Papers by Sergio Arturo Gutierrez Salinas
Engineering with Computers
This paper presents an algorithm to generate a new kind of polygonal mesh obtained from triangula... more This paper presents an algorithm to generate a new kind of polygonal mesh obtained from triangulations. Each polygon is built from a terminal-edge region surrounded by edges that are not the longestedge of any of the two triangles that share them. The algorithm is termed Polylla and is divided into three phases. The first phase consists of labeling each edge of the input triangulation according to its size; the second phase builds polygons (simple or not) from terminaledges regions using the label system; and the third phase transforms each non simple polygon into simple ones. The final mesh contains polygons with convex and non convex shape. Since Voronoi based meshes are currently the most used polygonal meshes, we compare some geometric properties of our meshes against constrained Voronoi meshes. Several experiments were run to compare the shape and size of polygons, the number of final mesh points and polygons. For the same input, Polylla meshes contain less polygons than Voronoi meshes and
arXiv (Cornell University), Apr 11, 2022
This paper * presents a GPU parallel algorithm to generate a new kind of polygonal meshes obtaine... more This paper * presents a GPU parallel algorithm to generate a new kind of polygonal meshes obtained from Delaunay triangulations. To generate the polygonal mesh, the algorithm first uses a classification system to label each edge of an input triangulation; second it builds polygons (simple or not) from terminal-edge regions using the label system, and third it transforms each non-simple polygon from the previous phase into simple ones, convex or not convex polygons. We show some preliminary experiments to test the scalability of the algorithm and compare it with the sequential version. We also run a very simple test to show that these meshes can be useful for the virtual element method.
XXII Workshop de Investigadores en Ciencias de la Computación (WICC 2020, El Calafate, Santa Cruz), 2020
Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics, 2017
Today's massive amount of biological sequence data has the potential to rapidly advance our under... more Today's massive amount of biological sequence data has the potential to rapidly advance our understanding of life's processes. However, since analyzing biological sequences is a very expensive computing task, users face a formidable challenge in trying to analyze these data on their own. Cloud computing o ers access to a large amount of computing resources in an on-demand and pay-per-use fashion, which is a practical way for people to analyze these huge data sets. However, many people are still reluctant to outsource biological sequences to the cloud because they contain sensitive information that should be kept secret for ethical, security, and legal reasons. One of the most fundamental and frequently used computational tools for biological sequence analysis is pairwise sequence alignment (PSA). Previous works for securely solving PSAs at the cloud su er from poor scalability, i.e., they are unable to exploit the cloud's infrastructure to solve PSAs in parallel because resourcelimited users need to be constantly involved in the computations. In this paper, we develop a secure outsourcing algorithm that allows users to solve an arbitrary number of PSAs in parallel at the cloud. Compared with previous works, our algorithm can reduce computing time of a large number of PSAs by more than 50% with as few as 5 computing nodes at the cloud.
XXII Workshop de Investigadores en Ciencias de la Computación (WICC 2020, El Calafate, Santa Cruz), 2020
2017 IEEE Trustcom/BigDataSE/ICESS, 2017
Modern organizations have collected vast amounts of data created by various systems and applicati... more Modern organizations have collected vast amounts of data created by various systems and applications. Scientists and engineers have a strong desire to advance scientific and engineering knowledge from such massive data. QR factorization is one of the most fundamental mathematical tools for data analysis. However, conducting QR factorization of a matrix requires high computational complexity. This incurs a formidable challenge in efficiently analyzing large-scale data sets by normal users or small companies on traditional resource limited computers. To overcome this limitation, industry and academia propose to employ cloud computing that can offer abundant computing resources. This, however, raises privacy concerns because users' data may contain sensitive information that needs to be hidden for ethical, legal, or security reasons. To this end, we propose a privacy-preserving outsourcing algorithm for efficiently performing large-scale QR factorization. We implement the proposed algorithm on the Amazon Elastic Compute Cloud (EC2) platform and a laptop. The experiment results show significant time saving for the user.
2018 IEEE Global Communications Conference (GLOBECOM), 2018
Tensor decompositions are very powerful tools for analyzing multi-dimensional multi-modal data. P... more Tensor decompositions are very powerful tools for analyzing multi-dimensional multi-modal data. Particularly, CP tensor decomposition is one of the most fundamental tensor decomposition models. However, it is usually computationally expensive to conduct CP tensor decompositions on a largescale tensor by common algorithms like alternative least squares (ALS). To address this issue, one widely recognized solution is to adopt cloud computing. However, this raises privacy concerns due to the private information carried by a tensor. Previous algorithms for privacy-preserving outsourcing of tensor decompositions and other related computations require heavy communication cost. In this paper, we first develop an efficient tensor transformation scheme to protect the private information carried by elements' values of a tensor. Then we design a privacy-preserving outsourcing algorithm for ALS based CP tensor decompositions. We implement our proposed algorithm on a laptop and Amazon EC2 cloud and offer experiment results to show the significant computing time-savings.
Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security, 2016
The massive amount of data that is being collected by today's society has the potential to advanc... more The massive amount of data that is being collected by today's society has the potential to advance scientific knowledge and boost innovations. However, people often lack sufficient computing resources to analyze their large-scale data in a cost-effective and timely way. Cloud computing offers access to vast computing resources on an on-demand and pay-per-use basis, which is a practical way for people to analyze their huge data sets. However, since their data contain sensitive information that needs to be kept secret for ethical, security, or legal reasons, many people are reluctant to adopt cloud computing. For the first time in the literature, we propose a secure outsourcing algorithm for large-scale quadratic programs (QPs), which is one of the most fundamental problems in data analysis. Specifically, based on simple linear algebra operations, we design a low-complexity QP transformation that protects the private data in a QP. We show that the transformed QP is computationally indistinguishable under a chosen plaintext attack (CPA), i.e., CPA-secure. We then develop a parallel algorithm to solve the transformed QP at the cloud, and efficiently find the solution to the original QP at the user. We implement the proposed algorithm on the Amazon Elastic Compute Cloud (EC2) and a laptop. We find that our proposed algorithm offers significant time savings for the user and is scalable to the size of the QP.
2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), 2017
The booming growth and popularity of mobile devices have led to the surge of various mobile appli... more The booming growth and popularity of mobile devices have led to the surge of various mobile applications. Many mobile applications, such as online video, gaming, are essentially computation-intensive, and hence can quickly deplete mobile devices' battery energy. To address this issue, academia and industry have proposed mobile edge computing (MEC) that can enable mobile devices to automatically offload computations to the edge servers located within the radio access networks of cellular operators. However, energy-hungry wireless communications incur extra energy consumption that may offset the energy saving due to computation offloading. To this end, we design an energy-efficient autonomic offloading scheme by jointly considering the physical layer design and application running latency. Specifically, we first mathematically model the energy consumption of a mobile application in MEC environment by taking into account the energy consumption incurred by the interactions among the tasks for the same application, which is largely ignored by previous studies. Then, we identify task execution flows based on a task interaction matrix, and formulate the maximum of the task flow's latencies as the application's latency. Finally, we formulate an energyefficient offloading problem, which is generally NP-hard, and develop an efficient heuristic method to solve the problem. We present extensive simulation results to show that our proposed scheme can achieve significant reduction (up to 20% around) in energy consumption compared with previous schemes.
El presente articulo muestra el diseno de un dispositivo haptico concebido para aplicaciones biom... more El presente articulo muestra el diseno de un dispositivo haptico concebido para aplicaciones biomedicas, especificamente para medicina quirurgica en la que el operador, a traves del sentido del tacto, sienta y manipule objetos simulados en un ambiente tridimensional y tele-operado. La interfaz haptica que se presenta corresponde a un robot tipo serie, con una arquitectura de cuatro grados de libertad que le permite al usuario posicionar y orientar el efector final en el entorno de trabajo. Para el estudio de los movimientos del robot se parte del modelado geometrico y dinamico del mismo, hasta la implementacion de un controlador por par calculado. Finalmente, se realiza la simulacion de la interfaz haptica en un ambiente virtual. Abstract: This paper presents the design of haptic device that is conceived for biomedical applications. Specifically, the device can be used for surgery training allowing the user to feel and handle simulated objects a tridimensional and tele-operated envi...
1Laboratorio de Analítica de Datos, Departamento de Ingeniería en Sistemas de Información. Facult... more 1Laboratorio de Analítica de Datos, Departamento de Ingeniería en Sistemas de Información. Facultad Regional Mendoza/Universidad Tecnológica Nacional Rodríguez 273 (M5502AJE) Mendoza 2Laboratorio de Gobierno Electrónico, Departamento de Ingeniería en Sistemas de Información. Facultad Regional Mendoza/Universidad Tecnológica Nacional Rodríguez 273 (M5502AJE) Mendoza 3Laboratorio de Investigación en Cómputo Paralelo/Distribuido Departamento de Ingeniería en Sistemas de Información Facultad Regional Mendoza/Universidad Tecnológica Nacional Rodríguez 273 (M5502AJE) Mendoza, +54 261 5244579 4Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)
2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2016
One of the most salient features of smart city is to utilize big data to make our lives more conv... more One of the most salient features of smart city is to utilize big data to make our lives more convenient and more intelligent. This is usually achieved through solving a series of large-scale common and fundamental problems such as linear systems of equations, linear programs, etc. However, it is a very challenging task for resource-limited clients and small companies to solve such problems as the data volume keeps increasing. With cloud computing, an alternative is to solve complex problems by outsourcing them to the cloud. Nonetheless, data privacy is one of the main concerns. Many previous works on privacy-preserving outsourcing are based on cryptographic techniques like homomorphic encryption and have very high computational complexity, which may not be practical for big data applications. In this paper, we design an efficient privacy-preserving outsourcing algorithm based on arithmetic operations only for large-scale convex separable programming problems. Specifically, we first develop an efficient transformation scheme to preserve data privacy. Then we linearize the convex functions with arbitrary accuracy and solve the problem by outsourcing it to the cloud. The client can efficiently verify the correctness of the returned results to prevent any malicious behavior of the cloud. Implementations on Amazon Elastic Compute Cloud (EC2) platform show that the proposed scheme provides significant time savings.
