Category Archives: research

Phil 10.18.18

7:00 – 9:00, 12:00 – ASRC PhD

  • Reading the New Yorker piece How Russia Helped Swing the Election for Trump, about Kathleen Hall Jamieson‘s book Cyberwar: How Russian Hackers and Trolls Helped Elect a President—What We Don’t, Can’t, and Do Know. Some interesting points with respect to Adversarial Herding:
    • Jamieson’s Post article was grounded in years of scholarship on political persuasion. She noted that political messages are especially effective when they are sent by trusted sources, such as members of one’s own community. Russian operatives, it turned out, disguised themselves in precisely this way. As the Times first reported, on June 8, 2016, a Facebook user depicting himself as Melvin Redick, a genial family man from Harrisburg, Pennsylvania, posted a link to DCLeaks.com, and wrote that users should check out “the hidden truth about Hillary Clinton, George Soros and other leaders of the US.” The profile photograph of “Redick” showed him in a backward baseball cap, alongside his young daughter—but Pennsylvania records showed no evidence of Redick’s existence, and the photograph matched an image of an unsuspecting man in Brazil. U.S. intelligence experts later announced, “with high confidence,” that DCLeaks was the creation of the G.R.U., Russia’s military-intelligence agency.
    • Jamieson argues that the impact of the Russian cyberwar was likely enhanced by its consistency with messaging from Trump’s campaign, and by its strategic alignment with the campaign’s geographic and demographic objectives. Had the Kremlin tried to push voters in a new direction, its effort might have failed. But, Jamieson concluded, the Russian saboteurs nimbly amplified Trump’s divisive rhetoric on immigrants, minorities, and Muslims, among other signature topics, and targeted constituencies that he needed to reach. 
  • Twitter released IRA dataset (announcement, archive), and Kate Starbird’s group has done some preliminary analysis
  • Need to do something about the NESTA Call for Ideas, which is due “11am on Friday 9th November
  • Continuing with Market-Oriented Programming
    • Some thoughts on what the “cost” for a trip can reference
      • Passenger
        • Ticket price
          • provider: Current price, refundability, includes taxes
            • carbon
            • congestion
            • other?
          • consumer: Acceptable range
        • Travel time
        • Departure time
        • Arrival time (plus arrival time confidence)
        • comfort (legroom, AC)
        • Number of stops (related to convenience)
        • Number of passengers
        • Time to wait
        • Externalities like airport security, which adds +/- 2 hours to air travel
      • Cargo
        • Divisibility (ship as one or more items)
        • Physical state for shipping (packaged, indivisible solid, fluid, gas)
          • Waste to food grade to living (is there a difference between algae and cattle? Pets? Show horses?
          • Refrigerated/heated
          • Danger
          • Stability/lifespan
          • weight
      • Aggregators provide simpler combinations of transportation options
    • Any exchange that supports this format should be able to participate. Additionally, each exchange should contain a list of other exchanges that a consumer can request, so we don’t need another level of hierarchy. Exchanges could rate other exchanges as a quality measure
      • It also occurs to me that there could be some kind of peer-to-peer or mesh network for degraded modes. A degraded mode implies a certain level of emergency, which would affect the (now small-scale) allocation of resources.
    • Some stuff about Mobility as a Service. Slide deck (from Canada Intelligent Transportation Service), and an app (Whim)
  • PSC AI/ML working group 9:00 – 12:00, plus writeup
    • PSC will convene a working group meeting on Thursday, Oct. 18 from 9am – 10am to identify actions and policy considerations related to advancing the use of AI solutions in government. Come prepared to share your ideas and experience. We would welcome your specific feedback on these questions:
      • How can PSC help make the government a “smarter buyer” when it comes to AI/ML?
      • How are agencies effectively using AI/ML today?
      • In what other areas could these technologies be deployed in government today?
        • Looking for bad sensors on NOAA satellites
      • What is the current federal market and potential future market for AI/ML?
      • Notes:
        • How to help our members – federal contracts. Help make the federal market frictionless
        • Kevin – SmartForm? What are the main gvt concerns? Is it worry about False positives?
          • Competitiveness – no national strategy
          • Appropriate use, particularly law enforcement
          • Robotic Process Automation (RPA) Security, Compliancy, and adoption. Compliancy testing.
          • Data trust. Humans make errors. When ML makes the same errors, it’s worse.
        • A system that takes time to get accurate watching people perform is not the kind of system that the government can buy.
          • This implies that there has to be immediate benefit, and can have the possibility of downstream benefit.
        • Dell would love to participate (in what?) Something about cloud
        • Replacing legacy processes with better approaches
        • Fedramp-like compliance mechanism for AI. It is a requirement if it is a cloud service.
        • Perceived, implicit bias is the dominant narrative on the government side. Specific applications like facial recognition
        • Take a look at all the laws that might affect AI, to see how the constraints are affecting adoption/use with an eye towards removing barriers
        • Chris ?? There isn’t a very good understanding or clear linkage between the the promise and the current problems, such as staffing, tagged data, etc
        • What does it mean to be reskilled and retrained in an AI context?
        • President’s Management Agenda
        • The killer app is cost savings, particularly when one part of government is getting a better price than another part.
        • Federal Data Strategy
        • Send a note to Kevin about data availability. The difference between NOAA sensor data (clean and abundant), vs financial data, constantly changing spreadsheets that are not standardized. Maybe the creation of tools that make it easier to standardize data than use artisanal (usually Excel-based) solutions. Wrote it up for Aaron to review. It turned out to be a page.

