Sharif Digital Repository

Recent years witnessed a steep rise in data generation and, consequently, the widespread adoption of software solutions able to support data-intensive applications. Many companies currently engage in data-intensive processes, however, fully embracing a data-driven paradigm is still cumbersome, and establishing a production-ready and fine-tuned deployment is time-consuming. This situation calls for innovative models and techniques to streamline the process of deployment configuration for Big Data applications. Moreover, many companies are using Cloud deployed clusters, which represent a cost-effective alternative to installation on premises. Accurate and fast prediction of the execution time... 

Companies depend on mining data to grow their business more than ever. To achieve optimal performance of Big Data analytics workloads, a careful configuration of the cluster and the employed software framework is required. The lack of flexible and accurate performance models, however, render this a challenging task. This paper fills this gap by presenting accurate performance prediction models based on Stochastic Activity Networks (SANs). In contrast to existing work, the presented models consider multiple work queues, a critical feature to achieve high accuracy in realistic usage scenarios. We first introduce a monolithic analytical model for a multi-queue YARN cluster running DAG-based Big... 

Companies depend on mining data to grow their business more than ever. To achieve optimal performance of Big Data analytics workloads, a careful configuration of the cluster and the employed software framework is required. The lack of flexible and accurate performance models, however, render this a challenging task. This article fills this gap by presenting accurate performance prediction models based on Stochastic Activity Networks (SANs). In contrast to existing work, the presented models consider multiple work queues, a critical feature to achieve high accuracy in realistic usage scenarios. We first introduce a monolithic analytical model for a multi-queue YARN cluster running DAG-based... 

Nowadays, many enterprises commit to the extraction of actionable knowledge from huge datasets as part of their core business activities. Applications belong to very different domains such as fraud detection or one-to-one marketing, and encompass business analytics and support to decision making in both private and public sectors. In these scenarios, a central place is held by the MapReduce framework and in particular its open source implementation, Apache Hadoop. In such environments, new challenges arise in the area of jobs performance prediction, with the needs to provide Service Level Agreement guarantees to the enduser and to avoid waste of computational resources. In this paper we... 

Porous gelatin-chitosan microcarriers (MCs) with the size of 350 ± 50 μm were fabricated with blends of different gelatin/chitosan (G/C) weight ratio using an electrospraying technique. Response surface methodology (RSM) was used to study the quantitative influence of process parameters, including blend ratio, voltage, and syringe pump flow rate, on MCs diameter and density. In the following, MCs of the same diameter and different G/C weight ratio (1, 2, and 3) were fabricated and their porosity and biocompatibility were investigated via SEM images and MTT assay, respectively. The results showed that mesenchymal stem cells (MSCs) could attach, proliferate, and spread on fabricated porous MCs... 

In large databases, availability of labeled training data is mostly prohibitive in classification. Semi-supervised algorithms are employed to tackle the lack of labeled training data problem. Video databases are the epitome for such a scenario; that is why semi-supervised learning has found its niche in it. Graph-based methods are a promising platform for semi-supervised video classification. Based on the multiview characteristic of video data, different features have been proposed (such as SIFT, STIP and MFCC) which can be utilized to build a graph. In this paper, we have proposed a new classification method which fuses the results of manifold regularization over different graphs. Our... 

Numerical simulation of resin transfer molding (RTM) is known as a useful method to analyze the process before the mold is actually built. In thick parts, the resin flow is no longer two-dimensional and must be simulated in a fully three-dimensional space. This article presents numerical simulations of three-dimensional non-isothermal mold filling of the RTM process. The control volume/finite element method (CV/FEM) is used in this study. Numerical formulation for resin flow is based on the concept of nodal partial saturation at the flow front. This approach permits to include a transient term in the working equation, removing the need for calculation of time step to track the flow front in... 

A process cycle of resin transfer molding (RTM) consists of two sequential stages, i.e. filling and curing stages. These two stages are interrelated in non-isothermal processes so that the curing stage is dominated by the resin flow as well as temperature and conversion distributions during the filling stage. Therefore, it is necessary to take into account both filling and curing stages to analyze the process cycle accurately. In this paper, a full three-dimensional process cycle simulation of RTM is performed. Full three-dimensional analysis is necessary for thick parts or parts having complex shape. A computer code is developed based on the control volume/finite element method (CV/FEM).... 

This paper contains analysis and simulation of recent dynamic models, describing human emotion. The pharmacological discussions lead to a new model of drug taking, which also have a better performance for description of psychological and psychiatric phenomena. Studying the effects of the order of fractional model, obtains an advantage of the fractional order model over the integer order one  

Voluminous amount of videos in today’s world has made the subject of objective (or semi-objective) classification of videos to be very popular. Among the various descriptors used for video classification, SIFT and LIFT can lead to highly accurate classifiers. But, SIFT descriptor does not consider video motion and LIFT is time-consuming. In this paper, a robust descriptor for semi-supervised classification based on video content is proposed. It holds the benefits of LIFT and SIFT descriptors and overcomes their shortcomings to some extent. For extracting this descriptor, the SIFT descriptor is first used and the motion of the extracted keypoints are then employed to improve the accuracy of... 

The 2D hypersonic real gas flow has been analyzed on an adaptive unstructured grid using Roe's Flux Difference Splitting and AUSM schemes. In high temperature and hypersonic regime, the flow is extremely compressible and ideal gas assumption is not valid. In fact in these flows, due to changes in the flow properties, composition of fluid elements will also change. To solve steady and unsteady 2D Euler' equations for real gases, assumption of a general equation of state for real gases in equilibrium is considered. We use an unstructured Delaunay triangulation and adapt it in high gradient areas. Results are compared with known numerical and exact solutions. The scheme is convergent, and... 

Unidirectional experiments were carried out for the evaluation of the unsaturated permeability of the reinforcement during the injection and the saturated permeability. Saturated permeability was found to govern the permanent flow established after filling of the cavity. The unsaturated permeability was evaluated by a linear regression in function of the position of the resin front during transient flow experiments performed at constant injection pressure. The difference between these two permeability values is also related to the pore structure of the fiber bed  

Vehicular edge computing is a progressing technology which provides processing resources to the internet of vehicles using the edge servers deployed at roadside units. Vehicles take advantage by offloading their computationintensive tasks to this infrastructure. However, concerning time-sensitive applications and the high mobility of vehicles, cost-efficient task offloading is still a challenge. This paper establishes a computation offloading strategy based on deep Q-learning algorithm for vehicular edge computing networks. To jointly minimize the system cost including offloading failure rate and the total energy consumption of the offloading process, the vehicle tasks offloading problem is... 

Data stream prediction is challenging when concepts drift, processing time, and memory constraints come into account. Concept drift refers to changes in data distribution over time that reduces prediction systems’ accuracy. We present a method for handling concept drift with a domain adaptation approach (CDDA) in a data stream. The proposed method passively deals with the concept drift by using the domain adaptation approaches with multiple sources while reducing the model execution time and memory consumption. We introduce two variants of CDDA to transfer the information in the multi-source windows to the target window: weighted multi-source CDDA and multi-source feature alignment CDDA.... 

The matching basis in field extentions is in-troduced by S. Eliahou and C. Lecouvey in [2]. In this paper we define the minimal and maximal linear matching property for field extensions and prove that if K is not algebraically closed, then K has minimal linear matching property. In this paper we will prove that algebraic number fields have maximal linear matching property. We also give a shorter proof of a result established in [6] on the fundamental theorem of algebra