Category Archives: Conferences

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. Nomad-Flocking-Stampede2
    • Length: Up to 4 pages (excluding references)
    • Submission deadline: October 1, 2018
    • Notification date: mid-November, 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 8.29.18

7:00 – 4:30 ASRC MKT

  • This Is How Russian Propaganda Actually Works In The 21st Century (plus Kate Starbird’s twitter thoughts)
    • The Russian government discreetly funded a group of seemingly independent news websites in Eastern Europe to pump out stories dictated to them by the Kremlin, BuzzFeed News and its reporting partners can reveal.
  • How Right Wing is Right Wing Populism? Using multilingual CNNs on party manifestos.
    • Right wing populist parties in Europe are clearly different from other right wing parties in their rhetoric and electoral appeal. Some observers see substantive differences between right wing populists and other right wing parties, with populists supporting the welfare state and gender equality more than other right wing parties, often as part of an anti-immigration and anti-Muslim agenda. We test this claim using novel data produced by a multilingual convolutional neural net on political party platforms for the years 1990 to 2015 from the Manifesto Corpus. We find no systematic differences between right wing populists and non-populists on support for welfare and gender equality, though there is some evidence that more successful populists are more centrist.
  • Need to write up a 4 page blue sky paper for the 2019 iConference in DC
  • Realized that the poster had two herding DTW charts on the poster. Fixed and sent back. Hopefully it will get reprinted in time…
  • Uploaded the edited version and added them to the online presentation. Also saved out the mp4 files to use in the ppt version
  • Back to working on speech recognition. I’ve done a bunch of things that I’m documenting before I see if anything helped.
  • TL;DR – after much flailing, I found a page that actually helped. It’s a how-to (rather than quickstart) guide that includes a variety of interfaces including gcloud, Java and Python. And the gcloud command worked like a charm! All the flailing below is just for documentation on what NOT to do. Here’s what worked:
    PS D:\Development\Sandboxes\MapsFromPodcasts> gcloud ml speech recognize D:\Development\Sandboxes\MapsFromPodcasts\brook
    lyn.flac --language-code='en-US'
    {
      "results": [
        {
          "alternatives": [
            {
              "confidence": 0.98360395,
              "transcript": "how old is the Brooklyn Bridge"
            }
          ]
        }
      ]
    }
    

    Note that the audio file is the same as the one in the examples and is available from Google here: storage.googleapis.com/cloud-samples-tests/speech/brooklyn.flac

  • For historical documentation of my flailing
    • First I opened a new Powershell window and re-ran the commands. Yup: Capture
    • Then I stumbled on the SDK support page and found this link to what may be the answer to the question on stackoverflow. CaptureIt says to run
      gcloud auth application-default login --scopes=https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/userinfo.email
    • Which I did, which caused a lot of things to happen Capture
    • First, I’m really wondering about this: To generate an access token for other uses, run: gcloud auth application-default print-access-token. This is used in both commands, si I’m wondering what it’s actually doing. What’s happening to this generated  token? is it being stored on my machine?
    • Second, it looks like I need to point at the [C:\Users\philip.feldman\AppData\Roaming\gcloud\application_default_credentials.json] file rather than the one in the project. That or copy to the dev location. I’m trying the former Capture
    • Then, I got this again (https://cloud.google.com/sdk/auth_success): Capture
    • Lastly, I upgraded because it said I could. Nothing works yet, so why not? Capture
    • That brought up a window with all this info:
      Your current Cloud SDK version is: 213.0.0
      You will be upgraded to version: 214.0.0
      