Phil 10.3.18

7:00 – 5:30 ASRC MKT

  • Finished At Home in the Universe. Really good. I’ll work on writing up notes this evening. The Kindle clippings feature is awesome
  • The stampeding robots paper is up on ArXiv: Disrupting the Coming Robot Stampedes: Designing Resilient Information Ecologies
  • Dopamine modulates novelty seeking behavior during decision making.
  • Need to finish Antonio’s paper, but my sense at this point is to add our work as a discussion of edge conditions that come up in the discussion section?
    • Done. Sent a letter discussing NIST RCS
  • Need to write up the fitness landscape thoughts. One axis is distance to model which is has a decay radius from each agent. Another axis is the price of an item(with future discounting?). Another axis is cost by agent to acquire the item. Cluster behavior emerges from local agents trying to find the best model and acquire the most value? There is also some kind of explicit connection between individuals that needs to be handled (a tanker and a plane have a client-server relationship that requires them to move in a coordinated way)
    • There is also information that is within the agents, and information that is in the environment. There may be other types of information as well.
  • Get Matt rolling on the whitepaper? – done!
  • Watson backend to A2P?
  • Kibitzed Aaron on how to access style sheets
  • Got about halfway through speaking notes on Army BAA

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!Arxiv
  • 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 8.29.18

7:00 – 4:30 ASRC MKT

  • Editing videos
  • Need to think about short CHI paper about designing for culture/robot interactions. The trolly problem at scale? How would the sim be set up? The amount of randomness at the initial condition? Stiffness vs. connectivity? Beleif space is still important and is actually used as a concept in path planning
  • Visual Exploration and Comparison of Word Embeddings
    • Word embeddings are distributed representations for natural language words, and have been wildly used in many natural language processing tasks. The word embedding space contains local clusters with semantically similar words and meaningful directions, such as the analogy. However, there are different training algorithms and text corpora, which both have a different impact on the generated word embeddings. In this paper, we propose a visual analytics system to visually explore and compare word embeddings trained by different algorithms and corpora. The word embedding spaces are compared from three aspects, i.e., local clusters, semantic directions and diachronic changes, to understand the similarity and differences between word embeddings.
  • Much work on slides
  • Can’t get Google to recognise my account?
    curl.exe -H "Content-Type: application/json" -H "Authorization: Bearer "$(gcloud auth application-default print-access-token) https://speech.google
    apis.com/v1/speech:recognize -d @sync-request.json
    curl: (6) Could not resolve host: ya29.c.EloHBu32-0nBAqimi1Zumlot6rjGtGpUk27qTTESRLW4vtd1LY4ihxBIesU3ga-kmwCaM7YZS-JRo_KNjaC_bj13dWazBcKr4YtAEQYFzSpSBx3DwdS46DTt0bg
    {
      "error": {
        "code": 403,
        "message": "The request is missing a valid API key.",
        "status": "PERMISSION_DENIED"
      }
    }

    No idea what host: ya29.c.EloHBu32-0nBAqimi1Zumlot6rjGtGpUk27qTTESRLW4vtd1LY4ihxBIesU3ga-kmwCaM7YZS-JRo_KNjaC_bj13dWazBcKr4YtAEQYFzSpSBx3DwdS46DTt0bg is

  • Found a problem with the poster. There are two herding DTW charts. Must be reprinted

