Container-based microservice architecture for local IoT services

Abstract. Edge services are needed to save networking and computational resources on higher tiers, enable operation during network problems, and to help limiting private data propagation to higher tiers if the function needing it can be handled locally. MEC at access network level provides most of these features but cannot help when access network is down. Local services, in addition, help alleviating the MEC load and limit the data propagation even more, on local level. This thesis focuses on the local IoT service provisioning. Local service provisioning is subject to several requirements, related to resource/energy-efficiency, performance and reliability. This thesis introduces a novel way to design and implement a Docker container-based micro-service system for gadget-free future IoT (Internet of Things) network. It introduces a use case scenario and proposes few possible required micro-services as of solution to the scenario. Some of these services deployed on different virtual platforms along with software components that can process sensor data providing storage capacity to make decisions based on their algorithm and business logic while few other services deployed with gateway components to connect rest of the devices to the system of solution. It also includes a state-of-the-art study for design, implementation, and evaluation as a Proof-of-Concept (PoC) based on container-based microservices with Docker. The used IoT devices are Raspberry Pi embedded computers along with an Ubuntu machine with a rich set of features and interfaces, capable of running virtualized services. This thesis evaluates the solution based on practical implementation. In addition, the thesis also discusses the benefits and drawbacks of the system with respect to the empirical solution. The output of the thesis shows that the virtualized microservices could be efficiently utilized at the local and resource constrained IoT using Dockers. This validates that the approach taken in this thesis is feasible for providing such services and functionalities to the micro and nanoservice architecture. Finally, this thesis proposes numerous improvements for future iterations

Towards understanding the challenges faced by machine learning software developers and enabling automated solutions

Modern software systems are increasingly including machine learning (ML) as an integral component. However, we do not yet understand the difficulties faced by software developers when learning about ML libraries and using them within their systems. To fill that gap this thesis reports on a detailed (manual) examination of 3,243 highly-rated Q&A posts related to ten ML libraries, namely Tensorflow, Keras, scikitlearn, Weka, Caffe, Theano, MLlib, Torch, Mahout, and H2O, on Stack Overflow, a popular online technical Q&A forum. Our findings reveal the urgent need for software engineering (SE) research in this area. The second part of the thesis particularly focuses on understanding the Deep Neural Network (DNN) bug characteristics. We study 2,716 high-quality posts from Stack Overflow and 500 bug fix commits from Github about five popular deep learning libraries Caffe, Keras, Tensorflow, Theano, and Torch to understand the types of bugs, their root causes and impacts, bug-prone stage of deep learning pipeline as well as whether there are some common antipatterns found in this buggy software. While exploring the bug characteristics, our findings imply that repairing software that uses DNNs is one such unmistakable SE need where automated tools could be beneficial; however, we do not fully understand challenges to repairing and patterns that are utilized when manually repairing DNNs. So, the third part of this thesis presents a comprehensive study of bug fix patterns to address these questions. We have studied 415 repairs from Stack Overflow and 555 repairs from Github for five popular deep learning libraries Caffe, Keras, Tensorflow, Theano, and Torch to understand challenges in repairs and bug repair patterns. Our key findings reveal that DNN bug fix patterns are distinctive compared to traditional bug fix patterns and the most common bug fix patterns are fixing data dimension and neural network connectivity. Finally, we propose an automatic technique to detect ML Application Programming Interface (API) misuses. We started with an empirical study to understand ML API misuses. Our study shows that ML API misuse is prevalent and distinct compared to non-ML API misuses. Inspired by these findings, we contributed Amimla (Api Misuse In Machine Learning Apis) an approach and a tool for ML API misuse detection. Amimla relies on several technical innovations. First, we proposed an abstract representation of ML pipelines to use in misuse detection. Second, we proposed an abstract representation of neural networks for deep learning related APIs. Third, we have developed a representation strategy for constraints on ML APIs. Finally, we have developed a misuse detection strategy for both single and multi-APIs. Our experimental evaluation shows that Amimla achieves a high average accuracy of ∼80% on two benchmarks of misuses from Stack Overflow and Github

Pd And Cu Monometallic And Single Atom Alloy Catalysts For The Hydrogenation Of Biomass Based Chemicals

The liquid-phase selective hydrogenation ofbio-based platform molecules like furfural was studied with non-toxic Cu-based heterogeneous catalysts.The initial materials were synthesised via wet impregnation using various copper precursors (nitrate, acetate, and sulfate) at two different loadings. High Cu loading (5 wt%) led to the formation of well-defined nanoparticles, while lower loading (1 wt%) generated highly dispersed atomic and dimeric Cu species on the non-porous nano-Al2O3support. Copper sulfatederived catalysts severely reduced the selectivity of furfuryl alcohol from 94.6% to 0.8% and promoted acetalisation reactions instead. On the contrary, sulfur-free copper acetate derived catalysts were found optimal for catalysing this reaction. The research then focused on enhancing colloidally synthesised Cu catalysts by incorporating trace-amounts of Pd atoms via galvanic replacement. These materials were referred as single atom alloy catalysts (SAA), as EXAFS confirmed they were atomically dispersed Pd atoms on Cu nanoparticles. These SAA catalysts improved the furfural conversion to furfuryl alcohol compared to the monometallic catalysts, as they presented the advantages of Cu (high selectivity) and Pd (superior activity) monometallic catalysts, without the drawbacks (copper’s low activity and palladium’s poor selectivity). As a result, SAA proved to be optimal green/atom efficient catalysts. Finally, the synthesised materials were tested for the hydrogenation of crotonaldehyde. Crotonaldehyde was chosen as it lacked the directing group present in furfural (furan ring), so the catalysts can be examined when the C ═ O hydrogenation is not specifically preferred. The SAA catalysts improved the normalised catalytic activity by nineteen-fold when compared to the Pd100 benchmark catalyst, while maintaining the reactive pathway of the Cu nanoparticle host. In essence, the presence of Pd “fast-forwarded” the extent of the reaction. For the wet impregnation monometallic Cu materials, the acetate precursor catalysts (1 and 5 wt%) showed superior activity, while the 5 wt% sulfur-based was the worst

Identifying Classes Susceptible to Adversarial Attacks

Despite numerous attempts to defend deep learning based image classifiers, they remain susceptible to the adversarial attacks. This paper proposes a technique to identify susceptible classes, those classes that are more easily subverted. To identify the susceptible classes we use distance-based measures and apply them on a trained model. Based on the distance among original classes, we create mapping among original classes and adversarial classes that helps to reduce the randomness of a model to a significant amount in an adversarial setting. We analyze the high dimensional geometry among the feature classes and identify the k most susceptible target classes in an adversarial attack. We conduct experiments using MNIST, Fashion MNIST, CIFAR-10 (ImageNet and ResNet-32) datasets. Finally, we evaluate our techniques in order to determine which distance-based measure works best and how the randomness of a model changes with perturbation

Study of Characteristics Curves Top-Gated Graphene FET Using SILVACO TCAD

This work presents a SILVACO TCAD based fabrication and device simulation of a top-gated graphene field-effect transistor. Effects of channel length and channel doping concentrations on the characteristics curves (transfer and output characteristics) of the GFET are also investigated and analyzed physically to obtain more physical insight