Although deep learning models have shown promising results in solving problems related to image recognition or natural language processing, they do not match how the biological brain works. Some of the differences include the amount of energy consumed, the way neurons communicate, or the way they learn. To close the gap between artificial neural networks and biological ones, researchers proposed the spiking neural network. Layered Spiking Neural P systems (LSN P systems) are networks of spiking neurons used to solve various classification problems. In this paper, we study the LSN P systems in the context of a federated learning client-server architecture over horizontally partitioned data. We analyze the privacy implications of pre-trained LSN P systems through membership inference attacks. We also perform experiments to assess the performance of an LSN P system trained in the federated learning setup. Our findings suggest that LSN P systems demonstrate higher accuracy and faster convergence compared to federated algorithms based on either perceptron or spiking neural networks.

Kernel P systems have been introduced as a unifying formalism allowing to specify, simulate and analyse various problems. Several applications of this model have been considered and a powerful tool built in order to support their development and analysis. Testing represents an important aspect of any system analysis and correctness. In this paper we introduce for the first time a bounded test generation approach for kernel P systems by considering bounded input sequences. A learning algorithm for kernel P systems is based on learning X-machine models that are equivalent to these systems for sequences of steps up to a certain limit, ℓ. The Lℓ learning algorithm is used. The testing approach is then devised from the inferred X-machines. The method is applied to a case study illustrating the key parts of the approach.

EDMA-2017, the first International Workshop on Engineering Data- & Model-driven Applications, was held in conjunction with the 10th IEEE International Conference on Cyber, Physical and Social Computing (IEEE CPSCom-2017). EDMA-2017 provided authors a forum for presentations, discussions and sharing of current challenges, knowledge, expertise and solutions regarding trends and technologies for the use of data and computational models for dealing with complexity in industrial, engineering, cyber-physical and related domains. Contributions to the EDMA-2017 included relevant studies from across engineering, computer science and data science researchers on data (knowledge discovery, machine learning, big data analytics) and model-based methods (e.g. engineering and hybrid models for systems reliability, degradation and vehicle health management) delivering effective and efficient solutions to challenges associated with handling complexity in real-world engineering and industrial applications. Selected extended contributions are included in this special issue, authored by multidisciplinary international teams of researchers and focussing on cutting edge technologies in industrial environment image segmentation, application of neural networks for high-speed train wheel wear classification, and manufacturing job shop planning with hybrid machine learning. The list of selected papers includes: An ordered sparse subspace clustering algorithm based on p-Norm: the authors Liping Chen, Gongde Guo and Hui Wang extend initial work on noise-resilient sparse subspace clustering algorithm for image sequences and propose a new approach to effective clustering of sequential image data under heavy noise conditions, characteristic for industry environments, requiring video scene segmentation. The contributions use the wavelet-histogram of oriented gradient transform in the kernel view to extract from source images global features using wavelet processes, and local features in an optimized weighted form of a coefficient matrix, and denoising component techniques with Laplacian constraints. Analysis and prediction of high-speed train wheel wear based on SIMPACK and backpropagation neural networks: the authors Shuwen Wang, Hao Yan, Caixia Liu, Ning Fan, Xiaoming Liu and Chengguo Wang use a combination of engineering model-based dynamic simulation with real-world wear measurements to deliver predictive solutions for important aspects of safe operation of high-speed trains. Wheel wear is studied using SIMPACK dynamic models of high-speed train carriages and backpropagation neural network classifiers based on measurement data. By investigating normal and lateral contact forces of wear of wheels, and wheel-rail interfaces, and integrating real-world data with SIMPACK dynamic simulations and machine learning classifiers, the contributors provide insights useful for maintenance and safe operation of high-speed trains. Job shop planning and scheduling for manufacturers with manual operations, authors: Longzhi Yang, Jie Li, Fei Chao, Phil Hackney, Mark Flanagan study the manual collate and pack area completion time estimation for Point of Sale and Point of Purchase Manufacturing Scheduling for optimisation of machine use in the manufacturing industry. The authors build on searching the most cost-effective permutation of job operations based on the cost of each operation on the compatible machine and the relations between job operations. The contributions include algorithms for a new genetic algorithm for scheduling that integrates fuzzy learning and inferences for completion time estimation and expert in the loop initialisation and adaptive learning processes. The EDMA-2017 chairs and special issue editors thank authors, organisers and readers for the passion and contributions to apply machine learning research to engineering applications. Dr Daniel Neagu is Professor of Computing and a successful AI/ML/big data analytics scientist with 20+ years of research project leadership funded by national, international research programmes, also national and international industry. Professor Neagu leads Bradford's AI Research (AIRe) Group and has research expertise in Explainable AI, Machine Learning and big data algorithms, responsible data and model governance, and their applications in healthcare, predictive toxicology and engineering. Daniel Neagu has chaired a number of international conferences in data science and AI (eg IEEE DSS 2018, UKCI 2014), and is founder of the series of the International Workshop on Engineering Data- & Model-driven Applications (EDMA 2017-2021). Felician Campean, PhD, FRSS, is a Professor of Reliability Engineering and Director of the Automotive Research Centre at the University of Bradford, UK. He is an internationally recognised expert in reliability, and has established an extensive track record of research and knowledge transfer with the global automotive industry, spanning over 24 years. His current research interests include the development of novel model-based and data-driven approaches to enhance reliability and resilience of complex multi-disciplinary systems, throughout their lifecycle. He has co-founded the Advanced Automotive Analytics research laboratory in 2016, and currently leading a significant portfolio of projects driving the application of ML and AI to real world engineering problems. Dr Marian Gheorghe is Professor of Computational Models and Software Engineering at University of Bradford. Professor Marian Gheorghe's research interests are in computational models and software engineering. He has a special interest in new classes of unconventional computational models, especially membrane systems and their connections with other (unconventional) computational models - Petri nets, process algebras, L-systems, reactive systems etc. He is also interested in formally verifying such models by using model checking approaches. Professor Gheorghe is also interested in model-based testing and connections with formal verification. He has been working with large scale simulations, based on formal models, and contributed to building adequate software platforms for such approaches. He is also interested in applications of formal specifications and formal verification methods and tools in computational and synthetic biology, but also in autonomous systems. He served as the Chair of the Steering Committee of the Conference on Membrane Computing between 2010 - 2014, the main research forum of the membrane computing community, editing the proceedings of these events with Springer, in Lecture Notes in Computer Science Series. He has connections and active collaborations with many prestigious Universities and research groups in the world. Professor Gheorghe's research has been funded by EPSRC, EU, Royal Academy of Engineering, British Council and has been included in some international projects abroad as scientific advisor and collaborator.

