EVA: Emergency Vehicle Allocation

Emergency medicine plays a critical role in the development of a community, where the goal is to provide medical assistance in the shortest possible time. Consequently, the systems that support emergency operations need to be robust, efficient, and effective when managing the limited resources at their disposal. To achieve this, operators analyse historical data in search of patterns present in past occurrencesthat could help predict future call volume. This is a time consuming and very complex task that could be solved by the usage of machine learning solutions, which have been performed appropriately in the context of time series forecasting. Only after the future demands are known, the optimization of the distribution of available assets can be done, for the purpose of supporting high-density zones. The current works aim to propose an integrated system capable of supporting decision-making emergency operations in a real-time environment by allocating a set of available units within a service area based on hourly call volume predictions. The suggested system architecture employs a microservices approach along with event-based communications to enable real-time interactions between every component. This dissertation focuses on call volume forecasting and optimizing allocation components. A combination of traditional time series and deep learning models was used to model historical data from Virginal Beach emergency calls between the years 2010 and 2018, combined with several other features such as weather-related information. Deep learning solutions offered better error metrics, with WaveNet having an MAE value of 0.04. Regarding optimizing emergency vehicle location, the proposed solution is based on a Linear Programming problem to minimize the number of vehicles in each station, with a neighbour mechanism, entitled EVALP-NM, to add a buffer to stations near a high-density zone. This solution was also compared against a Genetic Algorithm that performed significantly worse in terms of execution time and outcomes. The performance of EVALP-NM was tested against simulations with different settings like the number of zones, stations, and ambulances.A medicina de emergência desempenha um papel fundamental no desenvolvimento da Sociedade, onde o objetivo é prestar assistência médica no menor tempo possível. Consequentemente, os sistemas que apoiam as operações de emergência precisam de ser robustos, eficientes e eficazes na gestão dos recursos limitados. Para isso, são analisados dados históricos no intuito de encontrar padrões em ocorrências passadas que possam ajudar a prever o volume futuro de chamadas. Esta é uma tarefa demorada e muito complexa que poderia ser resolvida com o uso de soluções de Machine Learning, que têm funcionado adequadamente no contexto da previsão de séries temporais. Só depois de conhecida a demanda futura poderá ser feita a otimização da distribuição dos recursos disponíveis, com o objetivo de suportar zonas de elevada densidade populacional. O presente trabalho tem como objetivo propor um sistema integrado capaz de apoiar a tomada de decisão em operações de emergência num ambiente de tempo real, atribuindo um conjunto de unidades disponíveis dentro de uma área de serviço com base em previsões volume de chamadas a cada hora. A arquitetura de sistema sugerida emprega uma abordagem de microserviços juntamente com comunicações baseadas em eventos para permitir interações em tempo real entre os componentes. Esta dissertação centra se nos componentes de previsão do volume de chamadas e otimização da atribuição. Foram usados modelos de séries temporais tradicionais e Deep Learning para modelar dados históricos de chamadas de emergência de Virginal Beach entre os anos de 2010 e 2018, combinadas com informações relacionadas ao clima. As soluções de Deep Learning ofereceram melhores métricas de erro, com WaveNet a ter um valor MAE de 0,04. No que diz respeito à otimização da localização dos veículos de emergência, a solução proposta baseia-se num problema de Programação Linear para minimizar o número de veículos em cada estação, com um mecanismo de vizinho, denominado EVALP-NM, para adicionar unidades adicionais às estações próximas de uma zona de alta densidade de chamadas. Esta solução foi comparada com um algoritmo genético que teve um desempenho significativamente pior em termos de tempo de execução e resultados. O desempenho do EVALP-NM foi testado em simulações com configurações diferentes, como número de zonas, estações e ambulâncias

Knowledge-based 2-D vision system synthesis

technical reportA knowledge-based approach to computer vision provides the needed flexibility for performing recognition and inspection of objects in a complex environment. A system is described which uses knowledge about the environment, sensors, and performance requirements to construct a functional configuration of sensors and algorithms. The system uses an object-based approach to synthesize both the analytical model for an object and the executable application system

Property Testing and Probability Distributions: New Techniques, New Models, and New Goals

In order to study the real world, scientists (and computer scientists) develop simplified models that attempt to capture the essential features of the observed system. Understanding the power and limitations of these models, when they apply or fail to fully capture the situation at hand, is therefore of uttermost importance. In this thesis, we investigate the role of some of these models in property testing of probability distributions (distribution testing), as well as in related areas. We introduce natural extensions of the standard model (which only allows access to independent draws from the underlying distribution), in order to circumvent some of its limitations or draw new insights about the problems they aim at capturing. Our results are organized in three main directions: (i) We provide systematic approaches to tackle distribution testing questions. Specifically, we provide two general algorithmic frameworks that apply to a wide range of properties, and yield efficient and near-optimal results for many of them. We complement these by introducing two methodologies to prove information-theoretic lower bounds in distribution testing, which enable us to derive hardness results in a clean and unified way. (ii) We introduce and investigate two new models of access to the unknown distributions, which both generalize the standard sampling model in different ways and allow testing algorithms to achieve significantly better efficiency. Our study of the power and limitations of algorithms in these models shows how these could lead to faster algorithms in practical situations, and yields a better understanding of the underlying bottlenecks in the standard sampling setting. (iii) We then leave the field of distribution testing to explore areas adjacent to property testing. We define a new algorithmic primitive of sampling correction, which in some sense lies in between distribution learning and testing and aims to capture settings where data originates from imperfect or noisy sources. Our work sets out to model these situations in a rigorous and abstracted way, in order to enable the development of systematic methods to address these issues

