https://cis.temple.edu/~wu/research/publications/Publication_files/handbook-part1.pdf
The 19 chapters in this section cover a wide range of topics across multiple layers: MAC (part of the data link layer), network, and applications. One chapter is devoted to the cross-layer architecture for ad hoc wireless networks. Several chapters deal with various efficient and scalable routing, including multicasting and geocasting, in ad hoc wireless networks. One chapter discusses routing ...
https://cis.temple.edu/~latecki/Courses/CIS581-02/MatCIS581-02/LectureHolzschuch/FillingPolygons.pdf
Dr Nicolas Holzschuch University of Cape Town e-mail: holzschu@cs.uct.ac.za
https://sites.temple.edu/pdames/files/2016/07/DamesTokekarKumarISRR2015.pdf
Abstract Target tracking is a fundamental problem in robotics research and has been the subject of detailed studies over the years. In this paper, we introduce a new formulation, based on the mathematical concept of random finite sets, that allows for tracking an unknown and dynamic number of mobile targets with a team of robots. We show how to employ the Probability Hypothesis Density filter ...
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/~jiewu/research/publications/Publication_files/Privacy-Preserving_Federated_Neural_Architecture_Search_With_Enhanced_Robustness_for_Edge_Computing.pdf
Abstract—With the development of large-scale artificial intelli-gence services, edge devices are becoming essential providers of data and computing power. However, these edge devices are not immune to malicious attacks. Federated learning (FL), while pro-tecting privacy of decentralized data through secure aggregation, struggles to trace adversaries and lacks optimization for hetero-geneity ...
http://timetree.temple.edu/public/data/pdf/vanTuinen2009Chap57.pdf
Abstract Living birds (~9500 species) are grouped into 20–28 orders, comprising the Subclass Neornithes of the Class Aves. With few exceptions, molecular phylogenetic analyses have sup-ported two morphologica l divisions within Neornithes, Paleognathae (ratites and tinamous) and Neognathae (a ll other living birds). Within Neogna tha e, there is uni-versal support for the recognition of two ...
https://cis.temple.edu/~latecki/Courses/AI-Fall12/Lectures/RandomFields_Sarah.pdf
Stochastic processes as Dynamic Bayesian Networks Dynamic Bayesian Network is a probabilistic graphical model that represents a sequence of random variables and their conditional dependencies.
https://teaching.temple.edu/sites/teaching/files/resource/pdf/TPACK.pdf
Abstract This paper describes a framework for teacher knowledge for technology integration called technological pedagogical content knowledge (originally TPCK, now known as TPACK, or technology, pedagogy, and content knowledge). This framework builds on Lee Shulman’s construct of pedagogical content knowledge (PCK) to include technology knowledge. The development of TPACK by teachers is ...
https://cis.temple.edu/~tuf80213/courses/temple/cis1051/cis1051.pdf
Course Description CIS 1051 introduces students to computers, computer programming, and problem solving using programs written in the Python language. Topics covered include the general characteristics of computers; techniques of problem solving and algorithm specifications; and the implementation, debugging, and testing of computer programs. The goal is to learn to solve small programming ...
https://cis.temple.edu/~latecki/Papers/VisualCurvatureCVPR07.pdf
a relation between multi-scale visual curvature and convexity of simple closed curves. To our best knowledge, the proposed definition of visual curvature is the first ever that applies to regular curves as defined in differential geometry as well as to turn angles of polygonal curves. Moreover, it yields stable curvature estimates of curves in digital images even under sever distortions.