# Phil 3.14.19

ASRC AIMS 7:00 – 4:00, PhD ML, 4:30 –

# Phil 11.7.18

Let the House Subcommittee investigations begin! Also, better redistricting?

7:00 – 5:00 ASRC PhD/BD

• Rather than Deep Learning with Keras, I’m starting on Grokking Deep Learning. I need better grounding
• Installed Jupyter
• After lunch, send follow-up emails to the technical POCs. This will be the basis for the white paper: Tentative findings/implications for design. Modify it on the blog page first and then use to create the LaTex doc. Make that one project, with different mains that share overlapping content.
• Characterizing Online Public Discussions through Patterns of Participant Interactions
• Public discussions on social media platforms are an intrinsic part of online information consumption. Characterizing the diverse range of discussions that can arise is crucial for these platforms, as they may seek to organize and curate them. This paper introduces a computational framework to characterize public discussions, relying on a representation that captures a broad set of social patterns which emerge from the interactions between interlocutors, comments and audience reactions. We apply our framework to study public discussions on Facebook at two complementary scales. First, we use it to predict the eventual trajectory of individual discussions, anticipating future antisocial actions (such as participants blocking each other) and forecasting a discussion’s growth. Second, we systematically analyze the variation of discussions across thousands of Facebook sub-communities, revealing subtle differences (and unexpected similarities) in how people interact when discussing online content. We further show that this variation is driven more by participant tendencies than by the content triggering these discussions.
• More latent space flocking from Innovation Hub
• You Share Everything With Your Bestie. Even Brain Waves.
•  Scientists have found that the brains of close friends respond in remarkably similar ways as they view a series of short videos: the same ebbs and swells of attention and distraction, the same peaking of reward processing here, boredom alerts there. The neural response patterns evoked by the videos — on subjects as diverse as the dangers of college football, the behavior of water in outer space, and Liam Neeson trying his hand at improv comedy — proved so congruent among friends, compared to patterns seen among people who were not friends, that the researchers could predict the strength of two people’s social bond based on their brain scans alone.

• Similar neural responses predict friendship
• Human social networks are overwhelmingly homophilous: individuals tend to befriend others who are similar to them in terms of a range of physical attributes (e.g., age, gender). Do similarities among friends reflect deeper similarities in how we perceive, interpret, and respond to the world? To test whether friendship, and more generally, social network proximity, is associated with increased similarity of real-time mental responding, we used functional magnetic resonance imaging to scan subjects’ brains during free viewing of naturalistic movies. Here we show evidence for neural homophily: neural responses when viewing audiovisual movies are exceptionally similar among friends, and that similarity decreases with increasing distance in a real-world social network. These results suggest that we are exceptionally similar to our friends in how we perceive and respond to the world around us, which has implications for interpersonal influence and attraction.
• Brain-to-Brain coupling: A mechanism for creating and sharing a social world
• Cognition materializes in an interpersonal space. The emergence of complex behaviors requires the coordination of actions among individuals according to a shared set of rules. Despite the central role of other individuals in shaping our minds, most cognitive studies focus on processes that occur within a single individual. We call for a shift from a single-brain to a multi-brain frame of reference. We argue that in many cases the neural processes in one brain are coupled to the neural processes in another brain via the transmission of a signal through the environment. Brain-to-brain coupling constrains and simplifies the actions of each individual in a social network, leading to complex joint behaviors that could not have emerged in isolation.
• Started reading Similar neural responses predict friendship

