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... 

In this paper, the Becchi-Rouet-Stora-Tyutin (BRST) symmetry transformation is presented for the noncommutative U(N) gauge theory. The nilpotency of the charge associated with this symmetry is then proved. As a consequence of the spacelike noncommutativity parameter, the Hubert space of physical states is determined by the cohomology space of the BRST operator as in the commutative case. Further, the unitarity of the S-matrix elements projected onto the subspace of the physical states is deduced. © 2003 The American Physical Society  

In this paper, the Becchi-Rouet-Stora-Tyutin (BRST) symmetry transformation is presented for the noncommutative [Formula Presented] gauge theory. The nilpotency of the charge associated with this symmetry is then proved. As a consequence of the spacelike noncommutativity parameter, the Hilbert space of physical states is determined by the cohomology space of the BRST operator as in the commutative case. Further, the unitarity of the S-matrix elements projected onto the subspace of the physical states is deduced. © 2003 The American Physical Society  

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... 

The aim of this study was to determine the workspace of surgeon's body for designing more efficient surgical robots in the operation rooms. Five wearable inertial sensors were placed near the wrist and elbow joints and also on the thorax of surgeons to track the orientation of upper limb. Assuming that the lengths of five segments of an upper limb were known, measurements of the inertial sensors were used to determine the position of the wrist and elbow joints via an established kinematic model. subsequently, to assess the workspace of surgeon upper body, raw data were collected in the arthroscopy and laparoscopy operations. Experimental results demonstrated that the workspaces of surgeon's... 

When properties of the displacing fluid are considered, the rising profile of the penetrating fluid in a capillary tube deviates from its classical Lucas-Washburn profile. Also, shape of capillary tube can affect the rising profile in different aspects. In this article, effect of capillary tube's shape on the vertical capillary motion in presence of gravity is investigated by considering the properties of the displacing fluid. According to the fact that the differential equation of the capillary rising for a non-simple wall type is very difficult to solve analytically, a finite element simulation model is used for this study. After validation of the simulation model with an experiment that... 

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.... 

A novel method based on EEG nonlinear analysis and analysis of steady-state visual evoked potentials (SSVEPs) has been processed. The EEG phase space is reconstructed, and some new geometrical features are extracted. Statistical analysis is carried out based on ANOVA, and most significant features are selected and then fed into a multi-class support vector machine (MSVM). Both offline and online phases are considered to fully address SSVEP detection. In the offline mode, the whole design evaluation, feature selection, and classifier training are performed. In the online scenario, the proposed method is evaluated and the detection rate is reported for both phases. Subject-dependent and... 

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... 

Multi-sensor tracking is widely used for augmentation of tracking accuracy using data fusion. A basic requirement for such applications is the real time temporal synchronization and spatial registration of two sensory data. In this study a new method for time and space coordination of two tracking sensor measurements has been presented. For spatial registration we used a body coordinate system and then applied the effect of the level arm. The time synchronization was done based on least mean square (LMS) error method. This method was implemented to synchronize the position and orientation of an object using Inertial (1IMU) and Optical (Optotrak) tracking systems. The results of synchronized... 

The fusion of the optical and inertial tracking systems seems an attractive solution to solve the shadowing problem of the optical tracking systems, and remove the time integration troubles of the inertial sensors. We developed a fusion algorithm for this purpose, based on the Kalman filter, and examined its efficacy to improve the position and orientation data, obtained by each individual system. Experimental results indicated that the proposed fusion algorithm could effectively estimate the 2 seconds missing data of the optical tracker