Phil 3.24.18

This mostly today, but also two projects to convert python to jupyter:
It occurs to me that there could be an IntelliJ plugin
Of course, all the work is in the *other* direction:

Phil 3.23.18

7:00 – 5:00 ASRC MKT

  • Influence of augmented humans in online interactions during voting events
    • Overwhelming empirical evidence has shown that online social dynamics mirrors real-world events. Hence, understanding the mechanisms leading to social contagion in online ecosystems is fundamental for predicting, and even manouvering, human behavior. It has been shown that one of such mechanisms is based on fabricating armies of automated agents that are known as social bots. Using the recent Italian elections as an emblematic case study, here we provide evidence for the existence of a special class of highly influential users, that we name “augmented humans”. They exploit bots for enhancing both their visibility and influence, generating deep information cascades to the same extent of news media and other broadcasters. Augmented humans uniformly infiltrate across the full range of identified clusters of accounts, the latter reflecting political parties and their electoral ranks.
  • Reddit and the Struggle to Detoxify the Internet
    • “Does free speech mean literally anyone can say anything at any time?” Tidwell continued. “Or is it actually more conducive to the free exchange of ideas if we create a platform where women and people of color can say what they want without thousands of people screaming, ‘Fuck you, light yourself on fire, I know where you live’? If your entire answer to that very difficult question is ‘Free speech,’ then, I’m sorry, that tells me that you’re not really paying attention.”
    • This is the difference between discussion and stampede. That seems like it should be statistically detectable.
  • Metabolic Costs of Feeding Predictively Alter the Spatial Distribution of Individuals in Fish Schools
    • We examined individual positioning in groups of swimming fish after feeding
    • Fish that ate most subsequently shifted to more posterior positions within groups
    • Shifts in position were related to the remaining aerobic scope after feeding
    • Feeding-related constraints could affect leadership and group functioning
    • I wonder if this also keeps the hungrier fish at the front, increasing the effectiveness of gradient detections
  • Listening to Invisibilia: The Pattern Problem. There is a section on using machine learning for sociology. Listening to get the author of the ML and Sociology study. Predictions were not accurate. Not published?
  • The Coming Information Totalitarianism in China
    • The real-name system has two purposes. One is the chilling effect, and it works very well on average netizens but not so much on activists. The other and the main purpose is to be able to locate activists and eliminate them from certain information/opinion platforms, in the same way that opinions of dissident intellectuals are completely eradicated from the traditional media.
  • More BIC – Done! Need to assemble notes
    • It is a central component of resolute choice, as presented by McClennen, that (unless new information becomes available) later transient agents recognise the authority of plans made by earlier agents. Being resolute just is recognising that authority (although McClennen’ s arguments for the rationality and psychological feasibility of resoluteness apply only in cases in which the earlier agents’ plans further the common ends of earlier and later agents). This feature of resolute choice is similar to Bacharach’ s analysis of direction, explained in section 5. If the relationship between transient agents is modelled as a sequential game, resolute choice can be thought of as a form of direction, in which the first transient agent plays the role of director; the plan chosen by that agent can be thought of as a message sent by the director to the other agents. To the extent that each later agent is confident that this plan is in the best interests of the continuing person, that confidence derives from the belief that the first agent identified with the person and that she was sufficiently rational and informed to judge which sequence of actions would best serve the person’s objectives. (pg 197)
  • Meeting with celer scientific
  • More TF with Keras. Really good progress

Phil 3.22.18

7:00 – 5:00 ASRC MKT

  • The ONR proposal is in!
  • Promoted the Odyssey thoughts to Phlog
  • More BIC
    • The problem posed by Heads and Tails is not that the players lack a common understanding of salience; it is that game theory lacks an adequate explanation of how salience affects the decisions of rational players. All we gain by adding preplay communication to the model is the realisation that game theory also lacks an adequate explanation of how costless messages affect the decisions of rational players. (pg 180)
  • More TF crash course
    • Invert the ratio for train and validation
    • Add the check against test data
  • Get started on LSTM w/Aaron?


