179 Matching Annotations
  1. Nov 2022
    1. n recent years, the neural network based topic modelshave been proposed for many NLP tasks, such as infor-mation retrieval [11], aspect extraction [12] and sentimentclassification [13]. The basic idea is to construct a neuralnetwork which aims to approximate the topic-word distri-bution in probabilistic topic models. Additional constraints,such as incorporating prior distribution [14], enforcing di-versity among topics [15] or encouraging topic sparsity [16],have been explored for neural topic model learning andproved effective.

      Neural topic models are often trained to mimic the behaviours of probabilistic topic models - I should come back and look at some of the works:

      • R. Das, M. Zaheer, and C. Dyer, “Gaussian LDA for topic models with word embeddings,”
      • P. Xie, J. Zhu, and E. P. Xing, “Diversity-promoting bayesian learning of latent variable models,”
      • M. Peng, Q. Xie, H. Wang, Y. Zhang, X. Zhang, J. Huang, and G. Tian, “Neural sparse topical coding,”
  2. Oct 2022
    1. It may be that the more concrete boundaries that having multiple instances provide can dampen down the cascades caused by the small world network effect. It is an interesting model to coexist between the silos with global scope and the personal domains beloved by the indieweb. In indieweb we have been saying ‘build things that you want for yourself’, but building things that you want for your friends or organisation is a useful step between generations.

      I'd say not just interesting, but also crucial. Where T and FB operate at generic level (despite FB pages as subgroups), the statistical, and IndieWeb on the personal (my site, my self-built tool), M works at group level or just above (bigger instances). That middle ground between singular and the statistical is where complexity resides and where it needs to be addressed and embraced. The network metaphor favors that intermediate level.

    1. The essential truth of every social network is that the product is content moderation, and everyone hates the people who decide how content moderation works.
    2. the problems with Twitter are not engineering problems. They are political problems. Twitter, the company, makes very little interesting technology; the tech stack is not the valuable asset. The asset is the user base: hopelessly addicted politicians, reporters, celebrities, and other people who should know better but keep posting anyway.

      Twitter's primary asset is not their technology, but their addicted user base.

    1. It was only on these social networking sites, where people have a podium, that I noticed quite a change in discourse. All of a sudden, you could read family and friends' thoughts on all types of subjects that are never uttered in person.
  3. Sep 2022
    1. Multidiscpl teams are different from heterogenous ones when it comes to learning. Dense networks useful for incremental steps, but hinder innovative steps (Vgl [[Lurking Weak Strong Ties 20040204063311]]) Provide team design principles.

      Full paper in Zotero

    1. translate those notions into stuff that I can tackle in my own sphere of influence. And to me those then make up the stuff that matters.

      Things that matter are a combination of things of interest plus sphere of influence/action radius. This can bring macro issues into a place where they can be addressed by micro actions that have meaning locally and contribute to the issue at scale. Contributes to the invisible hand of networks. Vgl [[Invisible hand of networks 20180616115141]]

  4. Aug 2022
    1. If you decentralize, the system will recentralize, but one layer up. Something new will be enabled by decentralization. That sounds like evolution through layering, like upward-spiraling complexity. That sounds like progress to me.

      Systems will centralise one step up from where it's decentralised. Interesting. My intuition is a bit 'softer' it's a rule of thumb for coalescence. Things might coalesce out of different needs/circumstances. The type of centralisation intended here, if it's about the silo's there's a external driver, that the easiest business models are found in centralisation as it creates asymmetric power for the centraliser. It's not a necessary outcome of the underlying distributedness, but something that others might need using that distributedness. If centrliasation isn't possible or allowed at some layer, it may well force external drivers for centralisation one layer up. Organisations as well as CoPs are mushrooms on the mycelium of human networks. Now that capital, location and finding colleagues can be done distributedly those mushrooms aren't always needed, and we see other types of mushrooms coalesce alongside classic organisations. Something like that?

    1. Mobile Network Hacking, IP Edition. by Karsten Nohl, Luca Melette & Sina Yazdanmehr. Black Hat. London. December 2-5, 2019. 47 minute video. https://www.blackhat.com/eu-19/briefings/schedule/index.html#mobile-network-hacking-ip-edition-17617

      Mobile networks have gone through a decade of security improvements ranging from better GSM encryption to stronger SIM card and SS7 configurations. These improvements were driven by research at this and other hacking conferences.

