# Phil 5.10.19

7:00 – 4:00 ASRC NASA GOES

• Tensorflow Graphics?
• An End-to-End AutoML Solution for Tabular Data at KaggleDays
• More dissertation writing. Added a bit on The Sorcerer’s Apprentice and finished my first pass at Moby-Dick
• Add pickling to MatrixScalar – done!
def save_class(the_class, filename:str):
print("save_class")
# Its important to use binary mode
dbfile = open(filename, 'ab')

# source, destination
pickle.dump(the_class, dbfile)
dbfile.close()

def restore_class(filename:str) -> MatrixScalar:
print("restore_class")
# for reading also binary mode is important
dbfile = open(filename, 'rb')
dbfile.close()
return db
• Added flag to allow unlimited input buffer cols. It automatically sizes to the max if no arg for input_size
• NOTE: Add a “notes” dict that is added to the setup tab for run information

# Phil 4.30.19

7:00 – 8:30 ASRC NASA

• Working through Panos’ comments on the CHIPLAY paper and incorporating them into the JASS paper
• ML day today

Datasets for testing

• Cur
• Creating a package for probability
• Juryroom meeting – progress is good. Sent the current draft of the JASS paper

# Phil 4.19.19

8:00 – 4:00 ASRC TL

• Updating working copies of the paper based on the discussion with Aaron M last night.
• Based on the diagrams of the weights that I could make with the MNIST model, I think I want to try to make a layer neuron/weight visualizer. This one is very pretty
• Need to start on framework for data generation and analysis with Zach this morning
• Got Flask working (see above for rant on how).
• Flask-RESTful provides an extension to Flask for building REST APIs. Flask-RESTful was initially developed as an internal project at Twilio, built to power their public and internal APIs.

# Phil 3.14.19

ASRC AIMS 7:00 – 4:00, PhD ML, 4:30 –

# Phil 3.11.19

7:00 – 10:00 ASRC PhD. Fun, long day.

• Understanding BERT Transformer: Attention isn’t all you need
• Word Vectors and NLP Modeling from BoW to BERT
• Since the advent of word2vec, neural word embeddings have become a go to method for encapsulating distributional semantics in text applications. This series will review the strengths and weaknesses of using pre-trained word embeddings and demonstrate how to incorporate more complex semantic representation schemes such as Semantic Role Labeling, Abstract Meaning Representation and Semantic Dependency Parsing into your applications.
• Artificial Intelligence and Global Security Initiative Research Agenda
• The Center for a New American Security’s Artificial Intelligence and Global Security Initiative explores these and other issues surrounding the AI revolution. Current AI technology is powerful, but also has a number of vulnerabilities, including susceptibility to spoofing (false data) and control problems. An arms race in AI where nations and other actors rush to use this technology for their advantage without any concern for safety would be harmful to everyone. It is vitally important for the technology and policy communities to come together to better understand the implications of the AI revolution for global security and how best to navigate the challenges ahead.
• One more pass through Antonio’s paper this evening – done
• Working on getting the Slack chats into the database. It turns out that there can be threaded discussions within channels: thread_ts, reply_count, reply_users_count, latest_reply, reply_users, replies are the variables. It’s not critical now, but it would be nice to read these in as well.
• We are over 10,000 rows!
• And it looks like the Google Keras team is going to run the dungeon
• Starting on SequenceAnalyzer. Not bat progress for a day
• Meeting with Wayne

# Phil 3.1.19

7:00 – ASRC

• Got accepted to the TF dev conference. The flight out is expensive… Sent Eric V. a note asking for permission to go, but bought tix anyway given the short fuse
• Working on white paper. The single file was getting unwieldy, so I broke it up
• Found Speeding up Parliamentary Decision Making for Cyber Counter-Attack, which argues for the possibility of pre-authorizing automated response
• Up to six pages. IN the middle of the cyberdefense section