# 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 7.31.18

7:00 – 6:00 ASRC MKT

• Thinking that I need to forward the opinion dynamics part of the work. How heading differs from position and why that matters
• Found a nice adversarial herding chart from The Economist
• Why Do People Share Fake News? A Sociotechnical Model of Media Effects
• Fact-checking sites reflect fundamental misunderstandings about how information circulates online, what function political information plays in social contexts, and how and why people change their political opinions. Fact-checking is in many ways a response to the rapidly changing norms and practices of journalism, news gathering, and public debate. In other words, fact-checking best resembles a movement for reform within journalism, particularly in a moment when many journalists and members of the public believe that news coverage of the 2016 election contributed to the loss of Hillary Clinton. However, fact-checking (and another frequently-proposed solution, media literacy) is ineffectual in many cases and, in other cases, may cause people to “double-down” on their incorrect beliefs, producing a backlash effect.
• Epistemology in the Era of Fake News: An Exploration of Information Verification Behaviors among Social Networking Site Users
• Fake news has recently garnered increased attention across the world. Digital collaboration technologies now enable individuals to share information at unprecedented rates to advance their own ideologies. Much of this sharing occurs via social networking sites (SNSs), whose members may choose to share information without consideration for its authenticity. This research advances our understanding of information verification behaviors among SNS users in the context of fake news. Grounded in literature on the epistemology of testimony and theoretical perspectives on trust, we develop a news verification behavior research model and test six hypotheses with a survey of active SNS users. The empirical results confirm the significance of all proposed hypotheses. Perceptions of news sharers’ network (perceived cognitive homogeneity, social tie variety, and trust), perceptions of news authors (fake news awareness and perceived media credibility), and innate intentions to share all influence information verification behaviors among SNS members. Theoretical implications, as well as implications for SNS users and designers, are presented in the light of these findings.
• Working on plan diagram – done
• Organizing PhD slides. I think I’m getting near finished
• Walked through slides with Aaron. Need to practice the demo. A lot.

# Phil 7.27.18

Ted Underwood

• my research is as much about information science as literary criticism. I’m especially interested in applying machine learning to large digital collections
• Git repo with code for upcoming book: Distant Horizons: Digital Evidence and Literary Change
• Do topic models warp time?
• The key observation I wanted to share is just that topic models produce a kind of curved space when applied to long timelines; if you’re measuring distances between individual topic distributions, it may not be safe to assume that your yardstick means the same thing at every point in time. This is not a reason for despair: there are lots of good ways to address the distortion. The mathematics of cosine distance tend to work better if you average the documents first, and then measure the cosine between the averages (or “centroids”).
• The Historical Significance of Textual Distances
• Measuring similarity is a basic task in information retrieval, and now often a building-block for more complex arguments about cultural change. But do measures of textual similarity and distance really correspond to evidence about cultural proximity and differentiation? To explore that question empirically, this paper compares textual and social measures of the similarities between genres of English-language fiction. Existing measures of textual similarity (cosine similarity on tf-idf vectors or topic vectors) are also compared to new strategies that use supervised learning to anchor textual measurement in a social context.

7:00 – 8:00 ASRC MKT

• Continued on slides. I think I have the basics. Need to start looking for pictures
• Sent response to the SASO folks about who’s presenting what.

• More flailing on A2P UI? Oh, yeah….
• More RNN/LSTM?
• Slow progress. Spent some time cleaning up my single neuron spreadsheet, which does make more sense now.
• Some papers and source pointed to by the text:

# Phil 7.26.18

7:00 – 5:30 ASRC

• This could be interesting. Includes predictive analytics: BigQuery ML
• Working on slides
• Working on RNNs and LSTMS. I would love to build a simple, explanatory model in Excel, but can’t find one.
• Helped Aaron flail on getting tab dates into the A2P GUI

# Phil 7.25.18

7:00 – 3:00 ASRC

• Send out email with meeting time
• Rather than excerpts from the talks, do a demo of the relevant bits with conclusions and implications. Get the laptop running all the pieces. That means Python and TF and all the other bits.
• Submitted tuition expenses
• Submitted Fall 2018 approval
• Got SASO travel approval!
• More DNN study
• Finished CNNs
• Working on embeddings and W2V. Thought I’d try it on the laptop, but keras can’t find it’s back end and I’m getting other weird errors. One of the big ones was that I didn’t install tk with python. Here’s the answer from stackoverflow:
• And now we’re waiting a very long time for a tf ‘hello world’ to run… But it did!
• Had to also install pydot and graphviz-2.38.msi. Then add the graphviz bin directory to the path.
• But now everything runs on the laptop, which will help with the demos!
• Skipped the GloVe and pre-trained embeddings. Ready to start on DNNs tomorrow.

