Session8 14:20 - 16:00 on 14th June 2019

ICCS 2019 Main Track (MT) Session 8

Time and Date: 14:20 - 16:00 on 14th June 2019

Room: 1.5

Chair: Eisha Nathan

225 Immersed boundary method halo exchange in a hemodynamics application [abstract]
Abstract: In recent years, highly parallelized simulations of blood flow resolving individual blood cells have been demonstrated. Simulating such dense suspensions of deformable particles in flow often involves a partitioned fluid-structure interaction (FSI) algorithm, with separate solvers for Eulerian fluid and Lagrangian cell grids, plus a solver - e.g., immersed boundary method - for their interaction. Managing data motion in parallel FSI implementations is increasingly important, particularly for inhomogeneous systems like vascular geometries. In this study, we evaluate the influence of Eulerian and Lagrangian halo exchanges on efficiency and scalability of a partitioned FSI algorithm for blood flow. We describe an MPI+OpenMP implementation of the immersed boundary method coupled with lattice Boltzmann and finite element methods. We consider how communication and recomputation costs influence the optimization of halo exchanges with respect to three factors: immersed boundary interaction distance, cell suspension density, and relative fluid/cell solver costs.
John Gounley, Erik W. Draeger and Amanda Randles
386 Evolution of Hierarchical Structure & Reuse in iGEM Synthetic DNA Sequences [abstract]
Abstract: Many complex systems, both in technology and nature, exhibit hierarchical modularity: smaller modules, each of them providing a certain function, are used within larger modules that perform more complex functions. Previously, we have proposed a modeling framework, referred to as Evo-Lexis, that provides insight to some fundamental questions about evolving hierarchical systems. The predictions of the Evo-Lexis model should be tested using real data from evolving systems in which the outputs can be well represented by sequences. In this paper, we investigate the time series of iGEM synthetic DNA dataset sequences, and whether the resulting iGEM hierarchies exhibit the qualitative properties predicted by the Evo-Lexis framework. Contrary to Evo-Lexis, in iGEM the amount of reuse decreases during the timeline of the dataset. Although this results in development of less cost-efficient and less deep Lexis-DAGs, the dataset exhibits a bias in reusing specific nodes more often than others. This results in the Lexis-DAGs to take the shape of an hourglass with relatively high H-score values and stable set of core nodes. Despite the reuse bias and stability of the core set, the dataset presents a high amount of diversity among the targets which is in line with modeling of Evo-Lexis.
Payam Siyari, Bistra Dilkina and Constantine Dovrolis
475 Computational design of superhelices by local change of the intrinsic curvature [abstract]
Abstract: Helices appear in nature at many scales, ranging from molecules to tendrils in plants. Organisms take advantage of the helical shape to fold, propel and assemble. For this reason, several applications in micro and nanorobotics, drug delivery and soft-electronics have been suggested. On the other hand, biomolecules can form complex tertiary structures made with helices to accomplish many different functions. A particular well-known case takes place during cell division when DNA, a double helix, is packaged into a super-helix – i.e., a helix made of helices – to prevent DNA entanglement. DNA super-helix formation requires auxiliary histone molecules, around which DNA is wrapped, in a "beads on a string" structure. The idea of creating superstructures from simple elastic filaments served as the inspiration to this work. Here we report a method to produce ribbons with complex shapes by periodically creating strains along the ribbons. Ribbons can gain helical shapes, and their helicity is ruled by the asymmetric contraction along the main axis. If the direction of the intrinsic curvature is locally changed, then a tertiary structure results, similar to the DNA wrapped structure. In this process, auxiliary structures are not required and therefore new methodologies to shape filaments, of interest to nanotechnology and biomolecular science, are proposed.
Pedro E. S. Silva, Maria Helena Godinho and Fernão Vístulo de Abreu
493 Spatial modeling of influenza outbreaks in Saint Petersburg using synthetic populations [abstract]
Abstract: In this paper, we model influenza propagation in the Russian setting using a spatially explicit model and a detailed human agent database as its input. The aim of the research is to assess the applicability of this modeling method using influenza incidence data for 2010-2011 epidemic outbreak in Saint Petersburg and to compare the simulation results with the output of the compartmental SEIR model for the same outbreak. For this purpose, a synthetic population of Saint Petersburg was built and used for the simulation via FRED open source modeling framework. The parameters related to the outbreak (background immunity level and effective contact rate) are assessed by calibrating the compartmental model to incidence data. We show that the current version of synthetic population allows the agent-based model to reproduce real disease incidence.
Vasiliy Leonenko, Alexander Lobachev and Georgiy Bobashev

