https://cis.temple.edu/~latecki/Courses/AI-Fall12/Lectures/GreatMatrixIntro.pdf
There are numerous excellent texts on matrix algebra, so we will make little ef- fort to prove most of the results given below. For statistical applications, concise introductions can be found in the chapters on matrix methods in Johnson and Wichern (1988) and Morrison (1976), while Dhrymes (1978) and Searle (1982) pro- vide a more extended treatment. Wilf’s (1978) short chapter on matrix ...
https://cis.temple.edu/~latecki/Papers/DSP_PAMI2014.pdf
Abstract—In this paper, we present a novel partition framework, called dense subgraph partition (DSP), to automatically, precisely and efficiently decompose a positive hypergraph into dense subgraphs. A positive hypergraph is a graph or hypergraph whose edges, except self-loops, have positive weights. We first define the concepts of core subgraph, conditional core subgraph, and disjoint ...
https://cis.temple.edu/~latecki/Courses/RobotFall08/BishopBook/Pages_from_PatternRecognitionAndMachineLearning-2.pdf
Probabilities play a central role in modern pattern recognition. We have seen in Chapter 1 that probability theory can be expressed in terms of two simple equations corresponding to the sum rule and the product rule. All of the probabilistic infer-ence and learning manipulations discussed in this book, no matter how complex, amount to repeated application of these two equations. We could ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/Fog_Edge_Computing_BC2020.pdf
This book also covers the security architectural design of fog/edge computing, including a comprehensive overview of vulnerabilities in fog/edge computing within multiple architectural levels, the security and intelligent management, the implementation of network-function-virtualization-enabled multicasting in part four. It explains how to use the blockchain to realize security services. The ...
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://www.fox.temple.edu/sites/fox/files/Frontiers-Machines-versus-Humans-The-Impact-of-Artificial-Intelligence-Chatbot-Disclosure-on-Customer-Purchases.pdf
Results in Table 5 with both OLS and tobit models consistently support the negative and signi ficant ef-fect of before conversation on call length for the samples of attempted calls, excluding nonresponses and hang-ups.