      ┌─────────────────────────────────────────────────┐
      │        These components will be updated.        │
      ├──────────────────────────┬────────────┬─────────┤
      │           Name           │  Version   │   Size  │
      ├──────────────────────────┼────────────┼─────────┤
      │ Cloud SDK Core Libraries │ 2018.08.24 │ 8.3 MiB │
      │ gcloud cli dependencies  │ 2018.08.24 │ 2.4 MiB │
      └──────────────────────────┴────────────┴─────────┘
      ┌─────────────────────────────────────────────────────────────────────┐
      │                 These components will be installed.                 │
      ├────────────────────────────┬─────────────────────┬──────────────────┤
      │            Name            │       Version       │       Size       │
      ├────────────────────────────┼─────────────────────┼──────────────────┤
      │ Bundled Python             │                     │                  │
      └────────────────────────────┴─────────────────────┴──────────────────┘
      
      The following release notes are new in this upgrade.
      Please read carefully for information about new features, breaking changes,
      and bugs fixed.  The latest full release notes can be viewed at:
        https://cloud.google.com/sdk/release_notes
      
      214.0.0 (2018-08-28)
        Breaking Changes
            ■ **(Cloud Bigtable)** Modified the arguments accepted by cbt
              createappprofile and cbt updateappprofile in the following ways:
              ≡ Removed etag argument from createappprofile.
              ≡ Renamed allow-transactional-writes option as transactional-writes.
              ≡ Added a force option to ignore warnings.
            ■ **(Cloud Bigtable)** Modified the specification for routing policies.
              A routing policy can be either "route-any" (previously of
              "multi_cluster_routing_use_any") or "route-to=".
            ■ **(Compute Engine)** Deprecated gcloud compute interconnects
              attachments create. Please use gcloud compute interconnects attachments
              dedicated create instead.
            ■ **(Compute Engine)** Removed deprecated --mode flag from gcloud
              compute networks create. Use --subnet-mode instead.
            ■ **(Compute Engine)** Removed deprecated gcloud compute networks
              switch-mode command. Use gcloud compute networks update
              --switch-to-custom-mode instead.
            ■ **(Compute Engine)** Removed deprecated gcloud compute xpn command
              group. Use gcloud compute shared-vpc instead.
      
        Cloud Bigtable
            ■ Restored the output of the cbt count command that was inadvertently
              removed in the previous release.
      
        Cloud Datalab
            ■ Updated the datalab component to the 20180820 release. Released
              changes are documented in its tracking issue at
              https://github.com/googledatalab/datalab/issues/2064
              (https://github.com/googledatalab/datalab/issues/2064).
      
        Cloud Dataproc
            ■ Added SCHEDULED_DELETE column to gcloud beta dataproc clusters list
              command output.
      
        Cloud Datastore Emulator
            ■ Released Cloud Datastore Emulator version 2.0.2.
              ≡ Improved backward compatibility with App Engine local development
                by keeping auto generated indexes in index file generated from
                previous runs.
      
        Cloud Functions
            ■ Promoted --runtime flag of gcloud functions deploy to GA.
      
        Compute Engine
            ■ Promoted the following flags to GA:
              ≡ --network-tier of gcloud compute <addresses|forwarding-rules>
                create
              ≡ --default-network-tier of gcloud compute project-info update
              ≡ --network-tier of gcloud compute instances
                <add-access-config|create>
              ≡ --network-tier of gcloud compute instance-templates create
            ■ Promoted gcloud compute instances simulate-maintenance-event to GA.
            ■ Promoted <get|set>-iam-policy and <add|remove>-iam-policy-bindings to
              beta in the following commands groups:
              ≡ gcloud compute sole-tenancy node-groups
              ≡ gcloud compute sole-tenancy node-templates
      
        Kubernetes Engine
            ■ Promoted --disk-type flag of gcloud container <clusters|node-pools>
              create to GA.
            ■ Promoted --default-max-pods-per-node flag of gcloud container
              clusters create to beta.
            ■ Promoted --max-pods-per-node flag of gcloud container node-pools
              create to beta.
            ■ Modified --monitoring-service flag of gcloud containers clusters
              update to enable Google Cloud Monitoring service with Kubernetes-native
              resource model.
            ■ Modified --logging-service flag of gcloud containers clusters update
              to enable Google Cloud Logging service with Kubernetes-native resource
              model.
            ■ Modified output of gcloud beta container clusters list for DEGRADED
              clusters to include reason for degradation.
            ■ Added --enable-private-nodes and --enable-private-endpoint to gcloud
              beta container clusters create.
            ■ Deprecated --private-cluster flag of gcloud beta container clusters
              create; use --enable-private-nodes instead.
      
