The brain is a highly reconfigurable machine capable of task-specific adaptations. The brain continually rewires itself for a more optimal configuration to solve problems. We propose a novel strategic synthesis algorithm for feedforward networks that draws directly from the brain's behaviours when learning. The proposed approach analyses the network and ranks weights based on their magnitude. Unlike existing approaches that advocate random selection, we select highly performing nodes as starting points for new edges and exploit the Gaussian distribution over the weights to select corresponding endpoints. The strategy aims only to produce useful connections and result in a smaller residual network structure. The approach is complemented with pruning to further the compression. We demonstrate the techniques to deep feedforward networks. The residual sub-networks that are formed from the synthesis approaches in this work form common sub-networks with similarities up to ~90%. Using pruning as a complement to the strategic synthesis approach, we observe improvements in compression.
Probabilistic Boolean Networks (PBNs) were introduced as a computational model for the study of complex dynamical systems, such as Gene Regulatory Networks (GRNs). Controllability in this context is the process of making strategic interventions to the state of a network in order to drive it towards some other state that exhibits favourable biological properties. In this paper we study the ability of a Double Deep Q-Network with Prioritized Experience Replay in learning control strategies within a finite number of time steps that drive a PBN towards a target state, typically an attractor. The control method is model-free and does not require knowledge of the network's underlying dynamics, making it suitable for applications where inference of such dynamics is intractable. We present extensive experiment results on two synthetic PBNs and the PBN model constructed directly from gene-expression data of a study on metastatic-melanoma.
Complexity theory has been used to study a wide range of systems in biology and nature but also business and socio-technical systems, e.g., see . The ultimate objective is to develop the capability of steering a complex system towards a desired outcome. Recent developments in network controllability  concerning the reworking of the problem of finding minimal control configurations allow the use of the polynomial time Hopcroft- Karp algorithm instead of exponential time solutions. Subsequent approaches build on this result to determine the precise control nodes, or drivers, in each minimal control configuration , . A browser-based analytical tool, CCTool1, for identifying such drivers automatically in a complex network has been developed in . One key characteristic of a complex system is that it continuously evolves, e.g., due to dynamic changes in the roles, states and behaviours of the entities involved. This means that in addition to determining driver nodes it is appropriate to consider an evolving topology of the underlying complex network, and investigate the effect of removing nodes (and edges) on the corresponding minimal control configurations. The work presented here focuses on arriving at a classification of the nodes based on the effect their removal has on controllability of the network.
Mobile apps feed on variety of users’ information to provide great services. Some of the features require more sensitive details such as contact list to connect with friends or a precise location to find a desired restaurant nearby. Handling per- sonal information is vital because the user would expect them to be processed in an appropriate manner. However, research has proven that some third-party apps accidentally or maliciously leak users’ personal details. Thus, researchers made huge effort to come up with tools that detect leakage attempts. In this thesis, we targeted several gaps to improve user experience with mobile privacy leakage prob- lem. Initially, we designed a system that evaluates mobile privacy protection tools. This system can be useful for developers to assess their tools, and users to evaluate offered solutions. 165 selected Android privacy protection apps have been tested using our system, and it was established that the most effective approach of mobile privacy protection requires modification on mobile operating system level in order to capture explicit and implicit leakages. That requirement makes it difficult to find “off-the-shelve” protection tool. Therefore, it was decided to assist the user in selecting safe apps as a precaution step before problems occur. To achieve that, it is important to understand how mobile users form their decision when selecting apps. 1,100 crowdsourcing participants have been recruited to study their perceived trust of subjective and objective ratings of mobile apps’ privacy. This experiment guided us to design new interfaces that could assist decision making towards more privacy-friendly mobile apps, which was our most recent work. A newly designed interface, which communicates objective privacy ratings to the user, has been pro- posed. We have also conducted several user-studies involving 300 participants to evaluate our proposed app’s efficiency, the result ultimately showed that users were more motivated to engage in privacy-related decisions.