Phil 8.27.18

7:00 – 5:00 ASRC MKT

  • Good chat with Barbara yesterday. She suggests horse racing podcasts, since the question is always the same “who’s going to win today” and the information to discuss is much more constrained. Additionally, there is the wagering information that could be used to determine the level of consensus?
  • Found an idiom translator! “Swing of the pendulum” occurs at least in French, German and Italian
  • Downloaded the new videos Need to put them in the ppt when the slides stabilize
  • Pinged Wayne about getting together today
  • Changed the questions page to have English, Italian, French and German terms for belief space
  • Another example of diversity injection (twitter)
  • Working on podcast text handling
      • Created the MapsFromPodcasts project in Development
      • Created an new key and downloaded the key json file
      • Installed Google Cloud Tools (213.0.0), following the directions of this page. Wow. Lots of stuff!
        Output folder: D:\Programs\GoogleCloudAPI
        Downloading Google Cloud SDK core.
        Extracting Google Cloud SDK core.
        Create Google Cloud SDK bat file: D:\Programs\GoogleCloudAPI\cloud_env.bat
        Installing components.
        Welcome to the Google Cloud SDK!
        Your current Cloud SDK version is: 213.0.0
        Installing components from version: 213.0.0
        +-----------------------------------------------------------------------------+
        | These components will be installed. |
        +-----------------------------------------------------+------------+----------+
        | Name | Version | Size |
        +-----------------------------------------------------+------------+----------+
        | BigQuery Command Line Tool | 2.0.34 | < 1 MiB |
        | BigQuery Command Line Tool (Platform Specific) | 2.0.34 | < 1 MiB |
        | Cloud SDK Core Libraries (Platform Specific) | 2018.06.18 | < 1 MiB |
        | Cloud Storage Command Line Tool | 4.33 | 3.6 MiB |
        | Cloud Storage Command Line Tool (Platform Specific) | 4.32 | < 1 MiB |
        | Cloud Tools for PowerShell | | |
        | Cloud Tools for PowerShell | 1.0.1.8 | 17.9 MiB |
        | Default set of gcloud commands | | |
        | Windows command line ssh tools | | |
        | Windows command line ssh tools | 2017.09.15 | 1.8 MiB |
        | gcloud cli dependencies | 2018.08.03 | 1.3 MiB |
        +-----------------------------------------------------+------------+----------+
        For the latest full release notes, please visit:
        https://cloud.google.com/sdk/release_notes
        #============================================================#
        #= Creating update staging area =#
        #============================================================#
        #= Installing: BigQuery Command Line Tool =#
        #============================================================#
        #= Installing: BigQuery Command Line Tool (Platform Spec... =#
        #============================================================#
        #= Installing: Cloud SDK Core Libraries (Platform Specific) =#
        #============================================================#
        #= Installing: Cloud Storage Command Line Tool =#
        #============================================================#
        #= Installing: Cloud Storage Command Line Tool (Platform... =#
        #============================================================#
        #= Installing: Cloud Tools for PowerShell =#
        #============================================================#
        #= Installing: Cloud Tools for PowerShell =#
        #============================================================#
        #= Installing: Default set of gcloud commands =#
        #============================================================#
        #= Installing: Windows command line ssh tools =#
        #============================================================#
        #= Installing: Windows command line ssh tools =#
        #============================================================#
        #= Installing: gcloud cli dependencies =#
        #============================================================#
        #= Creating backup and activating new installation =#
        #============================================================#
        Performing post processing steps...
        ..............................................................................................................................................................done.
        Update done!
        This will install all the core command line tools necessary for working with
        the Google Cloud Platform.
        For more information on how to get started, please visit:
        https://cloud.google.com/sdk/docs/quickstarts
        Google Cloud SDK has been installed!

         

     

    • Google is sooooooooooooooooooooo Unix/Linux
  • Meeting with Wayne
    • Fix slides some more
    • Email about demo and poster – done

