Publications
255 results found
Alrajeh D, Kramer J, Russo A, et al., 2015, Automated Support for Diagnosis and Repair, Commun. ACM, Vol: 58, Pages: 65-72, ISSN: 0001-0782
Lavygina A, Russo A, Dulay N, 2015, Integrating Privacy and Safety Criteria into Planning Tasks, 11th International Workshop on Security and Trust Management (STM), Publisher: SPRINGER INT PUBLISHING AG, Pages: 20-36, ISSN: 0302-9743
Athakravi D, Alrajeh D, Broda K, et al., 2015, Inductive Learning Using Constraint-Driven Bias, 24th International Conference on Inductive Logic Programming (ILP), Publisher: SPRINGER-VERLAG BERLIN, Pages: 16-32, ISSN: 0302-9743
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- Citations: 5
Russo AM, Rankothge W, Ma J, et al., 2015, Towards Making Network Function Virtualization a Cloud Computing Service, IFIP/IEEE Integrated Network Management Symposium (IM 2015), Publisher: IEEE, Pages: 89-97
Network Function Virtualization (NFV) is bringing closer the possibility to truly migrate enterprise datacenters into the cloud. However, moving applications from private datacenters to cloud centers is complicated because many of these applications require network-based services: firewalls, IDS, load balancers, etc. Furthermore, network centered applications such as the 3G/4G IP Multimedia Subsystem (IMS) have been excluded from services typically provided by datacenters. NFV makes network functions a first-class citizen, and therefore holds strong promise for both enterprise datacenters and more complex network services (e.g., IMS). However, for a Cloud Service Provider to offer such services, many research questions still need to be addressed: e.g., in order to scale up/down resources to satisfy traffic demands and guarantee QoS, when and where should new virtual network functions be instantiated? How can network configuration be updated on-demand to guarantee service chaining, especially in the events of virtual network function creation and deletion? To enable on-demand management of the datacenter network, traditional Cloud Computing Management must be rethought. In this paper, we introduce the concept of a Network Function Center (NFC): we discuss the expected functionality, the meaning of management in this new context, and present a prototype system that uses genetic algorithms to dynamically distribute server and network resources.
Maimari N, Towhidi L, Oh D, et al., 2014, A novel integrated platform for gene network inference and validation: beyond the dream consortium, Atherosclerosis, Vol: 237, Pages: e11-e11, ISSN: 0021-9150
Russo AM, Alrajeh D, Uchitel S, et al., 2014, Marrying Model Checking and Symbolic Learning, Communications of the ACM
Russo AM, Athakravi D, Corapi D, et al., 2014, Learning through Hypothesis Refinement using Answer Set Programming, 23rd International Conference on Inductive Logic Programming
Markitanis A, Corapi D, Russo A, et al., 2014, Learning user behaviours in real mobile domains, Latest Advances in Inductive Logic Programming, Pages: 43-51, ISBN: 9781783265084
With the emergence of ubiquitous computing, innovations in mobile phones are increasingly changing the way users lead their lives. To make mobile devices adaptive and able to autonomously respond to changes in user behaviours, machine learning techniques can be deployed to learn behaviour from empirical data. Learning outcomes should be rulebased enforcement policies that can pervasively manage the devices, and at the same time facilitate user validation when and if required. In this chapter we demonstrate the feasibility of non-monotonic Inductive Logic Programming (ILP) in the automated task of extraction of user behaviour rules through data acquisition in the domain of mobile phones. This is a challenging task as real mobile datasets are highly noisy and unevenly distributed. We present two applications, one based on an existing dataset collected as part of the Reality Mining group, and the other generated by a mobile phone application called ULearn that we have developed to facilitate a realistic evaluation of the accuracy of the learning outcome.
Smith J, Lavygina A, Ma J, et al., 2014, Learning to recognise disruptive smartphone notifications, 16th International Conference on Human-Computer Interaction with Mobile Devices and Services, Publisher: ACM, Pages: 121-124
Short term studies in controlled environments have shown that user behaviour is consistent enough to predict disruptive smartphone notifications. However, in practice, user behaviour changes over time (concept drift) and individual user preferences need to be considered. There is a lack of research on which methods are best suited for predicting disruptive smartphone notifications longer-term, taking into account varying error costs. In this paper we report on a 16 week field study comparing how well different learners perform at mitigating disruptive incoming phone calls.