Phil 3.21.18

7:00 – 6:00 ASRC MKT, with some breaks for shovelling

  • First day of spring. Snow on the ground and more in the forecast.
  • I’ve been thinking of ways to describe the differences between information visualizations with respect to maps. Here’s The Odyssey as a geographic map:
  • Odysseus'_Journey
  • The first thing that I notice is just how far Odysseus travelled. That’s about half of the Mediterranean! I thought that it all happened close to Greece. Maps afford this understanding. They are diagrams that support the plotting of trajectories.Which brings me to the point that we lose a lot of information about relationships in narratives. That’s not their point. This doesn’t mean that non-map diagrams don’t help sometimes. Here’s a chart of the characters and their relationships in the Odyssey:
  •  odyssey
  • There is a lot of information here that is helpful. And this I do remember and understood from reading the book. Stories are good about depicting how people interact. But though this chart shows relationships, the layout does not really support navigation. For example, the gods are all related by blood and can pretty much contact each other at will. This chart would have Poseidon accessing Aeolus and  Circe by going through Odysseus.  So this chart is not a map.
  • Lastly, is the relationship that comes at us through search. Because the implicit geographic information about the Odyssey is not specifically in the text, a search request within the corpora cannot produce a result that lets us integrate it
  • OdysseySearchJourney
  • There is a lot of ambiguity in this result, which is similar to other searches that I tried which included travel, sail and other descriptive terms. This doesn’t mean that it’s bad, it just shows how search does not handle context well. It’s not designed to. It’s designed around precision and recall. Context requires a deeper understanding about meaning, and even such recent innovations such as sharded views with cards, single answers, and pro/con results only skim the surface of providing situationally appropriate, meaningful context.
  • Ok, back to tensorflow. Need to update my computer first….
    • Updating python to 64-bit – done
    • Installing Visual Studio – sloooooooooooooooooooooowwwwwwwwwwwww. Done
    • Updating graphics drivers – done
    • Updating tensorflow
    • Updating numpy with intel math
  • At the Validation section in the TF crash course. Good progress. drilling down into all the parts of python that I’ve forgotten. And I got to make a pretty picture: TF_crash_course1

Phil 3.20.18

7:00 – 3:00 ASRC MKT

  • What (satirical) denying a map looks like. Nice application of believability.
  • Need to make a folder with all the CUDA bits and Visual Studio to get all my boxes working with GPU tensorflow
  • Assemble one-page resume for ONR proposal
  • More BIC
    • The fundamental principle of this morality is that what each agent ought to do is to co-operate, with whoever else is co-operating, in the production of the best consequences possible given the behaviour of non-co-operators’ (Regan 1980, p. 124). (pg 167)
    • Ordered On Social Facts
      • Are social groups real in any sense that is independent of the thoughts, actions, and beliefs of the individuals making up the group? Using methods of philosophy to examine such longstanding sociological questions, Margaret Gilbert gives a general characterization of the core phenomena at issue in the domain of human social life.

Back to the TF crash course

    • Had to update my numpy from Christoph Gohlke’s Unofficial Windows Binaries for Python Extension Packages. It’s wonderful, but WHY???
    • Also had this problem updating numpy
      D:\installed>pip3 install "numpy-1.14.2+mkl-cp37-cp37m-win_amd64.whl"
      numpy-1.14.2+mkl-cp37-cp37m-win_amd64.whl is not a supported wheel on this platform.
    • That was solved by installing numpy-1.14.2+mkl-cp36-cp36m-win_amd64.whl. Why cp36 works and cp 37 doesn’t is beyond me.
    • Discussions with Aaron about tasks between now and the TFDS
    • Left early due to snow


Phil 3.19.18

7:00 – 5:00 ASRC MKT

    • The Perfect Selfishness of Mapping Apps
      • Apps like Waze, Google Maps, and Apple Maps may make traffic conditions worse in some areas, new research suggests.
    • Cambridge Social Decision-Making Lab
    • More BIC
      • Schema 3: Team reasoning (from a group viewpoint) pg 153
        • We are the members of S.
        • Each of us identifies with S.
        • Each of us wants the value of U to be maximized.
        • A uniquely maximizes U.
        • Each of us should choose her component of A.
      • Schema 4: Team reasoning (from an individual viewpoint) pg 159
        • I am a member of S.
        • It is common knowledge in S that each member of S identifies
          with S.
        • It is common knowledge in S that each member of S wants the
          value of U to be maximized.
        • It is common knowledge in S that A uniquely maximizes U.
        • I should choose my component of A.
      • Schema 7: Basic team reasoning pg 161
        • I am a member of S.
        • It is common knowledge in S that each member of S identifies
          with S.
        • It is common knowledge in S that each member of S wants the
          value of U to be maximized.
        • It is common knowledge in S that each member of S knows his
          component of the profile that uniquely maximizes U.
        • I should choose my component of the profile that uniquely
          maximizes U.

