Quantifier-free logic for nondeterministic theories

AbstractWe develop a quantifier-free logic for deriving consequences of multialgebraic theories. Multialgebras are used as models for nondeterminism in the context of algebraic specifications. They are many sorted algebras with set-valued operations. Formulae are sequents over atoms allowing one to state set-inclusion or identity of 1-element sets (determinacy). We introduce a sound and weakly complete Rasiowa–Sikorski (R–S) logic for proving multialgebraic tautologies. We then extend this system for proving consequences of specifications based on translation of finite theories into logical formulae. Finally, we show how such a translation may be avoided—introduction of the specific cut rules leads to a sound and strongly complete Gentzen system for proving directly consequences of specifications. Besides giving examples of the general techniques of R–S and the specific cut rules, we improve the earlier logics for multialgebras by providing means to handle empty carriers (as well as empty result-sets) without the use of quantifiers, and to derive consequences of theories without translation into another format and without using general cut

Extended Content-boosted Matrix Factorization Algorithm for Recommender Systems

AbstractRecommender technologies have been developed to give helpful predictions for decision making under uncertainty. An extensive amount of research has been done to increase the quality of such predictions, currently the methods based on matrix factorization are recognized as one of the most efficient.The focus of this paper is to extend a matrix factorization algorithm with content awareness to increase prediction accuracy. A recommender system prototype based on the resulting Extended Content-Boosted Matrix Factorization Algorithm is designed, developed and evaluated. The algorithm has been evaluated by empirical evaluation, which starts with creating of an experimental design, then conducting off-line empirical tests with accuracy measurement.The result revealed further potential of the content awareness in matrix factorization methods, which has not been fully realized in the generalized alignment-biased algorithm by Nguyen and Zhu and uncovers opportunities for future research

Enforcement of Patterns by Constraint-Aware Model Transformations

Patterns are descriptions and solutions for recurring problems in software design and implementation. In this paper, some ideas towards a formal approach to the specification of patterns in model-driven engineering (MDE) is presented. The approach is based on the Diagram Predicate Framework which provides a formal approach to (meta)modelling, model transformation and model management in MDE. In particular, patterns are defined as diagrammatic specifications and constraint-aware model transformations are adapted to enforce patterns. Moreover, running examples are used to illustrate the facade design pattern in structural models

Co-Transformation of Type and Instance Graphs Supporting Merging of Types and Retyping

Algebraic graph transformation is a well-known rule-based approach to manipulate graphs that can be applied in several contexts. In this paper we use it in the context of model-driven engineering. Graph transformation rules usually specify changes to only one graph per application, however there are use cases such as model co-evolution where not only a single graph should be manipulated but also related ones. The co-transformation of type graphs together with their instance graphs has shown to be a promising approach to formalize model and meta-model co-evolution. In this paper, we extend our earlier work on co-evolution by allowing transformation rules that have less restrictions so that graph manipulations such as merging of types and retyping of graph elements are allowed

Well-formed Model Co-evolution with Customizable Model Migration

Model-driven engineering (MDE) is a software engineering discipline which focuses on models as the primary artifact of the software development process while programs are mainly generated by means of model-to-code transformations. In particular, modeling languages tailored to specific domains promise to increase the productivity and quality of software. Nevertheless due to e.g. evolving requirements, modeling languages evolve and existing models have to be migrated. Corresponding manual model migration is tedious and error-prone, therefore tools have been developed to (partly) automate this process. We follow the idea of considering such modeling language and model co-evolutions as related graph transformations ensuring a correct and unique typing of migrated models. In this paper, we present a general and formal construction of well-formed model migration schemes that are able to co-adapt any model of a given modeling language to a performed meta-model change. We show how appropriate model migration schemes can be constructed and discuss how they may be customized

A Reference Architecture for Data-Driven and Adaptive Internet-Delivered Psychological Treatment Systems: Software Architecture Development and Validation Study