Law M, Russo A, Broda K, 2014, Inductive Learning of Answer Set Programs, 14th European Conference on Logics in Artificial Intelligence (JELIA), Publisher: Springer, Pages: 311-325, ISSN: 0302-9743
Maimari N, Broda K, Kakas A, et al., 2014, Symbolic Representation and Inference of Regulatory Network Structures, Logical Modeling of Biological Systems, Publisher: John Wiley & Sons, Inc., Pages: 1-48, ISBN: 9781119005223
Smith J, Lavygina A, Russo A, et al., 2014, When Did Your Smartphone Bother You Last?, ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), Publisher: ASSOC COMPUTING MACHINERY, Pages: 409-414
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- Citations: 3
Turliuc C-R, Maimari N, Russo A, et al., 2013, On Minimality and Integrity Constraints in Probabilistic Abduction, LPAR Logic for Programming,Artificial Intelligence and Reasoning, Publisher: Springer Verlag
Russo AM, Alrajeh D, Miller R, et al., 2013, Reasoning about Triggered Scenarios in Logic Programming, Theory and Practice of Logic Programming, Vol: 13
Uchitel S, Alrajeh D, Ben-David S, et al., 2013, Supporting incremental behaviour model elaboration, Computer Science - Research and Development, Vol: 28, Pages: 279-293, ISSN: 1865-2034
Behaviour model construction remains a difficult and labour intensive task which hinders the adoption of model-based methods by practitioners. We believe one reason for this is the mismatch between traditional approaches and current software development process best practices which include iterative development, adoption of use-case and scenario-based techniques and viewpoint- or stakeholder-based analysis; practices which require modelling and analysis in the presence of partial information about system behaviour. Our objective is to address the limitations of behaviour modelling and analysis by shifting the focus from traditional behaviour models and verification techniques that require full behaviour information to partial behaviour models and analysis techniques, that drive model elaboration rather than asserting adequacy. We aim to develop sound theory, techniques and tools that facilitate the construction of partial behaviour models through model synthesis, enable partial behaviour model analysis and provide feedback that prompts incremental elaboration of partial models. In this paper we present how the different research threads that we have and currently are developing help pursue this vision as part of the "Partial Behaviour Modelling - Foundations for Iterative Model Based Software Engineering" Starting Grant funded by the ERC. We cover partial behaviour modelling theory and construction, controller synthesis, automated diagnosis and refinement, and behaviour validation. © 2012 Springer-Verlag Berlin Heidelberg.
Ma J, Le F, Wood D, et al., 2013, A declarative approach to distributed computing: Specification, execution and analysis, THEORY AND PRACTICE OF LOGIC PROGRAMMING, Vol: 13, Pages: 815-830, ISSN: 1471-0684
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- Citations: 7
Sykes D, Corapi D, Magee J, et al., 2013, Learning Revised Models For Planning In Adaptive Systems, 35th IEEE/ACM International Conference on Software Engineering, Publisher: IEEE/ACM, Pages: 63-71
Maimari N, Krams R, Turliuc C-R, et al., 2013, ARNI: Abductive inference of complex regulatory network structures, 11th International Conference, CMSB 2013, Pages: 235-237, ISSN: 0302-9743
Physical network inference methods use a template of molecular interaction to infer biological networks from high throughput datasets. Current inference methods have limited applicability, relying on cause-effect pairs or systematically perturbed datasets and fail to capture complex network structures. Here we present a novel framework, ARNI, based on abductive inference, that addresses these limitations. © Springer-Verlag 2013.
Alrajeh D, Russo A, Lockerbie J, et al., 2013, Computational Alignment of Goals and Scenarios for Complex Systems, 35th International Conference on Software Engineering (ICSE), Publisher: IEEE, Pages: 1249-1252
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- Citations: 1
Athakravi D, Broda K, Russo A, 2012, Predicate invention in inductive logic programming, Pages: 15-21
The ability to recognise new concepts and incorporate them into our knowledge is an essential part of learning. From new scientific concepts to the words that are used in everyday conversation, they all must have at some point in the past, been invented and their definition defined. In this position paper, we discuss how a general framework for predicate invention could be made, by reasoning about the problem at the meta-level using an appropriate notion of top theory in inductive logic programming.© Duangtida Athakravi, Krysia Broda, and Alessandra Russo.
Corapi D, Russo A, Lupu E, 2012, Inductive logic programming in answer set programming, Pages: 91-97, ISSN: 0302-9743
In this paper we discuss the design of an Inductive Logic Programming (ILP) system in Answer Set Programming (ASP) and more in general the problem of integrating the two. We show how to formalise the learning problem as an ASP program and provide details on how the optimisation features of modern solvers can be adapted to derive preferred hypotheses. © 2012 Springer-Verlag Berlin Heidelberg.
Alrajeh D, Russo A, Uchitel S, et al., 2012, Integrating model checking and inductive logic programming, Pages: 45-60, ISSN: 0302-9743
Inductive Logic Programming can be used to provide automated support to help correct the errors identified by model checking, which in turn provides the relevant context for learning hypotheses that are meaningful within the domain of interest. Model checking and Inductive Logic Programming can thus be seen as two complementary approaches with much to gain from their integration. In this paper we present a general framework for such an integration, discuss its main characteristics and present an overview of its application. © 2012 Springer-Verlag Berlin Heidelberg.
