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.


CAPTAIN: Comprehensive Composition Assistance for Photo Taking

MODELING, MONITORING AND SCHEDULING TECHNIQUES FOR NETWORK RECOVERY FROM MASSIVE FAILURES

MODELING, MONITORING AND SCHEDULING TECHNIQUES FOR NETWORK RECOVERY FROM MASSIVE FAILURES

Author:

Zad Tootaghaj, Diman

Graduate Program:

Computer Science and Engineering

Degree:

Doctor of Philosophy

Document Type:

Dissertation

Date of Defense:

May 23, 2018

Committee Members:

• Thomas F Laporta, Dissertation Advisor
• Thomas F Laporta, Committee Chair
• Ting He, Committee Member
• Nilanjan Ray Chaudhuri, Committee Member
• Marek Flaska, Outside Member

Keywords:

• Network Recovery
• Massive Disruption
• Stochastic Optimization
• Uncertainty
• Network Recovery Massive Disruption
• Uncertainty.
• Cascading Failures
• Interdependent Networks
• Power Grid
• Software-Defined Networking

Abstract:

This dissertation explores modeling, monitoring and scheduling techniques for network recovery from massive failures, with a focus on optimization methods under uncertain knowledge of failures. Large-scale failures in communication networks due to natural disasters or malicious attacks can severely affect critical communications and threaten lives of people in the affected area. In 2005, Hurricane Katrina led to outage of over 2.5 million lines in the BellSouth (now AT&T) network. In the absence of a proper communication infrastructure, rescue operation becomes extremely difficult. Progressive and timely network recovery is, therefore, a key to minimizing losses and facilitating rescue missions. Many prior works on failure detection and recovery assume full knowledge of failures and use a deterministic approach for the recovery phase. In real-world scenarios, however, the failure pattern might be unknown or only partially known. Therefore, classic recovery approaches may not work. To this end, I focus on network recovery assuming partial and uncertain knowledge of the failure locations. I first studied large-scale failures in a communication network. In particular, I proposed a new recovery approach under uncertain knowledge of failures. I proposed a progressive multi-stage recovery approach that uses the incomplete knowledge of failure to find a feasible recovery schedule. From the elements of this solution, I selected a node with highest centrality at each iteration step to repair and exploit as a monitor to increase the knowledge of network state, until all critical services are restored. The recovery problem can be addressed by giving different priority to three performance aspects including: 1) Demand loss, 2) computation time and 3) number of repairs (or repair cost). These aspects are in conflict with each other and I studied the trade-off among them. Next, I focused on failure recovery of multiple interconnected networks. In particular, I focused on the interaction between a power grid and a communication network. I modeled the cascading failures in a power gird using a DC power flow model. I tackled the problem of mitigating an ongoing cascade by formulating the minimum cost flow assignment problem as a linear programming optimization. The optimization aimed at finding a minimum cost DC power flow setting that stops the cascading failure, where the total cost is defined as the total weighted amount of unsatisfied load due to the re-distribution of the power in the generators and loads without violating the overload constraint at each line. Then, I focused on network monitoring techniques that can be used for diagnosing the performance of individual links for localizing soft failures (e.g. highly congested links) in a communication network. I studied the optimal selection of the monitoring paths to balance identifiability and probing cost. I considered four closely related optimization problems: (1) Max-IL-Cost that maximizes the number of identifiable links under a probing budget, (2) Max-Rank-Cost that maximizes the rank of selected paths under a probing budget, (3) Min-Cost-IL that minimizes the probing cost while preserving identifiability, and (4) Min-Cost-Rank that minimizes the probing cost while preserving rank. I showed that while (1) and (3) are hard to solve, (2) and (4) possess desirable properties that allow efficient computation, while providing good approximation to (1) and (3). I proposed an optimal greedy-based approach for (4) and proposed a (1-1/e)-approximation algorithm for (2). My experimental analysis revealed that, compared to several greedy approaches that directly solve the identifiability-based optimization (i.e. (1) and (3)), the proposed rank-based optimization (i.e. (2) and (4)) achieved better trade-offs in terms of identifiability and probing cost. Finally, I addressed, a minimum disruptive routing framework in software defined networks. I showed that flow disruption, congestion and violation of policies can occur during updates of flow tables in software defined networks. I aimed to minimize the update disruption and minimize the number of affected flows during the update, while taking into account link capacity constraints and the importance of various flows to upper-layer applications. I formulated the problem as an integer linear programming and showed that it is NP-Hard. I proposed two randomized rounding algorithms with bounded congestion and demand loss to solve this problem. In addition to a small SDN testbed, I performed a large-scale simulation study to evaluate my proposed approaches on real network topologies. Extensive experimental and simulation results show that the two random rounding approaches have a disruption cost close to the optimal while incurring a low congestion factor and a low demand loss.

