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Lecture Set 4 – Selection Structures - Temple University

https://cis.temple.edu/~friedman/cis071/Lecture4-web.doc

The book also uses flow charts, which are sometimes handy for visualizing what is going on with a decision structure. However, pseudo-code, if properly indented and written with a little care is a preferred mechanism for mapping out the logic of a decision before trying to program it (and having to worry about syntax).

Distributed System Design: An Overview* - Temple University

https://cis.temple.edu/~wu/teaching/Spring2018/distributed-computing-2018.pdf

1. In your opinion, what is the future of the computing and the field of distributed systems? 2. Use your own words to explain the differences between distributed systems, multiprocessors, and network systems. 3. Calculate (a) node degree, (b) diameter, (c) bisection width, and (d) the number of links for an nx n2-d mesh, an n x n2- d torus, and an n-dimensional hypercube.

TileSR: Accelerate On-Device Super-Resolution with Parallel Offloading ...

https://cis.temple.edu/~jiewu/research/publications/Publication_files/m37113-chen%20final.pdf

TileSR: Accelerate On-Device Super-Resolution with Parallel Ofloading in Tile Granularity Ning Chen1, Sheng Zhang1∗, Yu Liang2, Jie Wu3, Yu Chen1, Yuting Yan1, Zhuzhong Qian1 and Sanglu Lu1

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.

Microsoft Word - 1--How to Create a Canvas Course Shell from your ...

https://teaching.temple.edu/sites/teaching/files/1--How%20to%20Create%20a%20Canvas%20Course%20Shell%20from%20your%20Banner%20class%20list%20%281%29.pdf

Create new Canvas shell for a class listed in Banner Note: Only use this method for courses you plan to use Canvas with this semester.

HackerRank Solutions: Tree: Huffman Decoding – Vahid E-Portfolio

https://sites.temple.edu/vahid/2022/03/05/hackerrank-solutions-tree-huffman-decoding/

Problem Huffman coding assigns variable length codewords to fixed length input characters based on their frequencies. More frequent characters are assigned shorter codewords and less frequent characters are assigned longer codewords. All edges along the path to a character contain a code digit. If they are on the left side of the tree, they will be a 0 (zero). If on the right, they’ll be a 1 ...

Dweck Perils of Promise & Praise

https://teaching.temple.edu/sites/teaching/files/resource/pdf/Dweck-Perils%20%26%20Promises%20of%20Praise.pdf

The wrong kind of praise creates self-defeating behavior. The right kind motivates students to learn.

Rigidity, Computation, and Randomization in Network Localization

https://cis.temple.edu/~wu/teaching/Spring%202015/EGWY04.pdf

Abstract—In this paper we provide a theoretical foundation for the problem of network localization in which some nodes know their locations and other nodes determine their locations by measuring the distances to their neighbors. We construct grounded graphs to model network localization and apply graph rigidity theory to test the conditions for unique localizability and to construct uniquely ...

Student Research Projects – Temple's GSWS Program

https://sites.temple.edu/gsws/student-research-projects/

Here is a sample of prior student research projects Ari Gewirtzman, “Transgender Bodies: From Medicalization and Criminalization to Liberation” This research explores how the relationship between cultural biases, medical practices, and legislation impact trans patients through analyzing historical and legal documents. The results of this research will be relevant for improving trans ...

Nonlinear Dimensionality Reduction - Temple University

https://cis.temple.edu/~latecki/Courses/AI-Fall12/Lectures/NLDimRed.pdf

Linear Dimensionality Reduction: Based on a linear projection of the data