507 research outputs found
Auditable and performant Byzantine consensus for permissioned ledgers
Permissioned ledgers allow users to execute transactions against a data store, and retain proof of their execution in a replicated ledger. Each replica verifies the transactions’ execution and ensures that, in perpetuity, a committed transaction cannot be removed from the ledger. Unfortunately, this is not guaranteed by today’s permissioned ledgers, which can be re-written if an arbitrary number of replicas collude. In addition, the transaction throughput of permissioned ledgers is low, hampering real-world deployments, by not taking advantage of multi-core CPUs and hardware accelerators.
This thesis explores how permissioned ledgers and their consensus protocols can be made auditable in perpetuity; even when all replicas collude and re-write the ledger. It also addresses how Byzantine consensus protocols can be changed to increase the execution throughput of complex transactions. This thesis makes the following contributions:
1. Always auditable Byzantine consensus protocols. We present a permissioned ledger system that can assign blame to individual replicas regardless of how many of them misbehave. This is achieved by signing and storing consensus protocol messages in the ledger and providing clients with signed, universally-verifiable receipts.
2. Performant transaction execution with hardware accelerators. Next, we describe a cloud-based ML inference service that provides strong integrity guarantees, while staying compatible with current inference APIs. We change the Byzantine consensus protocol to execute machine learning (ML) inference computation on GPUs to optimize throughput and latency of ML inference computation.
3. Parallel transactions execution on multi-core CPUs. Finally, we introduce a permissioned ledger that executes transactions, in parallel, on multi-core CPUs. We separate the execution of transactions between the primary and secondary replicas. The primary replica executes transactions on multiple CPU cores and creates a dependency graph of the transactions that the backup replicas utilize to execute transactions in parallel.Open Acces
Sampling-Based Exploration Strategies for Mobile Robot Autonomy
A novel, sampling-based exploration strategy is introduced for Unmanned Ground Vehicles (UGV) to efficiently map large GPS-deprived underground environments. It is compared to state-of-the-art approaches and performs on a similar level, while it is not designed for a specific robot or sensor configuration like the other approaches. The introduced exploration strategy, which is called Random-Sampling-Based Next-Best View Exploration (RNE), uses a Rapidly-exploring Random Graph (RRG) to find possible view points in an area around the robot. They are compared with a computation-efficient Sparse Ray Polling (SRP) in a voxel grid to find the next-best view for the exploration. Each node in the exploration graph built with RRG is evaluated regarding the ability of the UGV to traverse it, which is derived from an occupancy grid map. It is also used to create a topology-based graph where nodes are placed centrally to reduce the risk of collisions and increase the amount of observable space. Nodes that fall outside the local exploration area are stored in a global graph and are connected with a Traveling Salesman Problem solver to explore them later
Scaling energy management in buildings with artificial intelligence
L'abstract è presente nell'allegato / the abstract is in the attachmen
Quantum Computing for Airline Planning and Operations
Classical algorithms and mathematical optimization techniques have beenused extensively by airlines to optimize their profit and ensure that regulationsare followed. In this thesis, we explore which role quantum algorithmscan have for airlines. Specifically, we have considered the two quantum optimizationalgorithms; the Quantum Approximate Optimization Algorithm(QAOA) and Quantum Annealing (QA). We present a heuristic that integratesthese quantum algorithms into the existing classical algorithm, whichis currently employed to solve airline planning problems in a state-of-the-artcommercial solver. We perform numerical simulations of QAOA circuits andfind that linear and quadratic algorithm depth in the input size can be requiredto obtain a one-shot success probability of 0.5. Unfortunately, we areunable to find performance guarantees. Finally, we perform experiments withD-wave’s newly released QA machine and find that it outperforms 2000Q formost instances
Accessibility of Health Data Representations for Older Adults: Challenges and Opportunities for Design
Health data of consumer off-the-shelf wearable devices is often conveyed to users through visual data representations and analyses. However, this is not always accessible to people with disabilities or older people due to low vision, cognitive impairments or literacy issues. Due to trade-offs between aesthetics predominance or information overload, real-time user feedback may not be conveyed easily from sensor devices through visual cues like graphs and texts. These difficulties may hinder critical data understanding. Additional auditory and tactile feedback can also provide immediate and accessible cues from these wearable devices, but it is necessary to understand existing data representation limitations initially. To avoid higher cognitive and visual overload, auditory and haptic cues can be designed to complement, replace or reinforce visual cues. In this paper, we outline the challenges in existing data representation and the necessary evidence to enhance the accessibility of health information from personal sensing devices used to monitor health parameters such as blood pressure, sleep, activity, heart rate and more. By creating innovative and inclusive user feedback, users will likely want to engage and interact with new devices and their own data