Graphene was first isolated in the lab in 2003 and this work was first published in 2004 by a research team at The University of Manchester. Since that date, graphene research has exploded due to its special properties. Phonons and molecular dynamic simulation provide valuable tools to study the molecular systems under different structure forms. They are helpful to study graphene ribbons and defects. On the other hand, many machine learning techniques were extensively used to analyse the enormous amounts of data resulted from the molecular simulations. As such, this thesis aimed to use one of the machine learning techniques to study phonons of graphene with single vacancy defect and graphene armchair nanoribbons. PCA can be used to transform the atomic velocities into orthogonal eigenvectors such that each eigenvector represents one of the phonon modes of graphene. This is helpful to visualize the atomic motion of a specific phonon mode. To provide orthogonal eigenvectors, PCA needs the data to be of gaussian distribution. The atomic velocities resulted from the molecular simulations follow gaussian distribution at the equilibrium state. Hence, the assumption of gaussian distribution needed by PCA is achieved. However, only some of the phonon modes can be calculated from the atomic velocities in their real space. Most of the phonon modes are calculated after transforming the atomic velocities to a reciprocal space (k space) using spatial Fourier transform. The k space atomic velocities are not following gaussian distribution. This thesis introduced a novel method to use PCA to isolate and visualize the phonon modes extracted from the k space velocities. To prove the feasibility of using PCA to isolate k space phonons, we conducted classical molecular simulations of graphene with different structures. The effect of single vacancy defect on graphene phonons was studied in comparison to the perfect graphene. In addition, the effect of the armchair ribbon width on graphene phonon modes was investigated. The results of the conducted molecular simulations were used with PCA to visualize some of the phonon modes of pristine graphene and armchair nanoribbons of graphene. We used PCA to present the evolution of the atomic motion of specific k space phonon modes of armchair ribbons: the first overtone of TA phonon mode and the highest overtone of TO phonon mode. The presented motions showed that the breathing like mode is a transition state between two opposite atomic motions of TA mode. In the method we introduced using PCA, we used the eigenvectors with the lowest eigenvalues to study the Fourier transformed atomic velocities. This method rotated the k space atomic velocities into the eigenvectors with the lowest eigenvalues which helped to isolate and visualize the k space phonon modes.
Nurulhuda A. Manaf, Andreas Antoniades, Sotiris Moschoyiannis (2018)SBVR2Alloy: an SBVR to Alloy compiler, In: Proceedings of 10th IEEE International Conference on Service Oriented Computing and Applications (IEEE SOCA 2017)
We present a compilation tool SBVR2Alloy which is used to automatically generate as well as validate service choreographies specified in structured natural language. The proposed approach builds on a model transformation between Semantics of Business Vocabulary and Rules (SBVR), an OMG standard for specifying business models in structured English, and the Alloy Analyzer which is a SAT based constraint solver. In this way, declarative specifications can be enacted via a standard constraint solver and verified for realisability and conformance.
We propose a set of optimization techniques for transforming a generic AI codebase so that it can be successfully deployed to a restricted serverless environment, without compromising capability or performance. These involve (1) slimming the libraries and frameworks (e.g., pytorch) used, down to pieces pertaining to the solution; (2) dynamically loading pre-trained AI/ML models into local temporary storage, during serverless function invocation; (3) using separate frameworks for training and inference, with ONNX model formatting; and, (4) performance-oriented tuning for data storage and lookup. The techniques are illustrated via worked examples that have been deployed live on geospatial data from the transportation domain. This draws upon a real-world case study in intelligent transportation looking at on-demand, realtime predictions of flows of train movements across the UK rail network. Evaluation of the proposed techniques shows the response time, for varying volumes of queries involving prediction, to remain almost constant (at 50 ms), even as the database scales up to the 250M entries. The query response time is important in this context as the target is predicting train delays. It is even more important in a serverless environment due to the stringent constraints on serverless functions’ runtime before timeout. The similarities of a serverless environment to other resource constrained environments (e.g., IoT, telecoms) means the techniques can be applied to a range of use cases.
The aim of this paper is to facilitate e-business transactions between small and medium enterprises (SMEs), in a way that respects their local autonomy, within a digital ecosystem. For this purpose, we distinguish transactions from services (and service providers) by considering virtual private transaction networks (VPTNs) and virtual service networks (VSNs). These two virtual levels are optimised individually and in respect to each other. The effect of one on the other, can supply us with stability, failure resistance and small-world characteristics on one hand and durability, consistency and sustainability on the other hand. The proposed network design has a dynamic topology that adapts itself to changes in business models and availability of SMEs, and reflects the highly dynamic nature of a digital ecosystem.
In this paper we present a model for coordinating distributed long running and multi-service transactions in Digital Business EcoSystems. The model supports various forms of service composition, which are translated into a tuples-based behavioural description that allows to reason about the required behaviour in terms of ordering, dependencies and alternative execution. The compensation mechanism warranties consistency, including omitted results, without breaking local autonomy. The proposed model is considered at the deployment level of SOA, rather than the realisation level, and is targeted to business transactions between collaborating SMEs as it respects the loose-coupling of the underlying services. © 2007 IEEE.
We describe a true-concurrent approach for managing dependencies between distributed and concurrent coordinator components of a long-running transaction. In previous work we have described how interactions specified in a scenario can be translated into a tuples-based behavioural description, namely vector languages. In this paper we show how reasoning against order-theoretic properties of such languages can reveal missing behaviours which are not explicitly described in the scenario but are still possible. Our approach supports the gradual refinement of scenarios of interaction into a complete set of behaviours that includes all desirable orderings of execution and prohibits emergent behaviour of the transaction. Crown Copyright © 2010.
