Pinned Repositories
Bayes-Net-PacMan
book_face_employees
c_block_tracer
stacks using c blocks
cleanrl
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
coding-interview-university
A complete computer science study plan to become a software engineer.
Covid-Testing
dynamic-memory-allocator
Created an allocator for the x86-64 architecture with the following features: Free lists segregated by size class, using first-fit policy within each size class. Immediate coalescing of blocks on free with adjacent free blocks. Boundary tags to support efficient coalescing. Block splitting without creating splinters. Allocated blocks with client-specified alignment. Free lists maintained using last in first out (LIFO) discipline. Implemented my own versions of the malloc, realloc, free, and memalign functions.
openelections-data-pa
Pre-processed election results for Pennsylvania elections
orourke-compc
Code from O'Rourke book "Computational Geometry algorithms in C" 1997
twittor
using graphs to track followers and followings
nniemiec36's Repositories
nniemiec36/dynamic-memory-allocator
Created an allocator for the x86-64 architecture with the following features: Free lists segregated by size class, using first-fit policy within each size class. Immediate coalescing of blocks on free with adjacent free blocks. Boundary tags to support efficient coalescing. Block splitting without creating splinters. Allocated blocks with client-specified alignment. Free lists maintained using last in first out (LIFO) discipline. Implemented my own versions of the malloc, realloc, free, and memalign functions.
nniemiec36/coding-interview-university
A complete computer science study plan to become a software engineer.
nniemiec36/Covid-Testing
nniemiec36/openelections-data-pa
Pre-processed election results for Pennsylvania elections
nniemiec36/twittor
using graphs to track followers and followings
nniemiec36/Bayes-Net-PacMan
nniemiec36/book_face_employees
nniemiec36/c_block_tracer
stacks using c blocks
nniemiec36/cleanrl
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
nniemiec36/Clickstream-Data
nniemiec36/Compute-Pearson-Correlation-Coefficient
CSE 532: HW 2
nniemiec36/CSE333UIDEV
CSE 333 Mock Website
nniemiec36/CSE545
stuff for 545
nniemiec36/dns-resolver
Part A (70 points) You will be implementing a DNS resolver. The resolver takes as input a domain name. Your resolver resolves this query by first contacting the root server, the top-level domain, all the way until the authoritative name server. You can assume that you have access to a library that can resolve a single iterative DNS query. The set of libraries that you may use are given in the Appendix. The libraries also perform complete DNS resolution, but you are *not allowed* to use that. 1. You can access the IP address of the root servers from https://www.iana.org/domains/root/servers. 2. Build a “dig”-like tool called “mydig”. The mydig tool takes as input the name of the domain you want to resolve. You should resolve the “A” record for the query. When run your program and enter the input “www.cnn.com”, your tool must display the output as shown below: QUESTION SECTION: www.cnn.com. IN A ANSWER SECTION: www.cnn.com. 262 IN A 151.101.209.67 Query time: How much time it took to resolve the query WHEN: Date and time of request You can either provide the input at command line or you can ask the user to enter an input. 3.Make sure you handle errors: for example, in case you cannot connect to any of the DNS Name Server, or you are not able to parse the DNS Response, etc, you will have to return an error. Along with the code, you need to submit an output file called “mydig_output”, that contains the expected output for running your mydig program. Please specify the input to your program before the output in the same file. In some cases, you will not be able to resolve the query to the complete IP address, but only get a “CNAME”. In this case, you will have to resolve the query completely. An example of such a query is google.co.jp. You will use two APIs to create a DNS request to each individual server. The first is to create a DNS query and the second is to send this query to the destination. Figuring out the right APIs is up to you, but both can be found in the library. However,as mentioned earlier you are not allowed to use the resolve function in the library. PART B (30 points) Your next task is to measure the performance of your DNS resolver from Part A. Pick 10 out of the top 25 Websites from alexa.com (http://www.alexa.com/topsites.) Experiment 1: Run your DNS resolver on each website 10 times and find the average time, 25th percentile, and 75th percentile to resolve the DNS for each of the 10 websites. Experiment 2: Now use your local DNS resolver and repeat the experiment (call this Local DNS). Find the average time to resolve the address for the 10 websites. Experiment 3: Change the DNS resolver to Google’s public DNS (The IP address of this public DNS is often 8.8.8.8, or 8.8.4.4, but you need to verify). Repeat the experiment one more time and call this result “Google DNS” You can use the dig command for experiments 2 and 3. For each of the 10 Website, plot the median, 25th percentile and 75th percentile values over the 10 runs and draw a graph. The x axis is the website, named 1 to 10. The y axis is the time taken to resolve the query. Each point will have three bars corresponding to the three experiments. The 25th and 75th percentile should be shown as a box plot.
nniemiec36/encryption_decryption
encrypt and decrypt using queues
nniemiec36/food_chain
food chain using trees
nniemiec36/frequency_list
compare two passages with a hash table
nniemiec36/hadoop-and-clusters
Objectives: Gain experience with a live hadoop-style (hdfs, spark) cluster. Implement a basic collaborative filtering recommendation system. Implement hypothesis testing with multi-test correction at scale. Gain experience navigating a cloud console to spin up a cluster. Work with moderately large data. Gain experience problem solving non-theoretical, practical issue of big data.
nniemiec36/icmp-and-arp
ICMP Ping and ARP Packet
nniemiec36/knn
nniemiec36/media_player
media player with linked lists
nniemiec36/nniemiec36.github.io
welcome to my site!
nniemiec36/Opioid-Pills-Analysis
CSE 532: HW 1
nniemiec36/pcap
PCAP Programming Task and flow-level information
nniemiec36/printers-and-pipes
C code project based on printers that could only "print" certain file extension types and piping them through printers in order to convert the file to another type.
nniemiec36/Pseudo-Distributed-Hadoop-And-Spark
CSE532: HW 4
nniemiec36/spark-and-minhash
Objectives: To gain experience programming in Spark To understand differences in implementations using Spark versus MapReduce or standard streaming. To better understand LSH through implementing portions of the algorithm Gain further experience with Spark. Gain further experience with data preprocessing. Explore a different modality of data: structured data.
nniemiec36/streaming-and-mapreduce
Objectives: To implement prototypical streaming algorithms. To think through implementation of a novel streaming algorithm under the constraints of limited memory. To implement parts of a map reduce system in order to become familiar with what it must do behind the scenes. To implement a map reduce version of a provided algorithm.
nniemiec36/Voting-Queueing-Model
nniemiec36/XML-Queries
CSE532: HW 5