      Meanwhile, the networks have also mushroomed in complexity by integrating an ever-growing number of IT technologies from SIP to WiFi, IPSec, and most notably web technologies.

      This talk illustrates the security shortcomings when merging IT protocols into mobile networks. We bring back hacking gadgets long thought to be mitigated, including intercepting IMSI catchers, remote SMS intercept, and universal caller ID spoofing.

      We explore which protection measures are missing from the mobile network and discuss how to best bring them over from the IT security domain into mobile networks.

    1. On the Internet there are many collective projects where users interact only by modifying local parts of their shared virtual environment. Wikipedia is an example of this.[17][18] The massive structure of information available in a wiki,[19] or an open source software project such as the FreeBSD kernel[19] could be compared to a termite nest; one initial user leaves a seed of an idea (a mudball) which attracts other users who then build upon and modify this initial concept, eventually constructing an elaborate structure of connected thoughts.[20][21]

      Just as eusocial creatures like termites create pheromone infused mudballs which evolve into pillars, arches, chambers, etc., a single individual can maintain a collection of notes (a commonplace book, a zettelkasten) which contains memetic seeds of ideas (highly interesting to at least themselves). Working with this collection over time and continuing to add to it, modify it, link to it, and expand it will create a complex living community of thoughts and ideas.

      Over time this complexity involves to create new ideas, new structures, new insights.

      Allowing this pattern to move from a single person and note collection to multiple people and multiple collections will tend to compound this effect and accelerate it, particularly with digital tools and modern high speed communication methods.

      (Naturally the key is to prevent outside selfish interests from co-opting this behavior, eg. corporate social media.)

  5. Jul 2022
  6. Jun 2022
    1. few other large platforms unwittingly dissolved the mortar of trust, belief in institutions, and shared stories that had held a large and diverse secular democracy together.
    1. What's become clear is that our relationships are experiencing a profound reset. Across generations, having faced a stark new reality, a decades-long trend1 reversed as people are now shifting their energy away from maintaining a wide array of casual connections to cultivating a smaller circle of the people who matter most.

      ‘how the demand for deeper human connection has sparked a profound reset in our relationships’.

      The Meta Foresight (formerly Facebook IQ) team conducted a survey of 36,000 adults across 12 markets.

      Among their key findings:

      72% of respondents said that the pandemic caused them to reprioritize their closest friends
      Young people are most open to using more immersive tech to foster connections (including augmented and virtual reality), though all users indicated that tech will play a bigger role in enhancing personal connections moving forward
      37% of people surveyed globally reported reassessing their life priorities as a result of the pandemic
      
    1. algorithmic radicalization is presumably a simpler problem to solve than the fact that there are people who deliberately seek out vile content. “These are the three stories—echo chambers, foreign influence campaigns, and radicalizing recommendation algorithms—but, when you look at the literature, they’ve all been overstated.”

      algorithmic radicalization

    2. “A lot of the stories out there are just wrong,” he told me. “The political echo chamber has been massively overstated. Maybe it’s three to five per cent of people who are properly in an echo chamber.” Echo chambers, as hotboxes of confirmation bias, are counterproductive for democracy. But research indicates that most of us are actually exposed to a wider range of views on social media than we are in real life, where our social networks—in the original use of the term—are rarely heterogeneous.
  7. May 2022
    1. Indeed, as David Haskell, a biologist and writer, notes, a tree is “a community of cells” from many species: “fungus, bacteria, protist, alga, nematode and plant.” And often “the smallest viable genetic unit [is] … the networked community.”

      Explore this idea....

      What does it look like quantitatively?

    1. the death of Gerri Santoro, a woman who died seeking an illegal abortion in Connecticut, ignited a renewed fervor among those seeking to legalize abortion. Santoro’s death, along with many other reported deaths and injuries also sparked the founding of underground networks such as The Jane Collective to offer abortion services to those seeking to end pregnancies.
  8. Apr 2022
    1. Social networks may thus be “sticky” because social integration provides both benefits that encourage staying and social deterrents to leaving, increasing the chances of persistence.

      Important point - persistence can be because of negative reasons.