# Phil 7.20.18

• David Peritz
• Political polarization, accompanied by negative partisanship, are striking features of the current political landscape. Perhaps these trends were originally confined to politicians and the media, but we recently reached the point where the majority of Americans report they would consider it more objectionable if their children married across party lines than if they married someone of another faith. Where did this polarization come from? And what it is doing to American democracy, which is housed in institutions that were framed to encourage open deliberation, compromise and consensus formation? In this talk, Professor David Peritz will examine some of the deeper forces in the American economy, the public sphere and media, political institutions, and even moral psychology that best seem to account for the recent rise in popular polarization.

Sent out a Doodle to nail down the time for the PhD review

Went looking for something that talks about the cognitive load for TIT-FOR-TAT in the Iterated Prisoner’s Dilemma and can’t find anything. Did find this though, that is kind of interesting: New tack wins prisoner’s dilemma. It’s a collective intelligence approach:

• Teams could submit multiple strategies, or players, and the Southampton team submitted 60 programs. These, Jennings explained, were all slight variations on a theme and were designed to execute a known series of five to 10 moves by which they could recognize each other. Once two Southampton players recognized each other, they were designed to immediately assume “master and slave” roles – one would sacrifice itself so the other could win repeatedly.
• Nick Jennings
• Professor Jennings is an internationally-recognized authority in the areas of artificial intelligence, autonomous systems, cybersecurity and agent-based computing. His research covers both the science and the engineering of intelligent systems. He has undertaken fundamental research on automated bargaining, mechanism design, trust and reputation, coalition formation, human-agent collectives and crowd sourcing. He has also pioneered the application of multi-agent technology; developing real-world systems in domains such as business process management, smart energy systems, sensor networks, disaster response, telecommunications, citizen science and defence.
• Sarvapali D. (Gopal) Ramchurn
• I am a Professor of Artificial Intelligence in the Agents, Interaction, and Complexity Group (AIC), in the department of Electronics and Computer Science, at the University of Southampton and Chief Scientist for North Star, an AI startup.  I am also the director of the newly created Centre for Machine Intelligence.  I am interested in the development of autonomous agents and multi-agent systems and their application to Cyber Physical Systems (CPS) such as smart energy systems, the Internet of Things (IoT), and disaster response. My research combines a number of techniques from Machine learning, AI, Game theory, and HCI.

7:00 – 4:30 ASRC MKT

• SASO Travel request
• SASO Hotel – done! Aaaaand I booked for August rather than September. Sent a note to try and fix using their form. If nothing by COB try email.
• Potential DME repair?
• Starting Deep Learning with Keras. Done with chapter one
• Two seedbank lstm text examples:
• Generate Shakespeare using tf.keras
• This notebook demonstrates how to generate text using an RNN with tf.keras and eager execution.This notebook is an end-to-end example. When you run it, it will download a dataset of Shakespeare’s writing. The notebook will then train a model, and use it to generate sample output.
• CharRNN
• This notebook will let you input a file containing the text you want your generator to mimic, train your model, see the results, and save it for future use all in one page.