A survey and tutorial on deep reinforcement learning algorithms for robotic manipulation
Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin by outlining the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. The many deep reinforcement learning algorithms, such as value-based methods, policy-based methods, and actor–critic approaches, that have been suggested for robotic manipulation tasks are then covered. We also examine the numerous issues that have arisen when applying these algorithms to robotics tasks, as well as the various solutions that have been put forth to deal with these issues. Finally, we highlight several unsolved research issues and talk about possible future directions for the subject
DEFINITION OF AN ADVANCED PROCESS FOR THE PRODUCTION OF LOW ENVIRONMENTAL IMPACT CONTAINERS AS POTENTIAL ALTERNATIVE TO PLASTICS
For decades, petroleum-based synthetic polymers, commonly known as plastics, have become one of the most appealing materials used for a wide variety of applications. Nevertheless, currently, conventional petroleum-based plastics represent a serious problem for global pollution because remain for hundreds of years in the environment when discarded. In order to reduce dependence on fossil resources, bioplastic materials are being proposed as safer and more sustainable alternatives. Bioplastics are bio-based and/or biodegradable materials, typically derived from renewable sources. Among different resources, food waste is attracting more and more attention in the research field of bioplastics’ production. The sources of food waste include household, commercial, industrial and agricultural residues. In fact, every year, around one-third of all food resources produced for human consumption are lost or wasted. Although European Union guidelines stated that food waste should preferentially be used as animal feed, in some cases, it became illegal because of disease control concerns and other times its nutritional value is very poor. On the other hand, the production of bioplastics from food waste is a renewable, sustainable process, in which materials are fabricated from carbon neutral resources, thus aligning itself with the principles of the circular bioeconomy. However, the conversion of fruit and vegetable by-products into eco-friendly materials with mechanical and hydrodynamic performances comparable to those of fuel-based plastics still remains a challenge. In this thesis, different approaches have been investigated for the valorization of fruit and vegetable wastes to produce low environmental impact materials, as a potential alternative to plastics with application in the field of food packaging. In the first section, apple waste and tomato peel by-products have been used as fillers to fabricate starch-based biocomposites. The mechanical characterization of the samples showed their suitability for covering purposes, since a ductile and soft behaviour was exhibited. In the second section, an avocado by-product extract has been incorporated to an ethyl cellulose matrix for the production of impregnated paper with enhanced durability. Since fruit wastes can contain potential pathogens and physical and chemical contaminants which can be released when used as additive for active packaging, a preliminary untargeted metabolomic characterization of the extract was conducted by LC-ESI(-)-Q Exactive-Orbitrap- MS/MS. The lipid components detected in the extract proved to be useful additives to improve paper hydrophobicity, preventing food browning and moisture loss. In general, the addition of all tested wastes (apple waste, tomato peel and avocado by-products) has proved to be useful to increase the biodegradability of the fabricated biomaterials. Hence, the environmental benefits associated with their recovery are proposed as a driving force to expand
their further use for these purposes. The upcycling of food waste through the production of value-added products is an ideal and practical end use, allowing to save huge economic and energy losses
Artificial intelligence for understanding the Hadith
My research aims to utilize Artificial Intelligence to model the meanings of Classical Arabic Hadith, which are the reports of the life and teachings of the Prophet Muhammad. The goal is to find similarities and relatedness between Hadith and other religious texts, specifically the Quran. These findings can facilitate downstream tasks, such as Islamic question- answering systems, and enhance understanding of these texts to shed light on new interpretations.
To achieve this goal, a well-structured Hadith corpus should be created, with the Matn (Hadith teaching) and Isnad (chain of narrators) segmented. Hence, a preliminary task is conducted to build a segmentation tool using machine learning models that automatically deconstruct the Hadith into Isnad and Matn with 92.5% accuracy. This tool is then used to create a well-structured corpus of the canonical Hadith books.
After building the Hadith corpus, Matns are extracted to investigate different methods of representing their meanings. Two main methods are tested: a knowledge-based approach and a deep-learning-based approach. To apply the former, existing Islamic ontologies are enumerated, most of which are intended for the Quran. Since the Quran and the Hadith are in the same domain, the extent to which these ontologies cover the Hadith is examined using a corpus-based evaluation. Results show that the most comprehensive Quran ontology covers only 26.8% of Hadith concepts, and extending it is expensive. Therefore, the second approach is investigated by building and evaluating various deep-learning models for a binary classification task of detecting relatedness between the Hadith and the Quran. Results show that the likelihood of the current models reaching a human- level understanding of such texts remains somewhat elusive
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