# Phil 2.22.19

7:00 – 4:00 ASRC

• Running Ellen’s dungeon tonight
• Wondering what to do next. Look at text analytics? List is in this post.
• But before we do that, I need to extract from the DB posts as text. And now I have something to do!
• Sheesh – tried to update the database and had all kinds of weird problems. I wound up re-injesting everything from the Slack files. This seems to work fine, so I exported that to replace the .sql file that may have been causing all the trouble.
• Here’s a thing using the JAX library, which I’m becoming interested in: Meta-Learning in 50 Lines of JAX
• The focus of Machine Learning (ML) is to imbue computers with the ability to learn from data, so that they may accomplish tasks that humans have difficulty expressing in pure code. However, what most ML researchers call “learning” right now is but a very small subset of the vast range of behavioral adaptability encountered in biological life! Deep Learning models are powerful, but require a large amount of data and many iterations of stochastic gradient descent (SGD). This learning procedure is time-consuming and once a deep model is trained, its behavior is fairly rigid; at deployment time, one cannot really change the behavior of the system (e.g. correcting mistakes) without an expensive retraining process. Can we build systems that can learn faster, and with less data?
• Meta-Learning: Learning to Learn Fast
• A good machine learning model often requires training with a large number of samples. Humans, in contrast, learn new concepts and skills much faster and more efficiently. Kids who have seen cats and birds only a few times can quickly tell them apart. People who know how to ride a bike are likely to discover the way to ride a motorcycle fast with little or even no demonstration. Is it possible to design a machine learning model with similar properties — learning new concepts and skills fast with a few training examples? That’s essentially what meta-learning aims to solve.
• Meta learning is everywhere! Learning to Generalize from Sparse and Underspecified Rewards
• In “Learning to Generalize from Sparse and Underspecified Rewards“, we address the issue of underspecified rewards by developing Meta Reward Learning (MeRL), which provides more refined feedback to the agent by optimizing an auxiliary reward function. MeRL is combined with a memory buffer of successful trajectories collected using a novel exploration strategy to learn from sparse rewards.
• Lingvo: A TensorFlow Framework for Sequence Modeling
• While Lingvo started out with a focus on NLP, it is inherently very flexible, and models for tasks such as image segmentation and point cloud classification have been successfully implemented using the framework. Distillation, GANs, and multi-task models are also supported. At the same time, the framework does not compromise on speed, and features an optimized input pipeline and fast distributed training. Finally, Lingvo was put together with an eye towards easy productionisation, and there is even a well-defined path towards porting models for mobile inference.
• Working on white paper. Still reading Command Dysfunction and making notes. I think I’ll use the idea of C&C combat as the framing device of the paper. Started to write more bits
• What, if anything, can the Pentagon learn from this war simulator?
• It is August 2010, and Operation Glacier Mantis is struggling in the fictional Saffron Valley. Coalition forces moved into the valley nine years ago, but peace negotiations are breaking down after a series of airstrikes result in civilian casualties. Within a few months, the Coalition abandons Saffron Valley. Corruption sapped the reputation of the operation. Troops are called away to a different war. Operation Glacier Mantis ends in total defeat.
• Created a post for Command Dysfunction here. Finished.

# Phil 1.11.18

7:00 – 5:00 ASRC NASA

• The Philosopher Redefining Equality (New Yorker profile of Elizabeth Anderson)
• She takes great pleasure in arranging information in useful forms; if she weren’t a philosopher, she thinks, she’d like to be a mapmaker, or a curator of archeological displays in museums.
• Trolling the U.S.: Q&A on Russian Interference in the 2016 Presidential Election
• Ryan Boyd and researchers from Carnegie Mellon University and Microsoft Research analyzed Facebook ads and Twitter troll accounts run by Russia’s Internet Research Agency (IRA) to determine how people with differing political ideologies were targeted and pitted against each other through this “largely unsophisticated and low-budget” operation. To learn more about the study and its findings, we asked Boyd the following questions:
• Boyd is an interesting guy. Here’s his twitter profile: Social/Personality Psychologist, Computational Social Scientist, and Occasional Software Developer.
• Applied for an invite to the TF Dev summit
• Work on text analytics?
• Extract data by groups, group, user and start looking at cross-correlations
• Started modifying post_analyzer.py
• PHP “story” generator?
• Updating IntelliJ
• More DB work