In this paper, we present a survey of deep learning approaches for cybersecurity intrusion detection, the datasets used, and a comparative study. Specifically, we provide a review of intrusion detection systems based on deep learning approaches. The dataset plays an important role in intrusion detection, therefore we describe 35 well-known cyber datasets and provide a classification of these datasets into seven categories; namely, network traffic-based dataset, electrical network-based dataset, internet traffic-based dataset, virtual private network-based dataset, android apps-based dataset, IoT traffic-based dataset, and internet-connected devices-based dataset. We analyze seven deep learning models including recurrent neural networks, deep neural networks, restricted Boltzmann machines, deep belief networks, convolutional neural networks, deep Boltzmann machines, and deep autoencoders. For each model, we study the performance in two categories of classification (binary and multiclass) under two new real traffic datasets, namely, the CSE-CIC-IDS2018 dataset and the Bot-IoT dataset. In addition, we use the most important performance indicators, namely, accuracy, false alarm rate, and detection rate for evaluating the efficiency of several methods.
In this paper we describe the application of a Deep Reinforcement Learning agent to the problem of control of Gene Regulatory Networks (GRNs). The proposed approach is applied to Random Boolean Networks (RBNs) which have extensively been used as a computational model for GRNs. The ability to control GRNs is central to therapeutic interventions for diseases such as cancer. That is, learning to make such interventions as to direct the GRN from some initial state towards a desired attractor, by allowing at most one intervention per time step. Our agent interacts directly with the environment; being an RBN, without any knowledge of the underlying dynamics, structure or connectivity of the network. We have implemented a Deep Q Network with Double Q Learning that is trained by sampling experiences from the environment using Prioritized Experience Replay. We show that the proposed novel approach develops a policy that successfully learns how to control RBNs significantly larger than previous learning implementations. We also discuss why learning to control an RBN with zero knowledge of its underlying dynamics is important and argue that the agent is encouraged to discover and perform optimal control interventions in regard to cost and number of interventions.
We apply a learning classifier system, XCSI, to the task of providing personalised suggestions for passenger onward journeys. Learn- ing classifier systems combine evolutionary computation with rule-based machine learning, altering a population of rules to achieve a goal through interaction with the environment. Here XCSI interacts with a simulated environment of passengers travelling around the London Underground network, subject to disruption. We show that XCSI successfully learns individual passenger preferences and can be used to suggest personalised adjustments to the onward journey in the event of disruption.
Random Boolean Networks (RBNs) are an arguably simple model which can be used to express rather complex behaviour, and have been applied in various domains. RBNs may be controlled using rule-based machine learning, specifically through the use of a learning classifier system (LCS) – an eXtended Classifier System (XCS) can evolve a set of condition-action rules that direct an RBN from any state to a target state (attractor). However, the rules evolved by XCS may not be optimal, in terms of minimising the total cost along the paths used to direct the network from any state to a specified attractor. In this paper, we present an algorithm for uncovering the optimal set of control rules for controlling random Boolean networks. We assign relative costs for interventions and ‘natural’ steps. We then compare the performance of this optimal rule calculator algorithm (ORC) and the XCS variant of learning classifier systems. We find that the rules evolved by XCS are not optimal in terms of total cost. The results provide a benchmark for future improvement.
We describe a formal approach to protocol design for dialogues between autonomous agents in a digital ecosystem that involve the exchange of arguments between the participants. We introduce a vector language-based representation of argumentation protocols, which captures the interplay between different agentspsila moves in a dialogue in a way that (a) determines the legal moves that are available to each participant, in each step, and (b) records the dialogue history. We use UML protocol state machines (PSMs) to model a negotiation dialogue protocol at both the individual participant level (autonomous agent viewpoint) and the dialogue level (overall interaction viewpoint). The underlying vector semantics is used to verify that a given dialogue was played out in compliance with the corresponding protocol.
The Electric Vehicles (EVs) market has seen rapid growth recently despite the anxiety about driving range. Recent proposals have explored charging EVs on the move, using dynamic wireless charging that enables power exchange between the vehicle and the grid while the vehicle is moving. Specifically, part of the literature focuses on the intelligent routing of EVs in need of charging. Inter-Vehicle communications (IVC) play an integral role in intelligent routing of EVs around a static charging station or dynamic charging on the road network. However, IVC is vulnerable to a variety of cyber attacks such as spoofing. In this paper, a probabilistic cross-layer Intrusion Detection System (IDS), based on Machine Learning (ML) techniques, is introduced. The proposed IDS is capable of detecting spoofing attacks with more than 90% accuracy. The IDS uses a new metric, Position Verification using Relative Speed (PVRS), which seems to have a significant effect in classification results. PVRS compares the distance between two communicating nodes that is observed by On-Board Units (OBU) and their estimated distance using the relative speed value that is calculated using interchanged signals in the Physical (PHY) layer.
