234 research outputs found
Just Us: Et Blockchain-basert Personvernvennlig Sosialt Nettverk
No full textDenne rapporten beskriver et sosialt nettverk laget ved hjelp av react native og Hyperledger Fabric for å gi brukere en større grad av kontroll over brukerdata. Resultatet av prosjektet viser at det er et potensiale for å anvende blockchain-teknologi i sosiale nettverk, men at det ikke alene kan påvirke hvordan nettverkstilbyderen behandler brukerdataene på vei gjennom nettverket
NFT as a proof of Digital Ownership-reward system integrated to a Secure Distributed Computing Blockchain Framework
No full textToday, the global economy is dependent on the Internet and computational resources. Although they are tightly interconnected, it is difficult to evaluate their degree of interdependence. Keeping up with the pace of technology can be a challenging task, mainly when updating the hardware and software infrastructure. Every day, corporations and governments are faced with this issue; most have been victims of cyber attacks, security breaches, and data leaks. The consequences are significant in monetary losses; damage remediation is unattainable, even impossible, in certain circumstances. The repercussions might include reputational damage, legal responsibility, and threats to national security (when attacks are carried out against critical infrastructures to control the resources of a country), to name a few. Similarly, data has become such an integral part of many industries that it is one of the most critical targets for attackers that often is encrypted by ransomware, stolen, or corrupted. Without data, many companies are not be able to continue operating as they do. The combination of all these factors complicates the ability of organizations to cooperate, trust, and share information in efforts to research and develop solutions for industry and government. A promising technology can assist in significantly reducing the damage caused by the security threats outlined above: Blockchain technology has proven to be one of the most promising inventions of the twenty-first century for transmitting and protecting information while offering high reliability and availability, low exposure to attacks, protected encrypted data, and accessible to the entities willing to participate. Blockchain enabled the possibility to embed immutable data and compiled source code known as ‘smart contract’ where certain rules can be programmed to create business workflows. This thesis report proposes a Blockchain-based infrastructure solution provided by ”Hyperledger Fabric” technology for companies to securely transmit and share information using the latest encryption and data storage technologies operating on the model of distributed systems and smart contracts. By presenting unique digital assets as Non-Fungible Tokens (NFT), the infrastructure is able to trust the integrity of the data, while protecting it from counterfeiting. Through the use of a Blockchain-based file storage system known as IPFS, and by connecting all the relevant elements together through a web-based application, it is possible to demonstrate that the implementation of such systems is feasible, highly scalable and a useful tool that many organizations can utilize to create new work systems and workflows for digital asset management
System for Workflow Design and Execution on Data Shared Between Untrusting Organizations for Analytics
No full textPerformance of complex analytics \& AI algorithms typically involves large amounts of data. The data may originate from multiple sources and is typically compiled and moved to a central location before it can be consumed by the algorithms, making this approach impractical for untrusting organizations interested to share analytics and results but not risking the exposure of the dataset in its entirety. Current approaches to support such a scenario for data consumption is to move the computation closer to the data instead of the other way around. But that involves writing code for distributed file systems like Hadoop File System (HDFS), which demands professional expertise in writing Map-Reduce jobs and parallel code design patterns. In this thesis, we demonstrate a proof of concept allowing organizations to share their datasets for consumption by inter-organizational workflows without exposing the data itself and avoiding distributed programming expertise. We propose an approach using Hyperledger Fabric for untrusting entities to advertise their datasets for consumption by other organizations without demanding extensive knowledge of writing distributed code, and all this without ever exposing the data itself to the user. Hence the analytics can be run on the data while maintaining ownership. A permissioned blockchain network is established using Hyperledger Fabric and organizations can join the mentioned consortium. A JupyterHub server is hosted on a Kubernetes cluster that services users with a Jupyter instance where users can explore the datasets available through our custom extension, write code and construct workflows running the algorithms on the datasets. The required datasets are consumed as persistent volumes when running the workflow; only exposing the data to the job requiring it. To ensure the privacy of sensitive information committed to the blockchain, organizations encrypt the sensitive information with keys that are internal to the organization
Decentralized Identity for Industrial Applications
No full textThis thesis looks at some aspects of current authorization to applications, then
explores new ways of solving this with emerging technology originated from the
ever expanding field of blockchain. This accumulates to an architecture that could
work for organizations that want to work together. This architecture is tried im-
plemented, then drafts the outcome of the process
Taking Computation to Data: Integrating Privacy-preserving AI techniques and Blockchain Allowing Secure Analysis of Sensitive Data on Premise
No full textPhD thesis in Information technologyWith the advancement of artificial intelligence (AI), digital pathology has seen significant progress in recent years. However, the use of medical AI raises concerns about patient data privacy. The CLARIFY project is a research project funded under the European Union’s Marie Sklodowska-Curie Actions (MSCA) program. The primary objective of CLARIFY is to create a reliable, automated digital diagnostic platform that utilizes cloud-based data algorithms and artificial intelligence to enable interpretation and diagnosis of wholeslide-images (WSI) from any location, maximizing the advantages of AI-based digital pathology.
