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 ...
https://cis.temple.edu/~latecki/Papers/Quan_DRBANET_ICIP_2022.pdf
ABSTRACT Due to the powerful ability to encode image details and semantics, many lightweight dual-resolution networks have been proposed in recent years. However, most of them ignore the benefit of boundary information. This paper introduces a lightweight dual-resolution network, called DRBANet, aim-ing to refine semantic segmentation results with the aid of boundary information. DRBANet also ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/Paper%206750%20Camera%20Ready%20Version.pdf
1 Introduction Federated learning (FL) [Koneˇcn ́y et al., 2017], a widely-used framework for distributed machine learning, is a signif-icant research focus. Most FL algorithms, such as the clas-sic FedAvg, fall into Synchronous Federated Learning (SFL). They require the server to wait for all selected clients’ lo-cal training and uploads before aggregating updates, and as-sume uniform ...
https://cis.temple.edu/~latecki//Courses/CIS166-Spring07/Lectures/ch7.4.pdf
Section 7.4 Closures of Relations Definition: The closure of a R relation with respect property P is the relation number of ordered R to obtain pairs property to
https://cis.temple.edu/~tug29203/21spring-3329/lectures/ch4a.pdf
Chapter 4 Network Layer: The Data Plane A note on the use of these Powerpoint slides:
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://cis.temple.edu/~tug29203/24spring-4319/lectures/ch1b-1.pdf
free (available) buffers: arriving packets dropped (loss) if no free buffers
https://cis.temple.edu/~latecki/Papers/ACM_MM2023.pdf
ABSTRACT Image stitching aims to align a pair of images in the same view. Generating precise alignment with natural structures is challeng-ing for image stitching, as there is no wider field-of-view image as a reference, especially in non-coplanar practical scenarios. In this paper, we propose an unsupervised image stitching frame-work, breaking through the coplanar constraints in homography ...
https://cis.temple.edu/~latecki/Papers/ChamferECCVFinal.pdf
2 Related work There is a large number of applications of chamfer matching in computer vision and in medical image analysis. Chamfer distance was first introduced by Barrow et al. [2] in 1977 with a goal of matching two collections of contour fragments. Until today chamfer matching is widely used in object detection and classifica-tion task due to its tolerance to misalignment in position ...
https://liberalarts.temple.edu/sites/liberalarts/files/Handbook_on_the_Economics.pdf
The largest part of the industry (75 percent) comprises guards or patrol ofi cers. With the rising employment of technology, security blends into the IT department, especially in the proprietary context. It is also important to note that in both segments of the indus-try some security employees spend part of their time in such non- security- related tasks as concierge- type activities.