          • Bacharach notes to himself the ‘hunch’ that this schema is ‘the basic rational capacity’ which leads to high in Hi-Lo, and that it ‘seems to be indispensable if a group is ever to choose the best plan in the most ordinary organizational circumstances’. Notice that Schema 7 does not require that the individual who uses it know everyone’s component of the profile that maximizes U.
      • His hypothesis is that group identification is an individual’s psychological response to the stimulus of a particular decision situation. It is not in itself a group action. (To treat it as a group action would, in Bacharach’ s framework, lead to an infinite regress.) In the theory of circumspect team reasoning, the parameter w is interpreted as a property of a psychological mechanism-the probability that a person who confronts the relevant stimulus will respond by framing the situation as a problem ‘for us’. The idea is that, in coming to frame the situation as a problem ‘for us’, an individual also gains some sense of how likely it is that another individual would frame it in the same way; in this way, the value of w becomes common knowledge among those who use this frame. (Compare the case of the large cube in the game of Large and Small Cubes, discussed in section 4 of the introduction.) Given this model, it seems that the ‘us’ in terms of which the problem is framed must be determined by how the decision situation first appears to each individual. Thus, except in the special case in which w == 1, we must distinguish S (the group with which individuals are liable to identify, given the nature of the decision situation) from T (the set of individuals who in fact identify with S). pg 163
    • Starting with the updates
      C:\WINDOWS\system32>pip3 install --upgrade tensorflow-gpu
      Collecting tensorflow-gpu
        Downloading tensorflow_gpu-1.6.0-cp36-cp36m-win_amd64.whl (85.9MB)
          100% |████████████████████████████████| 85.9MB 17kB/s
      Collecting termcolor>=1.1.0 (from tensorflow-gpu)
        Downloading termcolor-1.1.0.tar.gz
      Collecting absl-py>=0.1.6 (from tensorflow-gpu)
        Downloading absl-py-0.1.11.tar.gz (80kB)
          100% |████████████████████████████████| 81kB 6.1MB/s
      Collecting grpcio>=1.8.6 (from tensorflow-gpu)
        Downloading grpcio-1.10.0-cp36-cp36m-win_amd64.whl (1.3MB)
          100% |████████████████████████████████| 1.3MB 1.1MB/s
      Collecting numpy>=1.13.3 (from tensorflow-gpu)
        Downloading numpy-1.14.2-cp36-none-win_amd64.whl (13.4MB)
          100% |████████████████████████████████| 13.4MB 121kB/s
      Collecting astor>=0.6.0 (from tensorflow-gpu)
        Downloading astor-0.6.2-py2.py3-none-any.whl
      Requirement already up-to-date: six>=1.10.0 in c:\program files\python36\lib\site-packages (from tensorflow-gpu)
      Collecting tensorboard<1.7.0,>=1.6.0 (from tensorflow-gpu)
        Downloading tensorboard-1.6.0-py3-none-any.whl (3.0MB)
          100% |████████████████████████████████| 3.1MB 503kB/s
      Collecting protobuf>=3.4.0 (from tensorflow-gpu)
        Downloading protobuf-3.5.2.post1-cp36-cp36m-win_amd64.whl (958kB)
          100% |████████████████████████████████| 962kB 1.3MB/s
      Collecting gast>=0.2.0 (from tensorflow-gpu)
        Downloading gast-0.2.0.tar.gz
      Requirement already up-to-date: wheel>=0.26 in c:\program files\python36\lib\site-packages (from tensorflow-gpu)
      Requirement already up-to-date: html5lib==0.9999999 in c:\program files\python36\lib\site-packages (from tensorboard<1.7.0,>=1.6.0->tensorflow-gpu)
      Requirement already up-to-date: bleach==1.5.0 in c:\program files\python36\lib\site-packages (from tensorboard<1.7.0,>=1.6.0->tensorflow-gpu)
      Requirement already up-to-date: markdown>=2.6.8 in c:\program files\python36\lib\site-packages (from tensorboard<1.7.0,>=1.6.0->tensorflow-gpu)
      Requirement already up-to-date: werkzeug>=0.11.10 in c:\program files\python36\lib\site-packages (from tensorboard<1.7.0,>=1.6.0->tensorflow-gpu)
      Collecting setuptools (from protobuf>=3.4.0->tensorflow-gpu)
        Downloading setuptools-39.0.1-py2.py3-none-any.whl (569kB)
          100% |████████████████████████████████| 573kB 2.3MB/s
      Building wheels for collected packages: termcolor, absl-py, gast
        Running bdist_wheel for termcolor ... done