# Phil 7.19.18

7:00 – 3:00 ASRC MKT

• More on augmented athletics: Pinarello Nytro electric road bike review
• WhatsApp Research Awards for Social Science and Misinformation (\$50k – Applications are due by August 12, 2018, 11:59pm PST)
• Setting up meeting with Don for 3:30 Tuesday the 24th. He also gave me some nice leads on potential people for Dance my PhD:
• Dr. Linda Dusman
• Linda Dusman’s compositions and sonic art explore the richness of contemporary life, from the personal to the political. Her work has been awarded by the International Alliance for Women in Music, Meet the Composer, the Swiss Women’s Music Forum, the American Composers Forum, the International Electroacoustic Music Festival of Sao Paulo, Brazil, the Ucross Foundation, and the State of Maryland in 2004, 2006, and 2011 (in both the Music: Composition and the Visual Arts: Media categories). In 2009 she was honored as a Mid- Atlantic Arts Foundation Fellow for a residency at the Virginia Center for the Creative Arts. She was invited to serve as composer in residence at the New England Conservatory’s Summer Institute for Contemporary Piano in 2003. In the fall of 2006 Dr. Dusman was a Visiting Professor at the Conservatorio di musica “G. Nicolini” in Piacenza, Italy, and while there also lectured at the Conservatorio di musica “G. Verdi” in Milano. She recently received a Maryland Innovation Initiative grant for her development of Octava, a real-time program note system (octavaonline.com).
• Doug Hamby
• A choreographer who specializes in works created in collaboration with dancers, composers, visual artists and engineers. Before coming to UMBC he performed in several New York dance companies including the Martha Graham Dance Company and Doug Hamby Dance. He is the co-artistic director of Baltimore Dance Project, a professional dance company in residence at UMBC. Hamby’s work has been presented in New York City at Lincoln Center Out-of-Doors, Riverside Dance Festival, New York International Fringe Festival and in Brooklyn’s Prospect Park. His work has also been seen at Fringe Festivals in Philadelphia, Edinburgh, Scotland and Vancouver, British Columbia, as well as in Alaska. He has received choreography awards from the National Endowment for the Arts, Maryland State Arts Council, New York State Council for the Arts, Arts Council of Montgomery County, and the Baltimore Mayor’s Advisory Committee on Arts and Culture. He has appeared on national television as a giant slice of American Cheese.
• Sent out a note with dates and agenda to the committee for the PhD review thing. Thom can open up August 6th
• Continuing extraction of seed terms for the sentence generation. And it looks like my tasking for next sprint will be to put together a nice framework for plugging in predictive patterns systems like LSTM and multi-layer perceptrons.
• This seems to be working:
agentRelationships GreenFlockSh_1
sampleData 0.0
cell cell_[4, 6]
influences AGENT
influence GreenFlockSh_0 val =  0.8778825396520958
influence GreenFlockSh_2 val =  0.8859173062045552
influence GreenFlockSh_3 val =  0.9390368569108515
influence GreenFlockSh_4 val =  0.9774328763377834
influences SOURCE
influence UL_point val =  0.032906293611796644
• Sprint planning
• VP-613: Develop general TensorFlow/Keras NN format
• LSTM
• MLP
• CNN
• VP-616: SASO Preparation
• Slides
• Poster
• Demo

