https://sites.temple.edu/care/cira/
In September 2019, we started a dataset of Critical Infrastructures Ransomware Attacks (CIRAs). These are based on publicly disclosed incidents in the media or security reports. This dataset (version 12.15) now has 2,119 records assembled from publicly disclosed incidents between November 2013 and March 31, 2025, and has been mapped to the MITRE ATT&CK Framework. To date, we have fulfilled ...
https://cis.temple.edu/~yanwang/publications.html
Yucheng Xie, Xiaonan Guo, Yan Wang, Jerry Cheng, and Yingying Chen. "Universal targeted adversarial attacks against mmwave-based human activity recognition." In Network Security Empowered by Artificial Intelligence, pp. 177-211. Cham: Springer Nature Switzerland, 2024. New!
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 ...
https://sites.temple.edu/pdames/files/2016/07/DamesTokekarKumarISRR2015.pdf
Abstract Target tracking is a fundamental problem in robotics research and has been the subject of detailed studies over the years. In this paper, we introduce a new formulation, based on the mathematical concept of random finite sets, that allows for tracking an unknown and dynamic number of mobile targets with a team of robots. We show how to employ the Probability Hypothesis Density filter ...
https://community.mis.temple.edu/mis2402sec004fall2025/schedule/mis2402-assignment01/
Visit the post for more.Web Application Development MIS 2402.004 Fall 2025 Lauren Kerner
https://sites.temple.edu/trail/files/2021/11/XieXinDamesIROS2021.pdf
Zhanteng Xie, Pujie Xin, and Philip Dames Abstract—This paper proposes a novel neural network-based control policy to enable a mobile robot to navigate safety through environments filled with both static obstacles, such as tables and chairs, and dense crowds of pedestrians. The network architecture uses early fusion to combine a short history of lidar data with kinematic data about nearby ...
http://sff.temple.edu/
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https://community.mis.temple.edu/mis5203sec003spring2020/files/2019/01/COBIT-2019-Framework-Introduction-and-Methodology_res_eng_1118.pdf
About ISACA Nearing positive potential its 50th year, of technology. ISACA®(isaca.org) Technology powers global today’s association world helping and ISACA individuals knowledge, credentials, leverages the expertise of its half-million engaged professionals in information and cyber their organizations. education and community to advance their careers and transform equips professionals ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/FedCPD.pdf
Both challenges pertain to optimizing personalized feder-ated learning, yet their solutions don’t cross-apply. Parame-ter decoupling protects local knowledge to prevent forgetting but falls short on sharing global insights, thus struggling with generalization. On the other hand, prototype learning curbs overfitting and boosts generalization by sharing class proto-types, yet it misses ...
https://campusoperations.temple.edu/sites/campusoperations/files/documents/EHRS/Occupational-Safety/Personal%20Fall%20Arrest%20System%20Inspection%20Checklist.pdf
Personal Fall Arrest System Inspection Checklist • Always follow manufacturer specifics for inspection criteria, this form does not replace manufacturer inspection checklist.