ICCS 2019 Main Track (MT) Session 16

Time and Date: 14:20 - 16:00 on 14th June 2019

Room: 1.3

Chair: Cantin Guillaume

233 An Agent-Based Model for Emergent Opponent Behavior [abstract]
Abstract: Organized crime, insurgency and terrorist organizations have a large and undermining impact on societies. This highlights the urgency to better understand the complex dynamics of these individuals and organizations in order to timely detect critical social phase transitions that form a risk for society. In this paper we introduce a new multi-level modelling approach that integrates insights from complex systems, criminology, psychology, and organizational studies with agent-based modelling. We use a bottom-up approach to model the active and adaptive reactions by individuals to the society, the economic situation and law enforcement activity. This approach enables analyzing the behavioral transitions of individuals and associated micro processes, and the emergent networks and organizations influenced by events at meso- and macro-level. At a meso-level it provides an experimentation analysis modelling platform of the development of opponent organization subject to the competitive characteristics of the environment and possible interventions by law enforcement. While our model is theoretically founded on findings in literature and empirical validation is still work in progress, our current model already enables a better understanding of the mechanism leading to social transitions at the macro-level. The potential of this approach is illustrated with computational results.
Koen van der Zwet, Ana Isabel Barros, Tom van Engers and Bob van der Vecht
352 Fine-Grained Label Learning via Siamese Network for Cross-modal Information Retrieval [abstract]
Abstract: Cross-modal information retrieval aims to search for semantically relevant data from various modalities when given a query from one modality. For text-image retrieval, a common solution is to map texts and images into a common semantic space and measure their similarity directly. Both the positive and negative examples are used for common semantic space learning. Existing work treats the positive/negative text-image pairs as equally positive/negative. However, we observe that many positive examples resemble the negative ones in some degrees and vice versa. These “hard examples” are challenging for existing models. In this paper, we aim to assign fine-grained labels for the examples to capture the degrees of “hardness”, thus enhancing cross-modal correlation learning. Specifically, we propose a siamese network on both the positive and negative examples to obtain their semantic similarities. For each positive/negative example, we use the text description of the image in the example to calculate its similarity with the text in the example. Based on these similarities, we assign fine-grained labels to both the positives and negatives and introduce these labels to a pairwise similarity loss function. The loss function benefits from the labels to increase the influence of hard examples on the similarity learning while maximizing the similarity of relevant text-image pairs and minimizing the similarity of irrelevant pairs. We conduct extensive experiments on the English Wikipedia, Chinese Wikipedia, and TVGraz datasets. Compared with state-of-the-art models, our model achieves significant improvement on the retrieval performance by incorporating with fine-grained labels.
Yiming Xu, Jing Yu, Jingjing Guo, Yue Hu and Jianlong Tan
354 MeshTrust: A CDN-centric Trust Model for Reputation Management on Video Traffic [abstract]
Abstract: Video applications today are more often deploying content delivery networks (CDNs) for content delivery. However, by decoupling the owner of the content and the organization serving it, CDNs could be abused by attackers to commit network crimes. Traditional flow-level measurements for generating reputation of IPs and domain names for video applications are insufficient. In this paper, we present MeshTrust, a novel approach that assessing reputation of service providers on video traffic automatically. We tackle the challenge from two aspects: the multi- tenancy structure representation and CDN-centric trust model. First, by mining behavioral and semantic characteristics, a Mesh Graph consisting of video websites, CDN nodes and their relations is constructed. Second, we introduce a novel CDN-centric trust model which transforms Mesh Graph into Trust Graph based on extended network embedding methods. Based on the labeled nodes in Trust Graph, a reputation score can be easily calculated and applied to real-time reputation management on video traffic. Our experiments show that MeshTrust can differentiate normal and illegal video websites with accuracy approximately 95% in a real cloud environment.
Xiang Tian, Yujia Zhu, Zhao Li, Chao Zheng, Qingyun Liu and Yong Sun
451 Optimizing spatial accessibility of company branches network with constraints [abstract]
Abstract: The ability of customer data collection in enterprise corporate information systems leads to the emergence of customer-centric algorithms and approaches. In this study, we consider the problem of choosing a candidate branch for closing based on the overall expected level of dissatisfaction of company customers with the location of remaining branches. To measure the availability of branches for individuals, we extract points of interests from the traces of visits using the clustering algorithm to find centers of interests. The following questions were further considered: (i) to which extent does spatial accessibility influence the choice of company branches by the customers? (ii) which algorithm provides a better trade-off between accuracy and computational complexity? These questions were studied in application to a bank branches network. In particular, data and domain restrictions from our bank-partner (one of the largest regional banks in Russia) were used. The results show that: (i) spatial accessibility significantly influences customers' choice (65%–75% of customers choose one of the top 5 branches by accessibility after closing the branch), (ii) the proposed greedy algorithm provides optimal solution in almost all of cases, (iii) output of the greedy algorithm may be further improved with a local search algorithm, (iv) instance of a problem with several dozens of branches and up to million customers may be solved with near-optimal quality in dozens of seconds.
Oleg Zaikin, Ivan Derevitskii, Klavdiya Bochenina and Janusz Holyst

