Doctor of Philosophy

dissertationInteractive editing and manipulation of digital media is a fundamental component in digital content creation. One media in particular, digital imagery, has seen a recent increase in popularity of its large or even massive image formats. Unfortunately, current systems and techniques are rarely concerned with scalability or usability with these large images. Moreover, processing massive (or even large) imagery is assumed to be an off-line, automatic process, although many problems associated with these datasets require human intervention for high quality results. This dissertation details how to design interactive image techniques that scale. In particular, massive imagery is typically constructed as a seamless mosaic of many smaller images. The focus of this work is the creation of new technologies to enable user interaction in the formation of these large mosaics. While an interactive system for all stages of the mosaic creation pipeline is a long-term research goal, this dissertation concentrates on the last phase of the mosaic creation pipeline - the composition of registered images into a seamless composite. The work detailed in this dissertation provides the technologies to fully realize interactive editing in mosaic composition on image collections ranging from the very small to massive in scale

Big Data and Large-scale Data Analytics: Efficiency of Sustainable Scalability and Security of Centralized Clouds and Edge Deployment Architectures

One of the significant shifts of the next-generation computing technologies will certainly be in the development of Big Data (BD) deployment architectures. Apache Hadoop, the BD landmark, evolved as a widely deployed BD operating system. Its new features include federation structure and many associated frameworks, which provide Hadoop 3.x with the maturity to serve different markets. This dissertation addresses two leading issues involved in exploiting BD and large-scale data analytics realm using the Hadoop platform. Namely, (i)Scalability that directly affects the system performance and overall throughput using portable Docker containers. (ii) Security that spread the adoption of data protection practices among practitioners using access controls. An Enhanced Mapreduce Environment (EME), OPportunistic and Elastic Resource Allocation (OPERA) scheduler, BD Federation Access Broker (BDFAB), and a Secure Intelligent Transportation System (SITS) of multi-tiers architecture for data streaming to the cloud computing are the main contribution of this thesis study

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

An Interactive Relaxation Approach for Anomaly Detection and Preventive Measures in Computer Networks

It is proposed to develop a framework of detecting and analyzing small and widespread changes in specific dynamic characteristics of several nodes. The characteristics are locally measured at each node in a large network of computers and analyzed using a computational paradigm known as the Relaxation technique. The goal is to be able to detect the onset of a worm or virus as it originates, spreads-out, attacks and disables the entire network. Currently, selective disabling of one or more features across an entire subnet, e.g. firewalls, provides limited security and keeps us from designing high performance net-centric systems. The most desirable response is to surgically disable one or more nodes, or to isolate one or more subnets.The proposed research seeks to model virus/worm propagation as a spatio-temporal process. Such models have been successfully applied in heat-flow and evidence or gestalt driven perception of images among others. In particular, we develop an iterative technique driven by the self-assessed dynamic status of each node in a network. The status of each node will be updated incrementally in concurrence with its connected neighbors to enable timely identification of compromised nodes and subnets. Several key insights used in image analysis of line-diagrams, through an iterative and relaxation-driven node labeling method, are explored to help develop this new framework

Machine Learning and other Computational-Intelligence Techniques for Security Applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

A General Framework for Motion Sensor Based Web Services

With the development of motion sensing technology, motion sensor based services have been put into a wide range of applications in recent years. Demand of consuming such service on mobile devices has already emerged. However, as most motion sensors are specifically designed for some heavyweight clients such as PCs or game consoles, there are several technical challenges prohibiting motion sensor from being used by lightweight clients such as mobile devices, for example: There is no direct approach to connect the motion sensor with mobile devices. Most mobile devices don't have enough computational power to consume the motion sensor outputs. To address these problems, I have designed and implemented a framework for publishing general motion sensor functionalities as a RESTful web service that is accessible to mobile devices via HTTP connections. In the framework, a pure HTML5 based interface is delivered to the clients to ensure good accessibility, a websocket based data transferring scheme is adopted to guarantee data transferring efficiency, a server side gesture pipeline is proposed to reduce the client side computational burden and a distributed architecture is designed to make the service scalable. Finally, I conducted three experiments to evaluate the framework's compatibility, scalability and data transferring performance

Hybrid intelligent framework for automated medical learning

This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up-to-date medical learning models regarding both the computational and cost the quality of returned solutions.publishedVersio

Hybrid intelligent framework for automated medical learning

This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up-to-date medical learning models regarding both the computational and cost the quality of returned solutionspublishedVersio

Design and Optimization of Mobile Cloud Computing Systems with Networked Virtual Platforms

A Mobile Cloud Computing (MCC) system is a cloud-based system that is accessed by the users through their own mobile devices. MCC systems are emerging as the product of two technology trends: 1) the migration of personal computing from desktop to mobile devices and 2) the growing integration of large-scale computing environments into cloud systems. Designers are developing a variety of new mobile cloud computing systems. Each of these systems is developed with different goals and under the influence of different design constraints, such as high network latency or limited energy supply. The current MCC systems rely heavily on Computation Offloading, which however incurs new problems such as scalability of the cloud, privacy concerns due to storing personal information on the cloud, and high energy consumption on the cloud data centers. In this dissertation, I address these problems by exploring different options in the distribution of computation across different computing nodes in MCC systems. My thesis is that "the use of design and simulation tools optimized for design space exploration of the MCC systems is the key to optimize the distribution of computation in MCC." For a quantitative analysis of mobile cloud computing systems through design space exploration, I have developed netShip, the first generation of an innovative design and simulation tool, that offers large scalability and heterogeneity support. With this tool system designers and software programmers can efficiently develop, optimize, and validate large-scale, heterogeneous MCC systems. I have enhanced netShip to support the development of ever-evolving MCC applications with a variety of emerging needs including the fast simulation of new devices, e.g., Internet-of-Things devices, and accelerators, e.g., mobile GPUs. Leveraging netShip, I developed three new MCC systems where I applied three variations of a new computation distributing technique, called Reverse Offloading. By more actively leveraging the computational power on mobile devices, the MCC systems can reduce the total execution times, the burden of concentrated computations on the cloud, and the privacy concerns about storing personal information available in the cloud. This approach also creates opportunities for new services by utilizing the information available on the mobile device instead of accessing the cloud. Throughout my research I have enabled the design optimization of mobile applications and cloud-computing platforms. In particular, my design tool for MCC systems becomes a vehicle to optimize not only the performance but also the energy dissipation, an aspect of critical importance for any computing system

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, pre-processing 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