        Stored in directory: C:\Users\philip.feldman\AppData\Local\pip\Cache\wheels\de\f7\bf\1bcac7bf30549e6a4957382e2ecab04c88e513117207067b03
        Running bdist_wheel for absl-py ... done
        Stored in directory: C:\Users\philip.feldman\AppData\Local\pip\Cache\wheels\3c\0f\0a\6c94612a8c26070755559045612ca3645fea91c11f2148363e
        Running bdist_wheel for gast ... done
        Stored in directory: C:\Users\philip.feldman\AppData\Local\pip\Cache\wheels\8e\fa\d6\77dd17d18ea23fd7b860e02623d27c1be451521af40dd4a13e
      Successfully built termcolor absl-py gast
      Installing collected packages: termcolor, absl-py, setuptools, protobuf, grpcio, numpy, astor, tensorboard, gast, tensorflow-gpu
        Found existing installation: setuptools 38.4.0
          Uninstalling setuptools-38.4.0:
            Successfully uninstalled setuptools-38.4.0
        Found existing installation: protobuf 3.5.1
          Uninstalling protobuf-3.5.1:
            Successfully uninstalled protobuf-3.5.1
        Found existing installation: numpy 1.13.0+mkl
          Uninstalling numpy-1.13.0+mkl:
            Successfully uninstalled numpy-1.13.0+mkl
        Found existing installation: tensorflow-gpu 1.4.0
          Uninstalling tensorflow-gpu-1.4.0:
            Successfully uninstalled tensorflow-gpu-1.4.0
      Successfully installed absl-py-0.1.11 astor-0.6.2 gast-0.2.0 grpcio-1.10.0 numpy-1.14.2 protobuf-3.5.2.post1 setuptools-39.0.1 tensorboard-1.6.0 tensorflow-gpu-1.6.0 termcolor-1.1.0
    • That caused the following items to break when I tried running “”
      "C:\Program Files\Python36\python.exe" D:/Development/Sandboxes/TensorflowPlayground/HelloPackage/
      Traceback (most recent call last):
        File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\platform\", line 75, in preload_check
        File "C:\Program Files\Python36\lib\ctypes\", line 348, in __init__
          self._handle = _dlopen(self._name, mode)
      OSError: [WinError 126] The specified module could not be found
      During handling of the above exception, another exception occurred:
      Traceback (most recent call last):
        File "D:/Development/Sandboxes/TensorflowPlayground/HelloPackage/", line 28, in 
          import tensorflow as tf
        File "C:\Program Files\Python36\lib\site-packages\tensorflow\", line 24, in 
          from tensorflow.python import *
        File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\", line 49, in 
          from tensorflow.python import pywrap_tensorflow
        File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\", line 30, in 
        File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\platform\", line 82, in preload_check
          % (build_info.cudart_dll_name, build_info.cuda_version_number))
      ImportError: Could not find 'cudart64_90.dll'. TensorFlow requires that this DLL be installed in a directory that is named in your %PATH% environment variable. Download and install CUDA 9.0 from this URL:
    • Installing Visual Studio for the DLLs before I install the Cuda parts
    • Downloading cuda_9.0.176_win10.exe from here There are also two patches
    • Next set of errors
      Traceback (most recent call last):
        File "D:/Development/Sandboxes/TensorflowPlayground/HelloPackage/", line 28, in 
          import tensorflow as tf
        File "C:\Program Files\Python36\lib\site-packages\tensorflow\", line 24, in 
          from tensorflow.python import *
        File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\", line 49, in 
          from tensorflow.python import pywrap_tensorflow
        File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\", line 30, in 
        File "C:\Program Files\Python36\lib\site-packages\tensorflow\python\platform\", line 97, in preload_check
          % (build_info.cudnn_dll_name, build_info.cudnn_version_number))
      ImportError: Could not find 'cudnn64_7.dll'. TensorFlow requires that this DLL be installed in a directory that is named in your %PATH% environment variable. Note that installing cuDNN is a separate step from installing CUDA, and this DLL is often found in a different directory from the CUDA DLLs. You may install the necessary DLL by downloading cuDNN 7 from this URL:
  • Looking for cudnn64_7.dll here?
  • Aaaand that seems to be working!
  • Tweaked ONR proposal with Aaron. Discovered that there is one page per PI, so we need to make one-page resumes.