Computational Finance and Business Intelligence (CFBI) Session 2

Time and Date: 14:20 - 16:00 on 14th June 2019

Room: 0.3

Chair: Yong Shi

339 Portfolio Selection based on Hierarchical Clustering and Inverse-variance Weighting [abstract]
Abstract: This paper presents a remarkable model for portfolio selection using inverse-variance weighting and machine learning techniques such as hierarchical clustering algorithms. This method allows building diversified portfolios that have a good balance sector exposure and style exposure, respect to momentum, size, value, short-term reversal, and volatility. Furthermore, we compare performance for seven hierarchical algorithms: Single, Complete, Average, Weighted, Centroid, Median and Ward Linkages. Results show that the Average Linkage algorithm has the best Cophenetic Correlation Coefficient. The proposed method using the best linkage criteria is tested against real data over a two-year dataset of one-minute American stocks returns. The portfolio selection model achieves a good financial return and an outstanding result in the annual volatility of 3.2%. The results suggest good behavior in performance indicators with a Sharpe ratio of 0.89, an Omega ratio of 1.16, a Sortino ratio of 1.29 and a beta to S&P of 0.26.
Andrés Arévalo, Diego León and German Hernandez
356 A computational Technique for Asian option pricing model [abstract]
Abstract: In the present work, the European style fixed strike Asian call option with arithmetic and continuous averaging is numerically evaluated where the volatility, the risk free interest rate and the dividend yield are functions of the time. A finite difference scheme consisting of second order HODIE scheme for spatial discretization and two-step backward differentiation formula for temporal discretization is applied. The scheme is proved to be second order accurate in space and time both. The numerical results are in accordance with analytical results.
Manisha and S Chandra Sekhara Rao
489 Improving portfolio optimization using weighted link prediction in dynamic stock networks [abstract]
Abstract: Portfolio optimization in stock markets has been investigated by many researchers. It looks for a subset of assets able to maintain a good trade-o control between risk and return. Several algorithms have been proposed to portfolio management. These algorithms use known return and correlation data to build subset of recommended assets. Dynamic stock correlation networks, whose vertices represent stocks and edges represent the correlation between them along the time, can also be used as input by these algorithms. This study proposes the denition of the constants of the classic mean-variance analysis using machine learning and weighted link prediction in stock networks (named as MLink). To assess the performance of MLink, experiments were performed using real data from the Brazilian Stock Exchange. In these experiments, MLink was compared with mean-variance analysis (MVA), a popular methods for portfolio optimization. According to the experimental results, the use of weighted link prediction in stock networks as input produced the best performance in the portfolio optimization task, resulting in a capital increase of 41% in 84 days.
Douglas Castilho, João Gama, Leandro Mundim and André de Carvalho