This paper presents a true-concurrent approach to formalising integration of Small-to-Medium Enterprises (SMEs) with Web services. Our approach formalises common notions in service-oriented computing such as conversations (interactions between clients and web services), multi-party conversations (interactions between multiple web services) and coordination protocols, which are central in a transactional environment. In particular, we capture long-running transactions with recovery and compensation mechanisms for the underlying services in order to ensure that a transaction either commits or is successfully compensated for. © 2008 Springer-Verlag Berlin Heidelberg.
One of the barriers for the adoption of Electric Vehicles (EVs) is the anxiety around the limited driving range. Recent proposals have explored charging EVs on the move, using dynamic wireless charging which enables power exchange between the vehicle and the grid while the vehicle is moving. In this article, we focus on the intelligent routing of EVs in need of charging so that they can make most efficient use of the so-called Mobile Energy Disseminators (MEDs) which operate as mobile charging stations. We present a method for routing EVs around MEDs on the road network, which is based on constraint logic programming and optimization using a graph-based shortest path algorithm. The proposed method exploits Inter-Vehicle (IVC) communications in order to eco-route electric vehicles. We argue that combining modern communications between vehicles and state of the art technologies on energy transfer, the driving range of EVs can be extended without the need for larger batteries or overtly costly infrastructure. We present extensive simulations in city conditions that show the driving range and consequently the overall travel time of electric vehicles is improved with intelligent routing in the presence of MEDs.
With REST becoming a dominant architectural paradigm for web services in distributed systems, more and more use cases are applied to it, including use cases that require transactional guarantees. We believe that the loose coupling that is supported by RESTful transactions, makes this currently our preferred interaction style for digital ecosystems (DEs). To further expand its value to DEs, we propose a RESTful transaction model that satisfies both the constraints of recoverable transactions and those of the REST architectural style. We then show the correctness and applicability of the model.
In this paper we are concerned with providing support for business activities in moving from value chains to value networks. We describe a fully distributed P2P architecture which reflects the dynamics of business processes that are not governed by a single organisation. The temporary virtual networks of long-term business transactions are used as the building block of the overall scale-free business network. The design is based on dynamically formed permanent clusters resulting in a topology that is highly resilient to failures (and attacks) and is capable of reconfiguring itself to adapt to changes in business models and respond to global failures of conceptual hubs. This fosters an environment where business communities can evolve to meet emerging business opportunities and achieve sustainable growth within a digital ecosystem.
Steering a complex system towards a desired outcome is a challenging task. The lack of clarity on the system’s exact architecture and the often scarce scientific data upon which to base the op- erationalisation of the dynamic rules that underpin the interactions between participant entities are two contributing factors. We describe an analytical approach that builds on Fuzzy Cognitive Map- ping (FCM) to address the latter and represent the system as a complex network. We apply results from network controllability to address the former and determine minimal control configurations - subsets of factors, or system levers, which comprise points for strategic intervention in steering the system. We have implemented the combination of these techniques in an analytical tool that runs in the browser, and generates all minimal control configurations of a complex network. We demonstrate our approach by reporting on our experience of working alongside industrial, local-government, and NGO stakeholders in the Humber region, UK. Our results are applied to the decision-making process involved in the transition of the region to a bio-based economy.
With REST becoming the dominant architectural paradigm for web services in distributed systems, more and more use cases are applied to it, including use cases that require transactional guarantees. We propose a RESTful transaction model that satisfies both the constraints of transactions and those of the REST architectural style. We then apply the isolation theorems to prove the robustness of its properties on a formal level.
Deployed AI platforms typically ship with bulky system architectures which present bottlenecks and a high risk of failure. A serverless deployment can mitigate these factors and provide a cost-effective, automatically scalable (up or down) and elastic real-time on-demand AI solution. However, deploying high complexity production workloads into serverless environments is far from trivial, e.g., due to factors such as minimal allowance for physical codebase size, low amount of runtime memory, lack of GPU support and a maximum runtime before termination via timeout. In this paper we propose a set of optimization techniques and show how these transform a codebase which was previously incompatible with a serverless deployment into one that can be successfully deployed in a serverless environment; without compromising capability or performance. The techniques are illustrated via worked examples that have been deployed live on rail data and realtime predictions on train movements on the UK rail network. The similarities of a serverless environment to other resource constrained environments (IoT, Mobile) means the techniques can be applied to a range of use cases.
