offense:
Kali Linux, kali.org,
Hachthissite.org: web application exploration
HackThebox.eu which provides vulnerable machines to attack
microcoruption.com, low level binary exploitation practice
Defense:
national collegiate cyber defense competition, natioalccdc.org, defending a pwned network
hivestorm.org, 4 hours to secure system
DOE cyberforcecompeition.com, two weeks to fix a broken network, two day to defend from attack.
virtualbox.org
cyber-guild.org
overthewire.org, learn Linux terminal
Coding problem 4
yield, coLab, connected to host running time
Q&A
UML assignment and LucidChart?
https://covid.cdc.gov/covid-data-tracker/#datatracker-home
zoom, sharing and recording
socrative
iPad drawing
Generators, example to show that it is faster. On CoLab, need to "Connected to host runtime"
https://oira.utk.edu/major-field-assessment-reporting-schedule/
No computer science in recent years, but many other engineering major.
lucidchart usage.
draw arrows, UML library
socrative questions on UML
lucidChart exercise on Animal example.
For online course, need to convert Socrative question into Multiple choices on Canvas.
Physical Biology of the Cell (2nd ed) by Phillips, Kondev, Theriot and Garcia (ISBN: 0815344503)
http://www.rpgroup.caltech.edu/aph161/syllabus
SO, experimental design, data analysis
outlier tests
law of large number
IQR test
stratified sampling, https://en.wikipedia.org/wiki/Stratified_sampling
cluster sampling, https://en.wikipedia.org/wiki/Cluster_sampling
MS thesis topics suggested for M. A.
Useful ones:
https://github.com/rahulvigneswaran/Intrusion-Detection-Systems
https://github.com/cstub/ml-ids
https://github.com/rambasnet/DeepLearning-IDS
https://github.com/vinayakumarr/Network-Intrusion-Detection
https://github.com/ymirsky/Kitsune-py
https://github.com/prashantnagawade/Intrusion-Detection-and-Prevention-System
https://github.com/niteshsarode/Intrusion-Detection-System-using-Machine-Learning
https://github.com/clazarom/DeepLearning_IDS
Kaggle competition, leaderboard
https://www.kaggle.com/c/fcupdf1920
https://github.com/marzekan/Anomaly_based_IDS
https://github.com/imRP26/Network-based-Intrusion-Detection-Systems
https://github.com/lukehsiao/ml-ids
https://github.com/prabhant/Network-Intrusion-detection-with-machine-learning
https://github.com/cytopia/git-ids
https://github.com/kyralmozley/ids
https://github.com/anandankit95/Anomaly-Based-Intrusion-Detection-
https://github.com/brijshah/Intrusion-Detection-System
https://github.com/nsslabcuus/AI_Security
https://github.com/alik604/cyber-security
Data sets:
https://github.com/jivoi/awesome-ml-for-cybersecurity
Ferrag 2019
Useless ones:
https://github.com/OWASP/Intelligent-Intrusion-Detection-System
zoom
socrative
Update github,
=>Integer Set
=>Animal, inheritance, constructor inheritance, method override
=>class variable versus instance variable. Rabbit tag
=> MITperson ID
cell fate
https://pubmed.ncbi.nlm.nih.gov/33472063/
temporal and spatial issues of gene activities -> diseases
context dependent function, interactions
| Equations | |
Verify references,
grant numbers: corrects
Figures and equations, Eq 2 K should be on top of the arrow.
Figure 2, 3 resolution is too low in the online
deep learining prediction interaction from protein sequence
https://www.biorxiv.org/content/10.1101/2021.01.22.427866v1.full.pdf
zoom
Review what Hangman game. (This is done in 30 minute)
Testing of function, develop the game.
OOP, stopped at the Wild Example.
genetic perturbation on protein concentration, PPI from string database.
Weiqun Li, Georgian Washington Univ. hichub
D-Script
http://cb.csail.mit.edu/cb/dscript/
We might need to simulate two time series with pre-defined lag and linear combination to test co-integration analysis,
For example, we can try: Y(t) = a * sin(X(t-DetlaT)) + b + noise
Where DeltaT is the lag.
We can use Cross-correlation to verify the lag of DeltaT and linear combination.
Things have been pretty exciting after the MocSec competition results went out. I came across a CompTia bundle for Sec+ Cert practices and elearning plus vouchers.
I'm requesting the professional development resources be used to purchase this bundle and recommending it be used for current and future SFS scholars to fulfill their obligations.
This is the link to the in question bundle: Sec+
https://store.comptia.org/comptia-security-complete-bundle/p/SEC-501-BDCO-21A-C
What is CertMaster Learn?