Background: Internet-delivered psychological treatment (IDPT) systems are software applications that offer psychological treatments via the internet. Such IDPT systems have become one of the most commonly practiced and widely researched forms of psychotherapy. Evidence shows that psychological treatments delivered by IDPT systems can be an effective way of treating mental health morbidities. However, current IDPT systems have high dropout rates and low user adherence. The primary reason is that the current IDPT systems are not flexible, adaptable, and personalized as they follow a fixed tunnel-based treatment architecture. A fixed tunnel-based architecture follows predefined, sequential treatment content for every patient, irrespective of their context, preferences, and needs. Moreover, current IDPT systems have poor interoperability, making it difficult to reuse and share treatment materials. There is a lack of development and documentation standards, conceptual frameworks, and established (clinical) guidelines for such IDPT systems. As a result, several ad hoc forms of IDPT models exist. Consequently, developers and researchers have tended to reinvent new versions of IDPT systems, making them more complex and less interoperable. Objective: This study aimed to design, develop, and evaluate a reference architecture (RA) for adaptive systems that can facilitate the design and development of adaptive, interoperable, and reusable IDPT systems. Methods: This study was conducted in collaboration with a large interdisciplinary project entitled INTROMAT (Introducing Mental Health through Adaptive Technology), which brings together information and communications technology researchers, information and communications technology industries, health researchers, patients, clinicians, and patients’ next of kin to reach its vision. First, we investigated previous studies and state-of-the-art works based on the project’s problem domain and goals. On the basis of the findings from these investigations, we identified 2 primary gaps in current IDPT systems: lack of adaptiveness and limited interoperability. Second, we used model-driven engineering and Domain-Driven Design techniques to design, develop, and validate the RA for building adaptive, interoperable, and reusable IDPT systems to address these gaps. Third, based on the proposed RA, we implemented a prototype as the open-source software. Finally, we evaluated the RA and open-source implementation using empirical (case study) and nonempirical approaches (software architecture analysis method, expert evaluation, and software quality attributes). Results: This paper outlines an RA that supports flexible user modeling and the adaptive delivery of treatments. To evaluate the proposed RA, we developed an open-source software based on the proposed RA. The open-source framework aims to improve development productivity, facilitate interoperability, increase reusability, and expedite communication with domain experts. Conclusions: Our results showed that the proposed RA is flexible and capable of adapting interventions based on patients’ needs, preferences, and context. Furthermore, developers and researchers can extend the proposed RA to various health care interventions.publishedVersio

A higher-order transformation approach to the formalization and analysis of BPMN using graph transformation systems

The Business Process Modeling Notation (BPMN) is a widely used standard notation for defining intra- and inter-organizational workflows. However, the informal description of the BPMN execution semantics leads to different interpretations of BPMN elements and difficulties in checking behavioral properties. In this article, we propose a formalization of the execution semantics of BPMN that, compared to existing approaches, covers more BPMN elements while also facilitating property checking. Our approach is based on a higher-order transformation from BPMN models to graph transformation systems. To show the capabilities of our approach, we implemented it as an open-source web-based tool

Towards a Multi Metamodelling Approach for Developing Distributed Healthcare Applications

Model Driven Engineering (MDE) uses formal methods to build mathematically rigorous models of complex systems. Metamodelling plays an important role in MDE as it is used to specify domain specific modelling languages. However, the potential of metamodelling has not been fully explored. Current approaches of MDE are often at a low level of abstraction and lack domain concepts for specifying behavior. In previous work, we proposed a multi metamodelling approach that captures the complexity of systems by using a metamodelling hierarchy, built from individually defined metamodels, each capturing different aspects of a healthcare domain. In this paper, we focus on modelling distributed healthcare applications and present an example from the healthcare domain. We address certain modelling aspects related to distributed applications such as process modelling, using message passing communication, and coordination of processes and resources

Internet-delivered mental health treatment systems in Scandinavia – A usability evaluation

Mental health problems are a major public health concern worldwide. Approximately 50% of the population will experience mental problems during their life. Traditional treatment is based on psychopharmacotherapy or psychotherapy, with face-to-face interaction between the patient and the therapist. New technologies such as Internet-delivered treatments are seen as an opportunity to offer more scalable and cost-efficient treatments in the field of mental health. Despite the growing interest and new evidence supporting the effect of Internet-delivered treatments is it remarkably little research on how the technology and the usability of Internet-delivered treatment programs affects the treatment. In this paper, we propose a set of evaluation criteria for evaluating the usability and the responsive design of Internet-delivered treatment systems. By our knowledge we are the first to include usability and universal design principles in the evaluation of Internet-delivered treatment systems. Our findings indicate that despite the good treatment results and proven clinical effects, the systems in general have several issues regarding usability, universal design and outdated technology. Based on our findings we propose that there should be established guidelines for testing the usability and technology of Internet-delivered treatment systems.publishedVersio

Model-Driven Automatic Question Generation for a Gamified Clinical Guideline Training System

Clinical practice guidelines (CPGs) are a cornerstone of modern medical practice since they summarize the vast medical literature and provide care recommendations based on the current best evidence. However, there are barriers to CPG utilization such as lack of awareness and lack of familiarity of the CPGs by clinicians due to ineffective CPG dissemination and implementation. This calls for research into effective and scalable CPG dissemination strategies that will improve CPG awareness and familiarity. We describe a model-driven approach to design and develop a gamified e-learning system for clinical guidelines where the training questions are generated automatically. We also present the prototype developed using this approach. We use models for different aspects of the system, an entity model for the clinical domain, a workflow model for the clinical processes and a game engine to generate and manage the training sessions. We employ gamification to increase user motivation and engagement in the training of guideline content. We conducted a limited formative evaluation of the prototype system and the users agreed that the system would be a useful addition to their training. Our proposed approach is flexible and adaptive as it allows for easy updates of the guidelines, integration with different device interfaces and representation of any guideline.acceptedVersio