Efficient Protocols for Multi-Party Computation

Secure Multi-Party Computation (MPC) allows a group of parties to compute a join function on their inputs without revealing any information beyond the result of the computation. We demonstrate secure function evaluation protocols for branching programs, where the communication complexity is linear in the size of the inputs, and polynomial in the security parameter. Our result is based on the circular security of the Paillier\u27s encryption scheme. Our work followed the breakthrough results by Boyle et al. [9; 11]. They presented a Homomorphic Secret Sharing scheme which allows the non-interactive computation of Branching Programs over shares of the secret inputs. Their protocol is based on the Decisional Diffie-Hellman Assumption. Additionally, we offer a verification technique to directly check correctness of the actual computation, rather than the absence of a potential error as in [9]. This results in fewer repetitions of the overall computation for a given error bound. We also use Paillier’s encryption as the underlying scheme of publicly perceptual hashing. Perceptual hashing allows the computation of a robust fingerprint of media files, such that the fingerprint can be used to detect the same object even if it has been modified in per- ceptually non-significant ways (e.g., compression). The robustness of such functions relies on the use of secret keys both during the computation and the detection phase. We present examples of publicly evaluatable perceptual hash functions which allow a user to compute the perceptual hash of an image using a public key, while only the detection algorithm will use the secret key. Our technique can be used to encourage users to submit intimate images to blacklist databases to stop those images from ever being posted online – indeed using a publicly evaluatable perceptual hash function the user can privately submit the fingerprint, without ever revealing the image. We present formal definitions for the security of perceptual hash, a general theoretical result that uses Fully Homomorphic Encryption, and a specific construction using Paillier’s encryption. For the latter we show via extensive implementation tests that the cryptographic overhead can be made minimal, resulting in a very efficient construction

LIPIcs, Volume 251, ITCS 2023, Complete Volume

LIPIcs, Volume 251, ITCS 2023, Complete Volum

Tools and Algorithms for the Construction and Analysis of Systems

This open access two-volume set constitutes the proceedings of the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2021, which was held during March 27 – April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The total of 41 full papers presented in the proceedings was carefully reviewed and selected from 141 submissions. The volume also contains 7 tool papers; 6 Tool Demo papers, 9 SV-Comp Competition Papers. The papers are organized in topical sections as follows: Part I: Game Theory; SMT Verification; Probabilities; Timed Systems; Neural Networks; Analysis of Network Communication. Part II: Verification Techniques (not SMT); Case Studies; Proof Generation/Validation; Tool Papers; Tool Demo Papers; SV-Comp Tool Competition Papers

Secure multi-party protocols under a modern lens

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 263-272).A secure multi-party computation (MPC) protocol for computing a function f allows a group of parties to jointly evaluate f over their private inputs, such that a computationally bounded adversary who corrupts a subset of the parties can not learn anything beyond the inputs of the corrupted parties and the output of the function f. General MPC completeness theorems in the 1980s showed that every efficiently computable function can be evaluated securely in this fashion [Yao86, GMW87, CCD87, BGW88] using the existence of cryptography. In the following decades, progress has been made toward making MPC protocols efficient enough to be deployed in real-world applications. However, recent technological developments have brought with them a slew of new challenges, from new security threats to a question of whether protocols can scale up with the demand of distributed computations on massive data. Before one can make effective use of MPC, these challenges must be addressed. In this thesis, we focus on two lines of research toward this goal: " Protocols resilient to side-channel attacks. We consider a strengthened adversarial model where, in addition to corrupting a subset of parties, the adversary may leak partial information on the secret states of honest parties during the protocol. In presence of such adversary, we first focus on preserving the correctness guarantees of MPC computations. We then proceed to address security guarantees, using cryptography. We provide two results: an MPC protocol whose security provably "degrades gracefully" with the amount of leakage information obtained by the adversary, and a second protocol which provides complete security assuming a (necessary) one-time preprocessing phase during which leakage cannot occur. * Protocols with scalable communication requirements. We devise MPC protocols with communication locality: namely, each party only needs to communicate with a small (polylog) number of dynamically chosen parties. Our techniques use digital signatures and extend particularly well to the case when the function f is a sublinear algorithm whose execution depends on o(n) of the n parties' inputs.by Elette Chantae Boyle.Ph.D