https://community.mis.temple.edu/mis5202online2016/files/2016/03/Internal-Control-Using-COBIT-5_whp_eng_0316.pdf
Internal Control in COBIT In COBIT® terms, a control can be any enabler that supports the achievement of one or more objectives (control objectives). These objectives are the desired result or purpose from the implementation of a relevant process, practice, principle, tool, organizational unit, symbol or other capability. A control practice is a key mechanism that supports the achievement of ...
https://www.fox.temple.edu/sites/fox/files/documents/CVs/xueming-luo-cv.pdf
Bio: Xueming Luo is the Charles Gilliland Distinguished Chair Professor of Marketing, Professor of Strategy, and Professor of MIS, and Founder/ Director of the Global Institute for Artificial Intelligence & Business Analytics in the Fox School of Business at Temple University. He is an interdisciplinary thought-leader in leveraging AI/ML algorithms, text/audio/image/video big data ...
https://www.fox.temple.edu/academics/fox-phd/admissions
A Complete Application to the Fox School of Business PhD Program includes: Current Resume / C.V. Personal Statement of Goals Two References (to be completed online by your recommenders) English language transcripts from all post-secondary institutions attended. Unofficial transcripts are acceptable for application review. Official GMAT / GRE Results (test results cannot be more than five years ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/Distributed_Deep_Multi-Agent_Reinforcement_Learning_for_Cooperative_Edge_Caching_in_Internet-of-Vehicles.pdf
Abstract—Edge caching is a promising approach to reduce duplicate content transmission in Internet-of-Vehicles (IoVs). Sev-eral Reinforcement Learning (RL) based edge caching methods have been proposed to improve the resource utilization and reduce the backhaul trafic load. However, they only obtain the local sub-optimal solution, as they neglect the influence from environments by other ...
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 ...