Phil 3.17.18

This came to me as a herding paper title for SASO 2018: This one simple trick can disrupt digital societies. Too much?

First International workshop on Socio-Cognitive Systems

  •  In this workshop we want to explore the interactions between cognitive and social aspects of “socio-cognitive systems” – that is where the social and cognitive aspects are studied together. The workshop connects elements of IJCAI/ECAI, AAMAS and ICML. Of course, modelling these systems in terms of Multi-Agent Systems seems intuitive, but also would require special attention to the social concepts in these MAS. The cognitive abilities of the agents should adapt themselves to the social context and development, which connects this area to machine learning in a social context. 
  • Deadline for submissions: 1st May 2018
  • Notification of acceptance: May/June 2018
  • Camera-ready copy of papers: June 2018
  • Workshop: 14 or 15 July 2018
  • Stockholm, co-located with IJCAI

Symmetric generative methods and t-SNE: a short survey

  • In data visualization, a family of methods is dedicated to the symmetric numerical matrices which contain the distances or similarities between high-dimensional data vectors. The method t-Distributed Stochastic Neighbor Embedding has been recently introduced for data visualization. Leading to competitive nonlinear embeddings which are able to reveal the natural classes, several variants have been developed. For comparison purposes, it is presented the recent generative alternative methods (Glove, probabilistic CA, LSPM, LargeVis, SBM) in the literature for nonlinear embedding via low dimensional positions.


  • étudier is a small Python program that uses Selenium and requests-html to drive a non-headless browser to collect a citation graph around a particular Google Scholar citation. The resulting network is written out as a Gephi file and a D3 visualization using networkx.

Phil 3.16.18

7:00 – 4:00 ASRC MKT

    • Umwelt
      • In the semiotic theories of Jakob von Uexküll and Thomas A. Sebeokumwelt (plural: umwelten; from the German Umwelt meaning “environment” or “surroundings”) is the “biological foundations that lie at the very epicenter of the study of both communication and signification in the human [and non-human] animal”.[1] The term is usually translated as “self-centered world”.[2] Uexküll theorised that organisms can have different umwelten, even though they share the same environment. The subject of umwelt and Uexküll’s work is described by Dorion Sagan in an introduction to a collection of translations.[3] The term umwelt, together with companion terms umgebungand innenwelt, have special relevance for cognitive philosophers, roboticists and cyberneticians, since they offer a solution to the conundrum of the infinite regress of the Cartesian Theater.
    • Benjamin Kuipers
      • How Can We Trust a Robot? (video)
        • Advances in artificial intelligence (AI) and robotics have raised concerns about the impact on our society of intelligent robots, unconstrained by morality or ethics
      • Socially-Aware Navigation Using Topological Maps and Social Norm Learning
        • We present socially-aware navigation for an intelligent robot wheelchair in an environment with many pedestrians. The robot learns social norms by observing the behaviors of human pedestrians, interpreting detected biases as social norms, and incorporating those norms into its motion planning. We compare our socially-aware motion planner with a baseline motion planner that produces safe, collision-free motion. The ability of our robot to learn generalizable social norms depends on our use of a topological map abstraction, so that a practical number of observations can allow learning of a social norm applicable in a wide variety of circumstances. We show that the robot can detect biases in observed human behavior that support learning the social norm of driving on the right. Furthermore, we show that when the robot follows these social norms, its behavior influences the behavior of pedestrians around it, increasing their adherence to the same norms. We conjecture that the legibility of the robot’s normative behavior improves human pedestrians’ ability to predict the robot’s future behavior, making them more likely to follow the same norm.
    • Erin’s defense
      • Nice slides!
      • Slide 4 – narrowing from big question to dissertation topic. Nice way to set up framing
      • Intellectual function vs. adaptive behavior
      • Loss of self-determination
      • Maker culture as a way of having your own high-dimensional vector? Does this mean that the maker culture is inherently more exploratory when compared to …?
      • “Frustration is an easy way to end up in off-task behavior”
      • Peer learning as gradient descent?
      • Emic ethnography
      • Pervasive technology in education
      • Turn-taking
      • Antecedent behavior consequence theory
      • Reducing the burden on the educators. Low-level detection and to draw attention to the educator and annotate. Capturing and labeling
      • Helina – bring the conclusions back to the core questions
      • Diversity injection works! Mainstream students gained broader appreciation of students with disability
      • Q: Does it make more sense to focus on potentially charismatic technologies that will include the more difficult outliers even if it requires a breakthrough? Or to make incremental improvements that can improve accessibility to some people with disabilities faster?
      • Boris analytic software