CertMaster Learn is a self-paced, comprehensive online learning experience that helps you gain the knowledge and practical skills necessary to be successful on your CompTIA certification exam, and in your IT career.
Interactive and flexible, CertMaster Learn is the ideal first step in your training journey. Instructional lessons are combined with videos, practice questions, and performance-based questions to provide hours of content aligned with the CompTIA exam objectives. A Learning Plan helps you stay on track with your studies, while robust analytics bring awareness of your strengths and weaknesses.
Topics Covered
Lesson 1: Comparing and Contrasting Attacks
Lesson 2: Comparing and Contrasting Security Controls
Lesson 3: Assessing Security Posture with Software Tools
Lesson 4: Explaining Basic Cryptography Concepts
Lesson 5: Implementing a Public Key Infrastructure
Lesson 6: Implementing Identity and Access Management Controls
Lesson 7: Managing Access Services and Accounts
Lesson 8: Implementing a Secure Network Architecture
Lesson 9: Installing and Configuring Security Appliances
Lesson 10: Installing and Configuring Wireless and Physical Access Security
Lesson 11: Deploying Secure Host, Mobile, and Embedded Systems
Lesson 12: Implementing Secure Network Access Protocols
Lesson 13: Implementing Secure Network Applications
Lesson 14: Explaining Risk Management and Disaster Recovery Concepts
Lesson 15: Summarizing Secure Application Development Concepts
Lesson 16: Explaining Organizational Security Concepts
https://www.amazon.com/Essential-Department-Chair-Comprehensive-Reference/dp/1118123743/
CRA depart chair training
SIGCSE depart head training.
data = c(rep(1, n/4), rep(2, n/4), rep(3, n/4), rep(4, n/4))
in a tree-graph: leaf nodes have degree=1, branching nodes have degree >= 3, and continuous nodes have degree=2. So, we can tabulate the leaf nodes, branching nodes, their time-intervals to estimate RLS, --Hong
If the autoencoder uses only linear activations and the cost function is the mean squared error (MSE), then it basically performing Principal Component Analysis.
Reference:
Zoom, enable screen sharing, recording
socrative
midterm grades
There is a student version of the ipynb file. Let student try on the palimdrome exercise. Be aware that input are single character list.
Java live-coding problem in one-hour
tennis play scores,
https://en.wikipedia.org/wiki/Tennis_scoring_system
Doman: Tweet ~ sentiment.
Peyton: RLS modeling
REU1: covid19 transmission
REU2: mutation ~ temperature.
REU3: Aging, network controllability
REU4: CLS --> RLS using GO network (biological implication?)
https://singlecell.broadinstitute.org/single_cell
Aging keyword return several single cell study project, aging mouse brain, aging haematopoietic stem cells
https://www.cell.com/cell-stem-cell/fulltext/S1934-5909(21)00051-5?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1934590921000515%3Fshowall%3Dtrue
Single-cell transcriptome and whole-genome sequencing of HSPCs from individuals with MPNs
This paper use scRNA to detect somatic mutations. It showed tumor mutaiton occured dacades ago. The tumor cells have a selective growth advantage.
search space
standard deviation
central tendency
regression to the mean
iteration
recursion
mean, median, mode
random sampling
http://www.open-access.bcu.ac.uk/6073/1/__staff_shares_storage%20500mb_Library_ID112668_Rwanda_HowToWriteAPhDProposal.pdf
https://elifesciences.org/articles/22939
https://ir.canterbury.ac.nz/bitstream/handle/10092/13419/Jun%2c%20Hyun_Master%27s%20Thesis.pdf?sequence=1&isAllowed=y
high lethality and more mutation?
https://github.com/Lopezurrutia/DSB_2018
https://github.com/alexxijielu/yeast_segmentation
it seems that many people are studying how control theory can be applied to improve deep learning. On the NSF conference call, many people from Electric Engineer with control theory expertise are in the call.
