Enabling efficient publicly verifiable outsourcing computation for matrix multiplication

Outsourcing heavy computational tasks to remote cloud server, which accordingly significantly reduce the computational burden at the end hosts, represents an effective and practical approach towards extensive and scalable mobile applications and has drawn increasing attention in recent years. However, due to the limited processing power of the end hosts yet the keen privacy concerns on the outsourced data, it is vital to ensure both the efficiency and security of the outsourcing computation in the cloud computing. In this paper, we address the issue by developing a publicly verifiable outsourcing computation proposal. In particular, considering a large amount of applications of matrix multiplication in large datasets and image processing, we propose a publicly verifiable outsourcing computation scheme for matrix multiplication in the amortized model. Security analysis demonstrates that the proposed scheme is provable secure by blinding input and output in a simple way. By comparing the developed scheme with existing proposals, we show that our proposal is more efficient in terms of functionality, as well as the computation, communication and storage overhead.

Enabling efficient and secure outsourcing of large matrix multiplications

With the growing popularity of cloud computing, outsourced computing has attracted much research effort recently. A computationally weak client is capable of delegating its heavy computing tasks, such as large matrix multiplications, to the cloud server. Critical requirements for such tasks include the need to guarantee the unforgeability of computing results and the preservation of the privacy of clients. On one hand, the result computed by the cloud server needs to be verified since the cloud server cannot be fully honest. On the other hand, as the data involved in computing may contain some sensitive information of the client, the data should not be identified by the cloud server. In this paper, we address these above issues by developing an Efficient and Secure Outsourcing scheme for Large Matrix Multiplication, named ESO- LMM. Security analysis demonstrates that ESO-LMM achieves the security requirements in terms of unforgeability of proof and privacy protection of outsourced data. Furthermore, performance evaluation indicates that ESO-LMM is much more efficient compared with the existing works in terms of computation, communication and storage overhead.

Supporting scientists in re-engineering sequential programs to parallel using model-driven engineering

Developing complex computational-intensiveand data-intensive scientific applications requires effectiveutilization of the computational power of the availablecomputing platforms including grids, clouds, clusters, multicoreand many-core processors, and graphical processingunits (GPUs). However, scientists who need to leverage suchplatforms are usually not parallel or distributed programmingexperts. Thus, they face numerous challenges whenimplementing and porting their software-based experimentaltools to such platforms. In this paper, we introduce asequential-to-parallel engineering approach to help scientistsin engineering their scientific applications. Our approach isbased on capturing sequential program details, plannedparallelization aspects, and program deployment details usinga set of domain-specific visual languages (DSVLs). Then, usingcode generation, we generate the corresponding parallelprogram using necessary parallel and distributedprogramming models (MPI, OpenCL, or OpenMP). Wesummarize three case studies (matrix multiplication, N-Bodysimulation, and signal processing) to evaluate our approach.

Optimization and matrix constructions for classification of data

Max-plus algebras and more general semirings have many useful applications and have been actively investigated. On the other hand, structural matrix rings are also well known and have been considered by many authors. The main theorem of this article completely describes all optimal ideals in the more general structural matrix semirings. Originally, our investigation of these ideals was motivated by applications in data mining for the design of centroid-based classification systems, as well as for the design of multiple classification systems combining several individual classifiers.

Parallel matrix inversion techniques

In this paper, we present techniques for inverting sparse, symmetric and positive definite matrices on parallel and distributed computers. We propose two algorithms, one for SIMD implementation and the other for MIMD implementation. These algorithms are modified versions of Gaussian elimination and they take into account the sparseness of the matrix. Our algorithms perform better than the general parallel Gaussian elimination algorithm. In order to demonstrate the usefulness of our technique, we implemented the snake problem using our sparse matrix algorithm. Our studies reveal that the proposed sparse matrix inversion algorithm significantly reduces the time taken for obtaining the solution of the snake problem. In this paper, we present the results of our experimental work.

Online blind source separation using incremental nonnegative matrix factorization with volume constraint

Online blind source separation (BSS) is proposed to overcome the high computational cost problem, which limits the practical applications of traditional batch BSS algorithms. However, the existing online BSS methods are mainly used to separate independent or uncorrelated sources. Recently, nonnegative matrix factorization (NMF) shows great potential to separate the correlative sources, where some constraints are often imposed to overcome the non-uniqueness of the factorization. In this paper, an incremental NMF with volume constraint is derived and utilized for solving online BSS. The volume constraint to the mixing matrix enhances the identifiability of the sources, while the incremental learning mode reduces the computational cost. The proposed method takes advantage of the natural gradient based multiplication updating rule, and it performs especially well in the recovery of dependent sources. Simulations in BSS for dual-energy X-ray images, online encrypted speech signals, and high correlative face images show the validity of the proposed method.

Physical activity of children: a global matrix of grades comparing 15 countries

The Active Healthy Kids Canada (AHKC) Report Card on Physical Activity for Children and Youth has been effective in poweringthe movement to get kids moving by influencing priorities, policies, and practice in Canada. The AHKC Report Card process wasreplicated in 14 additional countries from 5 continents using 9 common indicators (Overall Physical Activity, Organized SportParticipation, Active Play, Active Transportation, Sedentary Behavior, Family and Peers, School, Community and Built Environment,and Government Strategies and Investments), a harmonized process and a standardized grading framework. The 15 ReportCards were presented at the Global Summit on the Physical Activity of Children in Toronto on May 20, 2014. The consolidatedfindings are summarized here in the form of a global matrix of grades. There is a large spread in grades across countries for mostindicators. Countries that lead in certain indicators lag in others. Overall, the grades for indicators of physical activity (PA) aroundthe world are low/poor. Many countries have insufficient information to assign a grade, particularly for the Active Play and Familyand Peers indicators. Grades for Sedentary Behaviors are, in general, better in low income countries. The Community and BuiltEnvironment indicator received high grades in high income countries and notably lower grades in low income countries. There wasa pattern of higher PA and lower sedentary behavior in countries reporting poorer infrastructure, and lower PA and higher sedentarybehavior in countries reporting better infrastructure, which presents an interesting paradox. Many surveillance and researchgaps and weaknesses were apparent. International cooperation and cross-fertilization is encouraged to tackle existing challenges,understand underlying mechanisms, derive innovative solutions, and overcome the expanding childhood inactivity crisis.