This paper proposes a correct-by-construction method to build realizable choreographies described using conversation protocols (CPs). We define a new language consisting of an operators set for incremental construction of CPs. We suggest an asynchronous model described with the Event-B method and its refinement strategy, ensuring the scalability of our approach.

Existing adiabatic quantum computers are tailored towards minimizing the energies of Ising models. The quest for implementations of pattern recognition or machine learning algorithms on such devices can thus be seen as the quest for Ising model (re-)formulations of their objective functions. In this paper, we present Ising models for the tasks of binary clustering of numerical and relational data and discuss how to set up corresponding quantum registers and Hamiltonian operators. In simulation experiments, we numerically solve the respective Schrödinger equations and observe our approaches to yield convincing results.

We solve an important problem for the liner shipping industry called the Liner Shipping Fleet Repositioning Problem (LSFRP). The LSFRP poses a large financial burden on liner shipping firms. During repositioning, vessels are moved between services in a liner shipping network. Shippers wish to reposition vessels as cheaply as possible without disrupting the cargo flows of the network. The LSFRP is characterized by chains of interacting activities with a multi-commodity flow over paths defined by the activities chosen. Despite its great industrial importance, the LSFRP has received little attention in the literature. We introduce a novel mathematical model of the LSFRP with cargo flows based on a carefully constructed graph and evaluate it on real world data from our industrial collaborator.

This work focuses on a nonlinear robust control of a human arm-like robot arm by using a bio-inspired method based human arm musculoskeletal characteristics, mainly consisting of multi-joint viscosity and multi-joint stiffness. The multi-joint viscosity and multi-joint stiffness are used in designing a bio-inspired operator controller, and the time-varying on estimated human arm multi-joint viscoelasticity (HAMV) data is fed to the designed controller in simulation. Using the designed control architecture, the sufficient robust stable conditions are derived in the presence of uncertainties of modelling and measurement errors, and the control output tracking performance is also realized.

The data warehouse design methodologies require a novel approach in the Big Data context, because the methodologies have to provide solutions to face the issues related to the 5 Vs (Volume, Velocity, Variety, Veracity, and Value). So it is mandatory to support the designer through automatic techniques able to quickly produce a multidimensional schema using and integrating several data sources, which can be also unstructured and, therefore, need an ontologybased reasoning. Accordingly, the methodologies have to adopt agile techniques, in order to change the multidimensional schema as the business requirements change, without a complete design process. Furthermore, hybrid approaches must be used instead of the traditional data-driven or requirement-driven approaches, in order to avoid missing the adhesion to user requirements and to produce a valuable multidimensional schema compliant with data sources. In the paper, we perform a metric comparison among different methodologies, in order to demon‐ strate that methodologies classified as hybrid, ontology-based, automatic, and agile are tailored for the Big Data context.

The proposed study is to analyze and visualize properties of the social network constructed from a dataset based on Enron mail dataset, and utilize sentiment analysis as an additional source of information to study employees’ relationships in a company. We concluded that when social network analysis is used in conjunction with emotion detection, it is possible to see the positive or negative areas where the company must work to promote a healthy organizational culture and uncover possible organizational issues in a timely manner.

The optimization of a large number of decision variables, so called large scale global optimization (LSGO) remains challenging for existing heuristics. Inspired by the concept of global best (gbest) guided strategy, this paper proposes a gbest-guided covariance matrix adaptation evolution strategy (GCMA-ES) where the gbest information is utilized in the search equation to guide the exploitation process. The GCMA-ES can take advantages from both the CMA-ES and the gbest-guided strategy. Its performance is demonstrated on the CEC 2010 LSGO benchmarks.

The segmentation of larger organs in CT is a well studied problem. For lungs and liver, state of the art methods reach Dice Scores above 0.9. However, these methods are not as reliable on smaller organs such as pancreas, thyroid, adrenal glands and gallbladder, even though a good segmentation of these organs is needed for example for radiotherapy planning. In this work, we present a new method for the segmentation of such small organs that does not require any deformable registration to be performed. We encode regional context in the form of anatomical context and shape features. These are used within an iterative procedure where, after an initial labelling of all organs using local context only, the segmentation of small organs is refined using regional context. Finally, the segmentations are regularised by shape voting. On the Visceral Challenge 2015 dataset, our method yields a substantially higher sensitivity and Dice score than other forest-based methods for all organs. By using only affine registrations, it is also computationally highly efficient.

Often, different segments of a video may be more or less attractive for people depending on their experience in watching it. Due to this subjectiveness, the challenging task of automatically predicting whether a video segment is interesting or not has attracted a lot of attention. Current solutions are usually based on learning models trained with features from different modalities. In this paper, we propose a late fusion with rank aggregation methods for combining ranking models learned with features of different modalities and by different learning-to-rank algorithms. The experimental evaluation was conducted on a benchmarking dataset provided for the Predicting Media Interestingness Task at the MediaEval 2016. Two different modalities and four learning-to-rank algorithms are considered. The results are promising and show that the rank aggregation methods can be used to improve the overall performance, reaching gains of more than 10% over state-of-the-art solutions.

