Category Archives: Development

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 9.21.18

7:00 – 4:00 ASRC MKT

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

Phil 8.30.18

7:00 – 5:00  ASRC MKT

  • Target Blue Sky paper for iSchool/iConference 2019: The chairs are particularly looking for “Blue Sky Ideas” that are open-ended, possibly even “outrageous” or “wacky,” and present new problems, new application domains, or new methodologies that are likely to stimulate significant new research. 
  • I’m thinking that a paper that works through the ramifications of this diagram as it relates to people and machines. With humans that are slow responding with spongy, switched networks the flocking area is large. With a monolithic densely connected system it’s going to be a straight line from nomadic to stampede. 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 8.27.18

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 8.10.18

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 8.3.18

7:00 – 3:30 ASRC MKT

  • Slides and walkthrough – done!
  • Ramping up on SASO
  • Textricator is a tool for extracting text from computer-generated PDFs and generating structured data (CSV or JSON). If you have a bunch of PDFs with the same format (or one big, consistently formatted PDF) and you want to extract the data to CSV or JSON, _Textricator_ can help! It can even work on OCR’ed documents!
  • LSTM links for getting back to things later
  • Who handles misinformation outbreaks?
    • Misinformation attacks— the deliberate and sustained creation and amplification of false information at scale — are a problem. Some of them start as jokes (the ever-present street sharks in disasters) or attempts to push an agenda (e.g. right-wing brigading); some are there to make money (the “Macedonian teens”), or part of ongoing attempts to destabilise countries including the US, UK and Canada (e.g. Russia’s Internet Research Agency using troll and bot amplification of divisive messages).

      Enough people are writing about why misinformation attacks happen, what they look like and what motivates attackers. Fewer people are activelycountering attacks. Here are some of them, roughly categorised as:

      • Journalists and data scientists: Make misinformation visible
      • Platforms and governments: Reduce misinformation spread
      • Communities: directly engage misinformation
      • Adtech: Remove or reduce misinformation rewards

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 EconomistBrexit
  • 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.

9:00 – ASRC IRAD

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: python_fix
    • 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

Listening to We Can’t Talk Anymore? Understanding the Structural Roots of Partisan Polarization and the Decline of Democratic Discourse in 21st Century America. Very Tajfel

  • 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 m2_0229_670
  • 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 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