Lobo J, Ma J, Russo A, et al., 2012, Declarative distributed computing, Pages: 454-470, ISBN: 9783642307423
In this paper we present a language to write distributed applications. We provide an operational semantics of a single computational node based on Datalog. We then introduce a framework that can capture the semantics of a network of computational nodes working together. The framework can express several communication models (e.g. synchronous vs. asynchronous) and can be used to check many properties of the distributed computation under the different communication models. The framework is developed using Answer Set Programs. © 2012 Springer-Verlag Berlin Heidelberg.
Alrajeh D, Kramer J, van Lamsweerde A, et al., 2012, Generating obstacle conditions for requirements completeness, 34th International Conference on Software Engineering, Publisher: IEEE, Pages: 705-715, ISSN: 1558-1225
Missing requirements are known to be among the major causes of software failure. They often result from a natural inclination to conceive over-ideal systems where the software-to-be and its environment always behave as expected. Obstacle analysis is a goal-anchored form of risk analysis whereby exceptional conditions that may obstruct system goals are identified, assessed and resolved to produce complete requirements. Various techniques have been proposed for identifying obstacle conditions systematically. Among these, the formal ones have limited applicability or are costly to automate. This paper describes a tool-supported technique for generating a set of obstacle conditions guaranteed to be complete and consistent with respect to the known domain properties. The approach relies on a novel combination of model checking and learning technologies. Obstacles are iteratively learned from counterexample and witness traces produced by model checking against a goal and converted into positive and negative examples, respectively. A comparative evaluation is provided with respect to published results on the manual derivation of obstacles in a real safety-critical system for which failures have been reported.
Athakravi D, Corapi D, Russo A, et al., 2012, Handling change in normative specifications, Declarative Agent Languages and Technologies X, DALT 2012, Pages: 1-19, ISSN: 0302-9743
Normative frameworks provide a means to address the governance of open systems, offering a mechanism to express responsibilities and permissions of the individual participants with respect to the entire system without compromising their autonomy. In order to meet requirements careful design is crucial. Tools that support the design process can be of great benefit. In this paper, we describe and illustrate a methodology for elaborating normative specifications. We utilise use-cases to capture desirable and undesirable system behaviours, employ inductive logic programming to construct elaborations, in terms of revisions and extensions, of an existing (partial) normative specification and provide justifications as to why certain changes are better than others. The latter can be seen as a form of impact analysis of the possible elaborations, in terms of critical consequences that would be preserved or rejected by the changes. The main contributions of this paper is a (semi) automated process for controlling the elaboration of normative specifications and a demonstration of its effectiveness through a proof-of-concept case study. © Springer-Verlag Berlin Heidelberg 2013.
Molloy I, Dickens L, Morisset C, et al., 2012, Risk-based security decisions under uncertainty, Pages: 157-168
This paper addresses the making of security decisions, such as access-control decisions or spam filtering decisions, under uncertainty, when the benefit of doing so outweighs the need to absolutely guarantee these decisions are correct. For instance, when there are limited, costly, or failed communication channels to a policy-decision-point. Previously, local caching of decisions has been proposed, but when a correct decision is not available, either a policy-decision-point must be contacted, or a default decision used. We improve upon this model by using learned classifiers of access control decisions. These classifiers, trained on known decisions, infer decisions when an exact match has not been cached, and uses intuitive notions of utility, damage and uncertainty to determine when an inferred decision is preferred over contacting a remote PDP. Clearly there is uncertainty in the predicted decisions, introducing a degree of risk. Our solution proposes a mechanism to quantify the uncertainty of these decisions and allows administrators to bound the overall risk posture of the system. The learning component continuously refines its models based on inputs from a central policy server in cases where the risk is too high or there is too much uncertainty. We have validated our models by building a prototype system and evaluating it with requests from real access control policies. Our experiments show that over a range of system parameters, it is feasible to use machine learning methods to infer access control policies decisions. Thus our system yields several benefits, including reduced calls to the PDP, reducing latency and communication costs; increased net utility; and increased system survivability.
Alrajeh D, Kramer J, Russo A, et al., 2012, Elaborating Requirements using Model Checking and Inductive Learning, IEEE Transactions on Software Engineering, ISSN: 0098-5589
Dickens L, Molly I, Lobo J, et al., 2012, Learning Stochastic Models of Information Flow, 28th IEEE International Conference on Data Engineering (ICDE), Publisher: IEEE Computer Society, Pages: 570-581, ISSN: 1063-6382
Becker M, Russo A, Sultana N, 2012, Foundations of Logic-Based Trust Management, IEEE Symposium on Security and Privacy 2012, Publisher: IEEE Computer Society, Pages: 161-175, ISSN: 1081-6011
Alrajeh D, Kramer J, Russo A, et al., 2012, Learning from Vacuously Satisfiable Scenario-Based Specifications, 15th International Conference on Fundamental Approaches to Software Engineering (FASE), Publisher: SPRINGER-VERLAG BERLIN, Pages: 377-393, ISSN: 0302-9743
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- Citations: 7
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