# Phil 11.6.18

7:00 – 2:00 ASRC PhD/BD

• Today’s big though: Maps are going top be easier than I thought. We’ve been doing  them for thousands of years with board games.
• Worked with Aaron on slides, including finding fault detection using our technologies. There is quite a bit, with pioneering work from NASA
• Called and left messages for Dr. Wilkins and Dr. Palazzolo. Need to send a follow-up email to Dr. Palazzolo and start on the short white papers
• Leaving early to vote
• The following two papers seem to be addressing edge stiffness
• Model of the Information Shock Waves in Social Network Based on the Special Continuum Neural Network
• The article proposes a special class of continuum neural network with varying activation thresholds and a specific neuronal interaction mechanism as a model of message distribution in social networks. Activation function for every neuron is fired as a decision of the specific systems of differential equations which describe the information distribution in the chain of the network graph. This class of models allows to take into account the specific mechanisms for transmitting messages, where individuals who, receiving a message, initially form their attitude towards it, and then decide on the further transmission of this message, provided that the corresponding potential of the interaction of two individuals exceeds a certain threshold level. The authors developed the original algorithm for calculating the time moments of message distribution in the corresponding chain, which comes to the solution of a series of Cauchy problems for systems of ordinary nonlinear differential equations.
• A cost-effective algorithm for inferring the trust between two individuals in social networks
• The popularity of social networks has significantly promoted online individual interaction in the society. In online individual interaction, trust plays a critical role. It is very important to infer the trust among individuals, especially for those who have not had direct contact previously in social networks. In this paper, a restricted traversal method is defined to identify the strong trust paths from the truster and the trustee. Then, these paths are aggregated to predict the trust rate between them. During the traversal on a social network, interest topics and topology features are comprehensively considered, where weighted interest topics are used to measure the semantic similarity between users. In addition, trust propagation ability of users is calculated to indicate micro topology information of the social network. In order to find the topk most trusted neighbors, two combination strategies for the above two factors are proposed in this paper. During trust inference, the traversal depth is constrained according to the heuristic rule based on the “small world” theory. Three versions of the trust rate inference algorithm are presented. The first algorithm merges interest topics and topology features into a hybrid measure for trusted neighbor selection. The other two algorithms consider these two factors in two different orders. For the purpose of performance analysis, experiments are conducted on a public and widely-used data set. The results show that our algorithms outperform the state-of-the-art algorithms in effectiveness. In the meantime, the efficiency of our algorithms is better than or comparable to those algorithms.
• Back to LSTMs. Made a numeric version of “all work and no play in the jack_torrance generator
• Reading in and writing out weight files. The predictions seems to be working well, but I have no insight into the arguments that go into the LSTM model. Going to revisit the Deep Learning with Keras book

# Phil 10.31.18

7:00 – ASRC PhD

• Read this carefully today: Introducing AdaNet: Fast and Flexible AutoML with Learning Guarantees
• Today, we’re excited to share AdaNet, a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on our recent reinforcement learning and evolutionary-based AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture, but also for learning to ensemble to obtain even better models.
• What about data from simulation?
• Github repo
• AdaNet is a lightweight and scalable TensorFlow AutoML framework for training and deploying adaptive neural networks using the AdaNet algorithm [Cortes et al. ICML 2017]. AdaNet combines several learned subnetworks in order to mitigate the complexity inherent in designing effective neural networks. This is not an official Google product.
• Tutorials: for understanding the AdaNet algorithm and learning to use this package
• Welcome to adanet! For a tour of this python package’s capabilities, please work through the following notebooks:
• This looks like it’s based deeply the cloud AI and Machine Learning products, including cloud-based hyperparameter tuning.
• Time series prediction is here as well, though treated in a more BigQuery manner
• In this blog post we show how to build a forecast-generating model using TensorFlow’s DNNRegressor class. The objective of the model is the following: Given FX rates in the last 10 minutes, predict FX rate one minute later.
• Text generation:
• Cloud poetry: training and hyperparameter tuning custom text models on Cloud ML Engine
• Let’s say we want to train a machine learning model to complete poems. Given one line of verse, the model should generate the next line. This is a hard problem—poetry is a sophisticated form of composition and wordplay. It seems harder than translation because there is no one-to-one relationship between the input (first line of a poem) and the output (the second line of the poem). It is somewhat similar to a model that provides answers to questions, except that we’re asking the model to be a lot more creative.
• Codelab: Google Developers Codelabs provide a guided, tutorial, hands-on coding experience. Most codelabs will step you through the process of building a small application, or adding a new feature to an existing application. They cover a wide range of topics such as Android Wear, Google Compute Engine, Project Tango, and Google APIs on iOS.
Codelab tools on GitHub

