Image Retrieval, Smart Cropping, Aesthetic Scoring, Photography

Article pages:

http://sites.psu.edu/farshidfarhat/2011/05/01/risk-of-attack-coefficient-effect-on-availability-of-ad-hoc-networks/risk_of_attack_coefficient_effect_on_availability-14in557/

http://sites.psu.edu/farshidfarhat/2011/05/01/game-theoretic-approach-to-mitigate-packet-dropping-in-wireless-ad-hoc-networks/gametheoretic_approach_to_mitigate_packet_droppin-1dq28nd/

http://sites.psu.edu/farshidfarhat/2011/05/01/multi-dimensional-correlation-steganalysis/mdcs-2amhh8o/

http://sites.psu.edu/farshidfarhat/2011/06/01/image-steganalysis-based-on-svd-and-noise-estimation-improve-sensitivity-to-spatial-lsb-embedding-families/is_svd_ns-wqe9jh/

http://sites.psu.edu/farshidfarhat/2012/09/01/eigenvalues-based-lsb-steganalysis/evsteg-21gioiz/

http://sites.psu.edu/farshidfarhat/piakap-2mno7yt/

http://sites.psu.edu/farshidfarhat/2015/11/01/evaluating-the-combined-impact-of-node-architecture-and-cloud-workload-characteristics-on-network-traffic-and-performancecost/iiswc-1ngzkee/

http://sites.psu.edu/farshidfarhat/2017/05/01/detecting-dominant-vanishing-points-in-natural-scenes-with-application-to-composition-sensitive-image-retrieval/vp-labels-1rfu6wi/

http://sites.psu.edu/farshidfarhat/2015/07/07/fork-join-queue-modeling-and-optimal-scheduling-in-parallel-programming-frameworks/ms_thesis_mrmo_v22-2hocxhk/

http://sites.psu.edu/farshidfarhat/2015/07/01/optimal-placement-of-cores-caches-and-memory-controllers-in-network-on-chip/noc-2an8f6v/

http://sites.psu.edu/farshidfarhat/2014/07/01/modeling-and-optimization-of-straggling-mappers/mrmo_cloud_v23-28rj6wz/

http://sites.psu.edu/farshidfarhat/2017/05/26/discovering-triangles-in-portraits-for-supporting-photographic-creation/tmm17-portrait-11q12gt/

http://sites.psu.edu/farshidfarhat/2017/05/22/skeleton-matching-with-applications-in-severe-weather-detection/skeletonmatching_kamani-1mcrsox/

http://sites.psu.edu/farshidfarhat/2017/05/01/detecting-dominant-vanishing-points-in-natural-scenes-with-application-to-composition-sensitive-image-retrieval/vp-landscape-21ti4it/

http://sites.psu.edu/farshidfarhat/2016/12/05/shape-matching-using-skeleton-context-for-automated-bow-echo-detection/bow_echo_detection-2id499d/

http://sites.psu.edu/farshidfarhat/2017/10/05/cage-a-contention-aware-game-theoretic-model-for-heterogeneous-resource-assignment/pid5002569-u4y18l/

http://sites.psu.edu/farshidfarhat/2017/08/24/calculate-att-shares-in-a-shared-data-plan/att_2017_08-1g4fma2/

Pix pages:

http://sites.psu.edu/farshidfarhat/2015/07/07/fork-join-queue-modeling-and-optimal-scheduling-in-parallel-programming-frameworks/picture1-1w1a9lz/

http://sites.psu.edu/farshidfarhat/dsc03966-1d0lk5l/

http://sites.psu.edu/farshidfarhat/dsc03983-1yr7ewy/

http://sites.psu.edu/farshidfarhat/cropped-aaeaaqaaaaaaaamjaaaajdljy2nlzwfiltu3odqtngy5ms04zjkxlwi3mdg0n2yxn2u4oa-10shm9c-jpg/

http://sites.psu.edu/farshidfarhat/2010/04/25/locally-multipath-adaptive-routing-protocol-resilient-to-selfishness-and-wormholes/lmar-2m4133b/

http://sites.psu.edu/farshidfarhat/2009/04/25/an-extended-authentication-and-key-agreement-protocol-of-umts/eakap-2iv1ct0/

http://sites.psu.edu/farshidfarhat/2013/12/10/performance-modeling-and-optimization-of-mapreduce/mrmo_mascots_v10-194k61g/

http://sites.psu.edu/farshidfarhat/2015/12/15/thumbnail-generation-by-smart-cropping/3_cropped-2a1baam/

http://sites.psu.edu/farshidfarhat/2015/12/15/thumbnail-generation-by-smart-cropping/3-2d6lari/

http://sites.psu.edu/farshidfarhat/2015/12/15/thumbnail-generation-by-smart-cropping/eagle-1l7s0ua/

http://sites.psu.edu/farshidfarhat/2015/12/15/thumbnail-generation-by-smart-cropping/eagle_cr-2a5ec52/

personal.psu.edu/fuf111

Publications

• Discovering Triangles in Portraits for Supporting Photographic Creation, S He, Z Zhou, F Farhat, JZ Wang, IEEE Transactions on Multimedia, Aug 2017.

• Intelligent Portrait Composition Assistance — Integrating Deep-learned Models and Photography Idea Retrieval, F Farhat, MM Kamani, S Mishra, JZ Wang, July 2017.

• Skeleton Matching with Applications in Severe Weather Detection, MM Kamani, F Farhat, S Wistar, JZ Wang, Applied Soft Computing, May 2017.

• Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval, Z Zhou, F Farhat, JZ Wang, IEEE Transactions on Multimedia, May 2017.

• Shape matching using skeleton context for automated bow echo detection, MM Kamani, F Farhat, S Wistar, JZ Wang, Big Data (Big Data), 2016 IEEE International Conference on, 901-908, Dec 2016.