Phil 8.23.18

7:00 – 5:30 ASRC MKT

dlr99umvaaed9rk

  • Slides
    • Groups/tribes stay the same, but the topics change
    • Past polarizing topics:
      • Confederate statues
      • Kneeling for the national anthem
      • #blacklivesmatter
      • Hoodies
      • Crack cocaine
      • 1968 Olympics Black Power salute
      • Alabama bus boycott
    • Stiffening a group creates a stampede (In-group high SIH)
    • Adding group-invisible diversity disrupts the velocity and direction of a stampede
    • Arendt/Moscovici slide “So we’re doomed, right! Except…”
    • See what velocity of the disrupted stampede looks like
  • Why Trump Supporters Believe He Is Not Corrupt
    • The answer may lie in how Trump and his supporters define corruption. In a forthcoming book titled How Fascism Works, the Yale philosophy professor Jason Stanley makes an intriguing claim. “Corruption, to the fascist politician,” he suggests, “is really about the corruption of purity rather than of the law. Officially, the fascist politician’s denunciations of corruption sound like a denunciation of political corruption. But such talk is intended to evoke corruption in the sense of the usurpation of the traditional order.”
  • Climate science proposals are being reviewed by Ryan Zinke’s old football buddy. Seriously.
    • But what if the corruption isn’t hidden at all, but right out in the open? What if, when it’s identified, the perpetrator doesn’t apologize, or demonstrate any remorse or shame, and there’s no punishment? What then? We don’t really have good narratives around what happens in that situation, which is why the Trump administration so often leaves us sputtering and gawking. It can’t just be a motley collection of incompetent grifters, each misruling their own little fiefdom, trying to stay in their boss’s good graces, succeeding less through wits than a congenital lack of shame and the unstinting institutional support of GOP donors. Can it?

Phil 8.19.18

7:00 – 5:30 ASRC MKT

  • Had a thought that the incomprehension that comes from misalignment that Stephens shows resembles polarizing light. I need to add a slider that enables influence as a function of alignment. Done
    • Getting the direction cosine between the source and target belief
      double interAgentDotProduct = unitOrientVector.dotProduct(otherUnitOrientVector);
      double cosTheta = Math.min(1.0, interAgentDotProduct);
      double beliefAlignment = Math.toDegrees(Math.acos(cosTheta));
      double interAgentAlignment = (1.0 - beliefAlignment/180.0);
    • Adding a global variable that sets how much influence (0% – 100%) influence from an opposing agent. Just setting it to on/off, because the effects are actually pretty subtle
  • Add David’s contributions to slide one writeup – done
  • Start slide 2 writeup
  • Find casters for Dad’s walker
  • Submit forms for DME repair
    • Drat – I need the ECU number
  • Practice talk!
    • Need to reduce complexity and add clearly labeled sections, in particular methods
  • I need to start paying attention to attention
  • Also, keeping this on the list How social media took us from Tahrir Square to Donald Trump by Zeynep Tufekci
  • Social Identity Threat Motivates Science – Discrediting Online Comments
    • Experiencing social identity threat from scientific findings can lead people to cognitively devalue the respective findings. Three studies examined whether potentially threatening scientific findings motivate group members to take action against the respective findings by publicly discrediting them on the Web. Results show that strongly (vs. weakly) identified group members (i.e., people who identified as “gamers”) were particularly likely to discredit social identity threatening findings publicly (i.e., studies that found an effect of playing violent video games on aggression). A content analytical evaluation of online comments revealed that social identification specifically predicted critiques of the methodology employed in potentially threatening, but not in non-threatening research (Study 2). Furthermore, when participants were collectively (vs. self-) affirmed, identification did no longer predict discrediting posting behavior (Study 3). These findings contribute to the understanding of the formation of online collective action and add to the burgeoning literature on the question why certain scientific findings sometimes face a broad public opposition.

Phil 8.8.18

7:00 – 4:00 ASRC MKT

  • Oh, look, a new Tensorflow (1.10). Time to break things. I like the BigTable integration though.
  • Learning Meaning in Natural Language Processing — A Discussion
    • Last week a tweet by Jacob Andreas triggered a huge discussion on Twitter that many people have called the meaning/semantics mega-thread. Twitter is a great medium for having such a discussion, replying to any comment allows to revive the debate from the most promising point when it’s stuck in a dead-end. Unfortunately Twitter also makes the discussion very hard to read afterwards so I made three entry points to explore this fascinating mega-thread:

      1. a summary of the discussion that you will find below,
      2. an interactive view to explore the trees of tweets, and
      3. commented map to get an overview of the main points discussed:
  • The Current Best of Universal Word Embeddings and Sentence Embeddings
    • This post is thus a brief primer on the current state-of-the-art in Universal Word and Sentence Embeddings, detailing a few

      • strong/fast baselines: FastText, Bag-of-Words
      • state-of-the-art models: ELMo, Skip-Thoughts, Quick-Thoughts, InferSent, MILA/MSR’s General Purpose Sentence Representations & Google’s Universal Sentence Encoder.

