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A Survival Guide to AI and Teaching pt.4: Make AI Your Friend

https://sites.temple.edu/edvice/2023/06/21/a-survival-guide-to-ai-and-teaching-pt-4-make-ai-your-friend/

Allow students to submit an AI-generated first draft of a paper early in the semester, then focus their efforts on the revision process. Students submit both the AI draft and their revised version in a collaborative document (such as MS Word or Google Docs) so that you can track changes and see the version history.

I Can Hear You Without a Microphone: Live Speech Eavesdropping From ...

https://cis.temple.edu/~yu/research/Icanhearyou-INFOCOM2023.pdf

Abstract—Recent literature advances motion sensors mounted on smartphones and AR/VR headsets to speech eavesdropping due to their sensitivity to subtle vibrations. The popularity of mo-tion sensors in earphones has fueled a rise in their sampling rate, which enables various enhanced features. This paper investigates a new threat of eavesdropping via motion sensors of earphones by developing ...

CENTAUR WARFIGHTING: THE FALSE CHOICE OF HUMANS VS. AUTOMATION - Sites

https://sites.temple.edu/ticlj/files/2017/02/30.1.Scharre-TICLJ.pdf

Paul Scharre* Much of the debate on autonomous weapons presumes a choice between human versus autonomous decision-making over targeting and engagement decisions. In fact, in many situations, human-machine teaming in engagement decisions will not only be possible but preferable. Hybrid human-machine cognitive architectures will be able to leverage the precision and reliability of automation ...

Joint Optimization of DNN Partition and Scheduling for Mobile Cloud ...

https://cis.temple.edu/~jiewu/research/publications/Publication_files/ICPP_2021_Duan.pdf

ABSTRACT Reducing the inference time of Deep Neural Networks (DNNs) is critical when running time sensitive applications on mobile devices. Existing research has shown that partitioning a DNN and ofloading a part of its computation to cloud servers can reduce the inference time. The single DNN partition problem has been extensively in-vestigated recently. However, in real-world applications, a ...

Adaptive Procedural Generation in Minecraft - Temple University

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

1 Abstract Minecraft has been the focus of much AI research in past years. Most recently, interest has risen in procedural generation of settlements in Minecraft, largely due to a annual competition established in 2018 called the Generative Design in Minecraft Competition. Inspired by this recent research, we aim to develop a set of algorithms that are capable of building a realistic ...

Mutual Risk Sharing and Fintech: The Case of Xiang Hu Bao

https://www.fox.temple.edu/sites/fox/files/documents/Cummins%20Conference%202022/XiangHuBao-main-2022-03-23.pdf

The significant progress in information technologies promotes new venues in risk sharing and risk management practices (OECD, 2017). Just as peer-to-peer (P2P) lending platforms connected un-or under-financed borrowers to lenders, emerging fintech platforms can also be leveraged to reach traditionally un-insured or under-insured customers.2 This is exemplified by Xiang Hu Bao (XHB, meaning ...

TPAMI-0673-0906-2 1..11 - Temple University

https://cis.temple.edu/~latecki/Papers/PAMIshape08.pdf

Xiang Bai and Longin Jan Latecki, Senior Member, IEEE Abstract—This paper proposes a novel graph matching algorithm and applies it to shape recognition based on object silhouettes. The main idea is to match skeleton graphs by comparing the geodesic paths between skeleton endpoints. In contrast to typical tree or graph matching methods, we do not consider the topological graph structure. Our ...