https://cis.temple.edu/~lafollet/2168/2168Syllabus.html
Introductory knowledge of the Java programming language. Working knowledge of the basic ideas of procedural abstraction and elementary data abstraction and how to put these ideas to work in solving problems of moderate complexity.
https://cis.temple.edu/~jfiore/2024/fall/1068/handouts/syllabus/
You'll be developing software this semester using the Java Programming Language. While it's helpful to have your own computer, there are several on-campus computer labs with all of the software necessary for the course.
https://cis.temple.edu/~wu/teaching/Spring2018/distributed-computing-2018.pdf
meeting-time-scheduling ::= t:= 0; *[ t:= a(t) t:= b(t) t:= c(t) ] Communication and Synchronization One-way communication: sendand receive Two -way communication: RPC(Sun), RMI(Java and CORBA), and rendezvous(Ada) Several design decisions: One-to one or one-to-many Synchronous or asynchronous
https://cis.temple.edu/~pwang/9991-PJ/Reports/OzkanKilic.pdf
2 LIDA LIDA3 is a Java based cognitive framework using Global Workspace Theory4. The architecture ties in well with both neuroscience and cognitive psychology, but it deals most thoroughly with “lower level” aspects of intelligence, handling more advanced aspects like language and reasoning only somewhat sketchily.
https://scholarshare.temple.edu/bitstreams/b1ecc345-7a7c-4776-a366-9b5585e714d6/download
London: J. Murray; 1859. Vaughan TG. IcyTree: rapid browser-based visualization for phylogenetic trees and networks. Bioinformatics. 2017;33:btx155. Kreft Ł, Botzki A, Coppens F, Vandepoele K, Van Bel M. PhyD3: a phylogenetic tree viewer with extended phyloXML support for functional genomics data visualization. Bioinformatics. 2017;33(18):2946 ...
https://careercenter.temple.edu/students/grow/build-your-resume
Add computer/database skills like Python or JAVA; Microsoft Office: PowerPoint, Word, or Excel; Adobe Creative Cloud; or social media platforms that you may be comfortable using.
https://cis.temple.edu/~jiewu/research/publications/Publication_files/m37113-chen%20final.pdf
Abstract—Recent years have witnessed the unprecedented performance of convolutional networks in image super-resolution (SR). SR involves upscaling a single low-resolution image to meet application-specific image quality demands, making it vital for mobile devices. However, the excessive computational and memory requirements of SR tasks pose a challenge in mapping SR networks on a single ...