    2. persistence is the product of not only individual processes but also relational ones
    3. Students in a gateway biology course were randomly assigned to complete a control or values affirmation exercise, a psychological intervention hypothesized to have positive social effects. By the end of the term, affirmed students had an estimated 29% more friends in the course on average than controls. Affirmation also prompted structural changes in students’ network positions such that affirmed students were more central in the overall course friendship network.
  9. Feb 2022
    1. When I hear people in a variety of contexts talking about “building community” for students or colleagues (or, customers), I worry about that, too.  Is the motivation an additive one?  “Let’s give them more people to connect with and rely on?”  Or is it intended to be a kind of capture?

      What an enormous challenge for those of us in faculty development and other "community-building" businesses. Are we actually serving when we help people acculturate? We might be. We also might be trying to capture peoples' time and attention and loyalty.

  10. Jan 2022
    1. https://vimeo.com/232545219

      from: Eyeo Conference 2017

      Description

      Robin Sloan at Eyeo 2017 | Writing with the Machine | Language models built with recurrent neural networks are advancing the state of the art on what feels like a weekly basis; off-the-shelf code is capable of astonishing mimicry and composition. What happens, though, when we take those models off the command line and put them into an interactive writing environment? In this talk Robin presents demos of several tools, including one presented here for the first time. He discusses motivations and process, shares some technical tips, proposes a course for the future — and along the way, write at least one short story together with the audience: all of us, and the machine.

      Notes

      Robin created a corpus using If Magazine and Galaxy Magazine from the Internet Archive and used it as a writing tool. He talks about using a few other models for generating text.

      Some of the idea here is reminiscent of the way John McPhee used the 1913 Webster Dictionary for finding words (or le mot juste) for his work, as tangentially suggested in Draft #4 in The New Yorker (2013-04-22)

      Cross reference: https://hypothes.is/a/t2a9_pTQEeuNSDf16lq3qw and https://hypothes.is/a/vUG82pTOEeu6Z99lBsrRrg from https://jsomers.net/blog/dictionary


      Croatian acapella singing: klapa https://www.youtube.com/watch?v=sciwtWcfdH4


      Writing using the adjacent possible.


      Corpus building as an art [~37:00]

      Forgetting what one trained their model on and then seeing the unexpected come out of it. This is similar to Luhmann's use of the zettelkasten as a serendipitous writing partner.

      Open questions

      How might we use information theory to do this more easily?

      What does a person or machine's "hand" look like in the long term with these tools?

      Can we use corpus linguistics in reverse for this?

      What sources would you use to train your model?

      References:

      • Andrej Karpathy. 2015. "The Unreasonable Effectiveness of Recurrent Neural Networks"
      • Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, et al. "Generating sentences from a continuous space." 2015. arXiv: 1511.06349
      • Stanislau Semeniuta, Aliaksei Severyn, and Erhardt Barth. 2017. "A Hybrid Convolutional Variational Autoencoder for Text generation." arXiv:1702.02390
      • Soroush Mehri, et al. 2017. "SampleRNN: An Unconditional End-to-End Neural Audio Generation Model." arXiv:1612.07837 applies neural networks to sound and sound production
    1. https://www.youtube.com/watch?v=z3Tvjf0buc8

      graph thinking

      • intuitive
      • speed, agility
      • adaptability

      ; graph thinking : focuses on relationships to turn data into information and uses patterns to find meaning

      property graph data model

      • relationships (connectors with verbs which can have properties)
      • nodes (have names and can have properties)

      Examples:

      • Purchase recommendations for products in real time
      • Fraud detection

      Use for dependency analysis

  11. Dec 2021
    1. What is P2P (peer-to-peer) and what can you do with it? https://www.itpedia.nl/wp-content/uploads/2018/12/fingerworld.jpg.webp In a sense, Peer to Peer (P2P) networks are the social networks of the Internet. Every peer is equal to the others, and every peer has the same rights and duties as the others. Peers are clients and servers at the same time.

    1. It is impossible to think without writing; at least it is impossible in any sophisticated or networked (anschlußfähig) fashion.

      The sentiment that it is impossible to think without writing is patently wrong. While it's an excellent tool, it takes an overly textual perspective and completely ignores the value of orality an memory in prehistory.

      Modern culture has lost so many of our valuable cultural resources that we have completely forgotten that they even existed.

      Oral cultures certainly had networked thought, Luhmann and others simply can't imagine how it may have worked. We're also blinded by the imagined size of societies in pre-agricultural contexts. The size and scope of cities and city networks makes the history of writing have an outsized appearance.

      Further, we don't have solid records of these older netowrks, a major drawback of oral cultures which aren't properly maintained, but this doesn't mean that they didn not exist.