Phil 3.15.18

8:30 – 4:30 ASRC MKT

Phil 3.14.18

7:00 – 4:00 ASRC MKT

  • Cannot log into my timesheet
  • Continuing along with TF. Got past the introductions and to the beginning of the coding.
  • Myanmar: UN blames Facebook for spreading hatred of Rohingya (The Guardia)
    • ‘Facebook has now turned into a beast’, says United Nations investigator, calling network a vehicle for ‘acrimony, dissension and conflict’
  • Related to the above (which was pointed out by the author in this tweet)
  • Keynote: Susan Dumais
    • Better Together: An Interdisciplinary Perspective on Information Retreival
    • A solution to plato’s problem – latent semantic indexing
    • The road to LSI
    • LSI paper as dimension reduction Dumas et al 1988,
    • Search and context
      • Ranked list of 10 blue links
      • Need to understand the context in which they occur. Documents are intricately linked
      • Search is doe to accomplish something (picture of 2 people pointing at a chart/map?)
      • Short and long term models of interest (Bennett et al 2012)
      • Stuff I’ve Seen (2003) Becomes LifeBrowser
    • Future directions
      • ML will take over IR for better or worst
      • Moving from a world that indexe strings to a world that indexes things
      • Bing is doing pro/con with questions, state maintained dialog
  • Here and Now: Reality-Based Information Retrieval. [Perspective Paper]
    Wolfgang Büschel, Annett Mitschick and Raimund Dachselt

    • Perspective presentation on AR-style information retreival.
    • Maybe an virtual butler that behaves like an invisible freind?
  • A Study of Immediate Requery Behavior in Search.
    Haotian Zhang, Mustafa Abualsaud and Mark Smucker
  • Exploring Document Retrieval Features Associated with Improved Short- and Long-term Vocabulary Learning Outcomes.
    Rohail Syed and Kevyn Collins-Thompson
  • Switching Languages in Online Searching: A Qualitative Study of Web Users’ Code-Switching Search Behaviors.
    Jieyu Wang and Anita Komlodi
  • A Comparative User Study of Interactive Multilingual Search Interfaces.
    Chenjun Ling, Ben Steichen and Alexander Choulos

Phil 3.13.18

7:00 – 5:00 ASRC MKT

  • Sent T a travel request for the conference. Yeah, it’s about as late as it could be, but I just found out that I hadn’t registered completely…
  • Got Tensorflow running on my laptop. Can’t get Python 2.x warnings to not show. Grrrr.
  • Had to turn off privacy badger to get the TF videos to play. Nicely done
  • Information Fostering – Being Proactive with Information Seeking and Retrieval [Perspective Paper]
    Chirag Shah

    • Understanding topic, task, and intention
    • People are boxed in when looking for information. Difficult to encouraging broad thinking
    • Ryan White – tasks? Cortana?
    • What to do when things go bad:
  • The Role of the Task Topic in Web Search of Different Task Types.
    Daniel Hienert, Matthew Mitsui, Philipp Mayr, Chirag Shah and Nicholas Belkin
  • Juggling with Information Sources, Task Type, and Information Quality
    Yiwei Wang, Shawon Sarkar and Chirag Shah

    • Doing tasks in a study has an odd bias that drives users to non-social information sources. Since the user is not engaged in a “genuine” task, the request of other people isn’t considered as viable.
  • ReQuIK: Facilitating Information Discovery for Children Through Query Suggestions.
    Ion Madrazo, Oghenemaro Anuyah, Nevena Dragovic and Maria Soledad Pera

    • LSTM model + hand-coded heuristics combined deep and wide. LSTM produces 92% accuracy, Hand-rolled 68%, both 94%
    • Wordnet-based similarity
  • Improving exploration of topic hierarchies: comparative testing of simplified Library of Congress Subject Heading structures.
    Jesse David Dinneen, Banafsheh Asadi, Ilja Frissen, Fei Shu and Charles-Antoine Julien

    • Pruning large scale structures to support visualization
    • Browsing complexity calculations
    • Really nice. Dynamically pruned trees, with the technical capability for zooming at a local level
  • Fixation and Confusion – Investigating Eye-tracking Participants’ Exposure to Information in Personas.
    Joni Salminen, Jisun An, Soon-Gyo Jung, Lene Nielsen, Haewoon Kwak and Bernard J. Jansen

    • LDA topic extraction
    • Eyetribe – under $200. Bought by Facebook
    • Attribute similarity as a form of diversity injection
  • “I just scroll through my stuff until I find it or give up”: A Contextual Inquiry of PIM on Private Handheld Devices.
    Amalie Jensen, Caroline Jægerfelt, Sanne Francis, Birger Larsen and Toine Bogers

    • contextual inquiry – good at uncovering tacit interactions
    • Looking at the artifacts of PIM
  • Augmentation of Human Memory: Anticipating Topics that Continue in the Next Meeting
    Seyed Ali Bahrainian and Fabio Crestani

