Foundation Models shift the interest to adapting models instead of creating proprietary models from scratch. Despite this change, performing hyperparameter optimization (HPO) is still needed. Users adapting systems powered by those models on proprietary data should not considerably increase the overall resource footprint with extensive hyperparameter search. Given that this footprint is also proportional to the data used in HPO, we aim to investigate how a user can effectively reduce the amount of data used, leveraging the deep learning model's empirical facility to output the expected correct result for an item in the dataset.

Abstract Cyber Physical Systems (CPS) are becoming more ubiquitous, complex and powerful as well as more and more present in our daily life. The inherent benefit and comfort come with an environmental impact at every step of their life‐cycle. This impact is significant and unfortunately often ignored today. As cyber‐physical systems tend to be ‘invisible’, there is a need for awareness of the underlying infrastructure and required resources, early in the design phases. In this article, the environmental impact considerations in the early stages of the implementation and opportunities to improve design choices with a people‐planet‐system perspective are discussed. The authors discuss the aspects related to system configuration, data management and the overall goal and functionalities supported by the CPS. Through a specific smart home case, the potential of considering life‐cycle assessment of both the devices and data management is illustrated. By explicitly considering different configurations, it will be possible to analyse the environmental impacts of the design decisions. Our research in progress targets a design approach to converge into an equilibrium between utility, performance, and minor environmental impact of smart systems.

Machine learning (ML) systems based on deep neural networks are more present than ever in software solutions for numerous industries. Their inner workings relying on models learning with data are as helpful as they are mysterious for non-expert people. There is an increasing need to make the design and development of those solutions accessible to a more general public while at the same time making them easier to explore. In this paper, to address this need, we discuss a proposition of a new assisted approach, centered on the downstream task to be performed, for helping practitioners to start using and applying Deep Learning (DL) techniques. This proposal, supported by an initial testbed UI prototype, uses an externalized form of knowledge, where JSON files compile different pipeline metadata information with their respective related artifacts (e.g., model code, the dataset to be loaded, good hyperparameter choices) that are presented as the user interacts with a conversational agent to suggest candidate solutions for a given task.

Typical business process management studios provide support for process design through generic languages such as BPMN. This brings several shortcomings related to process governance over time, process ambiguity and complexity for non-technical users. Domain-specific process languages have the potential to correct these issues but they require strong enterprise tool support and integration in order to be successfully adopted. This paper proposes a mechanism for generating intuitive yet feature-rich graphical process studios for various business domains that are fully integrated with standard business process management solutions. It reduces the need for costly development and maintenance of such studios while ensuring that business users have consistent access to the ever-evolving enterprise body of knowledge. The approach uses model-based transformations to generate and support the entire infrastructure required by the studios. This includes the graphical user interface, the conversion capabilities to and from BPMN, embedding of real-time monitoring data from business process engines and service oriented platforms, live multi-user collaboration support, process governance and evolution, domain know-how management, as well as service-level agreement monitoring. The approach has been fully prototyped and integrated with enterprise-level tools and platforms.

Organizations' business processes become more and more complex and depend on services provided by external organizations. The global viewpoint given by choreography approach is required to better understand, build, survey and optimize complex processes. While existing proposals were made for defining choreographies, it is still difficult to bridge the gap between business process design and implementations. We present choreography with a graphical notation based on meta-modeling techniques as abstraction levels and multiple views. This will help us to fully exploit this currently underused concept. In order to address identified notation issues, several recommendations are put forward. An empirical experimentation of our work is finally presented bringing about several updates in our proposal.