1,720,973 research outputs found
Counter-Example Guided Abstract Refinement for Verification of Neural Networks
No full textIn the last few decades, the employment of machine learning (ML) models has been increasingly common in the Artificial Intelligence community, with a particular focus on neural networks (NNs). However, even though they are widely adopted, the lack of formal guarantees on their behavior still restrain their use in safety-critical applications, such as avionics and self-driving vehicles. Formal Verification has been proposed to tackle the reliability issues of NNs, but its complexity and the sheer size of the models of interest have been proven to be hard challenges. In this paper we present an enhancement of our verification algorithm based on counter-example guided abstraction refinement (CEGAR) and show how it performs with respect to other approximate star-based methods
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A comparison of declarative AI techniques for computer automated design of elevator systems
No full textLike other custom-built machinery, elevators are charecterized by a design process which includes selection, sizing and placement of components to fit a given configuration, satisfy users' requirements and adhere to stringent normative regulations. Unlike mass-produced items, the design process needs to be repeated almost from scratch each time a new configuration is considered. Since elevators are still designed mostly manually, project engineers must engage in time-consuming and error-prone activities over and over again, leaving little to be reused from one design to the next. Computer automated design can provide a cost-effective solution as it relieves the project engineer from such burdens. However, it introduces new challenges both in terms of efficiency - the search space for solutions grows exponentially in the number of component choices - and effectiveness - the perceived quality of the final design may not be as good as in the manual process. In this paper we compare three mainstream AI techniques that can provide problem-solving capabilities inside our tool LIFTCREATE for automated elevator design, namely Genetic Algorithms (GAs), Constraint Programming (CP) and Satisfiability Modulo Theories (SMT). A special-purpose heuristic search technique embedded in LIFTCREATE provides us with a yardstick to evaluate the solutions obtained with GAs, CP and SMT and to assess their feasibility for practical applications
Formal Verification of Neural Networks: a Case Study about Adaptive Cruise Control
No full textFormal verification of neural networks is a promising technique to improve their dependability for safety critical applications. Autonomous driving is one such application where the controllers supervising different functions in a car should undergo a rigorous certification process. In this paper we present an example about learning and verification of an adaptive cruise control function on an autonomous car. We detail the learning process as well as the attempts to verify various safety properties using the tool NEVER2, a new framework that integrates learning and verification in a single easy-to-use package intended for practictioners rather than experts in formal methods and/or machine learning
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Automated Design of Elevator Systems: Experimenting with Constraint-Based Approaches
No full textSystem configuration and design is a well-established topic
in AI. While many successful applications exists, there are still areas of
manufacturing where AI techniques find little or no application. We focus
on one such area, namely building and installation of elevator systems,
for which we are developing an automated design and configuration tool.
The questions that we address in this paper are: (i) What are the best
ways to encode some subtasks of elevator design into constraint-based
representations? (ii) What are the best tools available to solve the encodings? We contribute an empirical analysis to address these questions
in our domain of interest, as well as the complete set of benchmarks to
foster further researc
NeVer2: learning and verification of neural networks
No full textNeVer2 is an open-source, cross-platform tool aimed at designing, training, and verifying neural networks. It seamlessly integrates popular learning libraries with our verification backend, offering their functionalities via a graphical interface. Users can design the structure of a neural network by intuitively arranging blocks on a canvas. Subsequently, network training involves specifying dataset sources and hyperparameters through dialog boxes. After training, the verification process entails two steps: (i) incorporating input preconditions and output postconditions via dedicated blocks, and (ii) initiating verification with a simple “push-button” action. To our knowledge, there is currently no other publicly available tool that encompasses all these features. In this paper, we present a comprehensive description of NeVer2, illustrating its complete integration of design, training, and verification through examples. Additionally, we conduct experimental analyses on various verification benchmarks to illustrate the trade-off between completeness and computability using different algorithms. We also include a comparison with state-of-the-art tools such as α,β-CROWN and NNV for reference
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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