          Subscribe to these release notes at
          https://groups.google.com/forum/#!forum/google-cloud-sdk-announce
          (https://groups.google.com/forum/#!forum/google-cloud-sdk-announce).
      
      Do you want to continue (Y/n)?  Y
      
      ╔════════════════════════════════════════════════════════════╗
      ╠═ Creating update staging area                             ═╣
      ╠════════════════════════════════════════════════════════════╣
      ╠═ Uninstalling: Cloud SDK Core Libraries                   ═╣
      ╠════════════════════════════════════════════════════════════╣
      ╠═ Uninstalling: gcloud cli dependencies                    ═╣
      ╠════════════════════════════════════════════════════════════╣
      ╠═ Installing: Bundled Python                               ═╣
      ╠════════════════════════════════════════════════════════════╣
      ╠═ Installing: Cloud SDK Core Libraries                     ═╣
      ╠════════════════════════════════════════════════════════════╣
      ╠═ Installing: gcloud cli dependencies                      ═╣
      ╠════════════════════════════════════════════════════════════╣
      ╠═ Creating backup and activating new installation          ═╣
      ╚════════════════════════════════════════════════════════════╝
      
      Performing post processing steps...done.
      
      Update done!
      
      To revert your SDK to the previously installed version, you may run:
        $ gcloud components update --version 213.0.0
      
      Press any key to continue . . .
      
    • So now lets see what happens with a restarted PowerShell
    • Nope, same problem. I also tried deleting the environment variable completely and the behavior is the same. So I don’t think that the file with the data is being sent? Capture
    • Interesting, the app-roaming file is not the same as the file that google had me generate for the text recognition getting started page: Capture

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 8.24.18

7:00 – 4:00 ASRC MKT

  • Make more obvious the Inadvertent Social Information and Digital ISI
    • ISI
      • Trails
      • Visual clustering
      • Behavior around the commons (waterholes)
      • Presence of young
      • Mating behavior
      • etc.
    • DISI
      • Words and their overall source (Social media, website content, contributor content, auto-generated, etc)
      • Votes (likes, kudos, karma points)
      • Money (site income, blockchain ledger)
      • Linking (href, retweet, share)
      • Images & videos
  • Work more on behavior patterns of humans and animals
    • Highly organized (soccer match singing, marching, mass dancing events)
    • Wildebeest feeding, defending,migrating and stampeding
  • AutoKeras is a GitHub project that uses the ENAS algorithm. It can be installed using pip. Since it’s written in Keras it’s quite easy to control and play with, so you can even dive into the ENAS algorithm and try making some modifications. If you prefer TensorFlow or Pytorch, there’s also public code projects for those here and here!
  • From Zeynep’s twitter
    • So, Russian trolls amplified divisive content and helped spread vaccine misinformation.  Look, the challenge before us is to redefine *critical thinking* to include figuring out what to believe, not just how to be skeptical. Personal and institutional.
    • Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate
      •  Whereas bots that spread malware and unsolicited content disseminated antivaccine messages, Russian trolls promoted discord. Accounts masquerading as legitimate users create false equivalency, eroding public consensus on vaccination.
  • Trying to decode podcasts. Here’s my test, and here are the results from Google speech-to-text:
    • We were talking about the choices of who’s you can keep two of these three, I guess Adonis Alexander is along for the ride, huh? I thought I was about to I didn’t know I haven’t I haven’t sent it to him. Well, has he been out there? They might missing some guys got to hand. I kept thinking like if to say, they weren’t having these injuries. Like if they have like us to say, okay, but they have these reason iron Marshall and maybe he maybe he’s not available week one, but they don’t want to put them on IR prn’s things up. So maybe they have to add another running back like you so you have to create a roster spot I could imagine this is just speculation Alexander. Somehow gets the mysterious injury to put them on I are clearly my keys ready, right and they they would have five cornerbacks otherwise and you know, yeah, if you’re not going to be ready to go, but you may have to you know, go get okay. Yeah. I mean the he’s he’s a guy that I think is on based on these the way the wrong.
    • It’s pretty good as long as people aren’t stepping over each other verbally.
    • Good enough to try, I guess. Noisy data is life, right? Look for the bigger signal.
  • Here’s my current plan. It’s a half-assed first approach, but it should provide some insight.
    1. Download a season of a sports podcast and put each podcast into it’s own document Here’s the tutorial for Speech-to-text with REST
    2. Use Corpus Manager to convert, using BOW and create an ignore list for common words like “the”
    3. then read all the docs into LMN
    4. Then set the weight of each successive document (in time) so that its top
    5. Take the top ten words and save them to a file
    6. Try building a map

