HASLab

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This service produces reliable software systems in contexts where correctness, responsiveness, robustness and security are essential. It develops integrated research in three lines: formal methods for software development, reliable distributed systems and information security.

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Browsing HASLab by Author "5596"
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    Consistent comparison of symptom-based methods for COVID-19 infection detection
    ( 2023) Carlos Baquero ; 5596
    Background: During the global pandemic crisis, various detection methods of COVID-19-positive cases based on self-reported information were introduced to provide quick diagnosis tools for effectively planning and managing healthcare resources. These methods typically identify positive cases based on a particular combination of symptoms, and they have been evaluated using different datasets.Purpose: This paper presents a comprehensive comparison of various COVID-19 detection methods based on self-reported information using the University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), a large health surveillance platform, which was launched in partnership with Facebook.Methods: Detection methods were implemented to identify COVID-19-positive cases among UMD-CTIS participants reporting at least one symptom and a recent antigen test result (positive or negative) for six countries and two periods. Multiple detection methods were implemented for three different categories: rule-based approaches, logistic regression techniques, and tree-based machine-learning models. These methods were evaluated using different metrics including F1-score, sensitivity, specificity, and precision. An explainability analysis has also been conducted to compare methods.Results: Fifteen methods were evaluated for six countries and two periods. We identify the best method for each category: rule-based methods (F1-score: 51.48% -71.11%), logistic regression techniques (F1-score: 39.91% -71.13%), and tree-based machine learning models (F1-score: 45.07% -73.72%). According to the explainability analysis, the relevance of the reported symptoms in COVID-19 detection varies between countries and years. However, there are two variables consistently relevant across approaches: stuffy or runny nose, and aches or muscle pain.Conclusions: Regarding the categories of detection methods, evaluating detection methods using homogeneous data across countries and years provides a solid and consistent comparison. An explainability analysis of a tree-based machine-learning model can assist in identifying infected individuals specifically based on their relevant symptoms. This study is limited by the self-report nature of data, which cannot replace clinical diagnosis.
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    The CoronaSurveys System for COVID-19 Incidence Data Collection and Processing
    ( 2021) Agundez,AG ; Sanchez,I ; Roberts,JC ; Ojo,O ; Stavrakis,E ; Nicolaou,N ; Hernández Roig,HA ; Goessens,M ; Ortega,A ; Girault,B ; Georgiou,C ; Carlos Baquero ; Casari,P ; Anta,AF ; García,AG ; Frey,D ; 5596
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    The CoronaSurveys System for COVID-19 Incidence Data Collection and Processing
    ( 2021) Carlos Baquero ; Casari,P ; Anta,AF ; Garcia Garcia,A ; Frey,D ; Garcia Agundez,A ; Georgiou,C ; Girault,B ; Ortega,A ; Goessens,M ; Hernandez Roig,HA ; Nicolaou,N ; Stavrakis,E ; Ojo,O ; Roberts,JC ; Sanchez,I ; 5596
    CoronaSurveys is an ongoing interdisciplinary project developing a system to infer the incidence of COVID-19 around the world using anonymous open surveys. The surveys have been translated into 60 languages and are continuously collecting participant responses from any country in the world. The responses collected are pre-processed, organized, and stored in a version-controlled repository, which is publicly available to the scientific community. In addition, the CoronaSurveys team has devised several estimates computed on the basis of survey responses and other data, and makes them available on the project's website in the form of tables, as well as interactive plots and maps. In this paper, we describe the computational system developed for the CoronaSurveys project. The system includes multiple components and processes, including the web survey, the mobile apps, the cleaning and aggregation process of the survey responses, the process of storage and publication of the data, the processing of the data and the computation of estimates, and the visualization of the results. In this paper we describe the system architecture and the major challenges we faced in designing and deploying it.
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    Delta State Replicated Data Types
    ( 2018) Paulo Sérgio Almeida ; Ali Shoker ; Carlos Baquero ; 5607 ; 6172 ; 5596
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    The dynamics of remembering and forgetting
    ( 2022) Carlos Baquero ; Cabecinhas,R ; 5596
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    Efficient replication via timestamp stability
    ( 2021) Vítor Manuel Duarte ; Carlos Baquero ; Gotsman,A ; Sutra,P ; 6704 ; 5596
    Modern web applications replicate their data across the globe and require strong consistency guarantees for their most critical data. These guarantees are usually provided via state-machine replication (SMR). Recent advances in SMR have focused on leaderless protocols, which improve the availability and performance of traditional Paxos-based solutions. We propose Tempo - a leaderless SMR protocol that, in comparison to prior solutions, achieves superior throughput and offers predictable performance even in contended workloads. To achieve these benefits, Tempo timestamps each application command and executes it only after the timestamp becomes stable, i.e., all commands with a lower timestamp are known. Both the timestamping and stability detection mechanisms are fully decentralized, thus obviating the need for a leader replica. Our protocol furthermore generalizes to partial replication settings, enabling scalability in highly parallel workloads. We evaluate the protocol in both real and simulated geo-distributed environments and demonstrate that it outperforms state-of-the-art alternatives. © 2021 ACM.
