State-based and process-based value passing

State-based and process-based formalisms each come with their own distinct set of assumptions and properties. To combine them in a useful way it is important to be sure of these assumptions in order that the formalisms are combined in ways which have, or which allow, the intended combined properties. Consequently we cannot necessarily expect to take on state-based formalism and one process-based formalism and combine them and get something sensible, especially since the act of combining can have subtle consequences. Here we concentrate on value-passing, how it is treated in each formalism, and how the formalisms can be combined so as to preserve certain properties. Specifically, the aim is to take from the many process-based formalisms definitions that will best fit with our chosen stat-based formalism, namely Z, so that the fit is simple, has no unintended consequences and is as elegant as possible

The use of abstractions to solve large scheduling problems

This thesis investigates how abstractions can be used to improve performance in a railroad scheduling system that uses constraint programming. The idea behind abstractions is to solve a large problem in smaller parts and extract information from these parts. That information can then be used when solving the entire problem. Two different types of abstractions are introduced: Relations and Net abstractions. The use of relations builds orders between trips or parts of trips. These orders can be used to reduce the search necessary to find a solution to the scheduling problem. When using net abstraction, the problem is solved in an abstract search space, where it is easier to solve. The solution computed in the abstract search space is then used to reduce search when solving the problem in the original space. It is shown that these two types of abstraction can improve performance in problems with various settings. Relations can successfully be used in problems that have few solutions and are hard to solve. Net abstraction on the other hand works best for problems with many valid solutions

Reformulation in planning

Reformulation of a problem is intended to make the problem more amenable to efficient solution. This is equally true in the special case of reformulating a planning problem. This paper considers various ways in which reformulation can be exploited in planning

Model checking object-Z using ASM

A major problem with creating tools for Object-Z is that its high-level abstractions are difficult to deal with directly. Integrating Object-Z with a more concrete notation is a sound strategy. With this in mind, in this paper we introduce an approach to model-checking Object-Z specifications based on first integrating Object-Z with the Abstract State Machine (ASM) notation to get the notation OZ-ASM. We show that this notation can be readily translated into the specification language ASM-SL, a language that can be automatically translated into the language of the temporal logic model checker SMV

Automatic Verification of Parametric Specifications with Complex Topologies

The focus of this paper is on reducing the complexity in verification by exploiting modularity at various levels: in specification, in verification, and structurally. \begin{itemize} \item For specifications, we use the modular language CSP-OZ-DC, which allows us to decouple verification tasks concerning data from those concerning durations. \item At the verification level, we exploit modularity in theorem proving for rich data structures and use this for invariant checking. \item At the structural level, we analyze possibilities for modular verification of systems consisting of various components which interact. \end{itemize} We illustrate these ideas by automatically verifying safety properties of a case study from the European Train Control System standard, which extends previous examples by comprising a complex track topology with lists of track segments and trains with different routes

Compositional software verification based on game semantics

One of the major challenges in computer science is to put programming on a firmer mathematical basis, in order to improve the correctness of computer programs. Automatic program verification is acknowledged to be a very hard problem, but current work is reaching the point where at least the foundational�· aspects of the problem can be addressed and it is becoming a part of industrial software development. This thesis presents a semantic framework for verifying safety properties of open sequ;ptial programs. The presentation is focused on an Algol-like programming language that embodies many of the core ingredients of imperative and functional languages and incorporates data abstraction in its syntax. Game semantics is used to obtain a compositional, incremental way of generating accurate models of programs. Model-checking is made possible by giving certain kinds of concrete automata-theoretic representations of the model. A data-abstraction refinement procedure is developed for model-checking safety properties of programs with infinite integer types. The procedure starts by model-checking the most abstract version of the program. If no counterexample, or a genuine one, is found, the procedure terminates. Otherwise, it uses a spurious counterexample to refine the abstraction for the next iteration. Abstraction refinement, assume-guarantee reasoning and the L* algorithm for learning regular languages are combined to yield a procedure for compositional verification. Construction of a global model is avoided using assume-guarantee reasoning and the L* algorithm, by learning assumptions for arbitrary subprograms. An implementation based on the FDR model checker for the CSP process algebra demonstrates practicality of the methods

A LightGBM-Based EEG Analysis Method for Driver Mental States Classification

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)