• Detecting Vanishing Points in Natural Scenes with Application in Photo Composition Analysis, Z Zhou, F Farhat, JZ Wang, arXiv preprint arXiv:1608.04267, 2016.

• Optimal Placement of Cores, Caches and Memory Controllers in Network On-Chip, DZ Tootaghaj, F Farhat, arXiv preprint arXiv:1607.04298, 2016.

• Towards Stochastically Optimizing Data Computing Flows, F Farhat, DZ Tootaghaj, M Arjomand, arXiv preprint arXiv:1607.04334, 2016.

• Stochastic Modeling and Optimization of Stragglers, F Farhat, D Zad Tootaghaj, Y He, et al. IEEE Transaction on Cloud Computing, 2016.

• Fork-Join Queue Modeling and Optimal Scheduling in Parallel Programming Frameworks, F Farhat, EECS Thesis, The Pennsylvania State University, 2015.

• Evaluating the Combined Impact of Node Architecture and Cloud Workload Characteristics on Network Traffic and Performance/Cost, DZ Tootaghaj, F Farhat, M Arjomand, P Faraboschi et al. Workload characterization (IISWC), 2015 IEEE international symposium on, 203-212, 2015.

• Towards blind detection of low-rate spatial embedding in image steganalysis, F Farhat, S Ghaemmaghami, IET Image Processing 9 (1), 31-42, 2015.

• Modeling and optimization of straggling mappers, F Farhat, DZ Tootaghaj, A Sivasubramaniam, MT Kandemir, CR Das, Technical report, Technical Report CSE-14-006, Pennsylvania State University, 2014.

• Security Weaknesses in PGP Protocol, F Farhat, M Heydari, MR Aref, 2013.

• Eigenvalues-based LSB steganalysis, F Farhat, A Diyanat, S Ghaemmaghami, MR Aref, The ISC International Journal of Information Security 4 (2), 97-106, 2012.

• Image steganalysis based on SVD and noise estimation: Improve sensitivity to spatial LSB embedding families, A Diyanat, F Farhat, S Ghaemmaghami, TENCON 2011-2011 IEEE Region 10 Conference, 1266-1270, 2011.

• Multi-dimensional correlation steganalysis, F Farhat, A Diyanat, S Ghaemmaghami, MR Aref, Multimedia Signal Processing (MMSP), 2011 IEEE 13th International Workshop, 2011.

• Game-theoretic approach to mitigate packet dropping in wireless Ad-hoc networks, DZ Tootaghaj, F Farhat, MR Pakravan, MR Aref, Consumer Communications and Networking Conference (CCNC), 2011 IEEE, 163-165, 2011.

• Risk of attack coefficient effect on availability of Ad-hoc networks, DZ Tootaghaj, F Farhat, MR Pakravan, MR Aref, Consumer Communications and Networking Conference (CCNC), 2011 IEEE, 166-168, 2011.

• Private Identification, Authentication and Key Agreement Protocol with Security Mode Setup, F Farhat, S Salimi, A Salahi, IACR Cryptology ePrint Archive, 2011.

• Locally Multipath Adaptive Routing Protocol Resilient to Selfishness and Wormholes, F Farhat, MR Pakravan, M Salmasizadeh, MR Aref, International Conference on Information Security Practice and Experience, 2010.

• An Extended Authentication and Key Agreement Protocol of UMTS, F Farhat, S Salimi, A Salahi, International Conference on Information Security Practice and Experience, 2009.

 

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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.

Full manuscript > piakap

This entry was posted in ePrint, Research, Security and tagged Ahmad Salahi, Authentication, Authentication and Key Agreement Protocol in 4G, Identification, Key Agreement, Mobile Network, PIAKAP, Privacy, Security Protocol, Somayeh Salimi, UMTS on March 25, 2011.

Sites@PennState

Dig Data Computing

Big Visual Data, Image Retrieval and Deep Learning

Deep Learning at Penn State

Smart Cropping, Aesthetic Scoring, Vision and Recognition

Computer Vision and Pattern Recognition

Distributed and Parallel Systems, Network Security

STUDENT ASSOCIATION AT PENNSYLVANIA STATE UNIVERSITY

Big Visual Data Computing, Image Retrieval and Deep Learning, Smart Cropping, Aesthetic Scoring, Portrait Photography, Computer Vision and Pattern Recognition, Distributed and Parallel Systems, Network Security

Deep Learning in Photography

Intelligent Portrait Composition Assistance, Farshid Farhat, Mohammad Mahdi Kamani, Sahil Mishra, James Wang, ACM Multimedia 2017, Mountain View, CA, USA.

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.

Deep-learned Models and Photography Idea Retrieval

Art, Computer Vision, Conference Paper, Deep Learning, Image Processing, Machine Learning, Pattern Recognition, Photography, Research, Thesis and tagged 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.

Maryam Mirzakhani

Maryam Mirzakhani was first women to win maths' Fields Medal. She would be in our mind forever. #MaryamMirzakhani

in Farshid Farhat 's Twitter

Deep Learning @ Penn State

Integrating Deep-learned Models and Photography Idea Retrieval

Intelligent Portrait Composition Assistance (IPCA) – Integrating Deep-learned Models and Photography Idea Retrieval, Farshid Farhat, Mohammad Kamani, Sahil Mishra, James Wang, ACM Multimedia 2017, Mountain View, CA, USA.

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 perspectiveness, 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.

Full Text @ intelligent-portrait-composition-assistance-deep-learning-image-retrieval

Art, Computer Vision, Conference Paper, Deep Learning, Image Processing, Machine Learning, Pattern Recognition, Photography, Research, Thesis and tagged 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