In this paper we explore the concept of ldquoecosystemrdquo as a metaphor in the development of the digital economy. We argue that the modelling of social ecosystems as self-organising systems is also relevant to the study of digital ecosystems. Specifically, that centralised control structures in digital ecosystems militate against emergence of innovation and adaptive response to pressures or shocks that may impact the ecosystem. We hope the paper will stimulate a more holistic approach to gaining empirical and theoretical understanding of digital ecosystems.
We describe a translation of scenarios given in UML 2.0 sequence diagrams into a tuples-based behavioural model that considers multiple access points for a participating instance and exhibits true-concurrency. This is important in a component setting since different access points are connected to different instances, which have no knowledge of each other. Interactions specified in a scenario are modelled using tuples of sequences, one sequence for each access point. The proposed unfolding of the sequence diagram involves mapping each location (graphical position) onto the so-called component vectors. The various modes of interaction (sequential, alternative, concurrent) manifest themselves in the order structure of the resulting set of component vectors, which captures the dependencies between participating instances. In previous work, we have described how (sets of) vectors generate concurrent automata. The extension to our model with sequence diagrams in this paper provides a way to verify the diagram against the state-based model.
© 2003 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
In this paper we describe a formal model for the distributed coordination of long-running transactions in a Digital Ecosystem for business, involving Small and Medium Enterprises (SMEs). The proposed non-interleaving model of interaction-based service composition allows for communication between internal activities of transactions. The formal semantics of the various modes of service composition are represented by standard xml schemas. The current implementation framework uses suitable asynchronous message passing techniques and reflects the design decisions of the proposed model for distributed transactions in digital ecosystems.
The concept of a digital ecosystem (DE) has been used to explore scenarios in which multiple online services and resources can be accessed by users without there being a single point of control. In previous work we have described how the so-called transaction languages can express concurrent and distributed interactions between online services in a transactional environment. In this paper we outline how transaction languages capture the history of a long-running transaction and highlight the benefits of our true-concurrent approach in the context of DEs. This includes support for the recovery of a long-running transaction whenever some failure is encountered. We introduce an animation tool that has been developed to explore the behaviours of long-running transactions within our modelling environment. Further, we discuss how this work supports the declarative approach to the development of open distributed applications. © 2012 IEEE.
The distinct feature of this volume is its focus on mathematical models that identify the "core" concepts as first class modeling elements, and its providing of techniques for integrating and relating them.
We present a Peer-to-Peer network design which aims to support business activities conducted through a network of collaborations that generate value in different, mutually beneficial, ways for the participating organisations. The temporary virtual networks formed by long-term business transactions that involve the execution of multiple services from different providers are used as the building block of the underlying scale-free business network. We show how these local interactions, which are not governed by a single organisation, give rise to a fully distributed P2P architecture that reflects the dynamics of business activities. The design is based on dynamically formed permanent clusters of nodes, the so-called Virtual Super Peers (VSPs), and this results in a topology that is highly resilient to certain types of failure (and attacks). Furthermore, the proposed P2P architecture is capable of reconfiguring itself to adapt to the usage that is being made of it and respond to global failures of conceptual hubs. This fosters an environment where business communities can evolve to meet emerging business opportunities and achieve sustainable growth within a digital ecosystem.
In this paper, we describe a true-concurrent hierarchical logic interpreted over concurrent automata. Concurrent automata constitute a special kind of asynchronous transition system (ATS) used for modelling the behaviour of components as understood in component-based software development. Here, a component-based system consists of several interacting components whereby each component manages calls to and from the component using ports to ensure encapsulation. Further, a component can be complex and made of several simpler interacting components. When a complex component receives a request through one of its ports, the port delegates the request to an internal component. Our logic allows us to describe the different views we can have on the system. For example, the overall component interactions, whether they occur sequentially, simultaneously or in parallel, and how each component internally manages the received requests (possibly expressed at different levels of detail). Using concurrent automata as an underlying formalism we guarantee that the expressiveness of the logic is preserved in the model. In future work, we plan to integrate our truly-concurrent approach into the Edinburgh Concurrency Workbench. © 2007 Elsevier B.V. All rights reserved.
Rule-based machine learning focuses on learning or evolving a set of rules that represents the knowledge captured by the system. Due to its inherent complexity, a certain amount of fine tuning is required before it can be applied to a particular problem. However, there is limited information available to researchers when it comes to setting the corresponding run parameter values. In this paper, we investigate the run parameters of Learning Classifier Systems (LCSs) as applied to single-step problems. In particular, we study two LCS variants, XCS for reinforcement learning and UCS for supervised learning, and examine the effect that different parameter values have on enhancing the model prediction, increasing accuracy and reducing the resulting rule set size.
