https://www.temple.edu/academics/degree-programs/biologics-biosimilars-manufacturing-certificate-graduate-ph-bbmn-grad
Temple’s Certificate in Biologics and Biosimilars Manufacturing focuses on chemistry, manufacturing and controls aspects in the development and manufacture of these products, preparing students for positions in the biopharmaceutical industry and sharpening the skills and knowledge of those already working in this field.
https://news.temple.edu/news/2024-09-20/flappy-bird-return-after-10-year-hiatus-true-story-behind-world-s-most-viral-mobile
Temple University film and media arts faculty member Thomas Sharpe discusses the rise, fall and 2025 return of Flappy Bird, the world’s most viral mobile game.
https://community.mis.temple.edu/mis5208sp18/files/2018/01/Auditors%E2%80%99-Responsibility-for-Fraud-Detection.-1.pdf
Auditors will enter a much expanded arena of procedures to detect fraud as they implement SAS no. 99. The new standard aims to have the auditor’s consideration of fraud blended into the audit process and continually updated until the audit’s completion. SAS no. 99 describes a process in which the auditor (1) gathers information needed to identify material misstatement due to fraud, (2 ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/FedHAN.pdf
FedHAN: A Cache-Based Semi-Asynchronous Federated Learning Framework Defending Against Poisoning Attacks in Heterogeneous Clients Xiaoding Wang1 , Bin Ye1 , Li Xu1 , Lizhao Wu1 , Sun-Yuan Hsieh2 , Jie Wu3;4 and Limei Lin1 1College of Computer and Cyber Security, Fujian Provincial Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou 350117, China 2Department of ...
https://cis.temple.edu/~tug29203/18spring-3329/reading/Lab_7_Solutions.pdf
The answers below are based on the trace file tcp-ethereal-trace-1 in in
https://cis.temple.edu/~wu/research/publications/Publication_files/J-CT-2024-Balancing%20Privacy%20and%20Accuracy%20using%20Siginificant%20Gradient%20Protection%20in%20Federated%20Learning.pdf
Abstract—Previous state-of-the-art studies have demonstrated that adversaries can access sensitive user data by membership inference attacks (MIAs) in Federated Learning (FL). Intro-ducing differential privacy (DP) into the FL framework is an effective way to enhance the privacy of FL. Nevertheless, in differentially private federated learning (DP-FL), local gradients become excessively ...