# Phil 5.31.18

7:00 – ASRC MKT

• Via BBC Business Daily, found this interesting post on diversity injection through lunch table size:
• KQED is playing America Abroad – today on russian disinfo ops:
• Sowing Chaos: Russia’s Disinformation Wars
• Revelations of Russian meddling in the 2016 US presidential election were a shock to Americans. But it wasn’t quite as surprising to people in former Soviet states and the EU. For years they’ve been exposed to Russian disinformation and slanted state media; before that Soviet propaganda filtered into the mainstream. We don’t know how effective Russian information warfare was in swaying the US election. But we do know these tactics have roots going back decades and will most likely be used for years to come. This hour, we’ll hear stories of Russian disinformation and attempts to sow chaos in Europe and the United States. We’ll learn how Russia uses its state-run media to give a platform to conspiracy theorists and how it invites viewers to doubt the accuracy of other news outlets. And we’ll look at the evolution of internet trolling from individuals to large troll farms. And — finally — what can be done to counter all this?
• Some interesting papers on the “Naming Game“, a form of coordination where individuals have to agree on a name for something. This means that there is some kind of dimension reduction involved from all the naming possibilities to the agreed-on name.
• The Grounded Colour Naming Game
• Colour naming games are idealised communicative interactions within a population of artificial agents in which a speaker uses a single colour term to draw the attention of a hearer to a particular object in a shared context. Through a series of such games, a colour lexicon can be developed that is sufficiently shared to allow for successful communication, even when the agents start out without any predefined categories. In previous models of colour naming games, the shared context was typically artificially generated from a set of colour stimuli and both agents in the interaction perceive this environment in an identical way. In this paper, we investigate the dynamics of the colour naming game in a robotic setup in which humanoid robots perceive a set of colourful objects from their own perspective. We compare the resulting colour ontologies to those found in human languages and show how these ontologies reflect the environment in which they were developed.
• Group-size Regulation in Self-Organised Aggregation through the Naming Game
• In this paper, we study the interaction effect between the naming game and one of the simplest, yet most important collective behaviour studied in swarm robotics: self-organised aggregation. This collective behaviour can be seen as the building blocks for many others, as it is required in order to gather robots, unable to sense their global position, at a single location. Achieving this collective behaviour is particularly challenging, especially in environments without landmarks. Here, we augment a classical aggregation algorithm with a naming game model. Experiments reveal that this combination extends the capabilities of the naming game as well as of aggregation: It allows the emergence of more than one word, and allows aggregation to form a controllable number of groups. These results are very promising in the context of collective exploration, as it allows robots to divide the environment in different portions and at the same time give a name to each portion, which can be used for more advanced subsequent collective behaviours.
• More Bit by Bit. Could use some worked examples. Also a login so I’m not nagged to buy a book I own.
• Descriptive and injunctive norms – The transsituational influence of social norms.
• Three studies examined the behavioral implications of a conceptual distinction between 2 types of social norms: descriptive norms, which specify what is typically done in a given setting, and injunctive norms, which specify what is typically approved in society. Using the social norm against littering, injunctive norm salience procedures were more robust in their behavioral impact across situations than were descriptive norm salience procedures. Focusing Ss on the injunctive norm suppressed littering regardless of whether the environment was clean or littered (Study 1) and regardless of whether the environment in which Ss could litter was the same as or different from that in which the norm was evoked (Studies 2 and 3). The impact of focusing Ss on the descriptive norm was much less general. Conceptual implications for a focus theory of normative conduct are discussed along with practical implications for increasing socially desirable behavior.
• Construct validity centers around the match between the data and the theoretical constructs. As discussed in chapter 2, constructs are abstract concepts that social scientists reason about. Unfortunately, these abstract concepts don’t always have clear definitions and measurements.
• Simulation is a way of implementing theoretical constructs that are measurable and testable.
• Hyperparameter Optimization with Keras
• Recognizing images from parts Kaggle winner
• White paper
• Storyboard meeting
• The advanced analytics division(?) needs a modeling and simulation department that builds models that feed ML systems.
• Meeting with Steve Specht – adding geospatial to white paper

