Probabilistic Semantics for RoboChart A Weakest Completion Approach

We outline a probabilistic denotational semantics for the RoboChart language, a diagrammatic, domain-specific notation for de- scribing robotic controllers with their hardware platforms and operating environments. We do this using a powerful (but perhaps not so well known) semantic technique: He, Morgan, and McIver’s weakest completion semantics, which is based on Hoare and He’s Unifying Theories of Programming. In this approach, we do the following: (1) start with the standard semantics for a nondeterministic programming language; (2) propose a new probabilistic semantic domain; (3) propose a forgetful function from the probabilistic semantic domain to the standard semantic domain; (4) use the converse of the forgetful function to embed the standard semantic domain in the probabilistic semantic domain; (5) demonstrate that this embedding preserves program structure; (6) define the probabilistic choice operator. Weakest completion semantics guides the semantic definition of new languages by building on existing semantics and, in this case, tackling a notoriously thorny issue: the relationship between demonic and probabilistic choice. Consistency ensures that programming intuitions, development techniques, and proof methods can be carried over from the standard language to the probabilistic one. We largely follow He et al., our contribution being an explication of the technique with meticulous proofs suitable for mechanisation in Isabelle/UTP

Formal Aspects of Component Software - 15th International Conference, FACS 2018, Pohang, South Korea, October 10-12, 2018, Proceedings.

[No abstract available]11Nscopu

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Programming Languages and Systems

This open access book constitutes the proceedings of the 30th European Symposium on Programming, ESOP 2021, which was held during March 27 until 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 24 papers included in this volume were carefully reviewed and selected from 79 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems

Vérification automatique de la confidentialité différentielle

Ce rapport étudie la vérification quantitative de la confidentialité différentielle dans les systèmes distribués. Tout d’abord, nous examinons l’applicabilité de la vérification des modèles probabilistes pour fournir des garanties sur le comportement des système différentiellement confidentiels. Ensuite, nous concevons des méthodes qui extraient automatiquement les modèles des systèmes à partir d’une description de haut niveau, puis nous effectuons une vérification probabiliste de ces modèles. Nous développons à cette fin une nouvelle méthodologie de la vérification quantitative. Nous décrivons des méthodes formelles pour analyser un large éventail de propriétés de confidentialité, notamment la précision et la perte de la confidentialité. Nous avons également réexprimé la notion de confidentialité différentielle pour raisonner sur deux exécutions de programmes similaires. À notre connaissance, il s’agit des analyses de confidentialité les plus genérales pour les systèmes distribués. Deuxièmement, nous fournissons des preuves de couplage basées sur les relations de levage approximatives pour prouver la confidentialité différentielle dans les chaînes de Markov. Nous proposons également des algorithmes de vérification symbolique de la confidentialité. L’avantage de notre approche est que ces algorithmes peuvent être facilement implémentés dans n’importe quel outil de vérification de modèles probabilistes. Enfin, nous définissons une approche pour extraie des contre-exemples qui peuvent être utilisés pour fin de débogage similaires en fournissant une exécution qui viole la confidentialité.----------ABSTRACT: This report studies the quantitative verification of differential privacy in distributed systems. First, we examine the applicability of probabilistic model checking to provide guarantees on the behavior of differentially private systems. Next, we design methods that automatically extract the models of the systems from a high-level description, then we perform a probabilistic verification of these models. To this end, we are developing a new methodology for quantitative verification. We describe formal methods for analyzing a wide range of privacy properties, including accuracy and privacy loss. We have also re-expressed the notion of differential privacy to reason about two executions of similar programs. To our knowledge, this is the most general privacy analysis for distributed systems. Second, we provide evidence of coupling based on approximate lifting relationships to prove differential privacy in Markov chains. We also offer symbolic algorithm for verification of confidentiality. The advantage of our approach is that these algorithms can be easily implemented in any probabilistic model checker tool. Finally, we define an approach for extracting counterexamples that can be used for similar debugging purposes by providing an execution that violates confidentiality

University catalog, 2016-2017

The catalog is a comprehensive reference for your academic studies. It includes a list of all degree programs offered at MU, including bachelors, masters, specialists, doctorates, minors, certificates, and emphasis areas. It details the university wide requirements, the curricular requirements for each program, and in some cases provides a sample plan of study. The catalog includes a complete listing and description of approved courses. It also provides information on academic policies, contact information for supporting offices, and a complete listing of faculty members. -- Page 3