    • Social Interactions Log Analysis System (Bahrainian et. al)
    • Proactive augmentation of memory
    • LDA topic extraction
    • Recency effect could apply to distal ends of a JuryRoom discussion
  • Characterizing Search Behavior in Productivity Software.
    Horatiu Bota, Adam Fourney, Susan Dumais, Tomasz L. Religa and Robert Rounthwaite

Phil 3.12.18

7:00 – 7:00 ASRC

  • The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
    • Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic process that transcends the substrate in which it occurs, evolution’s creativity is not limited to nature. Indeed, many researchers in the field of digital evolution have observed their evolving algorithms and organisms subverting their intentions, exposing unrecognized bugs in their code, producing unexpected adaptations, or exhibiting outcomes uncannily convergent with ones in nature.
  • Analyzing Knowledge Gain of Users in Informational Search Sessions on the Web.
    Ujwal Gadiraju, Ran Yu, Stefan Dietze and Peter Holtz
  • Query Priming for Promoting Critical Thinking in Web Search.
    Yusuke Yamamoto and Takehiro Yamamoto

    • TruthFinder – consistency
    • CowSearch – provides supporting information for credibility judgements
    • Query priming only worked on university-educated participants. Explorer? Or not university educated are stampede?
  • Searching as Learning: Exploring Search Behavior and Learning Outcomes in Learning-related Tasks.
    Souvick Ghosh, Manasa Rath and Chirag Shah

    • Structures of the Life-World
    • Distinguish, organize and conclude are commonly used words by participants describing their tasks. This implies that learning, or at least the participant’s view of learning is building an inventory of facts. Hmm.
    • Emotional effect on cognitive behavior? It would be interesting to see if (particularly with hot-button issues), the emotion can lead to a more predictable dimension reduction.
  • Informing the Design of Spoken Conversational Search [Perspective Paper]
    Johanne R Trippas, Damiano Spina, Lawrence Cavedon, Hideo Joho and Mark Sanderson

    •  Mention to Johanne about spoken interface to SQL
    • EchoQuery
  • Style and alignment in information-seeking conversation.
    Paul Thomas, Mary Czerwinski, Daniel Mcduff, Nick Craswell and Gloria Mark

    • Conversational Style (Deborah Tannen) High involvement and High consideration.
    • Alignment. Match each others patterns of speech!
    • Joint action, interactive alignment, and dialog
      • Dialog is a joint action at different levels. At the highest level, the goal of interlocutors is to align their mental representations. This emerges from joint activity at lower levels, both concerned with linguistic decisions (e.g., choice of words) and nonlinguistic processes (e.g., alignment of posture or speech rate). Because of the high-level goal, the interlocutors are particularly concerned with close coupling at these lower levels. As we illustrate with examples, this means that imitation and entrainment are particularly pronounced during interactive communication. We then argue that the mechanisms underlying such processes involve covert imitation of interlocutors’ communicative behavior, leading to emulation of their expected behavior. In other words, communication provides a very good example of predictive emulation, in a way that leads to successful joint activity.
  • SearchBots: User Engagement with ChatBots during Collaborative Search.
    Sandeep Avula, Gordon Chadwick, Jaime Arguello and Robert Capra

