https://www.fox.temple.edu/directory/subodha-kumar-tuh48280
Biography Subodha Kumar is the Paul R. Anderson Distinguished Chair Professor of Statistics, Operations, and Data Science and the Founding Director of the Center for Business Analytics and Disruptive Technologies at Temple University’s Fox School of Business. He has a secondary appointment in Information Systems. He also serves as the Concentration Director for Ph.D. Program in Operations ...
https://cis.temple.edu/~mindyshi/
Candidate must have or be close to obtaining a Ph.D. in quantitative fields (include but not limited to Computer Science, Statistics, Mathematics, and Physics) and have demonstrated a high level of research productivity through publication in peer-reviewed conferences and journals. Experiences with machine learning or data privacy is required. As a team player working closely with cross ...
https://cis.temple.edu/~latecki/Courses/AI-Fall10/Lectures/ch5SVM.ppt
Outline What do we mean with classification, why is it useful Machine learning- basic concept Support Vector Machines (SVM) Linear SVM – basic terminology and some formulas Non-linear SVM – the Kernel trick An example: Predicting protein subcellular location with SVM Performance measurments Classification Everyday, all the time we classify things. Eg crossing the street: Is there a car ...
https://faculty.cst.temple.edu/~szyld/reports/randCholQR_rev2_report.pdf
In this paper, we first present and analyze a randomized algorithm called randQR for orthogonalizing the columns of a tall-and-skinny matrix with respect to a specific inner product. In order to reduce the cost of the computations, we propose to use “multisketching,” i.e., the use of two consecutive sketch matrices, within randQR. Using randQR with multisketching as a preconditioner for ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/ICC25_In-Kernel%20Traffic%20Sketching%20for%20Volumetric%20DDoS%20Detection-final.pdf
Abstract—Emerging network technologies like cloud comput-ing provide flexible services but also introduce vulnerabilities to host servers, such as exposure to Distributed Denial of Service (DDoS) attacks. Traditional host-based detection tools operate in the user space, which can delay detection because network traffic must pass through the kernel space first. Moving detection to the kernel ...
https://boyer.temple.edu/directory/helen-shoemark
Biography PhD, University of Melbourne, Australia MMEd, University of Kansas BM, University of Melbourne, Australia Dr. Helen Shoemark completed her training at The University of Melbourne and University of Kansas. She has more than 30 years’ experience as a clinical music therapist. Moving from special education and early intervention into pediatrics, she established the first program in ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/m37113-chen%20final.pdf
Abstract—Recent years have witnessed the unprecedented performance of convolutional networks in image super-resolution (SR). SR involves upscaling a single low-resolution image to meet application-specific image quality demands, making it vital for mobile devices. However, the excessive computational and memory requirements of SR tasks pose a challenge in mapping SR networks on a single ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/INFOCOM2024_PSFL%20Parallel-Sequential%20Federated%20Learning%20with%20Convergence%20Guarantees.pdf
Abstract—Federated Learning (FL) is a novel distributed learning paradigm which can coordinate multiple clients to jointly train a machine learning model by using their local data samples. Existing FL works can be roughly divided into two categories according to the modes of model training: Parallel FL (PFL) and Sequential FL (SFL). PFL can speed up each round of model training time through ...
https://www.fox.temple.edu/sites/fox/files/documents/CVs/xueming-luo-cv.pdf
Bio: Xueming Luo is the Charles Gilliland Distinguished Chair Professor of Marketing, Professor of Strategy, and Professor of MIS, and Founder/ Director of the Global Institute for Artificial Intelligence & Business Analytics in the Fox School of Business at Temple University. He is an interdisciplinary thought-leader in leveraging AI/ML algorithms, text/audio/image/video big data ...
https://cis.temple.edu/~latecki/Papers/GIFT-IEEEMM2017.pdf
Abstract—Projective analysis is an important solution in three-dimensional (3D) shape retrieval, since human visual perceptions of 3D shapes rely on various 2D observations from different viewpoints. Although multiple informative and discriminative views are utilized, most projection-based retrieval systems suffer from heavy computational cost, and thus cannot satisfy the basic requirement ...