https://ieeexplore.ieee.org/document/7348689
Machine learning accelerated computational fluid dynamics
https://arxiv.org/pdf/2102.01010.pdf
https://twitter.com/dkochkov1/status/1356440834921627650
Fig 1. old velocity goes into CNN block, --> filter constraints, --> advected, advecting velocity components --> tensor product --> divergence --> explicit time step --> pressure projection --> new velocity
flow are considered turbulent if Re >> 1
convection: heated air rise up
advected: horizontally transfer, https://en.wikipedia.org/wiki/Advection
learned solvers
https://www.simonsfoundation.org/grant/nsf-simons-research-collaborations-on-the-mathematical-and-scientific-foundations-of-deep-learning/
Collaboration on the Theoretical Foundations of Deep Learning
Peter Bartlett — Director, University of California, Berkeley
Bin Yu — Co-Investigator, University of California, Berkeley
Emmanuel Abbé — PI, Ecole polytechnique fédérale de Lausanne
Mikhail Belkin — PI, University of California, San Diego
Amit Daniely — PI, Hebrew University
Andrea Montanari — PI, Stanford University
Alexander Rakhlin — PI, Massachusetts Institute of Technology
Elchanan Mossel — Co-Investigator, Massachusetts Institute of Technology
Nike Sun — Co-Investigator, Massachusetts Institute of Technology
Roman Vershynin — PI, University of California, Irvine
Nathan Srebro — PI, Toyota Technological Institute at Chicago
Collaborative Research: Transferable, Hierarchical, Expressive, Optimal, Robust, Interpretable NETworks (THEORINET)
René Vidal — PI/Director, Johns Hopkins University
Mauro Maggioni — Co-Investigator, Johns Hopkins University
Joshua Vogelstein — Co-Investigator, Johns Hopkins University
Soledad Villar — Co-Investigator, Johns Hopkins University
Gitta Kutyniok — PI, Technical University of Berlin
Guillermo Sapiro — PI, Duke University
Ingrid Daubechies — Co-Investigator, Duke University
Rong Ge — Co-Investigator, Duke University
Alejandro Ribeiro — PI, University of Pennsylvania
Edgar Dobriban — Co-Investigator, University of Pennsylvania
Robert Ghrist — Co-Investigator, University of Pennsylvania
George Pappas — Co-Investigator, University of Pennsylvania
Yi Ma — PI, University of California, Berkeley
S. Shankar Sastry — Co-Investigator, University of California, Berkeley
Jacob Steinhardt — Co-Investigator, University of California, Berkeley
Emmanuel Candès — PI, Stanford University
https://www.coursera.org/learn/the-science-of-well-being
https://missing.csail.mit.edu/
https://exercism.io/
SFS internships, 2021
https://www.usajobs.gov/GetJob/ViewDetails/590335700
https://frb.taleo.net/careersection/2/jobdetail.ftl?lang=en&job=265427 (undergraduate only)
https://careers.gtri.gatech.edu/en-us/job/495963/security-research-student-intern-summer-2021-cipher%20GTRI
https://armyciviliansandtcareers.recsolu.com/external/requisitions/lQmHsPgqagj4b3uLti5QWw
https://www.pnnl.gov/internships
justic dept
https://www.justice.gov/criminal-ceos/computer-forensics-internship
FBI:
https://fbijobs.gov/students/grad-students
cmu:
https://cmu.taleo.net/careersection/2/jobdetail.ftl?job=0011K&tz=GMT-05%3A00&tzname=America%2FNew_York
NSA
https://www.intelligencecareers.gov/nsa/nsastudents.html
https://www.llnl.gov/join-our-team/careers/find-your-job/all/cybersecurity/3743990000018327
full time jobs
https://mitre.wd5.myworkdayjobs.com/en-US/MITRE/job/McLean-VA/Cybersecurity-and-Information-Security-New-Grad-positions_R101069-1
https://github.com/LarryJiang134/Image_manipulation_detection
https://github.com/XDJacobDX/OpenMFC/invitations
many real world matrix has low-rank,
rank of a matrix is the independent vectors (columns),
https://en.wikipedia.org/wiki/Rank_(linear_algebra)
Integrate multi-omics data with biological interaction networks using
Multi-view Factorization AutoEncoder (MAE)
Tianle Ma, Aidong Zhang, 2019, BMC genomics
My understanding the the deep learning its is not biological network. But biological nextwork adjacency matrix x inferred network matrix is used as a regularization term.
Ma-Zhang19 used feature interaction network -> Laplacian matrix of graph.
Ma-Zhang19 used encoder-decoder because this is similar to Matrix factorization, which need to keep the same dimensions of matrices, based on Hong's understanding.
Ma suggested that autoencoder basically works like PCA, scale input matrix into the orthological ones. So, autodencoder theoretically factorize matrix based on this rank, though in practice, the hidden variable dimension is a hyper-parameter decided by trial-and-errors. Ma suggested that network-based learning can be used after the encoder layer.
It seems that auto-encoder of the network input might be achieved by the "driver nodes".
https://www.paho.org/en/technical-documents-coronavirus-disease-covid-19#death-certification