New trend in Computational Photography

CAPTAIN: Comprehensive Composition Assistance for Photo Taking

Auction-based Resource Management in Computer Architecture
Professional portrait dataset
Enhanced Signal Recovery via Sparsity Inducing Image Priors

System and methods for UICC-based secure communication

System and methods for UICC-based secure communication US 9461993 B2
System and methods for uicc-based secure communication US 20150222631 A1

ABSTRACT
A system that incorporates the subject disclosure may include, for example, instructions which when executed cause a device processor to perform operations comprising sending a service request to a remote management server; receiving from the management server an authentication management function and an encryption key generator for execution by a secure element and an encryption engine for execution by a secure device processor, sending a request to establish a communication session with a remote device; and communicating with the remote device via a channel established using an application server. The secure element and the secure device processor authenticate each other using a mutual authentication keyset. The secure element, the secure device processor and the device processor each have a security level associated therewith; the security level associated with the secure device processor is intermediate between that of the secure element and that of the device processor. Other embodiments are disclosed.

Reference
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2		"3rd Generation Partnership Project; Technical Specification Group Core Network and Terminals; Remote APDU Structure for (U)SIM Toolkit applications", Release 10, 2012.
3		"3rd Generation Partnership Project; Technical Specification Group Core Network and Terminals; Secured packet structure for (Universal) Subscriber Identity Module (U)SIM Toolkit applications", Release 10, 2012.
4		"3rd Generation Partnership Project; Technical Specification Group Core Network and Terminals; UICC-terminal interface; Physical and logical characteristics", Release 10, 2011.
5		"3rd Generation Partnership Project; Technical Specification Group Core Network and Terminals; Universal Subscriber Identity Module (USIM) Application Toolkit (USAT)", Release 11, 2012.
6		"GlobalPlatform Card Confidential Card Content Management Card Specification v2.2-Amendment A", 2011.
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System and methods for UICC-based secure communication
SYSTEM AND METHODS FOR UICC-BASED SECURE COMMUNICATION 20150222631

ACQUINE

Aesthetic Rating for Photos

The developed engine gets the image and outputs a score showing the amount of the beauty inside the image! The code is also working on Android/iOS platform. The engine speed is good enough which makes it suitable for a real-time engine. The core computes aesthetic features from the image, and estimates the aesthetic score. Check out > ACQUINE

The contributors: Jia Li, James Wang, Farshid Farhat.

Computer Vision, Demo, Development, iOS, Research ACQUINE, Aesthetic Feature Extraction, Aesthetic Rating, Aesthetics Scoring, ALiPR, Android, CEO, CEOx2, GPU, Image Aesthetics, iOS, James Wang, Jia Li, Renderscript, Scoring Engine, Simplr

Contention-Aware Game-theoretic Model for Heterogeneous Resource Assignment

CAGE: A Contention-Aware Game-theoretic Model for Heterogeneous Resource Assignment

Traditional resource management systems rely on a centralized approach to manage users running on each resource. The centralized resource management system is not scalable for large-scale servers as the number of users running on shared resources is increasing dramatically and the centralized manager may not have enough information about applications' need. In this paper we propose a distributed game-theoretic resource management approach using market auction mechanism to find optimal strategy in a resource competition game. The applications learn through repeated interactions to choose their action on choosing the shared resources. Specifically, we look into two case studies of cache competition game and 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.

Draft > CAGE

Big Data, Conference Paper, Research Auction Theory, Biding Strategy, Computer Architecture, Contention Aware, Diman Tootaghaj, Distributed Resource Management, Game Theory, Resource Allocation, Resource Assignment, Resource Sharing on October 5, 2017.