Motivation: Formal verification is a computational approach that checks system correctness (in relation to a desired functionality). It has been widely used in engineering applications to verify that systems work correctly. Model checking, an algorithmic approach to verification, looks at whether a system model satisfies its requirements specification. This approach has been applied to a large number of models in systems and synthetic biology as well as in systems medicine. Model checking is, however, computationally very expensive, and is not scalable to large models and systems. Consequently, statistical model checking (SMC), which relaxes some of the constraints of model checking, has been introduced to address this drawback. Several SMC tools have been developed; however, the performance of each tool significantly varies according to the system model in question and the type of requirements being verified. This makes it hard to know, a priori, which one to use for a given model and requirement, as choosing the most efficient tool for any biological application requires a significant degree of computational expertise, not usually available in biology labs. The objective of this article is to introduce a method and provide a tool leading to the automatic selection of the most appropriate model checker for the system of interest. Results: We provide a system that can automatically predict the fastest model checking tool for a given biological model. Our results show that one can make predictions of high confidence, with over 90% accuracy. This implies significant performance gain in verification time and substantially reduces the 'usability barrier' enabling biologists to have access to this powerful computational technology. Availability and implementation: SMC Predictor tool is available at http://www.smcpredictor.com. Supplementary information: Supplementary data are available at Bioinformatics online.

In this paper we investigate the use of general topological spaces in connection with a generalised variant of membrane systems. We provide an approach which produces a fine grain description of local operations occurring simultaneously in sets of compartments of the system by restricting the interactions between objects. This restriction is given by open sets of a topology and multisets of objects associated with them, which dynamically change during the functioning of the system and which together define a notion of vicinity for the objects taking part in the interactions.