Declarative technologies have made great strides in expressivity between SQL and SBVR. SBVR models are more expressive that SQL schemas, but not as imminently executable yet. In this paper, we complete the architecture of a system that can execute SBVR models. We do this by describing how SBVR rules can be transformed into SQL DML so that they can be automatically checked against the database using a standard SQL query. In particular, we describe a formalization of the basic structure of an SQL query which includes aggregate functions, arithmetic operations, grouping, and grouping on condition. We do this while staying within a predicate calculus semantics which can be related to the standard SBVR-LF specification and equip it with a concrete semantics for expressing business rules formally. Our approach to transforming SBVR rules into standard SQL queries is thus generic, and the resulting queries can be readily executed on a relational schema generated from the SBVR model.
Concurrency control mechanisms such as turn-taking, locking, serialization, transactional locking mechanism, and operational transformation try to provide data consistency when concurrent activities are permitted in a reactive system. Locks are typically used in transactional models for assurance of data consistency and integrity in a concurrent environment. In addition, recovery management is used to preserve atomicity and durability in transaction models. Unfortunately, conventional lock mechanisms severely (and intentionally) limit concurrency in a transactional environment. Such lock mechanisms also limit recovery capabilities. Finally, existing recovery mechanisms themselves afford a considerable overhead to concurrency. This paper describes a new transaction model that supports release of early results inside and outside of a transaction, decreasing the severe limitations of conventional lock mechanisms, yet still warranties consistency and recoverability of released resources (results). This is achieved through use of a more flexible locking mechanism and by using two types of consistency graph. This provides an integrated solution for transaction management, recovery management and concurrency control. We argue that these are necessary features for management of long-term transactions within "digital ecosystems" of small to medium enterprises.
Random Boolean Networks (RBNs) are an arguably simple model which can be used to express rather complex behaviour, and have been applied in various domains. RBNs may be controlled using rule-based machine learning, speciﬁcally through the use of a learning classiﬁer system (LCS) – an eXtended Classiﬁer System (XCS) can evolve a set of condition-action rules that direct an RBN from any state to a target state (attractor). However, the rules evolved by XCS may not be optimal, in terms of minimising the total cost along the paths used to direct the network from any state to a speciﬁed attractor. In this paper, we present an algorithm for uncovering the optimal set of control rules for controlling random Boolean networks. We assign relative costs for interventions and‘natural’steps.We then compare the performance of this optimal rule calculator algorithm(ORC)and the XCS variant of learning classiﬁer systems.We ﬁnd that the rules evolved by XCS are not optimal in terms of total cost. The results provide a benchmark for future improvement.
A Razavi, Paul Krause, Sotiris Moschoyiannis (2010)Digital Ecosystems: challenges and proposed solutions, In: Handbook of research on P2P and grid systems for service-oriented computing: Models, Methodologies and Applicationspp. 1003-1031
Information Science Reference - Imprint of: IGI Global Publishing
In this paper we present a prototype of a tool that demonstrates how existing limitations in ensuring an agent’s compliance to an argumentation-based dialogue protocol can be overcome. We also present the implementation of compliance enforcement components for a deliberation dialogue protocol, and an application that enables two human participants to engage in an efficiently moderated dialogue, where all inappropriate utterances attempted by an agent are blocked and prevented from inclusion within the dialogue.
Modern software systems become increasingly complex as they are expected to support a large variety of different functions. We need to create more software in a shorter time, and without compromising the quality of the software. In order to build such systems efficiently, a compositional approach is required. This entails some formal technique for analysis and reasoning on local component properties as well as on properties of the composite. In this paper, we present a mathematical framework for the composition of software components, at a semantic modelling level. We describe a mathematical concept of a component and identify properties that ensure its potential behaviour can be captured. Based on that, we give a formal definition of composition and examine its effect on the individual components. We argue that properties of the individual components can, under certain conditions, be preserved in the composite. The proposed framework can be used for guiding the composition of components as it advocates formal reasoning about the composite before the actual composition takes place.
Sotiris Moschoyiannis, Leandros Maglaras, Nurulhuda A Manaf (2019)Trace-based Verification of Rule-based Service Choreographies, In: Proceedings of the 2018 IEEE 11th International Conference on Service-Oriented Computing and Applications (IEEE SOCA 2018)pp. 185-193
Institute of Electrical and Electronics Engineers (IEEE)
The service choreography approach has been proposed for describing the global ordering constraints on the observable message exchanges between participant services in service oriented architectures. Recent work advocates the use of structured natural language, in the form of Semantics of Business Vocabulary and Rules (SBVR), for specifying and validating choreographies. This paper addresses the verification of choreographies - whether the local behaviours of the individual participants conform to the global protocol prescribed by the choreography. We describe how declarative specifications of service choreographies can be verified using a trace-based model, namely an adaptation of Shields’ vector languages. We also use the so-called blackboard rules, which draw upon the Bach coordination language, as a middleware that adds reactiveness to this declarative setting. Vector languages are to trace languages what matrices are to linear transformations; they afford a more concrete representation which has advantages when it comes to computation or manipulation.