Biomedical and Bioinformatics Challenges for Computer Science (BBC) Session 3

Time and Date: 14:20 - 16:00 on 14th June 2019

Room: 0.4

Chair: Mario Cannataro

332 Combining Polynomial Chaos Expansions and Genetic Algorithm for the coupling of electrophysiological models [abstract]
Abstract: The number of computational models in cardiac research has grown over the last decades. Every year new models with different assumptions appear in the literature dealing with differences in interspecies cardiac properties. Generally, these new models update the physiological knowledge using new equations which reflect better the molecular basis of process. New equations require the fitting of parameters to previously known experimental data or even, in some cases, simulated data. This work studies and proposes a new method of parameter adjustment based on Polynomial Chaos and Genetic Algorithm to find the best values for the parameters upon changes in the formulation of ionic channels. It minimizes the search space and the computational cost combining it with a Sensitivity Analysis. We use the analysis of different models of L-type calcium channels to see that by reducing the number of parameters, the quality of the Genetic Algorithm dramatically improves. In addition, we test whether the use of the Polynomial Chaos Expansions improves the process of the Genetic Algorithm search. We find that it reduces the Genetic Algorithm execution in an order of 10³ times in the case studied here, maintaining the quality of the results. We conclude that polynomial chaos expansions can improve and reduce the cost of parameter adjustment in the development of new models.
Gustavo Novaes, Joventino Campos, Enrique Alvarez-Lacalle, Sergio Muñoz, Bernardo Rocha and Rodrigo Santos
365 A cloud architecture for the execution of medical imaging biomarkers [abstract]
Abstract: Digital Medical Imaging is increasingly being used in clinical routine and research. As a consequence, the workload in medical imaging departments in hospitals has multiplied by over 20 in the last decade. Medical Image processing requires intensive computing resources not available at hospitals, but which could be provided by public clouds. The article analyses the requirements of processing digital medical images and introduces a cloud-based architecture centred on a DevOps approach to deploying resources on demand, tailored to different types of workloads (based on the request of resources and the expected execution time). Results presented show a low overhead and high flexibility executing a lung disease biomarker on a public cloud.
Sergio López Huguet, Fabio García-Castro, Ángel Alberich-Bayarri and Ignacio Blanquer
371 A Self-Adaptive Local Search Coordination in Multimeme Memetic Algorithm for Molecular Docking [abstract]
Abstract: Molecular Docking is a methodology that deals with the problem of predicting the non-covalent binding of a receptor and a ligand at an atomic level to form a stable complex. Because the search space of possible conformations is vast, molecular docking is classified in computational complexity theory as a NP-hard problem. Because of the high complexity, exact methods are not efficient and several metaheuristics have been proposed. However, these methods are very dependent on parameter settings and search mechanism definitions, which requires approaches able to self-adapt these configurations along the optimization process. We proposed and developed a novel self-adaptive coordination of local search operators in a Multimeme Memetic Algorithm. The approach is based on the Biased Random Key Genetic Algorithm enhanced with four local search algorithms. The self-adaptation of methods and radius perturbation in local improvements works under a proposed probability function, which measures their performance to best guide the search process. The methods have been tested on a test set based on HIV-protease and compared to existing tools. Statistical test performed on the results shows that this approach reaches better results than a non-adaptive algorithm and is competitive with traditional methods.
Pablo Felipe Leonhart, Pedro Henrique Narloch and Marcio Dorn
428 Parallel CT reconstruction for multiple slices studies with SuiteSparseQR factorization package [abstract]
Abstract: Algebraic factorization methods applied to the discipline of Computerized Tomography (CT) Medical Imaging Reconstruction involve a high computational cost. Since these techniques are significantly slower than the traditional analytical ones and time is critical in this field, we need to employ parallel implementations in order to exploit the machine resources and obtain efficient reconstructions. In this paper, we analyze the performance of the sparse QR decomposition implemented on SuiteSparseQR factorization package applied to the CT reconstruction problem. We explore both the parallelism provided by BLAS threads and the use of the Householder reflections to reconstruct multiple slices at once efficiently. Combining both strategies, we can boost the performance of the reconstructions and implement a reliable and competitive method that gets high-quality CT images.
Mónica Chillarón, Vicente Vidal and Gumersindo Verdú Martín