Modeling and Optimization of MapReduce

ABSTRACT

MapReduce framework is widely used to parallelize batch jobs since it exploits a high degree of multi-tasking to process them. However, it has been observed that when the number of mappers increases, the map phase can take much longer than expected. This paper analytically shows that stochastic behavior of mapper nodes has a negative effect on the completion time of a MapReduce job, and continuously increasing the number of mappers without accurate scheduling can degrade the overall performance. We analytically capture the effects of stragglers (delayed mappers) on the performance. Based on an observed delayed exponential distribution (DED) of the response time of mappers, we then model the map phase by means of hardware, system, and application parameters. Mean sojourn time (MST), the time needed to sync the completed map tasks at one reducer, is mathematically formulated. Following that, we optimize MST by finding the task inter-arrival time to each mapper node. The optimal mapping problem leads to an equilibrium property investigated for different types of inter-arrival and service time distributions in a heterogeneous datacenter (i.e., a datacenter with different types of nodes). Our experimental results show the performance and important parameters of the different types of schedulers targeting MapReduce applications. We also show that, in the case of mixed deterministic and stochastic schedulers, there is an optimal scheduler that can always achieve the lowest MST.

[Tech Report] [Master Thesis] [IEEE Trans]

Last version > MapReduce_Performance_Optimization

This entry was posted in Conference Paper, Journal Paper, Research, Technical Report, Thesis Diman Tootaghaj, Fork-Join Queue, Mapper, MapReduce, Mean Sojourn Time, Modeling, MST, Reducer, Stochastic Optimization, Straggler, Straggling Mapper

PIAKAP

Authentication and Key Agreement Protocol in 4G

Private Identification, Authentication and Key Agreement Protocol with Security Mode Setup, Farshid Farhat, Somayeh Salimi, Ahmad Salahi.

Abstract
Identification, authentication and key agreement protocol of UMTS networks with security mode setup has some weaknesses in the case of mutual freshness of key agreement, DoS-attack resistance, and efficient bandwidth consumption. In this article we consider UMTS AKA and some other proposed schemes. Then we explain the known weaknesses of the previous frameworks suggested for the UMTS AKA protocol. After that we propose a new protocol called private identification, authentication, and key agreement protocol (PIAKAP), for UMTS mobile network. Our suggested protocol combines identification and AKA stages of UMTS AKA protocol while eliminates disadvantages of related works and brings some new features to improve the UMTS AKA mechanism. These features consist of reducing the interactive rounds of the UMTS AKA with security mode setup and user privacy establishment.

ePrint, Research, Security and tagged Ahmad Salahi, Authentication, Identification, Key Agreement, Mobile Network, PIAKAP, Privacy, Security Protocol, Somayeh Salimi, UMTS on March 25, 2011.

Bid Data Analytics

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Professional Photography using Deep Learning

ABSTRACT: Retrieving photography ideas corresponding to a given location facilitates the usage of smart cameras, where there is a high interest among amateurs and enthusiasts to take astonishing photos at anytime and in any location. Existing research captures some aesthetic techniques such as the rule of thirds, triangle, and perspective-ness, and retrieves useful feedbacks based on one technique. However, they are restricted to a particular technique and the retrieved results have room to improve as they can be limited to the quality of the query. There is a lack of a holistic framework to capture important aspects of a given scene and give a novice photographer informative feedback to take a better shot in his/her photography adventure. This work proposes an intelligent framework of portrait composition using our deep-learned models and image retrieval methods. A highly-rated web-crawled portrait dataset is exploited for retrieval purposes. Our framework detects and extracts ingredients of a given scene representing as a correlated hierarchical model. It then matches extracted semantics with the dataset of aesthetically composed photos to investigate a ranked list of photography ideas, and gradually optimizes the human pose and other artistic aspects of the composed scene supposed to be captured. The conducted user study demonstrates that our approach is more helpful than the other constructed feedback retrieval systems.

Art, Computer Vision, Conference Paper, Deep Learning, Image Processing, Machine Learning, Pattern Recognition, Photography, Research, Thesis ACM Multimedia, ACMM, Aesthetics, AI, Art Theory, Artificial Intelligence, CBIR, Computer Vision, Content-based Image Retrieval, Deep Learning, Deep-Learned Model Transfer, Human Pose, Human Pose Estimation, Image Aesthetics, Image Processing, Image Retrieval, Intelligent Portrait Composition Assistance, IPCA, Machine Learning, Object Detection, Pattern Recognition, Penn State Deep Learning, Photography, Photography Idea, Portrait, Portrait Composition, Portrait Dataset, Portrait Photography, Pose Estimation, Pose Recommendation, Scene Parsing, Step-by-Step-Guide on July 2, 2017.