Compensating inaccurate annotations to train 3D facial landmark localisation models

In this paper we investigate the impact of inconsistency in manual annotations when they are used to train automatic models for 3D facial landmark localization. We start by showing that it is possible to objectively measure the consistency of annotations in a database, provided that it contains replicates (i.e. repeated scans from the same person). Applying such measure to the widely used FRGC database we find that manual annotations currently available are suboptimal and can strongly impair the accuracy of automatic models learnt therefrom. To address this issue, we present a simple algorithm to automatically correct a set of annotations and show that it can help to significantly improve the accuracy of the models in terms of landmark localization errors. This improvement is observed even when errors are measured with respect to the original (not corrected) annotations. However, we also show that if errors are computed against an alternative set of manual annotations with higher consistency, the accuracy of the models constructed using the corrections from the presented algorithm tends to converge to the one achieved by building the models on the alternative,more consistent set

Robustness against Consistency Models with Atomic Visibility

To achieve scalability, modern Internet services often rely on distributed databases with consistency models for transactions weaker than serializability. At present, application programmers often lack techniques to ensure that the weakness of these consistency models does not violate application correctness. We present criteria to check whether applications that rely on a database providing only weak consistency are robust, i.e., behave as if they used a database providing serializability. When this is the case, the application programmer can reap the scalability benefits of weak consistency while being able to easily check the desired correctness properties. Our results handle systematically and uniformly several recently proposed weak consistency models, as well as a mechanism for strengthening consistency in parts of an application

A Framework for Transactional Consistency Models with Atomic Visibility

Modern distributed systems often rely on databases that achieve scalability by providing only weak guarantees about the consistency of distributed transaction processing. The semantics of programs interacting with such a database depends on its consistency model, defining these guarantees. Unfortunately, consistency models are usually stated informally or using disparate formalisms, often tied to the database internals. To deal with this problem, we propose a framework for specifying a variety of consistency models for transactions uniformly and declaratively. Our specifications are given in the style of weak memory models, using structures of events and relations on them. The specifications are particularly concise because they exploit the property of atomic visibility guaranteed by many consistency models: either all or none of the updates by a transaction can be visible to another one. This allows the specifications to abstract from individual events inside transactions. We illustrate the use of our framework by specifying several existing consistency models. To validate our specifications, we prove that they are equivalent to alternative operational ones, given as algorithms closer to actual implementations. Our work provides a rigorous foundation for developing the metatheory of the novel form of concurrency arising in weakly consistent large-scale databases

Coherent Integration of Databases by Abductive Logic Programming

We introduce an abductive method for a coherent integration of independent data-sources. The idea is to compute a list of data-facts that should be inserted to the amalgamated database or retracted from it in order to restore its consistency. This method is implemented by an abductive solver, called Asystem, that applies SLDNFA-resolution on a meta-theory that relates different, possibly contradicting, input databases. We also give a pure model-theoretic analysis of the possible ways to `recover' consistent data from an inconsistent database in terms of those models of the database that exhibit as minimal inconsistent information as reasonably possible. This allows us to characterize the `recovered databases' in terms of the `preferred' (i.e., most consistent) models of the theory. The outcome is an abductive-based application that is sound and complete with respect to a corresponding model-based, preferential semantics, and -- to the best of our knowledge -- is more expressive (thus more general) than any other implementation of coherent integration of databases

Blockchain-based database in an IoT environment: challenges, opportunities, and analysis

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. As Bitcoin and other cryptocurrencies become widely popular recently, the underlying concept—Blockchain—gets unprecedented attentions. One popular usage of Blockchain is a distributed replicated database. In this paper, we present initial studies on the challenges and opportunities of using Blockchain as a database for Internet-of-Things (IoT) applications. For IoT applications, latency is an important factor, whereas for application developers, consistency is an important property which specifies how the system orders the operations over blocks (that are stored in the Blockchain). However, consistency property of Blockchain-based database is not well studied, especially in the case when network is not synchronized and the system is dynamic—both are typical scenario in an IoT environment. Intuitively, Blockchain is designed to maintain a single ground truth—one can view the Blockchain itself as the order of the blocks that all participants should observe and respect. In most Blockchain designs, the participants will eventually converge to the same chain of blocks. However, there is very few study on the challenges of using Blockchains as a database in an IoT environment. This paper focuses on the enabling technology behind Bitcoin, Bitcoin Backbone Protocol (BBP). We first survey Blockchain-based IoT applications, and identify why it is necessary to use it as a database for IoT applications. Then we explore several reasonable consistency models for BBP-based database, and then show that such a database does not satisfy many consistency models under certain typical IoT environments. Moreover, we use simulation to study how network quality and system dynamic affect consistency. Finally, we propose a simple mechanism to make the BBP-based database satisfy both read-my-write and eventual consistency

Analyzing Consistency of Behavioral REST Web Service Interfaces

REST web services can offer complex operations that do more than just simply creating, retrieving, updating and deleting information from a database. We have proposed an approach to design the interfaces of behavioral REST web services by defining a resource and a behavioral model using UML. In this paper we discuss the consistency between the resource and behavioral models that represent service states using state invariants. The state invariants are defined as predicates over resources and describe what are the valid state configurations of a behavioral model. If a state invariant is unsatisfiable then there is no valid state configuration containing the state and there is no service that can implement the service interface. We also show how we can use reasoning tools to determine the consistency between these design models.Comment: In Proceedings WWV 2012, arXiv:1210.578