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 8.22.18

7:00 – 4:00 ASRC MKT

Phil 8.21.18

7:00 – 3:00 ASRC MKT

  • Rework the slides
    • Explicit introduction, lit review, methods, results, conclusion and discussion slides
    • Slide for the difference between opinion dynamics & consensus formation as a static end  and part of a dynamic process. (Tribe membership may be static, belief of the tribe is highly dynamic. It’s the story for the group)
    • Revisit stampede/flock/nomad slide in the conclusions
    • Lose the following slides:
      • Belief space
      • Theory slide replace with a slide that breaks out the to knobs of dimension reduction and social influence horizons. The slide is called “the simple trick” Explain how herding affects these knobs by presenting simple issues and making the network stiffer through weight and connection
    • Get rid of optical polarization
  • Fanning the Flames of Hate: Social Media and Hate Crime
    • This paper investigates the link between social media and hate crime using Facebook data. We study the case of Germany, where the recently emerged right-wing party Alternative für Deutschland (AfD) has developed a major social media presence. We show that right-wing anti-refugee sentiment on Facebook predicts violent crimes against refugees in otherwise similar municipalities with higher social media usage. To further establish causality, we exploit exogenous variation in major internet and Facebook outages, which fully undo the correlation between social media and hate crime. We further find that the effect decreases with distracting news events; increases with user network interactions; and does not hold for posts unrelated to refugees. Our results suggest that social media can act as a propagation mechanism between online hate speech and real-life violent crime.
  • Facebook is rating the trustworthiness of its users on a scale from zero to 1
    • Facebook has begun to assign its users a reputation score, predicting their trustworthiness on a scale from zero to 1.
    • Tessa Lyons, product manager who is in charge of fighting misinformation (video)
  • Social Science One
    • implements a new type of partnership between academic researchers and private industry to advance the goals of social science in understanding and solving society’s greatest challenges. The partnership enables academics to analyze the increasingly rich troves of information amassed by private industry in responsible and socially beneficial ways. It ensures the public maintains privacy while gaining societal value from scholarly research. And it enables firms to enlist the scientific community to help them produce social good, while protecting their competitive positions.
  • Lost Causes Is this fashion in economic theory (found via Twitter)?Causal
  • Poster printing – UMBC Commonvision

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 8.17.18

7:00 – 4:30 ASRC MKT

Phil 8.12.18

7:00 – 4:00 ASRC MKT

  • Having an interesting chat on recommenders with Robin Berjon on Twitter
  • Long, but looks really good Neural Processes as distributions over functions
    • Neural Processes (NPs) caught my attention as they essentially are a neural network (NN) based probabilistic model which can represent a distribution over stochastic processes. So NPs combine elements from two worlds:
      • Deep Learning – neural networks are flexible non-linear functions which are straightforward to train
      • Gaussian Processes – GPs offer a probabilistic framework for learning a distribution over a wide class of non-linear functions