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    Efficient replication via timestamp stability
    ( 2021) Carlos Baquero ; Vítor Manuel Duarte ; Gotsman,A ; Sutra,P ; 5596 ; 6704
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    Efficient synchronization of state-based CRDTs
    ( 2019) Paulo Sérgio Almeida ; Leitao,J ; Carlos Baquero ; Vítor Manuel Duarte ; 5607 ; 6704 ; 5596
    To ensure high availability in large scale distributed systems, Conflict-free Replicated Data Types (CRDTs) relax consistency by allowing immediate query and update operations at the local replica, with no need for remote synchronization. State-based CRDTs synchronize replicas by periodically sending their full state to other replicas, which can become extremely costly as the CRDT state grows. Delta-based CRDTs address this problem by producing small incremental states (deltas) to be used in synchronization instead of the full state. However, current synchronization algorithms for delta-based CRDTs induce redundant wasteful delta propagation, performing worse than expected, and surprisingly, no better than state-based. In this paper we: 1) identify two sources of inefficiency in current synchronization algorithms for delta-based CRDTs; 2) bring the concept of join decomposition to state-based CRDTs; 3) exploit join decompositions to obtain optimal deltas and 4) improve the efficiency of synchronization algorithms; and finally, 5) experimentally evaluate the improved algorithms. © 2019 IEEE.
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    Efficient Synchronization of State-based CRDTs
    ( 2019) Paulo Sérgio Almeida ; Leitao,J ; Carlos Baquero ; Vítor Manuel Duarte ; 5607 ; 6704 ; 5596
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    Global-Local View: Scalable Consistency for Concurrent Data Types
    ( 2018) Akkoorath,DD ; Brandão,J ; Bieniusa,A ; Carlos Baquero ; 5596
    Concurrent linearizable access to shared objects can be prohibitively expensive in a high contention workload. Many applications apply ad-hoc techniques to eliminate the need for synchronous atomic updates, which may result in non-linearizable implementations. We propose a new model which leverages such patterns for concurrent access to objects in a shared memory system. In this model, each thread maintains different views on the shared object: a thread-local view and a global view. As the thread-local view is not shared, it can be updated without incurring synchronization costs. These local updates become visible to other threads only after the thread-local view is merged with the global view. This enables better performance at the expense of linearizability. We discuss the design of several datatypes and evaluate their performance and scalability compared to linearizable implementations. © 2018, Springer International Publishing AG, part of Springer Nature.
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    Higher-order patterns in replicated data types
    ( 2019) Paulo Sérgio Almeida ; Leijnse,A ; Carlos Baquero ; 5607 ; 5596
    The design of Conflict-free Replicated Data Types traditionally requires implementing new designs from scratch to meet a desired behavior. Although there are composition rules that can guide the process, there has not been a lot of work explaining how existing data types relate to each other, nor work that factors out common patterns. To bring clarity to the field we explain underlying patterns that are common to flags, sets, and registers. The identified patterns are succinct and composable, which gives them the power to explain both current designs and open up the space for new ones. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.
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    Picking Publication Targets
    ( 2022) Carlos Baquero ; 5596
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    Probabilistic Causal Contexts for Scalable CRDTs
    ( 2023) Carlos Baquero ; 5596
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    Seeking Out Camille, and Being Open to Others
    ( 2021) Hill,RK ; Carlos Baquero ; 5596
    Robin K. Hill on overcoming biases against alternative views, and Carlos Baquero on his search for the elusive Camille Nous.
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    State-machine replication for planet-scale systems
    ( 2020) Perrin,M ; Sutra,P ; Vítor Manuel Duarte ; Carlos Baquero ; Rezende,TF ; Gotsman,A ; 6704 ; 5596
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    Time-limited Bloom Filter
    ( 2023) Paulo Sérgio Almeida ; Carlos Baquero ; 5607 ; 5596
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    Time-limited Bloom Filter
    ( 2023) Paulo Sérgio Almeida ; Carlos Baquero ; 5607 ; 5596
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    Using survey data to estimate the impact of the omicron variant on vaccine efficacy against COVID-19 infection
    ( 2023) Carlos Baquero ; 5596
    Symptoms-based detection of SARS-CoV-2 infection is not a substitute for precise diagnostic tests but can provide insight into the likely level of infection in a given population. This study uses symptoms data collected in the Global COVID-19 Trends and Impact Surveys (UMD Global CTIS), and data on variants sequencing from GISAID. This work, conducted in January of 2022 during the emergence of the Omicron variant (subvariant BA.1), aims to improve the quality of infection detection from the available symptoms and to use the resulting estimates of infection levels to assess the changes in vaccine efficacy during a change of dominant variant; from the Delta dominant to the Omicron dominant period. Our approach produced a new symptoms-based classifier, Random Forest, that was compared to a ground-truth subset of cases with known diagnostic test status. This classifier was compared with other competing classifiers and shown to exhibit an increased performance with respect to the ground-truth data. Using the Random Forest classifier, and knowing the vaccination status of the subjects, we then proceeded to analyse the evolution of vaccine efficacy towards infection during different periods, geographies and dominant variants. In South Africa, where the first significant wave of Omicron occurred, a significant reduction of vaccine efficacy is observed from August-September 2021 to December 2021. For instance, the efficacy drops from 0.81 to 0.30 for those vaccinated with 2 doses (of Pfizer/BioNTech), and from 0.51 to 0.09 for those vaccinated with one dose (of Pfizer/BioNTech or Johnson & Johnson). We also extended the study to other countries in which Omicron has been detected, comparing the situation in October 2021 (before Omicron) with that of December 2021. While the reduction measured is smaller than in South Africa, we still found, for instance, an average drop in vaccine efficacy from 0.53 to 0.45 among those vaccinated with two doses. Moreover, we found a significant negative (Pearson) correlation of around - 0.6 between the measured prevalence of Omicron in several countries and the vaccine efficacy in those same countries. This prediction, in January of 2022, of the decreased vaccine efficacy towards Omicron is in line with the subsequent increase of Omicron infections in the first half of 2022.
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    What Ever Happened to Peer-to-Peer Systems?
    ( 2022) Carlos Baquero ; 5596
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    A Year Embedded in the Crypto-NFT Space
    ( 2023) Carlos Baquero ; 5596