundant feeds at the network level using a Field-Programmable Gate Array (FPGA). With support for any messaging protocol, we evaluate our design using the NASDAQ TotalView-ITCH, OPRA, and ARCA data feed protocols, and provide two simultaneous outputs: one prioritising low latency, and one prioritising high reliability with three dynamically configurable windowing methods.
Our second contribution is a new ring-based architecture for low latency, parallel access to FPGA memory. Traditional FPGA memory is formed by grouping block memories (BRAMs) together and accessing them as a single device. Our architecture accesses these BRAMs independently and in parallel. Targeting memory-based computing, which stores pre-computed function results in memory, we benefit low latency applications that rely on: highly-complex functions; iterative computation; or many parallel accesses to a shared resource. We assess square root, power, trigonometric, and hyperbolic functions within the FPGA, and provide a tool to convert Python functions to our new architecture.
Our third contribution extends the ring-based architecture to support any FPGA processing element. We unify E heterogeneous processing elements within compute pools, with each element implementing the same function, and the pool serving D parallel function calls. Our implementation-agnostic approach supports processing elements with different latencies, implementations, and pipeline lengths, as well as non-deterministic latencies. Compute pools evenly balance access to processing elements across the entire application, and are evaluated by implementing eight different neural network activation functions within an FPGA.Open Acces
Deconstructing Gendered and Colonial Violence in the Re-Imagined Nineteenth-Century: Domestic Traumas in Neo-Victorianism on Screen
En esta tesis, examinamos el colapso del concepto idealizado de familia nuclear en las sociedades occidentales a través de la (mala) representación de los traumas de género y coloniales en el neovictorianismo audiovisual. Para ello, analizamos cuatro textos audiovisuales neovictorianos producidos en la última década: Crimson Peak (2015) de Guillermo del Toro, Penny Dreadful (2014-2016), Taboo (2017-presente) y Carnival Row (2019-presente) a través del marco teórico de los estudios de trauma. Nuestras hipótesis de partida son (1) estos textos audiovisuales neovictorianos aparentemente proporcionan una representación feminista de los traumas de género dentro de los confines de la familia nuclear y sus heroínas transgreden con éxito las convenciones de género victorianas. (2) Sin embargo, estas heroínas acaban siendo castigadas por sus transgresiones de género. Por lo tanto, los creadores de estos textos muestran una agenda postfeminista que manipula a la audiencia para que acepte representaciones e ideologías antifeministas. En lo que se refiere a la metodología de esta tesis, hemos llevado a cabo un análisis transversal o comparativo de las obras cinematográficas que componen nuestro corpus de análisis, en lugar de estudiarlas por separado. Así, la presente tesis se estructura en capítulos temáticos, donde cada uno de ellos se centra en un aspecto específico de los traumas de género y coloniales en relación con la familia nuclear. El capítulo 1 presenta los principales objetivos, metodología y estructura de la tesis. El capítulo 2 ofrece una visión general del campo del neovictorianismo, así como de su sinergia con el género gótico, tanto en la literatura como en la pantalla. El capítulo 3 se centra en los principales estudios sobre el trauma y sus aplicaciones en el campo de los estudios culturales, concretamente en el neovictorianismo. El capítulo 4 examina los textos cinematográficos que conforman nuestro corpus de análisis desde la perspectiva de las heterotopías de Foucault. El capítulo 5 analiza los tropos de la maternidad monstruosa y la paternidad ausente en el neovictorianismo audiovisual. El capítulo 6 examina el desmoronamiento de la familia neovictoriana a través de la trama del incesto, en particular el incesto entre hermanos y el incesto entre padre e hija. El capítulo 7 examina la dicotomía víctima-perpetrador en el neovictorianismo audiovisual, con especial énfasis en el concepto del trauma del perpetrador. El capítulo 8 recoge las principales conclusiones de esta tesis doctoral. En primer lugar, aunque estos textos neovictorianos audiovisuales parecen dar voz a sujetos históricamente marginados (como las mujeres o los individuos coloniales), finalmente reproducen a sus predecesores victorianos en la exaltación de los perpetradores blancos, masculinos y de clase media-alta. Esta ideología contradictoria del neovictorianismo audiovisual parece ser sintomática de su contexto de producción posfeminista. Por último, en esta tesis doctoral sostenemos que estos mensajes ideológicos transmitidos a través de la cultura popular deberían considerarse peligrosos para la causa feminista, ya que podrían manipular a las mujeres, haciéndoles creer que el statu quo patriarcal es insuperable.In this thesis, I examine the collapse of the idealised notion of the nuclear family in Western societies through the (mis)representation of gendered and colonial traumas in Neo-Victorianism on screen. In order to do so, I analyse four neo-Victorian screen texts produced in the last decade: Guillermo del Toro's Crimson Peak (2015), Penny Dreadful (2014-2016), Taboo (2017-present) and Carnival Row (2019-present) through the theoretical framework of Trauma Studies. My starting hypotheses are (1) these neo-Victorian screen texts seemingly provide a feminist representation of gendered traumas within the confines of the nuclear family and their heroines successfully transgress Victorian gender conventions. (2) Nonetheless, these heroines are eventually punished for their gender transgressions. Therefore, the creators of these screen texts display a postfeminist agenda to manipulate the audience into accepting anti-feminist representations and ideologies. Regarding the methodology of this thesis, I
have carried out a cross-sectional or comparative examination of the screen works that make up my corpus of analysis, rather than studying them separately. Thus, the present thesis is structured in thematic chapters, with each of them being devoted to a specific aspect of gendered and colonial traumas in relation to the nuclear family.
Chapter 1 presents the main objectives, methodology and structure of the thesis. Chapter 2 offers an overview of the field of neo-Victorianism and its synergy with the Gothic genre, both in literature and on screen. Chapter 3 introduces the main tenets of Trauma Studies and its application to the field of Cultural Studies (particularly to Neo-Victorianism). Chapter 4 examines the screen texts that conform my corpus of analysis from the perspective of Foucauldian heterotopias. Chapter 5 analyses the tropes of monstrous motherhood and absent fatherhood in Neo-Victorianism on screen. Chapter 6 scrutinizes the collapse of the neo-Victorian family through the incest plotline, particularly sibling incest and father-daughter incest. Chapter 7 examines the victim-perpetrator dichotomy in neo-Victorianism on screen, with a particular emphasis on the concept of perpetrator trauma. Chapter 8 gathers the main conclusions of this PhD thesis. First, even though these neo-Victorian screen texts seemingly give voice to historically marginalised subjects (such as women or colonized individuals), they finally replicate their Victorian predecessors in uplifting white, male, upper-middle-class perpetrators. This contradictory ideology in neo-Victorianism on screen appears to be symptomatic of its postfeminist context of production. Finally, in this PhD thesis I argue that these ideological messages conveyed through popular culture should be considered dangerous for the feminist cause, as they might mislead women into believing that the patriarchal status quo is unsurmountable
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