      Both have their advantages and drawbacks. In the limited data regime, GPs are preferable due to their probabilistic nature and ability to capture uncertainty. This differs from (non-Bayesian) neural networks which represent a single function rather than a distribution over functions. However the latter might be preferable in the presence of large amounts of data as training NNs is computationally much more scalable than inference for GPs. Neural Processes aim to combine the best of these two worlds.

  • How The Internet Talks (Well, the mostly young and mostly male users of Reddit, anyway)
    • To get a sense of the language used on Reddit, we parsed every comment since late 2007 and built the tool above, which enables you to search for a word or phrase to see how its popularity has changed over time. We’ve updated the tool to include all comments through the end of July 2017.
  • Add breadcrumbs to slides
  • Download videos – done! Put these in the ppt backup
  • Fix the DTW emergent population chart on the poster and in the slides. Print!
  • Set up the LaTex Army BAA framework
  • Olsson
  • Slide walkthough. Good timing. Working on the poster some more AdversarialHerding2

Phil 8.14.18

7:00 – 4:30 ASRC MKT

  • Presented LaTex talk/workshop. I think it needs to be a more focused SIGCHI workshop that steps through the transition from a template document to a document with all the needed parts
    • Will’s document then becomes a resource for how to do a particular task.
  • Promoted The Radio in Fascist Italy as a Phlog post. Need to add a takeaway section
  • Georgetown Law Technology Review (Vol 2, Issue 2)
  • More poster AdversarialHerding2
  • BAA work? Lots, actually. Dug though the Army’s and found many good leads
  • Add to the list of things to read: How social media took us from Tahrir Square to Donald Trump
    • To understand how digital technologies went from instruments for spreading democracy to weapons for attacking it, you have to look beyond the technologies themselves.

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
      • Reader
      • Illustrator
      • Photoshop
    • Microsoft suite
    • Express VPN

