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Abstract ■■■ The Internet of Services and Things is characterized as a distributed computing environment that will be populated by a large number of software services and things. Within this context, software systems will increasingly be built by reusing and composing together software services and things distributed over the Internet. This calls for new integration paradigms and patterns, formal composition theories, integration architectures, as well as flexible and dynamic composition and verification mechanisms. In particular, service- and thing-based systems pose new challenges for software composition and verification techniques, due to changing requirements, emerging behaviors, uncertainty, and dynamicity.

Background: Cognitive Remediation or Rehabilitation Treatment (CRT) is a psychosocial treatment method against cognitive deficits and their functional consequences. CRT is implemented in various disorders ranging from brain injury to neurological and mental disorders and is associated with positive results in terms of cognitive and social functioning. Similarly, the investigation of the effects of CRT in schizophrenia has shown that it could improve cognition and everyday functioning and that it might also be beneficial for the other clusters of symptoms of the disorder (positive and negative symptoms). Objective: To present the origins, process of development and characteristics of a CRT program for individuals with psychoses (schizophrenia and other psychotic disorders, bipolar disorder with psychotic features and bipolar depression, etc.), along with the experience of its pilot implementation and its perspectives under limited financial and human resources. Methods: The article will discuss the theoretical basis of a CRT program for patients with psychosis. It will also present the consecutive steps of its development, its structure and its pilot implementation. Finally, it will discuss its future perspectives. Results: The program is based on the experience from animal and human studies and on principles including interdisciplinary therapeutic team work, individualization of therapy, frequent practice of cognitive tasks, use of strategies and psychological support. It aims at the improvement of everyday functioning of individuals with psychoses through the enhancement of their cognition. It is preceded and followed by sessions assessing cognitive performance, symptoms and functioning and comprises 40 individual training sessions with a frequency of at least two sessions per week. The training sessions are divided into two parts including a) a cognitive exercises module and b) a social cognition module. Cognitive exercises include a program designed for elderly suffering from mild cognitive impairment and additional cognitive tasks involving the main cognitive domains of impaired performance in schizophrenia. The social cognition module has been arisen from a program administered to patients hospitalized in an acute psychiatric ward with the aim to facilitate and accelerate their discharge. The perspectives of the CRT program include the development of group training sessions and public awareness activities and the creation of similar CRT Units in Mental Health Centres and other Health Settings. Given that both the cognitive impairments and the methods used for their treatment have common features across different mental or neurological disorders (e.g. non-psychotic depression, dementia, brain injury, substance use disorders), they also include the implementation of the program onr individuals presenting with cognitive impairments beyond psychoses. Conclusions: The development of CRT programs in the context of a National Health Service and under conditions of limited funding is feasible and could satisfy unmet needs of patients having cognitive difficulties regardless of their aetiology.

Despite many advances that enable the application of model checking techniques to the verification of large systems, the state-explosion problem remains the main challenge for scalability. Compositional verification addresses this challenge by decomposing the verification of a large system into the verification of its components. Recent techniques use learning-based approaches to automate compositional verification based on the assume-guarantee style reasoning. However, these techniques are only applicable to finite-state systems. In this work, we propose a new framework that interleaves abstraction and learning to perform automated compositional verification of infinite-state systems. We also discuss the role of learning and abstraction in the related context of interface generation for infinite-state components.