Solving Problems with Uncertainties (SPU) Session 3

Time and Date: 14:20 - 16:00 on 14th June 2019

Room: 0.6

Chair: Vassil Alexandrov

467 On the estimation of the accuracy of numerical solutions in CFD problems [abstract]
Abstract: The task of assessing accuracy in mathematical modeling of gas-dynamic processes is of utmost importance and relevance. Modern software packages include a large number of models, numerical methods and algorithms that allow to solve most of the current CFD problems. However, the issue of obtaining a reliable solution in the absence of experimental data or any reference solution remains relevant. The paper provides a brief overview of some useful approaches to solving the problem, including such approaches as a multi-model approach, the study of an ensemble of solutions, the construction of a generalized numerical experiment.
Alexander Bondarev
499 "Why did you do that?" Explaining black box models with Inductive Synthesis [abstract]
Abstract: By their nature, the composition of black box models is opaque. This makes the ability to generate explanations for the response to stimuli challenging. The importance of explaining black box models has become increasingly important given the prevalence of AI and ML systems and the need to build legal and regulatory frameworks around them. Such explanations can also increase trust in these uncertain systems. In our paper we present RICE, a method for generating explanations of the behaviour of black box models by (1) probing a model to extract model output examples using sensitivity analysis; and (2) applying CNPInduce, a method for inductive logic program synthesis, to generate logic programs based on critical input-output pairs, and (3) interpreting the target program as a human-readable explanation. We demonstrate the application of our method by generating explanations of an artificial neural network trained to follow simple traffic rules in a hypothetical self-driving car simulation. We conclude with a discussion on the scalability and usability of our approach and its potential applications to explanation-critical scenarios.
Gorkem Pacaci, David Johnson, Steve McKeever and Andreas Hamfelt
510 Predictive Analytics with Factor Variance Association [abstract]
Abstract: Predictive Factor Variance Association (PFVA) is a machine learning algorithm that solves the multiclass problem. A set of feature samples is provided and a set of target classes. If a sample belongs to a class, then that column is marked as one or zero otherwise. PFVA will carry out Singular Value Decomposition in the standardized samples creating orthogonal linear combinations of the variables called Factors. For each linear combination, probabilities are estimated for a target class. Then a least squares curve fitting model is used to compute the probability that a particular sample belongs to a class or not. It can also give predictions based on regression for quantitative dependent variables and carry-out clustering of samples. The main advantage of our technique is a clear mathematical founda-tion using well-known concepts of linear algebra and probability.
Raul Ramirez-Velarde, Laura Hervert-Escobar and Neil Hernandez-Gress
536 Integration of ontological engineering and machine learning methods to reduce uncertainties in health risk assessment and recommendation systems [abstract]
Abstract: This research provides an approach that integrates the best from ontology engineering and machine learning methods in order to reduce some types of uncertainties in health risk assessment challenges and improve explainability of decision-making systems. The proposed approach is based on ontological knowledge base of health risk assessment having regard to medical, genetic, environmental and life style factors. To automate the knowledge base development, we propose integrating both traditional knowledge engineering methods and machine learning approach using collaborative knowledge base Freebase. We also come up with the idea of using Text Mining method based on lexico-syntactic patterns inherited from different sets and created by our own. Moreover, we use ontology engineering methods in order to explain machine learning results, unsupervised methods in particular. In the paper we present the case studies showing original methods and approaches solving problems with some kind of uncertainties in biomedicine decision making systems within BioGenom2.0 platform development. Because the platform use ontology driven reasoner there is no need to make changes in source code in order to tackle health risk assessment challenges using various of knowledge base focused on medical, genetic aspects and etc.
Svetlana Chuprina and Taisiya Kostareva

