Phil 3.27.18

7:00 – 6:00 ASRC MKT

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  • Continuing with Keras
    • The training process can be stopped when a metric has stopped improving by using an appropriate callback:
      keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto')
    • How to download and install quiver
    • Tried to get Tensorboard working, but it doesn’t connect to the data right?
    • Spent several hours building a neuron that learns in Excel. I’m very happy with it. What?! SingleNeuron
  • This is a really interesting thread. Stonekettle provoked a response that can be measured for variance, and also for the people (and bots?) who participate.
  • Listening to the World Affairs Council on The End of Authority, about social influence and misinformation
    • With so many forces undermining democratic institutions worldwide, we wanted a chance to take a step back and provide some perspective. Russian interference in elections here and in Europe, the rise in fake news and a decline in citizen trust worldwide all pose a danger. In this first of a three-part series, we focus on the global erosion of trust. Jennifer Kavanagh, political scientist at the RAND Corporation and co-author of “Truth Decay”, and Tom Nichols, professor at the US Naval War college and author of “The Death of Expertise,” are in conversation with Ray Suarez, former chief national correspondent for PBS NewsHour.
  • Science maps for kids
    • Dominic Walliman has created science infographics and animated videos that explore how the fields of biology, chemistry, computer science, physics, and mathematics relate.
  • The More you Know (Wikipedia) might serve as a template for diversity injection
  • A list of the things that Google knows about you via Twitter
  • Collective movement ecology
    • The collective movement of animals is one of the great wonders of the natural world. Researchers and naturalists alike have long been fascinated by the coordinated movements of vast fish schools, bird flocks, insect swarms, ungulate herds and other animal groups that contain large numbers of individuals that move in a highly coordinated fashion ([1], figure 1). Vividly worded descriptions of the behaviour of animal groups feature prominently at the start of journal articles, book chapters and popular science reports that deal with the field of collective animal behaviour. These descriptions reflect the wide appeal of collective movement that leads us to the proximate question of how collective movement operates, and the ultimate question of why it occurs (sensu[2]). Collective animal behaviour researchers, in collaboration with physicists, computer scientists and engineers, have often focused on mechanistic questions [37] (see [8] for an early review). This interdisciplinary approach has enabled the field to make enormous progress and revealed fundamental insights into the mechanistic basis of many natural collective movement phenomena, from locust ‘marching bands’ [9] through starling murmurations [10,11].
  • Starting to read Influence of augmented humans in online interactions during voting events
    • Massimo Stella (Scholar)
    • Marco Cristoforetti (Scholar)
    • Marco Cristoforetti (Scholar)
    • Abstract: 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.
    • Bruter and Harrison [19] shift the focus on the psychological in uence that electoral arrangements exert on voters by altering their emotions and behavior. The investigation of voting from a cognitive perspective leads to the concept of electoral ergonomics: Understanding optimal ways in which voters emotionally cope with voting decisions and outcomes leads to a better prediction of the elections.
    • Most of the Twitter interactions are from humans to bots (46%); Humans tend to interact with bots in 56% of mentions, 41% of replies and 43% of retweets. Bots interact with humans roughly in 4% of the interactions, independently on interaction type. This indicates that bots play a passive role in the network but are rather highly mentioned/replied/retweeted by humans.
    • bots’ locations are distributed worldwide and they are present in areas where no human users are geo-localized such as Morocco.
    • Since the number of social interactions (i.e., the degree) of a given user is an important estimator of the in uence of user itself in online social networks [17, 22], we consider a null model fixing users’ degree while randomizing their connections, also known as configuration model [23, 24].
    • During the whole period, bot bot interactions are more likely than random (Δ > 0), indicating that bots tend to interact more with other bots rather than with humans (Δ < 0) during Italian elections. Since interactions often encode the spread of a given content online [16], the positive assortativity highlights that bots share contents mainly with each other and hence can resonate with the same content, be it news or spam.

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