Model-Driven Engineering (MDE) has been successfully used in static program analysis. Frameworks like MoDisco inject the program structure into a model, available for further processing by query and transformation tools, e.g., for program understanding, reverseengineering, modernization. In this paper we present our first steps towards extending MoDisco with capabilities for dynamic program analysis. We build an injector for program execution traces, one of the basic blocks of dynamic analysis. Our injector automatically instruments the code, executes it and captures a model of the execution behavior of the program, coupled with the model of the program structure. We use the trace injection mechanism for model-driven impact analysis on test sets. We identify some scalability issues that remain to be solved, providing a case study for future efforts in improving performance of model-

This work couples the use of augmented and virtual reality, a tabletop display, and mobile devices (tablets and smartphones) to develop an innovative, system to support learner-centric anatomy education and training. The system provides a common tabletop interaction surface where a global view of an anatomical model is provided. This global view is available to all of the users (instructor and trainees) whom can interact with the model using the touch-sensitive tabletop display surface. In addition to this global view, each of the trainees has access to the model through a mobile device that is synchronized with the global view and provides each trainee with an individualized (local) view of the scene and interaction mechanisms. This paper outlines our integrated tabletop computer-tablet display and its use to facilitate virtual-based eye anatomy training.

In this paper, we target at similarity search among data supply chains, which plays essential role in optimizing the chain and extending its value. This problem is very challenging for application-oriented data supply chains because the high complexity of data supply chain makes the computation of similarity extremely complex and inefficiency. In this paper, we propose a feature space representation model based on key points, which can extract the key features from sub-sequences of the original data supply chain and simplify the original data supply chain into a feature vector form. Then, we formulate the similarity computation of key points based on the multi-scale features. Further, we propose an improved hierarchical clustering algorithm for similarity search over data supply chains. The main idea is to separate sub-sequences into disjoint groups such that each-group meets one specific clustering criteria, and thus the cluster containing the query object is the similarity search result. The experimental results show that the proposed approach is both effective and efficient for data supply chain retrieval.

s of Invited Talks Classical Problems to Make Quantum Computing a Reality Adam C. Whiteside, Austin G. Fowler Centre for Quantum Computation and Communication Technology, School of Physics, The University of Melbourne,Victoria, 3010, Australia Google Inc., Santa Barbara, CA 93117, USA (Dated: April 15, 2016) Recent experiments have shown exciting progress toward creating reliable quantum bits (qubits) that will make up tomorrow’s quantum computers. While experiments and engineers continue to make the physical side a reality, computer scientists and software engineers will be essential to getting the most out of such expensive hardware. An entire stack of classical software must be developed, requiring creative solutions to a broad range of problems. We provide an introduction to quantum computing and an overview of the problems left to face in an effort to inspire more research in these important areas. DEMONIC Programming: A Computational Language for Single-particle Equilibrium Thermodynamics, and its Formal Semantics Samson Abramsky and Dominic Horsman Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford, OX1 3QD, UK samson.abramsky@cs.ox.ac.uk Joint Quantum Centre Durham-Newcastle, Durham University, Department of Physics, Rochester Building, Science Laboratories, South Road, Durham DH1 3LE, UK dominic.horsman@durham.ac.uk Abstract. Maxwell’s Demon, ‘a being whose faculties are so sharpened that he can follow every molecule in its course’, has been the centre of much debate about his abilities to violate the second law of thermodynamics. Landauer’s hypothesis, that the Demon must erase its memory and incur a thermodynamic cost, has become the standard response to Maxwell’s dilemma, and its implications for the thermodynamics of computation reach into many areas of quantum and classical computing. It remains, however, still a hypothesis. Debate over the existence of an erasure cost for information has often centred around simple toy models of a single particle in a box. Despite their simplicity, the ability of these systems to accurately represent thermodynamics (specifically to satisfy the second law) and whether or not they display Landauer Erasure, has been a matter of ongoing argument. The recent Norton-Ladyman controversy is one such example. In this paper we give a computational language for formal reasoning about thermodynamic systems. We formalise the basic single-particle operations as statements in the language, and then show that the second law must be satisfied by any composition of these basic operations. This is done by finding a computational invariant of the system. We show, furthermore, that this invariant requires an erasure cost to exist within the system, equal to kT ln 2 for a bit of information: Landauer Erasure becomes a theorem of the formal system. The Norton-Ladyman controversy can therefore be resolved in a rigorous fashion, and moreover the formalism we introduce gives a set of reasoning tools for further analysis of Landauer erasure, which are provably consistent with the second law of thermodynamics. Maxwell’s Demon, ‘a being whose faculties are so sharpened that he can follow every molecule in its course’, has been the centre of much debate about his abilities to violate the second law of thermodynamics. Landauer’s hypothesis, that the Demon must erase its memory and incur a thermodynamic cost, has become the standard response to Maxwell’s dilemma, and its implications for the thermodynamics of computation reach into many areas of quantum and classical computing. It remains, however, still a hypothesis. Debate over the existence of an erasure cost for information has often centred around simple toy models of a single particle in a box. Despite their simplicity, the ability of these systems to accurately represent thermodynamics (specifically to satisfy the second law) and whether or not they display Landauer Erasure, has been a matter of ongoing argument. The recent Norton-Ladyman controversy is one such example. In this paper we give a computational language for formal reasoning about thermodynamic systems. We formalise the basic single-particle operations as statements in the language, and then show that the second law must be satisfied by any composition of these basic operations. This is done by finding a computational invariant of the system. We show, furthermore, that this invariant requires an erasure cost to exist within the system, equal to kT ln 2 for a bit of information: Landauer Erasure becomes a theorem of the formal system. The Norton-Ladyman controversy can therefore be resolved in a rigorous fashion, and moreover the formalism we introduce gives a set of reasoning tools for further analysis of Landauer erasure, which are provably consistent with the second law of thermodynamics.

