arXiv subjects

Farshid Farhat

Publications and source records attributed to Farshid Farhat.

5 recordsLinked to original sources

arXiv2018

CAPTAIN: Comprehensive Composition Assistance for Photo Taking

Many people are interested in taking astonishing photos and sharing with others. Emerging hightech hardware and software facilitate ubiquitousness and functionality of digital photography. Because composition matters in photography, researchers have leveraged some common composition techniques to assess the aesthetic quality of photos computationally. However, composition techniques developed by professionals are far more diverse than well-documented techniques can cover. We leverage the vast underexplored innovations in photography for computational composition assistance. We propose a comprehensive framework, named CAPTAIN (Composition Assistance for Photo Taking), containing integrated deep-learned semantic detectors, sub-genre categorization, artistic pose clustering, personalized aesthetics-based image retrieval, and style set matching. The framework is backed by a large dataset crawled from a photo-sharing Website with mostly photography enthusiasts and professionals. The work proposes a sequence of steps that have not been explored in the past by researchers. The work addresses personal preferences for composition through presenting a ranked-list of photographs to the user based on user-specified weights in the similarity measure. The matching algorithm recognizes the best shot among a sequence of shots with respect to the user's preferred style set. We have conducted a number of experiments on the newly proposed components and reported findings. A user study demonstrates that the work is useful to those taking photos.

cs.CV↗

arXiv2018

CARMA: Contention-aware Auction-based Resource Management in Architecture

As the number of resources on chip multiprocessors (CMPs) increases, the complexity of how to best allocate these resources increases drastically. Because the higher number of applications makes the interaction and impacts of various memory levels more complex. Also, the selection of the objective function to define what \enquote{best} means for all applications is challenging. Memory-level parallelism (MLP) aware replacement algorithms in CMPs try to maximize the overall system performance or equalize each application's performance degradation due to sharing. However, depending on the selected \enquote{performance} metric, these algorithms are not efficiently implemented, because these centralized approaches mostly need some further information regarding about applications' need. In this paper, we propose a contention-aware game-theoretic resource management approach (CARMA) using market auction mechanism to find an optimal strategy for each application in a resource competition game. The applications learn through repeated interactions to choose their action on choosing the shared resources. Specifically, we consider two cases: (i) cache competition game, and (ii) main processor and co-processor congestion game. We enforce costs for each resource and derive bidding strategy. Accurate evaluation of the proposed approach show that our distributed allocation is scalable and outperforms the static and traditional approaches.

cs.DC↗

arXiv2017

Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval

Linear perspective is widely used in landscape photography to create the impression of depth on a 2D photo. Automated understanding of linear perspective in landscape photography has several real-world applications, including aesthetics assessment, image retrieval, and on-site feedback for photo composition, yet adequate automated understanding has been elusive. We address this problem by detecting the dominant vanishing point and the associated line structures in a photo. However, natural landscape scenes pose great technical challenges because often the inadequate number of strong edges converging to the dominant vanishing point is inadequate. To overcome this difficulty, we propose a novel vanishing point detection method that exploits global structures in the scene via contour detection. We show that our method significantly outperforms state-of-the-art methods on a public ground truth landscape image dataset that we have created. Based on the detection results, we further demonstrate how our approach to linear perspective understanding provides on-site guidance to amateur photographers on their work through a novel viewpoint-specific image retrieval system.

cs.CV↗

arXiv2016

Optimal Placement of Cores, Caches and Memory Controllers in Network On-Chip

Parallel programming is emerging fast and intensive applications need more resources, so there is a huge demand for on-chip multiprocessors. Accessing L1 caches beside the cores are the fastest after registers but the size of private caches cannot increase because of design, cost and technology limits. Then split I-cache and D-cache are used with shared LLC (last level cache). For a unified shared LLC, bus interface is not scalable, and it seems that distributed shared LLC (DSLLC) is a better choice. Most of papers assume a distributed shared LLC beside each core in on-chip network. Many works assume that DSLLCs are placed in all cores; however, we will show that this design ignores the effect of traffic congestion in on-chip network. In fact, our work focuses on optimal placement of cores, DSLLCs and even memory controllers to minimize the expected latency based on traffic load in a mesh on-chip network with fixed number of cores and total cache capacity. We try to do some analytical modeling deriving intended cost function and then optimize the mean delay of the on-chip network communication. This work is supposed to be verified using some traffic patterns that are run on CSIM simulator.

cs.PF↗

arXiv2016

Towards Stochastically Optimizing Data Computing Flows

With rapid growth in the amount of unstructured data produced by memory-intensive applications, large scale data analytics has recently attracted increasing interest. Processing, managing and analyzing this huge amount of data poses several challenges in cloud and data center computing domain. Especially, conventional frameworks for distributed data analytics are based on the assumption of homogeneity and non-stochastic distribution of different data-processing nodes. The paper argues the fundamental limiting factors for scaling big data computation. It is shown that as the number of series and parallel computing servers increase, the tail (mean and variance) of the job execution time increase. We will first propose a model to predict the response time of highly distributed processing tasks and then propose a new practical computational algorithm to optimize the response time.

cs.DC↗