• Add the Range and Length section in my notes to the DARPA measurement section. Done. I need to start putting together the dissertation using these parts
• Read Open Source, Open Science, and the Replication Crisis in HCI. Broadly, it seems true, but trying to piggyback on GitHub seems like a shallow solution that repurposes something for coding – an ephemeral activity, to science, which is archival for a reason. Thought needs to be given to an integrated (collection, raw data, cleaned data, analysis, raw results, paper (with reviews?), slides, and possibly a recording of the talk with questions. What would it take to make this work across all science, from critical ethnographies to particle physics? How will it be accessible in 100 years? 500? 1,000? This is very much an HCI problem. It is about designing a useful socio-cultural interface. Some really good questions would be “how do we use our HCI tools to solve this problem?”, and, “does this point out the need for new/different tools?”.
• NASA AIMS meeting. Demo in 2 weeks. AIMS is “time series prediction”, A2P is “unstructured data”. Proove that we can actually do ML, as opposed to saying things.
• How about cross-point correlation? Could show in a sim?
• Meeting on Friday with a package
• We’ve solved A, here’s the vision for B – Z and a roadmap. JPSS is a near-term customer (JPSS Data)
• Getting actionable intelligence from the system logs
• Application portfolios for machine learning
• Umbrella of capabilities for Rich Burns
• New architectural framework for TTNC
• Software Engineering Division/Code 580
• A2P as a toolbox, but needs to have NASA-relevant analytic capabilities
• GMSEC overview

# Phil 10.2.18

7:00 – 5:00 ASRC Research

• Graph laplacian dissertation
• The spectrum of the normalized graph Laplacian can reveal structural properties of a network and can be an important tool to help solve the structural identification problem. From the spectrum, we attempt to develop a tool that helps us to understand the network structure on a deep level and to identify the source of the network to a greater extent. The information about different topological properties of a graph carried by the complete spectrum of the normalized graph Laplacian is explored. We investigate how and why structural properties are reflected by the spectrum and how the spectrum changes when compairing different networks from different sources.
• Universality classes in nonequilibrium lattice systems
• This article reviews our present knowledge of universality classes in nonequilibrium systems defined on regular lattices. The first section presents the most important critical exponents and relations, as well as the field-theoretical formalism used in the text. The second section briefly addresses the question of scaling behavior at first-order phase transitions. In Sec. III the author looks at dynamical extensions of basic static classes, showing the effects of mixing dynamics and of percolation. The main body of the review begins in Sec. IV, where genuine, dynamical universality classes specific to nonequilibrium systems are introduced. Section V considers such nonequilibrium classes in coupled, multicomponent systems. Most of the known nonequilibrium transition classes are explored in low dimensions between active and absorbing states of reaction-diffusion-type systems. However, by mapping they can be related to the universal behavior of interface growth models, which are treated in Sec. VI. The review ends with a summary of the classes of absorbing-state and mean-field systems and discusses some possible directions for future research.
• “The Government Spies Using Our Webcams:” The Language of Conspiracy Theories in Online Discussions
• Conspiracy theories are omnipresent in online discussions—whether to explain a late-breaking event that still lacks official report or to give voice to political dissent. Conspiracy theories evolve, multiply, and interconnect, further complicating efforts to limit their propagation. It is therefore crucial to develop scalable methods to examine the nature of conspiratorial discussions in online communities. What do users talk about when they discuss conspiracy theories online? What are the recurring elements in their discussions? What do these elements tell us about the way users think? This work answers these questions by analyzing over ten years of discussions in r/conspiracy—an online community on Reddit dedicated to conspiratorial discussions. We focus on the key elements of a conspiracy theory: the conspiratorial agents, the actions they perform, and their targets. By computationally detecting agent–action–target triplets in conspiratorial statements, and grouping them into semantically coherent clusters, we develop a notion of narrative-motif to detect recurring patterns of triplets. For example, a narrative-motif such as “governmental agency–controls–communications” appears in diverse conspiratorial statements alleging that governmental agencies control information to nefarious ends. Thus, narrative-motifs expose commonalities between multiple conspiracy theories even when they refer to different events or circumstances. In the process, these representations help us understand how users talk about conspiracy theories and offer us a means to interpret what they talk about. Our approach enables a population-scale study of conspiracy theories in alternative news and social media with implications for understanding their adoption and combating their spread
• Need to upload to ArXiv (try multiple tex files) – done!
• If I’m charging my 400 hours today, then start putting together text prediction. I’d like to try the Google prediction series to see what happens. Otherwise, there are two things I’d like to try with LSTMs, since they take 2 coordinates as inputs
• Use a 2D embedding space
• Use NLP to get a parts-of-speech (PoS) analysis of the text so that there can be a (PoS, Word) coordinate.
• Evaluate the 2 approaches on their ability to converge?
• Coordinating with Antonio about workshops. It’s the 2019 version of this: International Workshop on Massively Multi-Agent Systems (MMAS2018) in conjunction with IJCAI/ECAI/AAMAS/ICML 2018

