https://guides.temple.edu/qda/nvivo
Information for Temple students, researchers, and instructors interested in conducting qualitative data analysis
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https://community.mis.temple.edu/sgeorge/
Shaina George received 50 points for participating in the Fox IT Career Fair in Fall 2025 Shaina George received 100 points for part-time non-major work experience Shaina George received 50 points for attending an AIS professional development workshop in Spring 2025 Shaina George received 20 points for completing a volunteering activity Shaina George received 50 points for attending an AIS ...
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There is no password to take the quiz. If you receive a screen asking for a password, it is most likely due to connectivity issues or the Proctorio extension has not been installed. If this occurs, contact Proctorio by clicking on the grey shield in the upper right toolbar to live chat; support is available 24/7. If you receive the message: “Warning: This computer’s memory (RAM) is at or ...
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https://engineering.temple.edu/directory/damoon-soudbakhsh-tuk53604
Biography Damoon Soudbakhsh is an assistant professor in the Department of Mechanical Engineering at Temple University. His research interests are in the broad area of optimal modeling and control. He is working on advancing analytical tools and data-driven modeling techniques, with an emphasis on energy systems. He has industry experience in developing resilient and energy-efficient ...
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https://charlesstudy.temple.edu/event/14912913
This is a two-part workshop. The first part asks that you watch a recording that will demonstrate and compare five different Qualitative Data Analysis (QDA) tools – ATLAS.ti, NVivo, Dedoose, Taguette, and QualCoder. The second part, on the scheduled workshop date, is an interactive online Q/A session and consultation. The goals of the workshop are to learn about your research, assist you in ...
https://cis.temple.edu/~wu/research/publications/Publication_files/Joint%20Dynamic%20Grouping%20and%20Gradient%20Coding%20for%20Time-critical%20Distributed%20Machine%20Learning%20in%20Heterogeneous%20Edge%20Networks-FINAL-VERSION.pdf
Abstract—In edge networks, distributed computing resources have been widely utilized to collaboratively perform a machine learning task by multiple nodes. However, the model training time in heterogeneous edge networks is becoming longer because of excessive computation and delay caused by slow nodes, namely stragglers. The parameter server even abandons stragglers which fail to return ...
https://sites.temple.edu/trail/publications/
Pre-Prints/Under Review Alkesh Kumar Srivastava, Jared Levin, and Philip Dames. “Energy Efficient Multi Robot Package Delivery under Capacity-Constraints via Voronoi-Constrained Networks.” [arXiv] Alkesh Kumar Srivastava, Jared Levin, Alexander Derrico, and Philip Dames. “DELIVER: A System for LLM-Guided Coordinated Multi-Robot Pickup and Delivery using Voronoi-Based Relay Planning ...