  12. Nov 2021
    1. people reading the same book at the same time, exploring the same ideas…Norms around signalling you're interested in something, and the extent of your interest, would go far

      How do we find the connections we don't know we're looking for?

  13. Sep 2021
    1. "Human nature is not a machine to be built after a model, and set to do exactly the work prescribed for it, but a tree which requires to grow and develop itself on all sides, according to the tendency of the inward forces which make it a living thing. Such are the differences among human beings in their sources of pleasure, their susceptibilities of pain, and the operation on them of different physical and moral agencies, that unless there is a corresponding diversity in their modes of life, they neither obtain their fair share of happiness, nor grow up to the mental, moral, and aesthetic stature of which their nature is capable." John Stuart Mill, On Liberty (1859)
  14. Aug 2021
  15. Jul 2021
    1. Supply chains—starting with the factories upstream, running through the ports and rail yards and warehouses, and ending with retail—are large and complex systems. These systems need to be adaptive, and yet the news shows us they are not. 

      We need supply chains to route around problems in the same way that packets on the internet route around bottlenecks and broken connections.

  16. Jun 2021
  17. May 2021
  18. Apr 2021
    1. This looks fascinating. I'm not so much interested in the coding/programming part as I am the actual "working in public" portions as they relate to writing, thinking, blogging in the open and sharing that as part of my own learning and growth as well as for sharing that with a broader personal learning network. I'm curious what lessons might be learned within this frame or how educators and journalists might benefit from it.

    1. Others are asking questions about the politics of weblogs – if it’s a democratic medium, they ask, why are there so many inequalities in traffic and linkage?

      This still exists in the social media space, but has gotten even worse with the rise of algorithmic feeds.

  19. Mar 2021
    1. he Cyborg Manifesto, Donna Haraway talks about the possibility of networks. While the Facebook of 2021 strings us out along a spectrum and pushes us to either end, Haraway’s conception of a network in 1985 is “the profusion of space and identities and the permeability of boundaries in the personal body and the body politic.” I

      An interesting data point in the evolution of networks

    1. Particularly striking in 1971 was his call for advanced technology to support "learning webs": The operation of a peer-matching network would be simple. The user would identify himself by name and address and describe the activity for which he sought a peer. A computer would send him back the names and addresses of all those who had inserted the same description. It is amazing that such a simple utility has never been used on a broad scale for publicly valued activity.
  20. Feb 2021
    1. Cytoscape is an open source software platform for visualizing complex networks and integrating these with any type of attribute data. A lot of Apps are available for various kinds of problem domains, including bioinformatics, social network analysis, and semantic web.
  21. Jan 2021
  22. Dec 2020
    1. If you look at the same graph with distance 2, the layer of additionally visible nodes show how my new Notion might be connected to things like online identity, using the environment to store memory and layered access to information. This triggers additional thoughts during the writing process.

      Lovely. This is such a great insight that I can already see is going to help me a lot.

  23. Oct 2020
    1. In at least one instance, a foreign adversary was able to take advantage of a back door invented by U.S. intelligence, according to Juniper Networks Inc, which said in 2015 its equipment had been compromised. In a previously unreported statement to members of Congress in July seen by Reuters, Juniper said an unnamed national government had converted the mechanism first created by the NSA.

      NSA gets Juniper to put a backdoor in one of their products. The product gets compromised by a foreign government in 2015.

    1. Workplace Learning in Informal Networks

      Milligan, C., Littlejohn, A., & Margaryan, A. (2014). Workplace Learning in Informal Networks. Journal of Interactive Media in Education.

      Learning does not stop when an individual leaves formal education, but becomes increasingly informal, and deeply embedded within other activities such as work. This article describes the challenges of informal learning in knowledge intensive industries, highlighting the important role of personal learning networks. The article argues that knowledge workers must be able to self-regulate their learning and outlines a range of behaviours that are essential to effective learning in informal networks. The article identifies tools that can support these behaviours in the workplace and how they might form a personal work and learning environment.

      https://search.ebscohost.com/login.aspx?direct=true&AuthType=shib&db=eric&AN=EJ1034717&site=eds-live&scope=site&custid=uphoenix

      7/10

    1. Newport is an academic — he makes his primary living teaching computer science at a university, so he already has a built-in network and a self-contained world with clear moves towards achievement.