# Phil 9.21.18

7:00 – 4:00 ASRC MKT

• “Who’s idea was it to connect every idiot on the internet with every other idiot” PJ O’Rourke, Commonwealth Club, 2018
• Running Programs In Reverse for Deeper A.I.” by Zenna Tavares
• In this talk I show that inverse simulation, i.e., running programs in reverse from output to input, lies at the heart of the hardest problems in both human cognition and artificial intelligence. How humans are able to reconstruct the rich 3D structure of the world from 2D images; how we predict that it is safe to cross a street just by watching others walk, and even how we play, and sometimes win at Jenga, are all solvable by running programs backwards. The idea of program inversion is old, but I will present one of the first approaches to take it literally. Our tool ReverseFlow combines deep-learning and our theory of parametric inversion to compile the source code of a program (e.g., a TensorFlow graph) into its inverse, even when it is not conventionally invertible. This framework offers a unified and practical approach to both understand and solve the aforementioned problems in vision, planning and inference for both humans and machines.
• Bot-ivistm: Assessing Information Manipulation in Social Media Using Network Analytics
• Matthew Benigni
• Kenneth Joseph
• Kathleen M. Carley (Scholar)
• Social influence bot networks are used to effect discussions in social media. While traditional social network methods have been used in assessing social media data, they are insufficient to identify and characterize social influence bots, the networks in which they reside and their behavior. However, these bots can be identified, their prevalence assessed, and their impact on groups assessed using high dimensional network analytics. This is illustrated using data from three different activist communities on Twitter—the “alt-right,” ISIS sympathizers in the Syrian revolution, and activists of the Euromaidan movement. We observe a new kind of behavior that social influence bots engage in—repetitive @mentions of each other. This behavior is used to manipulate complex network metrics, artificially inflating the influence of particular users and specific agendas. We show that this bot behavior can affect network measures by as much as 60% for accounts that are promoted by these bots. This requires a new method to differentiate “promoted accounts” from actual influencers. We present this method. We also present a method to identify social influence bot “sub-communities.” We show how an array of sub-communities across our datasets are used to promote different agendas, from more traditional foci (e.g., influence marketing) to more nefarious goals (e.g., promoting particular political ideologies).
• Pinged Aaron M. about writing an article
• More iConf paper. Got a first draft on everything but the discussion section

