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://www.fox.temple.edu/sites/fox/files/documents/Cummins%20Conference%202022/RILA_Moenig_JRI_final.pdf
Registered index-linked annuities (RILAs) are increasingly popular equity-based re-tirement savings products o ered by U.S. life insurance companies. They combine features of xed-index annuities and traditional variable annuities (TVAs), o ering in-vestors equity exposure with downside protection in a tax-deferred setting. This article introduces RILAs to the academic literature by describing ...
https://cis.temple.edu/~latecki/Courses/AI-Fall12/Lectures/GreatMatrixIntro.pdf
and their associatedeigenvectorsof a matrix describe the geometry of the trans- formation associated with that matrix. Using the multivariate normal, we then develop the multivariate breeders’ equation and examine properties of Gaussian fitness functions. We conclude with some elementary concepts in vector calcu- lus, focusing on derivatives of vectors and finding local extrema of vector ...
https://cis-linux1.temple.edu/~jiewu/research/publications/Publication_files/ICPADS2024.pdf
Abstract—Federated Learning (FL) is an emerging privacy-preserving distributed machine learning paradigm that enables numerous clients to collaboratively train a global model without transmitting private datasets to the FL server. Unlike most existing research, this paper introduces a Data-Driven FL system in Unmanned Aerial Vehicle (UAV) networks, named DDFL, which features an innovative ...
https://cis.temple.edu/tagit/presentations/Neural-Symbolic%20Computing%20An%20Effective%20Methodology%20for%20Principled%20Integration%20of%20Machine%20Learning%20and%20Reasoning.pdf
“Neural-symbolic computing aims at reconciling the dominating symbolic and connectionist paradigms of AI under a principled foundation. In neural-symbolic computing, knowledge is represented in symbolic form, whereas learning and reasoning are computed by a neural network.”
https://cis.temple.edu/~jiewu/research/publications/Publication_files/Reinforcement_Learning-based_Dual-Identity_Double_Auction_in_Personalized_Federated_Learning.pdf
Reinforcement Learning-based Dual-Identity Auction in Personalized Federated Juan Li, Member, IEEE, Zishang Chen, Tianzi Wu, Fellow, IEEE, Yanmin
https://cis.temple.edu/~latecki/Courses/AI-Fall12/Lectures/RandomFields_Sarah.pdf
C. Sutton and A. McCallum, An Introduction to Conditional Random Fields for Relational Learning, MIT Press, 2006. D. Precup, Graphical Models, Probabilistic Reasoning in AI, McGill University, 2008. J. Cao and K.J. Worsley, Applications of Random Fields in Human Brain Mapping, American Mathematical Society, 1991.
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://cis.temple.edu/~latecki/Courses/AI-Fall12/Lectures/NLDimRed.pdf
Linear Dimensionality Reduction: Based on a linear projection of the data
https://scholarshare.temple.edu/bitstreams/1c1f1a6d-0f34-4234-8b39-41d9eeb397f0/download
of machine learning (ML) versus a traditional statistical model in predicting dental caries in