This paper presents a hybrid scatter search algorithm to solve the capacitated arc routing problem with refill points (CARP-RP). The vehicle servicing arcs must be refilled on the spot by using a second vehicle. This problem is addressed in real-world applications in many services systems. The problem consists on simultaneously determining the vehicles routes that minimize the total cost. In the literature is proposed an integer linear programming model to solve the problem. We propose a hybrid algorithm based on Scatter Search, Simulated Annealing and Iterated Local Search. Our method is tested with instances from the literature. We found best results in the objective function for the majority instances.

For most usual optimisation problems, the Nearer is Better assumption is true (in probability). Classical iterative algorithms take this property into account, either explicitly or implicitly, by forgetting some information collected during the process, assuming it is not useful any more. However, when the property is not globally true, i.e. for deceptive problems, it may be necessary to keep all the sampled points and their values, and to exploit this increasing amount of information. Such a basic Total Memory Optimiser is presented here. We experimentally show that this technique can outperform classical methods on small deceptive problems. As it gets very computing time expensive when the dimension of the problem increases, a few compromises are suggested to speed it up.

Survivability is a critical attribute of modern computer and communication systems. The assessment of survivability is mostly performed in a qualitative manner and thus cannot meet the need for more precise and solid evaluation of service loss or degradation in presence of failure/attack/disaster. This talk addresses the current research status of quantification of survivability. First, we carefully define survivability and contrast it with traditional measures such as reliability, availability and performability [2, 8, 7]. We use “survivability” as defined by the ANSI T1A1.2 committee – that is, the transient performance from the instant an undesirable event occurs until steady state with an acceptable performance level is attained [1]. Thus survivability can be seen as a generalization of recovery after a failure or any undesired event [3]. We then discuss probabilistic models for the quantification of survivability based on our chosen definition. Next, three case studies are presented to illustrate our approach. One case study is about the quantitative evaluation of several survivable architectures for the plain old telephone system (POTS) [5]. The second case study deals with the survivability quantification of communication networks [4] while the third is that of smart grid distribution automation networks [6]. In each case hierarchical models are developed to derive various survivability measures. Numerical results are provided to show how a comprehensive understanding of the system behavior after failure can be achieved through such models.

The web is being accessed increasingly by users for which an accurate geo-location is available, and increasing volumes of geo-tagged content are available on the web, including web pages, points of interest, and microblog posts. Studies suggest that each week, several billions of keyword-based queries are issued that have some form of local intent and that target geo-tagged web content with textual descriptions. This state of affairs gives prominence to spatial web data management, and it opens to a research area full of new and exciting opportunities and challenges. A prototypical spatial web query takes a user location and user-supplied keywords as arguments, and it returns content that is spatially and textually relevant to these arguments. Due perhaps to the rich semantics of geographical space and its importance to our daily lives, many different kinds of relevant spatial web query functionality may be envisioned. Based on recent and ongoing work by the speaker and his colleagues, the talk presents key functionality, concepts, and techniques relating to spatial web querying; it presents functionality that addresses different kinds of user intent; and it outlines directions for the future development of keyword-based spatial web querying. Bio. Christian S. Jensen is Obel Professor of Computer Science at Aalborg University, Denmark, and he was previously with Aarhus University for three years and spent a one-year sabbatical at Google Inc., Mountain View. His research concerns data management and data-intensive systems, and its focus is on temporal and spatio-temporal data management. Christian is an ACM and an IEEE Fellow, and he is a member of Academia Europaea, the Royal Danish Academy of Sciences and Letters, and the Danish Academy of Technical Sciences. He has received several national and international awards for his research. He is Editor-in-Chief of ACM Transactions on Database Systems. Using Conceptual Model Technologies for Understanding the Human Genome: From an “Homo Sapiens” to an “Homo Genius”

Computers have become an integral part of a vast range of coordination patterns among human activities which go far beyond mere calculation. The conceptual relevance of this new field of application of computers has been advocated by Carl Adam Petri (1926–2010) and Anatol W. Holt (1927–2010), two computer scientists best known for their contributions to the subject of Petri nets, a graphical formalism for describing the causal dependence of events in systems distributed in space. We outline some fundamental, mainly epistemological aspects of their vision of the computer as a “communication machine.”