In this paper we describe the application of a learning classifier system (LCS) variant known as the eXtended classifier system (XCS) to evolve a set of ‘control rules’ for a number of Boolean network instances. We show that (1) it is possible to take the system to an attractor, from any given state, by applying a set of ‘control rules’ consisting of ternary conditions strings (i.e. each condition component in the rule has three possible states; 0, 1 or #) with associated bit-flip actions, and (2) that it is possible to discover such rules using an evolutionary approach via the application of a learning classifier system. The proposed approach builds on learning (reinforcement learning) and discovery (a genetic algorithm) and therefore the series of interventions for controlling the network are determined but are not fixed. System control rules evolve in such a way that they mirror both the structure and dynamics of the system, without having ‘direct’ access to either.
We propose the use of structured natural language (English) in specifying service choreographies, focusing on the what rather than the how of the required coordination of participant services in realising a business application scenario. The declarative approach we propose uses the OMG standard Semantics of Business Vocabulary and Rules (SBVR) as a modelling language. The service choreography approach has been proposed for describing the global orderings of the invocations on interfaces of participant services. We therefore extend SBVR with a notion of time which can capture the coordination of the participant services, in terms of the observable message exchanges between them. The extension is done using existing modelling constructs in SBVR, and hence respects the standard specification. The idea is that users - domain specialists rather than implementation specialists - can verify the requested service composition by directly reading the structured English used by SBVR. At the same time, the SBVR model can be represented in formal logic so it can be parsed and executed by a machine.
Matthew R. Karlsen, Sotiris K. Moschoyiannis (2018)Optimal control rules for random Boolean networks, In: Complex Networks and their Applications: Proceedings of Complex Networks 2018 (The 7th International Conference on Complex Networks and Their Applications)812pp. 828-840
Springer International Publishing
A random Boolean network (RBN) may be controlled through the use of a learning classifier system (LCS) – an eXtended Classifier System (XCS) can evolve a rule set that directs an RBN from any state to a target state. However, the rules evolved may not be optimal, in terms of minimising the total cost of the paths used to direct the network from any state to a specified attractor. Here we uncover the optimal set of control rules via an exhaustive algorithm. The performance of an LCS (XCS) on the RBN control problem is assessed in light of the newly uncovered optimal rule set.
Deception exists in all aspects of life and is particularly evident on the Web. Deception includes child sexual predators grooming victims online, medical news headlines with little medical evidence or scientific rigour, individuals claiming others’ work as their own, and systematic deception of company shareholders and institutional investors leading to corporate collapses. This thesis explores the potential for automatic detection of deception. We investigate the nature of deception and the related cues, focusing in particular on Verbal Cues, and concluding that they cannot be readily generalised. We demonstrate how deception-specific features, based on sound hypotheses, can overcome related limitations by presenting approaches for three different examples of deception – namely Child Sexual Predator Detection (SPD), Authorship Identification (AI) and Intrinsic Plagiarism Detection (IPD). We further show how our approaches result in competitive levels of reliability. For SPD we develop our approach largely based on the commonality of requests for key personal information. To address AI, we introduce approaches based on a frequency-mean-variance and a frequency-only framework in order to detect strong associations between co-occurring patterns of a limited number of stopwords. Our IPD approaches are based on simple commonality of words at document level and usage of proper nouns; document sections lacking commonality can be identified as plagiarised. The frameworks of the International Workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse (PAN) competitions provided an independent evaluation of the approaches. The SPD approach obtained an F1 score of 0.48. F1 scores of 0.47, 0.53 and 0.57 were achieved in AI tasks for PAN2012, 2013 and 2014 respectively. IPD yielded an overall accuracy of 91%. Through post-competition adaptations we also show how to improve the approaches and the scores and demonstrate the importance of suitable datasets and how most approaches are not easily transferable between various types of deception.