      This is one of the key reasons people look to social media--for the connections and the network they don't have via non-digital means. Most of the people I've seen with large blogs or well-traveled websites have simply done a much better job of connecting and interacting with their audience and personal networks. To a great extent this is because they've built up a large email list to send people content directly. Those people then read their material and comment on their blogs.

      This is something the IndieWeb can help people work toward in a better fashion, particularly with better independent functioning feed readers.

    1. That is to say: if the problem has not been the centralized, corporatized control of the individual voice, the individual’s data, but rather a deeper failure of sociality that precedes that control, then merely reclaiming ownership of our voices and our data isn’t enough. If the goal is creating more authentic, more productive forms of online sociality, we need to rethink our platforms, the ways they function, and our relationships to them from the ground up. It’s not just a matter of functionality, or privacy controls, or even of business models. It’s a matter of governance.
    1. Thought leader and tech executive, John Ryan, provided valuable historical context both onstage and in his recent blog. He compared today’s social media platforms to telephone services in 1900. Back then, a Bell Telephone user couldn’t talk to an AT&T customer; businesses had to have multiple phone lines just to converse with their clients. It’s not that different today, Ryan asserts, when Facebook members can’t share their photos with Renren’s 150 million account holders. All of these walled gardens, he said, need a “trusted intermediary” layer to become fully interconnected.

      An apt analogy which I've used multiple times in the past.

    1. Micro.blog is not an alternative silo: instead, it’s what you build when you believe that the web itself is the great social network.

      So true!!!

    1. cyberinfrastructure is something more specific thanthe network itself, but it is something more general than a tool or a resource developed for a particular proj-ect, a range of projects, or, even more broadly, for a particular discipline.

      Mentioned in the video https://youtu.be/lelmXaSibrc?t=17m35s

    1. Mutual aid societies were built on the razed foundations of the old  guilds, and cooperatives and mass political parties then drew on the  experience of the mutual aid societies."

      This reminds me of the beginning of the Civil Rights movement that grew out of the civic glue that arose out of prior work relating to rape cases several years prior.

      I recall Zeynep Tufekci writing a bit about some of these tangential ideas in some of her social network writing. (Where's the link to that?)

  24. Aug 2020
  25. Jul 2020
  26. Jun 2020
  27. May 2020
  28. Apr 2020
    1. import all the necessary libraries into our notebook. LibROSA and SciPy are the Python libraries used for processing audio signals. import os import librosa #for audio processing import IPython.display as ipd import matplotlib.pyplot as plt import numpy as np from scipy.io import wavfile #for audio processing import warnings warnings.filterwarnings("ignore") view raw modules.py hosted with ❤ by GitHub View the code on <a href="https://gist.github.com/aravindpai/eb40aeca0266e95c128e49823dacaab9">Gist</a>. Data Exploration and Visualization Data Exploration and Visualization helps us to understand the data as well as pre-processing steps in a better way. 
    2. TensorFlow recently released the Speech Commands Datasets. It includes 65,000 one-second long utterances of 30 short words, by thousands of different people. We’ll build a speech recognition system that understands simple spoken commands. You can download the dataset from here.
    3. In the 1980s, the Hidden Markov Model (HMM) was applied to the speech recognition system. HMM is a statistical model which is used to model the problems that involve sequential information. It has a pretty good track record in many real-world applications including speech recognition.  In 2001, Google introduced the Voice Search application that allowed users to search for queries by speaking to the machine.  This was the first voice-enabled application which was very popular among the people. It made the conversation between the people and machines a lot easier.  By 2011, Apple launched Siri that offered a real-time, faster, and easier way to interact with the Apple devices by just using your voice. As of now, Amazon’s Alexa and Google’s Home are the most popular voice command based virtual assistants that are being widely used by consumers across the globe. 
    4. Learn how to Build your own Speech-to-Text Model (using Python) Aravind Pai, July 15, 2019 Login to Bookmark this article (adsbygoogle = window.adsbygoogle || []).push({}); Overview Learn how to build your very own speech-to-text model using Python in this article The ability to weave deep learning skills with NLP is a coveted one in the industry; add this to your skillset today We will use a real-world dataset and build this speech-to-text model so get ready to use your Python skills!
    1. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Use Keras if you need a deep learning library that: Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility). Supports both convolutional networks and recurrent networks, as well as combinations of the two. Runs seamlessly on CPU and GPU. Read the documentation at Keras.io. Keras is compatible with: Python 2.7-3.6.
    1. Networks  of civic engagement increase the potential cost to defectors who risk  benefits from future transactiaction. The same networks foster norms of  reciprocity that are reinforced by the networks of relationships in  which reputation is both balued and discussed. The same social networks  facilitate the flow of reputational information.