IEEE Internet of Things Journal, 2017
Electric power systems are critical infrastructure and are vulnerable to contingencies including ... more Electric power systems are critical infrastructure and are vulnerable to contingencies including natural disasters, system errors, malicious attacks, etc. These contingencies can affect the world's economy and cause great inconvenience to our daily lives. Therefore, security of power systems has received enormous attention for decades. Recently, the development of the Internet of Things (IoT) enables power systems to support various network functions throughout the generation, transmission, distribution, and consumption of energy with IoT devices (such as sensors, smart meters, etc.). On the other hand, it also incurs many more security threats. Cascading failures, one of the most serious problems in power systems, can result in catastrophic impacts such as massive blackouts. More importantly, it can be taken advantage by malicious attackers to launch physical or cyber attacks on the power system. In this paper, we propose and investigate cascading failure attacks (CFAs) from a stochastic game perspective. In particular, we formulate a zerosum stochastic attack/defense game for CFAs while considering the attack/defense costs, budget constraints, diverse load shedding costs, and dynamic states in the system. Then, we develop a Q-CFA learning algorithm that works efficiently in power systems without any a priori information. We also formally prove that the convergence of the proposed algorithm achieves a Nash equilibrium. Simulation results validate the efficacy and efficiency of the proposed scheme by comparisons with other state-of-theart approaches.
IEEE Transactions on Big Data, 2017
Solving large-scale sparse linear systems of equations (SLSEs) is one of the most common and fund... more Solving large-scale sparse linear systems of equations (SLSEs) is one of the most common and fundamental problems in big data, but it is very challenging for resource-limited users. Cloud computing has been proposed as a timely, efficient, and cost-effective way of solving such expensive computing tasks. Nevertheless, one critical concern in cloud computing is data privacy. Specifically, clients' SLSEs usually contain private information that should remain hidden from the cloud for ethical, legal, or security reasons. Many previous works on secure outsourcing of linear systems of equations (LSEs) have high computational complexity, and do not exploit the sparsity in the LSEs. More importantly, they share a common serious problem, i.e., a huge number of memory I/O operations. This problem has been largely neglected in the past, but in fact is of particular importance and may eventually render those outsourcing schemes impractical. In this paper, we develop an efficient and practical secure outsourcing algorithm for solving large-scale SLSEs, which has low computational and memory I/O complexities and can protect clients' privacy well. We implement our algorithm on Amazon Elastic Compute Cloud, and find that the proposed algorithm offers significant time savings for the client (up to 74%) compared to previous algorithms.
Catheter System for Vascular Re-Entry from a Sub-Intimal Space
Catheter systems and methods for crossing vascular occlusions
Consideraciones sobre las variaciones de mediano y largo plazo de oleaje en el diseno de obras maritimas
2013 Proceedings IEEE INFOCOM, 2013
With great advances in mobile devices, e.g., smart phones and tablets, location-based services (L... more With great advances in mobile devices, e.g., smart phones and tablets, location-based services (LBSs) have recently emerged as a very popular application in mobile networks. However, since LBS service providers require users to report their location information, how to preserve users' location privacy is one of the most challenging problems in LBSs. Most existing approaches either cannot fully protect users' location privacy, or cannot provide accurate LBSs. Many of them also need the help of a trusted third-party, which may not always be available. In this paper, we propose a geometric approach, called -, to provide realtime accurate LBSs while preserving users' location privacy without involving any third-party. Specifically, we first divide a user's region of interest (ROI), which is a disk centered at the user's location, into equal sectors. Then, we generate concealing disks (CDs), one for each sector, one by one to collaboratively and fully cover each of the sectors. We call the area covered by the CDs the concealing space, which fully contains the user's ROI. After rotating the concealing space with respect to the user's location, we send the rotated centers of the CDs along with their radii to the service provider, instead of the user's real location and his/her ROI. To investigate the performance of -, we theoretically analyze its privacy level and concealing cost. Extensive simulations are finally conducted to evaluate the efficacy and efficiency of the proposed schemes.
Ciencia y Tecnología del …, 2004
2004 Sergio Salinas M. / Manuel Contreras L. / Juan Fierro C. PROPAGACIÓN DE LA ONDA DE MAREA EN ... more 2004 Sergio Salinas M. / Manuel Contreras L. / Juan Fierro C. PROPAGACIÓN DE LA ONDA DE MAREA EN EL ESTRECHO DE MAGALLANES Ciencia y Tecnología del Mar, , año/vol. 27, número 002 Comité Oceanográfico Nacional Valparaíso, Chile pp. 5-20
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Papers by Sergio Arturo Gutierrez Salinas