Computational Optimization, Modelling and Simulation (COMS) Session 4

Time and Date: 14:20 - 16:00 on 14th June 2019

Room: 1.4

Chair: Xin-She Yang

27 Surrogate-based optimisation of tidal turbine arrays: A case study for the Faro-Olhão Inlet [abstract]
Abstract: This paper presents a study for estimating the size of a tidal turbine array for the Faro-Olhão Inlet (Potugal) using a surrogate optimisation approach. The method compromises problem formulation, hydro-morphodynamic modelling, surrogate construction and validation, and constraint optimisation. A total of 26 surrogates were built using linear RBFs as a function of two design variables: number of array rows and number of Tidal Energy Converters (TECs) per row. Surrogates describe array performance and environmental effects associated with hydrodynamic and morphological aspects of the multi inlet lagoon. Validated surrogates were employed to formulate a constraint optimisation model. Results evidence that the largest array size that satisfies performance and environmental constraints is made of 3 rows and 10 TECs per row.
Eduardo González-Gorbeña, André Pacheco, Theocharis A. Plomaritis, Óscar Ferreira, Cláudia Sequeira and Theo Moura
240 Time-dependent link travel time approximation for large-scale dynamic traffic simulations [abstract]
Abstract: Large-scale dynamic traffic simulations generate a sizeable amount of raw data that needs to be managed for analysis. Typically, big data reduction techniques are used to decrease redundant, inconsistent and noisy data as these are perceived to be more useful than the raw data itself. However, these methods are normally performed independently so it wouldn’t compete with the simulation’s computational and memory resources. In this paper, we are motivated in developing a data reduction technique that will be integrated into a simulation process and executed numerous times. Specifically, we are interested in reducing the size of each link’s time-dependent travel time data in a large-scale dynamic traffic simulation. The objective is to approximate the time-dependent link travel times along the y−axis to reduce memory consumption while insignificantly affecting the simulation results. An important aspect of the algorithm is its capability to restrict the maximum absolute error bound which avoids theoretically inconsistent results not accounted for by the dynamic traffic simulation model. One major advantage of the algorithm is its efficiency’s independence from input complexity such as the number of sampled data points, sampled data’s shape and irregularity of sampling intervals. Using a 10x10 grid network with variable time-dependent link travel time data complexities and absolute error bounds, the dynamic traffic simulation results show that the algorithm achieves around 80%−99% of link travel time data reduction using a small amount of computational resource.
Genaro Jr Peque, Hiro Harada and Takamasa Iryo
460 Evaluation of the Suitability of Intel Xeon Phi Clusters for the Simulation of Ultrasound Wave Propagation using Pseudospectral Methods [abstract]
Abstract: The ability to perform large-scale ultrasound simulations has generated significant interest in medical ultrasonics, including treatment planning in therapeutic ultrasound, and image reconstruction in photoacoustic tomography. However, routine execution of such simulations using modern pseudospectral methods is computationally very challenging. To enable fast simulation, a cluster of computers is typically used. Nowadays, the trend in parallel computing is towards the use of accelerated nodes where the hard work is offloaded from processors to accelerators. During last five years, Intel has released two generations of accelerators called Intel Xeon Phi. The goal of this paper is to investigate the parameters on both architectures with respect to current processors, and evaluate the suitability of accelerated clusters for the distributed simulation of ultrasound propagation in medical applications. The paper reveals that the former version of Xeon Phis, the Knight's Corner architecture, suffers from several flaws that reduces the performance far below the Haswell processors. On the other hand, the second generation called Knight's Landing shows a very promising performance comparable with current processors.
Filip Vaverka, Bradley Treeby and Jiří Jaroš