Abstract
In this paper we present a new static data type inference algorithm for logic programming. Without the need for declaring types for predicates, our algorithm is able to automatically assign types to predicates which, in most cases, correspond to the data types processed by their intended meaning. The algorithm is also able to infer types given data type definitions similar to data definitions in Haskell and, in this case, the inferred types are more informative, in general. We present the type inference algorithm, prove it is decidable and sound with respect to a type system, and, finally, we evaluate our approach on example programs that deal with different data structures.

Abstract
Motor controllers, such as the ones used in signalling systems, include critical embedded software. Alstom is a company that produces such embedded systems, which must follow complex certification processes that require formal modelling and analysis. The formal analysis of these real-time systems have to balance between including enough details to be useful and abstracting away enough details to be verifiable. This paper describes our work in the context of the European VALU3S project to integrate the analysis of such systems with the Uppaal model checker during the development cycle, involving both developers from Alstom and academic partners. We use special Excel tables to configure the underlying Uppaal models and requirements, bridging these two stakeholders. We follow Software Product Line Engineering principles, e.g., allowing features to be turned on and off and periodicities to be changed, and verify different properties for each of such configuration. We automate the instantiation and verification in Uppaal of a set of selected configurations via an open-source prototype tool named Uppex.

Abstract
To improve manufacturing processes, it is essential to find the root causes of occurring problems, in order to solve them permanently. Automatic Root Cause Analysis (ARCA) solutions aid analysts in finding such root causes, by using automatic data analysis to improve the digital decision. When trying to locate the root cause of a problem in a manufacturing process, a phenomenon can occur that disrupts the application of ARCA solutions. Overlap, as we denominated, is a phenomenon where local synchronicities in the manufacturing process lead to data where it is impossible to discern the influence of each location in the quality of products, which impedes automated diagnosis, especially when using classifiers. This paper identifies and defines overlap, and proposes a two-phase ARCA solution that uses factor-ranking algorithms, instead of classifiers. The proposed solution is evaluated in simulated and real case-study data. Results proved the presence of overlap in the datasets, and its negative impact on classifiers. The proposed solution has a positive performance detecting root causes even in the presence of overlap.

Abstract

Abstract
CloudAnchor is a multi-agent brokerage platform for the negotiation of Infrastructure as a Service cloud resources between Small and Medium Sized Enterprises, acting either as providers or consumers. This project entails the research, design, and implementation of a smart contract solution to permanently record and manage contractual and behavioural stakeholder data on a blockchain network. Smart contracts enable safe contract code execution, increasing trust between parties and ensuring the integrity and traceability of the chained contents. The defined smart contracts represent the inter-business trustworthiness and Service Level Agreements established within the platform. CloudAnchor interacts with the blockchain network through a dedicated Application Programming Interface, which coordinates and optimises the submission of transactions. The performed tests indicate the success of this integration: (i) the number and value of negotiated resources remain identical; and (ii) the run-time increases due to the inherent latency of the blockchain operation. Nonetheless, the introduced latency does not affect the brokerage performance, proving to be an appropriate solution for reliable partner selection and contractual enforcement between untrusted parties. This novel approach stores all brokerage strategic knowledge in a distributed, decentralised, and immutable database.

Abstract
This study presents the dependence of strain sensitivity on cavity length in conventional Fabry-Perot (F-P) sensors. A high number of F-P sensors were required and to ensure their reproducibility, a manufacturing process was developed to obtain similar sensors but with different types of lengths. A hollow-core silica tube was used to fabricate several F-P cavities by fusion splicing it between two sections of SMF28 fiber. The fabricated F-P has a varying length ranging from 15 to 2500 mu m. The cavities were measured under a microscope and the reflected spectrum was acquired for each one. Strain measurements were performed for a maximum strain of 1000 mu epsilon. The strain sensitivity showed a highly linear correlation with increment lambda(FSR). Small length variations for short cavities heavily affect the FSR value. The smallest and longest cavities present sensitivities of 8.71 and 2.68 pm/mu epsilon, respectively. Thermal characterization for low- and high-temperature regimes was also performed and is constant for tested sensors.