# Phil 8.30.18

7:00 – 5:00  ASRC MKT

• Target Blue Sky paper for iSchool/iConference 2019: The chairs are particularly looking for “Blue Sky Ideas” that are open-ended, possibly even “outrageous” or “wacky,” and present new problems, new application domains, or new methodologies that are likely to stimulate significant new research.
• I’m thinking that a paper that works through the ramifications of this diagram as it relates to people and machines. With humans that are slow responding with spongy, switched networks the flocking area is large. With a monolithic densely connected system it’s going to be a straight line from nomadic to stampede.
• Length: Up to 4 pages (excluding references)
• Submission deadline: October 1, 2018
• Final versions due: December 14, 2018
• First versions will be submitted using .pdf. Final versions must be submitted in .doc, .docx or La Tex.
• More good stuff on BBC Business Daily Trolling for Cash
• Anger and animosity is prevalent online, with some people even seeking it out. It’s present on social media of course as well as many online forums. But now outrage has spread to mainstream media outlets and even the advertising industry. So why is it so lucrative? Bonny Brooks, a writer and researcher at Newcastle University explains who is making money from outrage. Neuroscientist Dr Dean Burnett describes what happens to our brains when we see a comment designed to provoke us. And Curtis Silver, a tech writer for KnowTechie and ForbesTech, gives his thoughts on what we need to do to defend ourselves from this onslaught of outrage.
• Exposure to Opposing Views can Increase Political Polarization: Evidence from a Large-Scale Field Experiment on Social Media
• Christopher Bail (Scholar)
• There is mounting concern that social media sites contribute to political polarization by creating “echo chambers” that insulate people from opposing views about current events. We surveyed a large sample of Democrats and Republicans who visit Twitter at least three times each week about a range of social policy issues. One week later, we randomly assigned respondents to a treatment condition in which they were offered financial incentives to follow a Twitter bot for one month that exposed them to messages produced by elected officials, organizations, and other opinion leaders with opposing political ideologies. Respondents were re-surveyed at the end of the month to measure the effect of this treatment, and at regular intervals throughout the study period to monitor treatment compliance. We find that Republicans who followed a liberal Twitter bot became substantially more conservative post-treatment, and Democrats who followed a conservative Twitter bot became slightly more liberal post-treatment. These findings have important implications for the interdisciplinary literature on political polarization as well as the emerging field of computational social science.
• Setup gcloud tools on laptop – done
• Setup Tensorflow on laptop. Gave up un using CUDA 9.1, but got tf doing ‘hello, tensorflow’
• Marcom meeting – 2:00
• Get the concept of behaviors being a more scalable, dependable way of vetting information.
• Eg Watching the DISI of outrage as manifested in trolling
• “Uh. . . . not to be nitpicky,,,,,but…the past tense of drag is dragged, not drug.”: An overview of trolling strategies
• Dr Claire Hardaker (Scholar) (Blog)
• I primarily research aggression, deception, and manipulation in computer-mediated communication (CMC), including phenomena such as flaming, trolling, cyberbullying, and online grooming. I tend to take a forensic linguistic approach, based on a corpus linguistic methodology, but due to the multidisciplinary nature of my research, I also inevitably branch out into areas such as psychology, law, and computer science.
• This paper investigates the phenomenon known as trolling — the behaviour of being deliberately antagonistic or offensive via computer-mediated communication (CMC), typically for amusement’s sake. Having previously started to answer the question, what is trolling? (Hardaker 2010), this paper seeks to answer the next question, how is trolling carried out? To do this, I use software to extract 3,727 examples of user discussions and accusations of trolling from an eighty-six million word Usenet corpus. Initial findings suggest that trolling is perceived to broadly fall across a cline with covert strategies and overt strategies at each pole. I create a working taxonomy of perceived strategies that occur at different points along this cline, and conclude by refining my trolling definition.
• Citing papers
• FireAnt (Filter, Identify, Report, and Export Analysis Toolkit) is a freeware social media and data analysis toolkit with built-in visualization tools including time-series, geo-position (map), and network (graph) plotting.
• Fix marquee – done
• Export to ppt – done!
• include videos – done
• Center title in ppt:
• model considerations – done
• diversity injection – done
• Got the laptop running Python and Tensorflow. Had a stupid problem where I accidentally made a virtual environment and keras wouldn’t work. Removed, re-connected and restarted IntelliJ and everything is working!

# Phil 8.10.18

7:00 – ASRC MKT

• Finished the first pass through the SASO slides. Need to start working on timing (25 min + 5 min questions)
• Start on poster (A0 size)
• Sent Wayne a note to get permission for 899
• Started setting up laptop. I hate this part. Google drive took hours to synchronize
• Java
• Python/Nvidia/Tensorflow
• Intellij
• Visual Studio
• MikTex
• TexStudio
• Xampp
• Vim
• TortoiseSVN
• WinSCP
• 7-zip
• Creative Cloud
• Acrobat