My research as an early stage researcher for the CLARIFY project centers on securing information systems using machine learning and access control techniques. To achieve this goal, I extensively researched privacy protection technologies such as federated learning, differential privacy, dataset distillation, and blockchain. These technologies have different priorities in terms of privacy, computational efficiency, and usability. Therefore, we designed a computing system that supports different levels of privacy security, based on the concept: taking computation to data. Our approach is based on two design principles. First, when external users need to access internal data, a robust access control mechanism must be established to limit unauthorized access. Second, it implies that raw data should be processed to ensure privacy and security. Specifically, we use smart contractbased access control and decentralized identity technology at the system security boundary to ensure the flexibility and immutability of verification. If the user’s raw data still cannot be directly accessed, we propose to use dataset distillation technology to filter out privacy, or use locally trained model as data agent. Our research focuses on improving the usability of these methods, and this thesis serves as a demonstration of current privacy-preserving and secure computing technologies
Slimmable neural networks for edge devices
No full textWhile methods based on deep learning have witnessed major breakthroughs in machine perception and generative modeling, the problem of how to run neural networks within latency budget for edge devices remains unsolved. This thesis presents a new approach to train a single neural network executable at arbitrary widths for instant and adaptive accuracy-efficiency trade-offs at runtime.
First a simple and general method is presented to train a single neural network executable at different widths (number of channels in a layer). The width can be chosen from a predefined widths set to adaptively optimize accuracy-efficiency trade-offs at runtime. Instead of training individual networks with different width configurations, we train a shared network with switchable batch normalization. At runtime, the network can adjust its width on the fly according to on-device benchmarks and resource constraints, rather than downloading and offloading different models. Our trained networks, named slimmable neural networks, achieve ImageNet classification accuracy similar to (and in many cases better than) that of individually trained models of MobileNet v1, MobileNet v2, ShuffleNet and ResNet-50 at different widths. We also demonstrate better performance of slimmable models compared with individual ones across a wide range of applications including COCO bounding-box object detection, instance segmentation and person keypoint detection without tuning hyper-parameters. We visualize and discuss the learned features of slimmable networks.
Further, we propose a systematic approach to train universally slimmable networks (US-Nets), extending slimmable networks to execute at arbitrary width, and generalizing to networks both with and without batch normalization layers. In addition, we propose two improved training techniques for US-Nets, named the sandwich rule and the inplace distillation, to enhance training process and boost testing accuracy. We show improved performance of universally slimmable MobileNet v1 and MobileNet v2 on ImageNet classification task, compared with individually trained ones and 4-switch slimmable network baselines. We also evaluate the proposed US-Nets and improved training techniques on tasks of image super-resolution and deep reinforcement learning. Extensive ablation experiments on these representative tasks demonstrate the effectiveness of our proposed methods. Our discovery opens up the possibility to directly evaluate a FLOPs-Accuracy spectrum of network architectures. Finally, we demonstrate an application to search for channel number configurations based on proposed slimmable networks.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01The student, Jiahui Yu, accepted the attached license on 2019-02-14 at 14:34.The student, Jiahui Yu, submitted this Thesis for approval on 2019-02-14 at 14:42.This Thesis was approved for publication on 2019-02-15 at 11:18.DSpace SAF Submission Ingestion Package generated from Vireo submission #13390 on 2019-08-22 at 16:19:49Made available in DSpace on 2019-08-23T20:44:31Z (GMT). No. of bitstreams: 2
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Previous issue date: 2019-02-15Embargo set by: Seth Robbins for item 112252
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Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 112252
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Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 112252
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Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemLimited Restriction Lifted for Item 112252 on 2021-08-24T09:15:34Z
Arbuscular Mycorrhizal Fungi Enhance Tolerance to Drought Stress by Altering the Physiological and Biochemical Characteristics of Sugar Beet
No full textGlobal warming is contributing to an increase in the frequency of extreme climate events, leading to more frequent droughts that pose significant abiotic stressors affecting the growth and yield of sugar beet. To address the detrimental effects of drought stress on sugar beet seedlings, this study simulated a drought environment and examined the impact of arbuscular mycorrhizal fungi (AMF) symbiosis on seedling growth. The findings revealed that AMF inoculation under drought conditions enhanced the photosynthesis rate and increased the content of photosynthetic pigments in the leaves of sugar beet. Additionally, it effectively mitigated cell membrane damage in the seedlings, elevated the levels of osmoregulatory substances, and enhanced antioxidant enzyme activities in both leaves and roots. The inoculation of AMF regulates the physiological processes associated with sugar beet growth, alleviates the adverse effects of drought stress, and promotes seedling development. Consequently, AMF can be regarded as a valuable bioregulator in sugar beet cultivation under drought conditions, providing significant practical benefits for improving sugar beet yield
