https://cis.temple.edu/~apal/npa.pdf
Abstract Recent advances in radio and embedded systems have enabled the proliferation of wireless sensor networks. Wireless sensor networks are tremendously being used in different environments to perform various monitoring tasks such as search, rescue, disaster relief, target tracking and a number of tasks in smart environments. In many such tasks, node localization is inherently one of the ...
https://cis.temple.edu/~pwang/Publication/WHW-Realtime.pdf
Abstract. This paper compares the various conceptions of “real-time” in the context of AI, as different ways of taking the processing time into consideration when problems are solved. An architecture of real-time reasoning and learning is introduced, which is one aspect of the AGI system NARS. The basic idea is to form problem-solving processes flexibly and dynamically at run time by using ...
https://phlr.temple.edu/
Law is a powerful tool that can advance health and well-being. By developing and promoting the measurement and evaluation of the law’s impact, through the field of legal epidemiology, we strive to support evidence-based, proactive, and creative policies that advance public health and well-being for all.
https://cis-linux1.temple.edu/~tug29203/25fall-2033/lectures/ch2.pdf
Choose r objects in succession from a population of n distinct objects fa1; a1; ; ang, in such a way that an object once chosen is removed from the population Then we again get an ordered sample, but now there are n - 1 objects left after the rst choice, n - 2 objects left after the second choice, and so on.
https://scholarshare.temple.edu/bitstreams/48e9e752-7b37-40de-96f0-ed735aa84796/download
Longin Jan Latecki, Advisory Chair, Computer and Information Sciences Slobodan Vucetic, Computer and Information Sciences Haibin Ling, Computer and Information Sciences Jianbo Shi, External Member, University of Pennsylvania
https://cis.temple.edu/tagit/presentations/Hopfield%20Networks%20is%20all%20you%20need.pdf
Note that there are 2,500 pixels in each image, the size of the weight matrix will be 2500 × 2500, but only learned by ONE image. Two natural problems will arise. 1) How many patterns can one
https://law.temple.edu/wp-content/uploads/yearby-cv-june-2025.pdf
Temple University Beasley School of Law Philadelphia, PA Judge Clifford Scott Green Professor (Endowed Chair), August 2025 – present Member, Center for Public Health Law Research, August 2025 - present
https://cis.temple.edu/~latecki/Courses/AI-Fall10/Lectures/ch8Bayes.ppt
COMP20411 Machine Learning * Outline Background Probability Basics Probabilistic Classification Naïve Bayes Example: Play Tennis Relevant Issues Conclusions COMP20411 Machine Learning * Background There are three methods to establish a classifier a) Model a classification rule directly Examples: k-NN, decision trees, perceptron, SVM b) Model the probability of class memberships given input ...
https://cis.temple.edu/~latecki/Courses/CIS601-04/Projects/Dhiraj/Image%20Segmentation%20by.ppt
What is segmentation? It is to distinguish objects from background Types of segmentation edge based region based In the present project we use region based segmentation. Region based Segmentation A region-based method usually proceeds as follows: the image is partitioned into connected regions by grouping neighboring pixels of similar intensity levels. Adjacent regions are then merged under ...
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 ...