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Adaptive Procedural Generation in Minecraft - Temple University

https://cis.temple.edu/~wangp/5603-AI/Project/2022S/pattersonblaker/Ward_Patterson_Final_Report.pdf

For example, if we wanted to know what block was at a certain (x, y, z) coordinate in the world, we could make a GET request to the server at the blocks endpoint (i.e., "lo-calhost:9000/blocks") with the coordinates as parameters and it would return information about the block at those coordinates.

Xueming Luo - Fox School of Business

https://www.fox.temple.edu/directory/xueming-luo-tuf35198

Luo, X., Jia, N., Ouyang, E., & Fang, Z. (2024). Introducing machine‐learning‐based data fusion methods for analyzing multimodal data: An application of measuring trustworthiness of microenterprises.

Marco Shaojun Qin - Fox School of Business

https://www.fox.temple.edu/directory/marco-shaojun-qin-tuk40739

Qin, S.M., Jia, N., Luo, X., Liao, C., & Huang, Z. (2023). Perceived Fairness of Human Managers Compared with Artificial Intelligence in Employee Performance Evaluation.

FedCPD: Personalized Federated Learning with Prototype-Enhanced ...

https://cis.temple.edu/~jiewu/research/publications/Publication_files/FedCPD.pdf

Initially, we generate feature em-beddings on the local datasets Di = (x; y)Ni of client i us-ing the backbone network Wf;t+1 distributed by the server as follows.

AGI Introduction - Temple University

https://cis.temple.edu/~pwang/AGI-Intro.html

Artificial General Intelligence — A gentle introduction Pei Wang [On-line document since 2007, last updated in August 2025] [中文] [Español] [This page contains information about the field of Artificial General Intelligence (AGI), collected and organized according to my judgment, though efforts are made to avoid personal biases.]

Micro-influencers have a major influence on Generation Z

https://news.temple.edu/news/2025-03-18/micro-influencers-have-major-influence-generation-z

Temple University faculty member Jay Sinha has a new scholarly journal that outlines how micro-influencers can be key in targeting Generation Z consumers

Chat-GPT syllabus statement guidance

https://teaching.temple.edu/sites/teaching/files/resource/pdf/Chat-GPT%20syllabus%20statement%20guidance.pdf

Sample Syllabus Statements for the Use of AI Tools in Your Course The following guidance is provided to assist you in developing coherent policies on the use of generative AI tools in your course. Please adjust the guidance to fit your particular context. Remember also to note in specific assignment descriptions where AI use is allowed or disallowed.

Wang Lab - Temple University

https://cis.temple.edu/~yu/wanglab/index.html

About The W ireless and A dvanced N etworking G roup (WANG Lab) in the Department of Computer and Information Sciences at Temple University focuses on research that advances the way that people, devices and applications interact in emerging wireless networking, smart sensing, distributed computing, and artificial intelligence. Wang Lab was established in the University of North Carolina at ...

FedHAN: A Cache-Based Semi-Asynchronous Federated Learning Framework ...

https://cis.temple.edu/~jiewu/research/publications/Publication_files/FedHAN.pdf

[Koneˇcn ́y et al., 2017] Jakub Koneˇcn ́y, H. Brendan McMa-han, Felix X. Yu, Peter Richt ́arik, Ananda Theertha Suresh, and Dave Bacon. Federated learning: Strategies for improving communication efficiency. arXiv:1610.05492, 2017.

PowerPoint Presentation

https://cis.temple.edu/~latecki/Courses/AI-Fall11/Lectures/ch7EL.ppt

On each iteration t, we find a classifier h(x) that minimizes the error with respect to the distribution. Next we increase weights of training examples misclassified by h(x), and decrease weights of the examples correctly classified by h(x) The new distribution is used to train the next classifier, and the process is iterated.