Phil 3.11.18

7:00 – 5:00 ASRC MKT

  • Notes from Coursera Deep Learning courses by Andrew Ng. Cool notes by Tess Ferrandez <- nice Angular stuff here too
  • Kill Math project for math visualizations
    • The power to understand and predict the quantities of the world should not be restricted to those with a freakish knack for manipulating abstract symbols.
  • Leif Azzopardi
  • CHIIR 2018 DC today! I’m on after lunch! Impostor syndrome well spun up right now
    • Contextualizing Information Needs of Patients with Chronic Conditions Using Smartphones
      • Henna Kim
      • What about the OpenAPS project?
      • recognition that patients need pieces of information to accomplish health related work to better manage their condition to health and wellness
      • Information needs arise from talks???
      • Goals that patients pursue for a long period of time
  • Task-based Information Seeking in Different Study Settings
    • Yiwei Wang
    • People are influenced by their natural environment. Also the cognitive environment
    • What about nomadic/flock/stampede?
    • She needs a research browser!
    • Need for cognition
  • The Moderator Effect of Working Memory and Emotion on the Relationship between Information Overload and Online Health Information Quality
    • Yung-Sheng Chang
    • Information overload and information behavior/attitude
    • Overload is also the inability to simplify. Framing should help with incorporation
  • Exploring the effects of social contexts on task-based information seeking behavior
    • Eun Youp Rha
    • Socio-cultural context
    • A task is only recognizable within a certain context when people agree it is a task
    • Sociocultural mental processes. Perception, memory, Classification signification (Zerubavel, 1997)
      • Sociology of perception
      • Sociology of attention
      • Practice theory – Viewing human actions as regular performances of ritualized actions
    • How do tow communities in different places evolve different norms?
  • Distant Voices in the Dark: Understanding the incongruent information needs of fiction authors and readers
    • Carol Butler
    • Authors and readers interact with each other
    • What about The Martian?
    • Also, fanfiction?
    • Authors want to interact with other authors, readers with readers.
    • Also writing for peers where readers are assumed not to exist (technical publications)
    • Writing and reading is built around an industrial process (mass entertainment in general? What about theater?)
    • Stigma around self-publishing
    • Not much need to interact because they don’t get that much from each other. Also, the book has just been released and the readers haven’t read it. What question do you ask when you haven’t read the book yet? This leads to the “same stupid questions”
    • Library catalogs that incorporate social media. Sense is that it failed?
    • BookTube?
  • On the Interplay Between Search Behavior and Collections in Digital Libraries and Archives
    • Tessel Bogaard
    • Digital library, with text, meta information, clickstreams in logs
    • How do we let the domain curators understand their users
    • Family announcements are disproportionately popular. Short sessions, with few clicks and documents
    • WWII documents are from prolonged interactions
    • Grouping sessions using k medoid using user interactions  and facets. Use average silhouette widths (how similar are the clusters) Stability over time
    • Markov cahin analysis
    • Side by side comparison over teh whole data set
    • Session graph (published demo paper)
  • Creative Search: Using Search to Leverage Your Everyday Creativity
    • Yinglong Zhang
    • Creativity can be taught
    • To be creative, you need to acquire deep domain knowledge. High dimensions. Implies that thinking in low dimensions are creativity constraining.
    • Crowdsourcing tools (Yu, Kittur, and Kraut 2016)
    • Free form web curation (Kerne et. al)
  • Diversity-Enhanced Recommendation Interface and Evaluation
    • Chun-Hua Tsai
    • Diversity-enhanced interface design
    • Continuous Controlability and experience
    • Very LMN-like
    • Interface is swamped by familiarity. Minimum delta from current interfaces.
  • Towards Human-Like Conversational Search Systems
    • Mateusz Dubiel
    • More experience = more use.
    • Needs more conversational?
    • Enable navigation through converation?
    • Back chaining and forward chaining
    • Asking for clarification
    • Turn taking
  • Room 225
  • Journal of information research
  • Paul Thomas (MS Research)
  • Ryan White (MS Research)
  • Jimmy Lin (Ex Twitter)
  • Dianne Kelly.

Phil 3.9.18

8:00 – 4:30 ASRC MKT

  • Still working on the nomad->flocking->stampede slide. Do I need a “dimensions” arrow?
  • Labeled slides. Need to do timings – done
  • And then Aaron showed up, so lots of reworking. Done again!
  • Put the ONR proposal back in its original form
  • An overview of gradient descent optimization algorithm
    • Gradient descent is one of the most popular algorithms to perform optimization and by far the most common way to optimize neural networks. At the same time, every state-of-the-art Deep Learning library contains implementations of various algorithms to optimize gradient descent (e.g. lasagne’scaffe’s, and keras’ documentation). These algorithms, however, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by. This blog post aims at providing you with intuitions towards the behaviour of different algorithms for optimizing gradient descent that will help you put them to use.

Phil 3.8.18

7:00 – 5:00 ASRC

  • Another nice comment from Joanna Bryson on BBC Business Daily – The bias is seldom in the algorithm. Latent Semantic Indexing is simple arithmetic. The data contains the bias, and that’s from us. Fairness is a negotiated concept, which means that is is complicated. Requiring algorithmic fairness necessitates placing enormous power in the hands of those writing the algorithms.
  • The science of fake news (Science magazine)
    • The rise of fake news highlights the erosion of long-standing institutional bulwarks against misinformation in the internet age. Concern over the problem is global. However, much remains unknown regarding the vulnerabilities of individuals, institutions, and society to manipulations by malicious actors. A new system of safeguards is needed. Below, we discuss extant social and computer science research regarding belief in fake news and the mechanisms by which it spreads. Fake news has a long history, but we focus on unanswered scientific questions raised by the proliferation of its most recent, politically oriented incarnation. Beyond selected references in the text, suggested further reading can be found in the supplementary materials.
  • Incorporating Sy’s comments into a new slide deck
  • More ONR
  • Meeting with Shimei
    • Definitely use the ONR-specified headings
    • Research is looking good and interesting! Had to spend quite a while explaining lexical trajectories.
  • Ran through the slides with Sy again. Mostly finalized?