      If you want some background on what happened before 2017 😀, I recommend the nice post on word embeddings that Sebastian wrote last year and his intro posts.

  • Treeverse is a browser extension for navigating burgeoning Twitter conversations. right_pane
  • Detecting computer-generated random responding in questionnaire-based data: A comparison of seven indices
    • With the development of online data collection and instruments such as Amazon’s Mechanical Turk (MTurk), the appearance of malicious software that generates responses to surveys in order to earn money represents a major issue, for both economic and scientific reasons. Indeed, even if paying one respondent to complete one questionnaire represents a very small cost, the multiplication of botnets providing invalid response sets may ultimately reduce study validity while increasing research costs. Several techniques have been proposed thus far to detect problematic human response sets, but little research has been undertaken to test the extent to which they actually detect nonhuman response sets. Thus, we proposed to conduct an empirical comparison of these indices. Assuming that most botnet programs are based on random uniform distributions of responses, we present and compare seven indices in this study to detect nonhuman response sets. A sample of 1,967 human respondents was mixed with different percentages (i.e., from 5% to 50%) of simulated random response sets. Three of the seven indices (i.e., response coherence, Mahalanobis distance, and person–total correlation) appear to be the best estimators for detecting nonhuman response sets. Given that two of those indices—Mahalanobis distance and person–total correlation—are calculated easily, every researcher working with online questionnaires could use them to screen for the presence of such invalid data.
  • Continuing to work on SASO slides – close to done. Got a lot of adversarial herding FB examples from the House Permanent Committee on Intelligence. Need to add them to the slide. Sobering.
  • And this looks like a FANTASTIC ride out of Trento: ridewithgps.com/routes/27552411
  • Fixed the border menu so that it’s a toggle group

Phil 8.7.18

8:00 – ASRC MKT

  • Looking for discussion transcripts.
  • Podcasts
    • Do you get your heart broken by the Nationals, Wizards, Caps and Redskins every single year but you still come back for more? The DMV Sports Roundtable is the podcast for you – Washington’s sports teams from the fans’ perspective – and plenty of college coverage too.
    • Join UCB Theatre veterans Cody Lindquist & Charlie Todd as they welcome a panel of NYC’s most hilarious comedians, journalists, and politicians to chug two beers on stage and discuss the politics of the week. It’s like Meet The Press, but funnier and with more alcohol. Theme song by Tyler Walker.
    • Rasslin Roundtable: Wrestling podcast centered around the latest PPV
    • TSN 1290 Roundtable: Kevin Olszewski hosts the Donvito Roundtable, airing weekdays from 11am-1pm CT on TSN 1290 Winnipeg. Daily discussion about the Winnipeg Jets, the NHL, and whatever else is on his mind!
    • The Game Design Round Table Focusing on both digital and tabletop gaming, The Game Design Round Table provides a forum for conversation about critical issues to game design.
    • Story Works Round Table Before you can be a successful author, you have to write a great story. Each week, co-hosts, Alida Winternheimer, author and writing coach at Word Essential, Kathryn Arnold, emerging writer, & Robert Scanlon, author of the Blood Empire series, have conversations about the craft of writing fiction. They bring diverse experiences and talents to the table from both the traditional and indie worlds. Our goal is for each episode to be a fun, lively discussion of some aspect of story craft that that enlightens, as well as entertains.
  • Some good pix of bike-share graveyards in China that would be good stampede pix from The Atlantic (set 1) (set 2) Bicycles of various bike-sharing services are seen in Shanghai.
  • Starting back on the SASO slides. Based on Wayne’s comments, I’m reworking the Stephens’ slide
    • Flashes of Insight: Whole-Brain Imaging of Neural Activity in the Zebrafish (video)(paper)(paper)

Phil 7.1.18

On vacation, but oddly enough, I’m back on my morning schedule, so here I am in Bormio, Italy at 4:30 am.

I forgot my HDMI adaptor for the laptop. Need to order one and have it delivered to Zurich – Hmmm. Can’t seem to get it delivered from Amazon to a hotel. Will have to buy in Zurich

Need to add Gamerfate to the lit review timeline to show where I started to get interested in the problem – tried it but didn’t like it. I’d have to redo the timeline and I’m not sure I have the excel file

Add vacation pictures to slides – done!