This paper proposes a formal methodology to analyse bio-systems, in particular synthetic biology systems. An integrative analysis perspective combining different model checking approaches based on different property categories is provided. The methodology is applied to the synthetic pulse generator system and several verification experiments are carried out to demonstrate the use of our approach to formally analyse various aspects of synthetic biology systems.

The mathematician and logician Gr. C. Moisil (1906-1973) played a fundamental role in the introduction and the development of computer science in Romania and in raising the first generations of

on the occasion of his 65th birthday.

As unconventional computation matures and non-standard programming frameworks are demonstrated, the need for formal verification will become more prevalent. This is so because “programming” in unconventional substrates is difficult. In this paper we

Here we continue the study of bio-Turing machines introduced in [2] and further investigated in [17]. We introduce a restricted model of bio-Turing machine and we investigate its computational power, a hierarchy of languages accepted, and deterministic and nondeterministic variants. A comprehensive example illustrating the modelling power of the introduced machine ends the paper.

VLSI research, in its continuous push toward further miniaturisation, is seeking to break through the limitations of current circuit manufacture techniques by moving towards biomimetic methodologies that rely on self-assembly, selforganisation and evodevo-like processes. On the other hand, Systems and Synthetic biology's quest to achieve ever more detailed (multi)cell models are relying more and more on concepts derived from computer science and engineering such as the use of logic gates, clocks and pulse generator analogs to describe a cell's decision making behavior. This paper is situated at the crossroad of these two enterprises. That is, a novel method of non-conventional computation based on the encapsulation of simple gene regulatory-like networks within liposomes is described. Three transcription Boolean logic gates were encapsulated and simulated within liposomes self-assembled from DMPC (dimyristoylphosphatidylcholine) amphiphiles using an implementation of Dissipative Particle Dynamics (DPD) created with the NVIDIA CUDA framework, and modified to include a simple collision chemistry in a stochastic environment. The response times of the AND, OR and NOT gates were shown to be positively effected by the encapsulation within the liposome inner volume.

Self-assembly is a distributed, asynchronous mechanism that is pervasive across natural systems where hierarchical complex structures are built from the bottom-up. The lack of a centralised master plan, no external intervention, and preprogrammed interactions among entities are within its most relevant and technologically appealing properties. This paper tackles the self-assembly Wang tiles designability problem by means of artificial evolution. This research is centred in the use of tiles that are extended with rotation and probabilistic motion, and an evolutionary algorithm using the Morphological Image Analyses method as a fitness function. The obtained results support this approach as a successful engineering mechanism for the computer-aided design of self-assembled patterns.

Self-assembly is a powerful autopoietic mechanism ubiquitous throughout the natural world. It may be found at the molecular scale and also at astronomical scales. Self-assembly power lays in the fact that it is a distributed, not necessarily synchronous, control mechanism for the bottom-up manufacture of complex systems. Control of the assembly process is shared across a myriad of elemental components, none of which has either the storage or the computation capabilities to know and follow a master plan for the assembly of the intended system. In this paper we present an evolutionary algorithm which is capable of programming the so called "Wang tiles" for the self-assembly of two-dimensional squares.

Abstract. Stream X-machines are a general and powerful computational model. By coupling the control structure of a stream X-machine with a set of formal grammars a new machine called a generalised stream X-machine with underlying distributed grammars , acting as a translator, is obtained. By introducing this new mechanism a hierarchy of computational models is provided. If the grammars are of a particular class, say regular or context-free, then finite sets are translated into finite sets, when ? k , = k derivation strategies are used, and regular or context-free sets, respectively, are obtained for ? k , * and terminal derivation strategies. In both cases, regular or context-free grammars, the regular sets are translated into non-context-free languages. Moreover, any language accepted by a Turing machine may be written as a translation of a regular set performed by a generalised stream X-machine with underlying distributed grammars based on context-free rules, under = k derivation strategy. On the other hand the languages generated by some classes of cooperating distributed grammar systems may be obtained as images of regular sets through some X-machines with underlying distributed grammars. Other relations of the families of languages computed by generalised stream X-machines with the families of languages generated by cooperating distributed grammar systems are established. At the end, an example dealing with the specification of a scanner system illustrates the use of the introduced mechanism as a formal specification model.