      How can we build some of this into social media networks to increase the level of trust and facts?

    1. there’s nothing exceptional about human brains.

      But is there something exceptional about the societies we have built? And the culture (including everything: chairs, tables, houses, streets, etc, etc) that surrounds us? I mean: is consciousness something that we have as individuals? Or is is something collective that we feel individually? Like a node in a vast network that gets a feeling of the local consciousness that the whole network has, and feels as if it is his/her own consciousness...

  29. Feb 2020
    1. The wiki can be used as a semantic networking tool, a way to construct meaningful connections between topics, ideas or concepts. A semantic network is composed of nodes (such as wiki pages ) with meaningful links (hyperlinks) connecting them. A semantic network of wikis can help learners to organize their ideas and to convey that organisation of ideas to others (Jonassen et al, 1999, p.165)

      semantic networking tool: a way to construct meaningful connections between topics, ideas or concepts. A semantic network is composed of nodes (such as wiki pages) with meaningful links (hyperlinks) connecting them. The pages are nodes the hyperlinks are the meaningful links. You can also see how important a concept is by the times it appears in other pages.s

  30. Jan 2020
  31. Dec 2019
    1. "Most of the structuring forms I'll show you stem from the simple capability of being able to establish arbitrary linkages between different substructures, and of directing the computer subsequently to display a set of linked substructures with any relative positioning we might designate among the different substructures. You can designate as many different kinds of links as you wish, so that you can specify different display or manipulative treatment for the different types."
    2. "You usually think of an argument as a serial sequence of steps of reason, beginning with known facts, assumptions, etc., and progressing toward a conclusion. Well, we do have to think through these steps serially, and we usually do list the steps serially when we write them out because that is pretty much the way our papers and books have to present them—they are pretty limiting in the symbol structuring they enable us to use. Have you even seen a 'scrambled-text' programmed instruction book? That is an interesting example of a deviation from straight serial presentation of steps.3b6b "Conceptually speaking, however, an argument is not a serial affair. It is sequential, I grant you, because some statements have to follow others, but this doesn't imply that its nature is necessarily serial. We usually string Statement B after Statement A, with Statements C, D, E, F, and so on following in that order—this is a serial structuring of our symbols. Perhaps each statement logically followed from all those which preceded it on the serial list, and if so, then the conceptual structuring would also be serial in nature, and it would be nicely matched for us by the symbol structuring.3b6c "But a more typical case might find A to be an independent statement, B dependent upon A, C and D independent, E depending upon D and B, E dependent upon C, and F dependent upon A, D, and E. See, sequential but not serial? A conceptual network but not a conceptual chain. The old paper and pencil methods of manipulating symbols just weren't very adaptable to making and using symbol structures to match the ways we make and use conceptual structures. With the new symbol-manipulating methods here, we have terrific flexibility for matching the two, and boy, it really pays off in the way you can tie into your work.3b6d This makes you recall dimly the generalizations you had heard previously about process structuring limiting symbol structuring, symbol structuring limiting concept structuring, and concept structuring limiting mental structuring.
  32. Nov 2019
    1. HGT typically adds new catabolic routes to microbial metabolic networks. This increases the chance of new metabolic interactions between bacteria
  33. Jul 2019
    1. Compared with neural networks configured by a pure grid search,we find that random search over the same domain is able to find models that are as good or betterwithin a small fraction of the computation time.
  34. Jun 2019
    1. Throughout the past two decades, he has been conducting research in the fields of psychology of learning and hybrid neural network (in particular, applying these models to research on human skill acquisition). Specifically, he has worked on the integrated effect of "top-down" and "bottom-up" learning in human skill acquisition,[1][2] in a variety of task domains, for example, navigation tasks,[3] reasoning tasks, and implicit learning tasks.[4] This inclusion of bottom-up learning processes has been revolutionary in cognitive psychology, because most previous models of learning had focused exclusively on top-down learning (whereas human learning clearly happens in both directions). This research has culminated with the development of an integrated cognitive architecture that can be used to provide a qualitative and quantitative explanation of empirical psychological learning data. The model, CLARION, is a hybrid neural network that can be used to simulate problem solving and social interactions as well. More importantly, CLARION was the first psychological model that proposed an explanation for the "bottom-up learning" mechanisms present in human skill acquisition: His numerous papers on the subject have brought attention to this neglected area in cognitive psychology.
    1. However, this doesn’t mean that Min-Max scaling is not useful at all! A popular application is image processing, where pixel intensities have to be normalized to fit within a certain range (i.e., 0 to 255 for the RGB color range). Also, typical neural network algorithm require data that on a 0-1 scale.