Sugar accumulation stage in sugar beets is a key stage in response to continuous cropping soil microbial community assembly
No full textAimsContinuous cropping effects are a major constraint to the sugar beet industry. Although the microbial community of continuously cropped sugar beets has been studied, the effect of continuous cropping on microbial symbiotic networks and their function during plant development is unclear.MethodsWe analyzed bulk soil and rhizosphere from continuously cropped sugar beet at four growth stages using amplicon and metagenome sequencing and explored the microbial composition, co-occurrence networks, and potential functions of the microbiome at each plant developmental stage. Soil metrics were correlated with microbial communities, and sugar beet from fields with a maize-beet crop rotation acted as a control group.ResultsContinuous cropping and the plant developmental stage had far-reaching effects on plant compartment microbial diversity, composition, and cross-kingdom networks, with the strongest effects observed in the rhizosphere of plants at the sugar accumulation stage. Metagenomic analyses further showed that continuous cropping profoundly affects the assembly and function of the soil microbiome at the host developmental stage. Significant changes in the compositions of the fungal and bacterial communities were observed as the plants developed especially during the sugar accumulation stage, as disease-associated pathogens increased and became the core microbial population in the continuously cropped group.ConclusionsContinuous cropping alters the structure of the microbial core population and resulting in very strong selective regulation of the composition and potential function of the soil microbiome during plant development
Model-based myelin water fraction mapping: analyses and improvement
No full textIn this thesis, the problem of model-based myelin water fraction (MWF) mapping is addressed. We first focus on three of the most widely used signal models for T2*-myelin water imaging (MWI), i.e., the NNLS-multi-exponential model, the magnitude-3-exponential model, and the complex-3-exponential model, and investigate their sensitivities to practical perturbations such as random noise and field-related structured errors. We demonstrate through both Cramér-Rao lower bound (CRLB) analyses and Monte Carlo simulations that the three signal models are all very unstable inherently. Comparatively speaking, however, we demonstrate the theoretical advantage of the 3-exponential models over the multi-exponential model in handling noise, and the practical advantage of the magnitude models over the complex model in handling phase-related perturbations for T2*-MWI. We also illustrate the necessity and effects of incorporating various types of constraints for additional sensitivity gain.
Using the insights obtained in the sensitivity analyses, we then propose a new MWF fitting scheme that leverages an improved signal model and a set of more effective constraints. In particular, a relaxed magnitude-3-exponential model with additional frequency compensation terms is introduced to better represent voxels with large field variations; a set of statistical distributions learned from in vivo training data is further imposed on the model parameters for additional constraints. Using phantom simulation and in vivo experiments, we then evaluate and compare the proposed method with several popular conventional MWF fitting schemes to demonstrate the improved accuracy and robustness of the proposed method.
In this thesis, a literature review on the study of myelin and the development of MWF mapping is provided at the start of the work. Background materials on the CRLB theories are also provided to facilitate reading.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-12-01The student, Jiahui Xiong, accepted the attached license on 2020-11-17 at 12:52.The student, Jiahui Xiong, submitted this Thesis for approval on 2020-11-17 at 13:03.This Thesis was approved for publication on 2020-11-18 at 14:00.DSpace SAF Submission Ingestion Package generated from Vireo submission #15895 on 2021-03-04 at 16:31:59Made available in DSpace on 2021-03-05T21:45:32Z (GMT). No. of bitstreams: 2
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Previous issue date: 2020-11-18Embargo set by: Seth Robbins for item 117288
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Emulsion Droplets Entering and Spreading at the Air and Water Interface Studied by Drop Shape Tensiometry
No full textThis research investigated the entering and spreading phenomena of emulsion droplets at the air-water interface, as it relates to ring formation and oil spreading in beverage emulsions. Drop shape tensiometry was employed to observe the adsorption, entering or spreading of oil droplets. A flavor emulsion, resembling a beverage was employed as the model system for this thesis. The spreading coefficient was not an effective indicator for oil spreading at the air-water interface in the complex emulsion systems. In biopolymer based emulsion systems, the dilational elastic modulus is a better parameter than surface tension for predicting the entering and spreading events. It was also shown that instability may be prevented by addition of a low molecular weight emulsifier. For the first time, confocal microscopy was employed to observe adsorption and spreading of oil droplets in model beverage emulsions
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