The 'connected world' forces us to think about 'interoperability' as a primary requirement when building health care databases in the present day. Whilst semantic interoperability has made a major contribution to data utilisation between systems it often has not been able to integrate some large heterogeneous datasets required for research. As health data gets 'bigger' and complex, we are required to shift to rapid and flexible ways of resolving problems related to semantic interoperability. Ontological approaches accelerate implementing interoperability due to the availability of robust tools and technology frameworks that promote reuse. This thesis reports the results of a mixed methods study that proposes a pragmatic methodology that maximises the use of ontologies across a multilayered research readiness model which can be used in data-driven health care research projects. The research examined evidence for the use of ontologies across a majority of layers in the reference model. The first part of the thesis examines the methods used for assessing readiness to participate in research across six dimensions of health care. It reports on existing ontological elements that boosts research readiness and also proposes ontological extensions for modelling the semantics of data sources and research study requirements. The second part of the thesis presents an ontology toolkit that supports rapid development of ontologies that can be used in health care research projects. It provides details of how an ontology toolkit for creating health care ontologies was developed through the consensus of a panel of informatics experts and clinicians. This toolkit evolved further to include a series of ontological building blocks that assist clinicians to rapidly build ontologies.
Guaranteeing the correct coordination of distributed applications that are built up as networks of autonomous participants, e.g., software components, web services, online resources, software as a service (SaaS) peers, is inherently challenging. This is obvious when the current distributed applications involve a collaboration between loosely-coupled services on distinct providers; the ordering of interactions that may further affects the dependencies between different participants, including control flow dependencies (e.g., a given service invocation must occur before another one), time constraints, and transactional dependencies. This complexity of the development of distributed applications illustrates how important the techniques and approaches for designing and coordinating the service interactions between distinct participant services to ensure that the overall goal of the collaboration between participant services is achieved. Standardisation efforts to date have resulted in the Web Services Choreography Description Language (WS-CDL), a specification protocol advocated by W3C. WS-CDL and other modeling languages (e.g., UML2) provide various divergent semantics and less user-friendly graphical notation. On the other hand the formal approach would allow unambiguous specification and verification of the intended collaboraton. In this research work, a declarative approach was proposed for specifying coordination of distributed applications involving distinct participant services which is being able to verify that it is correct. The proposed approach could captures and describing the complex interactions that involves the ordering of service interaction based on the given global constraints. A new model using a declarative approach, an OMG standard Semantics of Business Vocabulary and Rules (SBVR) model was introduced for specifying service choreography. This SBVR model is then formulated and transformed into Alloy model using Alloy Analyzer for verification. A fully automated SBVR2Alloy tool was implemented for transforming from the developed SBVR model into the Alloy model. This proposed model is targeted to enable the practitioners (business analysts, developers) to devise and set up the service choreographies that realise their collaborations by generating the automated verifiable choreography model.
As one of the world’s most devastating diseases of mankind, tuberculosis is a global health crisis. Despite extensive research into the disease spanning more than a century, it remains the number one killer due to a single causative and infectious agent, Mycobacterium tuberculosis, which is one of the human pathogens. The bacterium is able to persist as a long-term infection, known as latent tuberculosis. One characteristic of persister cells is that they are phenotypically tolerant to the action of antibiotics; a trait which has important implications in tuberculosis chemotherapy. Observing persistence is an important element of the latent tuberculosis study. To gain insight into persistence of tuberculosis, confocal microscopy is used to capture the cell growth and division behaviour in a microfluidic device so that a large amount of time-lapse image data are collected. It is very challenging for human observers to grasp them directly. In this work, we aim to develop a system that is able to track and analyse the cell growth patterns automatically. Specially, a major task is to observe any unusual behaviour, such as persistence. We first presented an approach to extract cell properties evolving between consecutive frames by feeding cell segmentation and tracking results from one frame to the next. Each individual cell is obtained by integrating the Distance Regularised Level Set Evolution model with cell septum and membrane function. It was then tracked by minimising a single cell trajectory energy function along time-lapse series. Our experiments showed that cell growth and division can be measured automatically by applying this scheme. Comparing with other existing algorithms, our results showed the efficiency of the approach when testing on different datasets. The proposed approach has demonstrated great potential for large scale bacterial cell growth analysis. We further investigated the deep convolutional neural networks with a hierarchical visual tracking approach. We demonstrated that this approach can robustly segment and track individual cells from different microscopy image types, such as phase-contrast and bright-field microscopy images. Comparing with previous methods, the convolutional neural networks have significantly improved accuracy in cell segmentation thus minimising manual correction effort. We also outlined several rules for designing and optimising deep convolutional neural networks for this study. We believed that deep convolutional neural network approach is robust for segmenting and tracking various strains of bacteria cells. With above analysis, we obtained a large number of cell growth features over several generations. We discovered that the loss of phenotypic inheritance causes increased frequency of persisters. We also illustrated that cell growth and division was most consistent with the adder model in a single generation. These novel observations can be accounted for the generation and maintenance of phenotypic variation and provide potential new targets for the development of novel therapeutic strategies that address persistence in bacterial infections.