Some random thoughts

  • When using the belief space example of the table, note that if we sum up all the discussions about tables, we would be able to build a pretty god map of what matters to people with regards to tables
  • Manifold learning is what intelligent systems do as a way of determining relationships between things (see curse of dimensionality). As groups of individuals, we need to coordinate our manifold learning activities so that we can us the power of group cognition. When looking at how manifold learning schemes like t-sne and particularly embedding systems such as word2vec create their own unique embeddings, it becomes clear that our machines are not yet engaged in group cognition, except in the simplest way of re-using trained networks and copied hyperparameters. This is very prone to stampedes
  • In conversation at dinner, Mike M mentioned that he’d like a language app that is able to indicate the centrality of a term an order that list so that it’s possible to learn a language in a “prioritized” way that can be context-dependent. I think that LMN with a few tweaks could do that.

Continuing the Evolution of Cooperation. A thing that strikes me is that once a TIT FOR TAT successfully takes over, then it becomes computationally easier to ALWAYS COOPERATE. That could evolve to become dominant and be completely vulnerable to ALWAYS DEFECT

Phil 6.27.18

7:00 – 12:00 ASRC MKT

  • Print out documents! Done. Got passport drive too.
  • Need to write an extractor that lets the user navigate the xml file containing influences of selected agents. This could be a sample-by sample network. Maybe two modes?
    • Select an agent and see all the other agents come in and out of influcene
    • Select an number of agents and only watch the mutual influence.
    • There is an integrated JavaFX charts that I could use, or it could be an uploaded webapp? JavaFX would be easier in the short term, but a webapp would help more with JuryRoom…
    • Another option would be Python, since that’s where the LSTM code will live.
    • On the whole, two days before leaving on travel is probably the wrong time to start coding
  • Fixed a bug in the xml file generation
  • copied the new jar file onto the thumb drive
  • copied the xml file onto the thumb drive

12:00 – 4:00 ASRC A2P

  • Pomoting things to QA – done! Or at least, up to date with the excel files

Phil 6.26.18

7:00 – 5:00 ASRC MKT

  • Started back with the Evolution of Cooperation
  • Social loafing (Scholar results)
    • In social psychologysocial loafing is the phenomenon of a person exerting less effort to achieve a goal when they work in a group than when they work alone. This is seen as one of the main reasons groups are sometimes less productive than the combined performance of their members working as individuals, but should be distinguished from the accidental coordination problems that groups sometimes experience. Research on social loafing began with rope pulling experiments by Ringelmann, who found that members of a group tended to exert less effort in pulling a rope than did individuals alone. In more recent research, studies involving modern technology, such as online and distributed groups, have also shown clear evidence of social loafing. Many of the causes of social loafing stem from an individual feeling that his or her effort will not matter to the group.
  • NELA2017 contains almost every news article from 92 sources between April 2017 and October 2017, amounting to over 136K articles. This data set is the first release of NELA datasets. This version of the data set can be found on github and a full description and use cases can be found in our 2018 ICWSM paper.
  • Submitted “One Simple Trick” final to SASO
  • Updated ArXive
  • Fixed a bug that prevented population interactions in FlockingAgentManager.initializeAgents():
                // add to the global list
                allBoidsList.add(fs);
    
                // add a pointer to the global list to each shape
                fs.setFlockingShapeList(allBoidsList);
    
                // Add to the flock so that we can get flock headings
                List flock = flockListsMap.get(flockName);
                flock.add(fs);

    Seriously, what was I thinking?

  • Continued GUI tweaking. I think it looks pretty good, and it fits (mostly) on my laptop Version6.26.18
  • Verified that the influences record agents from different flocks and sources.
  • Copied all CI 2018 things I can think of onto the thumb drive

Phil 6.22.18

7:00 – 5:30 ASRC MKT

  • Twitter experiment on a fake Gary Indiana secession. IFTTT retweeting leads to interesting behavior.
  • Fixed FlockingShape casting by adding a customDrawStep(GraphicsContext gc) to the SmartShape base class that’s called from draw().
  • Add records to each agent that store a list of source and agent influences at each time sample. It should include the name of the item and the amount of influence. Probably save as an XML file, since it has too many dimensions. The file could then be used to create terms or spreadsheets.
    • Started on CAInfluence class which will be added to CA classes in an arrayList in BaseCA;
  • More file conversion with Bob – and everything worked great until I try one after Bob leaves. Ka-BOOM!
    • Installed all the packages to get everything to run in the debugger. Found what appears to be a perfectly good line “range” that causes the problem? Will start debugging on Wednesday.
  • Project MERCATOR proposal
  • Meeting with Sy