      Use min-max scaling for image processing & neural networks.

  35. Apr 2019
    1. Most of these near clones have and will fail. The reason that matching the basic proof of work hurdle of an Status as a Service incumbent fails is that it generally duplicates the status game that already exists. By definition, if the proof of work is the same, you're not really creating a new status ladder game, and so there isn't a real compelling reason to switch when the new network really has no one in it.

      This presumes that status is the only reason why people would join such a network. It also underlines the fact that the platform needs to be easy and simple to use, otherwise no one enters it and uses it as the tool first before the network exists.

  36. Mar 2019
  37. Feb 2019
    1. y, these occurrences –which 38arguablycan be considered the norm rather than the exception in related taxa –may 39provide useful evidence of relatednes

      This is very true.

      And one reason more why especially palaeontologists should stop ignoring distance-based networks (following the Farris'ian Dogma that "distance = phenetic", but see Felsenstein, 2004, Inferring Phylogenies) as a tool to explore the non-trivial signal in their data sets — some application examples posted at the Genealogical World of Phylogenetic Networks; see also Denk & Grimm, Rev. Pal. Pal. 2009, Bomfleur et al., BMC Evol. Biol. 2015, —, PeerJ, 2017. Even in the absence of reticulation, evolving morphologies do not provide tree-like signal, because synapomorphies, characters fully compatible with the true tree, are rare, homoiologies common, and convergences, characters incompatible with the true tree, inevitable.

      The less tree-like the signal and the more different the individual probabilities for change, the more misleading or ambiguous will be the parsimony tree reconstruction. Neighbour-nets may appear to be crude tools, but are quick-to-infer, designed to handle data incompatibility. Consensus networks are, in any possible aspect, more informative than a strict or majority rule consensus tree.

      Instead of trying to decide between equally and inevitable biased trees, we can just explore our data, using networks. See pic, depicting all potential synapomorphies, bold, symplesiomorphies, italics, and homoiologies that can be inferred directly from the crocodilian morphomatrix. Naturally including pseudo-synapomorphies (red) when compared to the provided molecular tree.

      PS That the way out of the dilemma is to embrace networks has been realised very early in evolutionary sciences (long before Hennig and Farris).

      Pic3

    1. “the true benefit of the academy is the interaction, the accessto the debate, to the negotiation of knowledge—not to the stale cataloging of content

      Once this particular light goes on in one's head, it may be impossible to turn it off. Yet we still need the so-called "stale" cataloging of content. We need foundational knowledge. Perhaps the academy has just made its function (again) more visible under connectivism? And we are in a creative tension of sorts with knowledge cataloging as an end in itself?

  38. Jan 2019
    1. Finally, a maincontribution of this research lies in the examination of the solicitation of expertise in a digitally-connected world, where widely distributed and diverse expertise must nevertheless be realized under highly localized conditions.

      Evokes crowdsourcing/peer production literature on expertise (Majchrzak et al, Faraj et al, Benkler et al, Kittur,et al.)

  39. Dec 2018
    1. It is based on reciprocity and a level of trust that each party is actively seeking value-added information for the other.

      Seems like this is a critical assumption to examine for current media literacy/misinformation discussions. As networks become very large and very flat, does this assumption of reciprocity and good faith hold? (I'm thinking, here, of people whose expertise I trust in one domain but perhaps not in another, or the fact that sometimes I'm talking to one part of my network and not really "actively seeking information" for other parts.)

    1. feed-forward network (also known as a multilayer perceptron)

      In the network, each layer has variety of cells, which connect to next layers cells.

  40. Nov 2018
    1. At Clark, we established networked communities to help professors from different disciplines share innovative pedagogies and ideas for leading student work on group projects.

      Specifically how is "networked communities" being used in this context? "Networked" how (technically, practically, and organizationally)?