# Phil 5.25.18

7:00 – 6:00 ASRC MKT

• Starting Bit by Bit
• I realized the hook for the white paper is the military importance of maps. I found A Revolution in Military Cartography?: Europe 1650-1815
• Military cartography is studied in order to approach the role of information in war. This serves as an opportunity to reconsider the Military Revolution and in particular changes in the eighteenth century. Mapping is approached not only in tactical, operational and strategic terms, but also with reference to the mapping of war for public interest. Shifts in the latter reflect changes in the geography of European conflict.
• Reconnoitering sketch from Instructions in the duties of cavalry reconnoitring an enemy; marches; outposts; and reconnaissance of a country; for the use of military cavalry. 1876 (pg 83)
• rutter is a mariner’s handbook of written sailing directions. Before the advent of nautical charts, rutters were the primary store of geographic information for maritime navigation.
• It was known as a periplus (“sailing-around” book) in classical antiquity and a portolano (“port book”) to medieval Italian sailors in the Mediterranean Sea. Portuguese navigators of the 16th century called it a roteiro, the French a routier, from which the English word “rutter” is derived. In Dutch, it was called a leeskarte (“reading chart”), in German a Seebuch (“sea book”), and in Spanish a derroterro
• Example from ancient Greece:
• From the mouth of the Ister called Psilon to the second mouth is sixty stadia.
• Thence to the mouth called Calon forty stadia.
• From Calon to Naracum, which last is the name of the fourth mouth of the Ister, sixty stadia.
• Hence to the fifth mouth a hundred and twenty stadia.
• Hence to the city of Istria five hundred stadia.
• From Istria to the city of Tomea three hundred stadia.
• From Tomea to the city of Callantra, where there is a port, three hundred stadia
• Battlespace
• Cyber-Human Systems (CHS)
• In a world in which computers and networks are increasingly ubiquitous, computing, information, and computation play a central role in how humans work, learn, live, discover, and communicate. Technology is increasingly embedded throughout society, and is becoming commonplace in almost everything we do. The boundaries between humans and technology are shrinking to the point where socio-technical systems are becoming natural extensions to our human experience – second nature, helping us, caring for us, and enhancing us. As a result, computing technologies and human lives, organizations, and societies are co-evolving, transforming each other in the process. Cyber-Human Systems (CHS) research explores potentially transformative and disruptive ideas, novel theories, and technological innovations in computer and information science that accelerate both the creation and understanding of the complex and increasingly coupled relationships between humans and technology with the broad goal of advancing human capabilities: perceptual and cognitive, physical and virtual, social and societal.
• Reworked Section 1 to incorporate all this in a single paragraph
• Long discussion about all of the above with Aaron
• Worked on getting the CoE together by CoB
• Do Diffusion Protocols Govern Cascade Growth?
• Continuing with creating the Simplest LSTM ever
• All work and no play makes jack a dull boy indexes alphabetically as :

# Phil 5.22.18

8:00 – 5:00 ASRC MKT

• EAMS meeting
• Rational
• Sensitivity knn. Marching cubes, or write into space. Pos lat/lon altitude speed lat lon (4 dimensions)
• Do they have flight path?
• Memory
• Retraining (batch)
• inference real time
• How will time be used
• Much discussion of simulation
• End-to-end Machine Learning with Tensorflow on GCP
• In this workshop, we walk through the process of building a complete machine learning pipeline covering ingest, exploration, training, evaluation, deployment, and prediction. Along the way, we will discuss how to explore and split large data sets correctly using BigQuery and Cloud Datalab. The machine learning model in TensorFlow will be developed on a small sample locally. The preprocessing operations will be implemented in Cloud Dataflow, so that the same preprocessing can be applied in streaming mode as well. The training of the model will then be distributed and scaled out on Cloud ML Engine. The trained model will be deployed as a microservice and predictions invoked from a web application. This lab consists of 7 parts and will take you about 3 hours. It goes along with this slide deck
• Slides
• Codelab
• Added in JuryRoom Text rough. Next is Research Browser
• Worked with Aaron on LSTM some more. More ndarray slicing experience:
import numpy as np
dimension = 3
size = 10
dataset1 = np.ndarray(shape=(size, dimension))
dataset2 = np.ndarray(shape=(size, dimension))
for x in range(size):
for y in range(dimension):
val = (y+1) * 10 + x +1
dataset1[x,y] = val
val = (y+1) * 100 + x +1
dataset2[x,y] = val

dataset1[:, 0:1] = dataset2[:, -1:]
print(dataset1)
print(dataset2)
• Results in:
[[301.  21.  31.]
[302.  22.  32.]
[303.  23.  33.]
[304.  24.  34.]
[305.  25.  35.]
[306.  26.  36.]
[307.  27.  37.]
[308.  28.  38.]
[309.  29.  39.]
[310.  30.  40.]]
[[101. 201. 301.]
[102. 202. 302.]
[103. 203. 303.]
[104. 204. 304.]
[105. 205. 305.]
[106. 206. 306.]
[107. 207. 307.]
[108. 208. 308.]
[109. 209. 309.]
[110. 210. 310.]]