Phil 8.9.18

7:00 – 3:00 ASRC MKT

  • Working on the herding slide
  • Animals Teach Robots to Find Their Way
    • Michael Milford – “I always regard spatial intelligence as a gateway to understanding higher-level intelligence. It’s the mechanism by which we can build on our understanding of how the brain works.”
  • Direct recordings of grid-like neuronal activity in human spatial navigation
    • Grid cells in the entorhinal cortex appear to represent spatial location via a triangular coordinate system. Such cells, which have been identified in rats, bats, and monkeys, are believed to support a wide range of spatial behaviors. By recording neuronal activity from neurosurgical patients performing a virtual-navigation task we identified cells exhibiting grid-like spiking patterns in the human brain, suggesting that humans and simpler animals rely on homologous spatial-coding schemes. Human grid cells
  • The cognitive map in humans: spatial navigation and beyond
    • The ‘cognitive map’ hypothesis proposes that brain builds a unified representation of the spatial environment to support memory and guide future action. Forty years of electrophysiological research in rodents suggest that cognitive maps are neurally instantiated by place, grid, border and head direction cells in the hippocampal formation and related structures. Here we review recent work that suggests a similar functional organization in the human brain and yields insights into how cognitive maps are used during spatial navigation. Specifically, these studies indicate that (i) the human hippocampus and entorhinal cortex support map-like spatial codes, (ii) posterior brain regions such as parahippocampal and retrosplenial cortices provide critical inputs that allow cognitive maps to be anchored to fixed environmental landmarks, and (iii) hippocampal and entorhinal spatial codes are used in conjunction with frontal lobe mechanisms to plan routes during navigation. We also discuss how these three basic elements of cognitive map based navigation—spatial coding, landmark anchoring and route planning—might be applied to nonspatial domains to provide the building blocks for many core elements of human thought.
  • Spatial scaffold effects in event memory and imagination
    • Jessica Robin
    • Spatial context is a defining feature of episodic memories, which are often characterized as being events occurring in specific spatiotemporal contexts. In this review, I summarize research suggesting a common neural basis for episodic and spatial memory and relate this to the role of spatial context in episodic memory. I review evidence that spatial context serves as a scaffold for episodic memory and imagination, in terms of both behavioral and neural effects demonstrating a dependence of episodic memory on spatial representations. These effects are mediated by a posterior-medial set of neocortical regions, including the parahippocampal cortex, retrosplenial cortex, posterior cingulate cortex, precuneus, and angular gyrus, which interact with the hippocampus to represent spatial context in remembered and imagined events. I highlight questions and areas that require further research, including differentiation of hippocampal function along its long axis and subfields, and how these areas interact with the posterior-medial network.
  • Identifying the cognitive processes underpinning hippocampal-dependent tasks (preprint, not peer-reviewed)
    • Autobiographical memory, future thinking and spatial navigation are critical cognitive functions that are thought to be related, and are known to depend upon a brain structure called the hippocampus. Surprisingly, direct evidence for their interrelatedness is lacking, as is an understanding of why they might be related. There is debate about whether they are linked by an underlying memory-related process or, as has more recently been suggested, because they each require the endogenous construction of scene imagery. Here, using a large sample of participants and multiple cognitive tests with a wide spread of individual differences in performance, we found that these functions are indeed related. Mediation analyses further showed that scene construction, and not memory, mediated (explained) the relationships between the functions. These findings offer a fresh perspective on autobiographical memory, future thinking, navigation, and also on the hippocampus, where scene imagery appears to play a highly influential role.
  • Home early to wait for FedEx. And here’s a fun thing: dkgpgukx0aatbal

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 8.2.18

7:00 – 5:00 ASRC MKT

  • Joshua Stevens (Scholar)
    • At Penn State I researched cartography and geovisual analytics with an emphasis on human-computer interaction, interactive affordances, and big data. My work focused on new forms of map interaction made possible by well constructed visual cues.
  • A Computational Analysis of Cognitive Effort
    • Cognitive effort is a concept of unquestionable utility in understanding human behaviour. However, cognitive effort has been defined in several ways in literature and in everyday life, suffering from a partial understanding. It is common to say “Pay more attention in studying that subject” or “How much effort did you spend in resolving that task?”, but what does it really mean? This contribution tries to clarify the concept of cognitive effort, by introducing its main influencing factors and by presenting a formalism which provides us with a tool for precise discussion. The formalism is implementable as a computational concept and can therefore be embedded in an artificial agent and tested experimentally. Its applicability in the domain of AI is raised and the formalism provides a step towards a proper understanding and definition of human cognitive effort.
  • Efficient Neural Architecture Search with Network Morphism
    • While neural architecture search (NAS) has drawn increasing attention for automatically tuning deep neural networks, existing search algorithms usually suffer from expensive computational cost. Network morphism, which keeps the functionality of a neural network while changing its neural architecture, could be helpful for NAS by enabling a more efficient training during the search. However, network morphism based NAS is still computationally expensive due to the inefficient process of selecting the proper morph operation for existing architectures. As we know, Bayesian optimization has been widely used to optimize functions based on a limited number of observations, motivating us to explore the possibility of making use of Bayesian optimization to accelerate the morph operation selection process. In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search by introducing a neural network kernel and a tree-structured acquisition function optimization algorithm. With Bayesian optimization to select the network morphism operations, the exploration of the search space is more efficient. Moreover, we carefully wrapped our method into an open-source software, namely Auto-Keras for people without rich machine learning background to use. Intensive experiments on real-world datasets have been done to demonstrate the superior performance of the developed framework over the state-of-the-art baseline methods.
  • I think I finished the Dissertation Review slides. Walkthrough tomorrow!