  41. Oct 2018
    1. Do neural networks dream of semantics?

      Neural networks in visual analysis, linguistics Knowledge graph applications

      1. Data integration,
      2. Visualization
      3. Exploratory search
      4. Question answering

      Future goals: neuro-symbolic integration (symbolic reasoning and machine learning)

  42. Aug 2018
    1. To start you thinking, here’s a quote from lead educator Jean Burgess. Jean considers how Twitter has changed since 2006 and reflects on her own use of the platform in the context of changing patterns of use. In response to the suggestion that Twitter is a dying social media platform, Jean states that: the narratives of decline around the place at the moment […] have to do with a certain loss of sociability. And to those of us for whom Twitter’s pleasures were as much to do with ambient intimacy, personal connections and play as they were to do with professional success theatre, celebrity and breaking news, this is a real, felt loss: sociability matters.
    1. Historically,researchers in diverse fields such as communication, sociology, law, and eco-nomics have argued that effective human systems organize people through acombination of hierarchical structures (e.g., bureaucracies), completely dis-tributed coordination mechanisms (e.g., markets), and social institutions ofvarious kinds (e.g., cultural norms). However, the rise of networked systemsand online platforms for collective intelligence has upended many of the as-sumptions and findings from this earlier research.

      Benkler argues that the process, motives, and cultural norms of online network-driven knowledge work are different than systems previously studied and should be re-evaluated.

    1. the internet may not be the most effective means of bringing work to an audience, particularly if you don’t already have some sort of access to an audience that will allow your work to be discovered

      Traditional scholarly publishing has a huge benefit of momentum - everyone is already there.

  43. Jul 2018
    1. Then I used Gephi, another free data analysis tool, to visualize the data as an entity-relationship graph. The coloured circles—called Nodes—represent Twitter accounts, and the intersecting lines—known as Edges—refer to Follow/Follower connections between accounts. The accounts are grouped into colour-coded community clusters based on the Modularity algorithm, which detects tightly interconnected groups. The size of each node is based on the number of connections that account has with others in the network.
    2. Using the open-source NodeXL tool, I collected and imported a complete list of accounts tweeting that exact phrase into a spreadsheet. From that list, I also gathered and imported an extended community of Twitter users, comprised of the friends and followers of each account. It was going to be an interesting test: if the slurs against Nemtsov were just a minor case of rumour-spreading, they probably wouldn't be coming from more than a few dozen users.
    1. The New Yorker’s Sasha Frere-Jones called Twitter a “self-cleaning oven,” suggesting that false information could be flagged and self-corrected almost immediately. We no longer had to wait 24 hours for a newspaper to issue a correction.
    1. Dissemination MechanismsFinally, we need to think about how this content is being disseminated. Some of it is being shared unwittingly by people on social media, clicking retweet without checking. Some of it is being amplified by journalists who are now under more pressure than ever to try and make sense and accurately report information emerging on the social web in real time. Some of it is being pushed out by loosely connected groups who are deliberately attempting to influence public opinion, and some of it is being disseminated as part of sophisticated disinformation campaigns, through bot networks and troll factories.
    1. The team found that the number of friends that pairs of individual have in common is strongly correlated with the strength of the tie between them, as measured in other ways. That’s regardless of whether people are linked by mobile-phone records or by social ties in rural Indian villages.
  44. Nov 2017
    1. Rather than framing everything at the course level, we should be deploying these technologies for the individual.26

      Obvious question: what about groups, communities, networks, and other supra-individual entities apart from the course/cohort model?

    1. As they stand, and especially with algorithmic reinforcement, “reactions” and “likes” are like megaphones for echo chambers and news outrage.

      This is something that's been nagging at me for the last couple of weeks.

      Does it all matter? Does that tweet, share, thumbs up, like really matter at all? If you/we/I share out of tweet of support, outrage, or indifference, does it really matter on the grand scale.

      Yes, I might have some likeminded individuals value it, read it, use it, share it. But, ultimately aren't we really just shouting into the echo chambers that have been built up for us thanks to these algorithms and networks? We're preaching to the choir.

      I'd like to think that open can/will combat this...but unsure.

      I think this is a post for Hybrid Ped or elsewhere. Lemme know if this resonates with anyone and you want to write it out.

  45. Oct 2017
    1. Another significant finding is that efforts of the membersof religious networks—in spite of their relatively closedcharacteristics—in terms of being at the center of a net-work and taking the brokerage role are, contrary to theliterature, highly developed

      This is an important finding that can help researchers better understand how this and similar religious networks operate.