Monthly Archives: September 2018

Phil 9.28.18

7:30 – 4:00 ASRC MKT

  • Stumbled on this podcast this morning: How Small Problems Snowball Into Big Disasters
  • How to Prepare for a Crisis You Couldn’t Possibly Predict
  • I’m trying to think about how this should be applied to human/machine ecologies. I think that simulation is really important because it lets one model patch compare itself against another model without real-world impacts. This has something to do with a shared, multi-instance environment simulation as well. The environment provides one level of transparent interaction, but there also needs to be some level of inadvertent social information that shows some insight into how a particular system is working.
    • When the simulation and the real world start to diverge for a system, that needs to be signaled
    • Systems need to be able to “look into” other simulations and compare like with like. So a tagged item (bicycle) in one sim is the same in another.
    • Is there an OS that hands out environments?
    • How does a decentralized system coordinate? Is there an answer in MMOGs?
  • Kate Starbird’s presentation was interesting as always. We had a chance to talk afterwards, and she’d like to see our work, so I’ve sent her links to the last two papers.
    I also met Bill Braniff, who is the director of the UMD Study of Terrorism and responses to Terrorism. He got papers too, with a brief description about how mapping could aid in the detection of radicalization patterns
    Then at lunch, I had a chance to meet with Roger Bostelman from NIST. He’s interested in writing standards for fleet and swarm vehicles, and is interested in making sure that standards mitigate the chance of stampeding autonomous vehicles, so I sent him the Blue Sky draft.
    And lastly, I got a phone call from Aaron who says that our project will be terminated December 31, after which there will be no more IR&D at ASRC. It was a nice run while it lasted. And they may change their minds, but I doubt it.

Phil 9.27.18

7:00 – 6:00 ASRC MKT

  • Writing your own LaTex class
  • Multiple facets of biodiversity drive the diversity–stability relationship
    • A substantial body of evidence has demonstrated that biodiversity stabilizes ecosystem functioning over time in grassland ecosystems. However, the relative importance of different facets of biodiversity underlying the diversity–stability relationship remains unclear. Here we use data from 39 grassland biodiversity experiments and structural equation modelling to investigate the roles of species richness, phylogenetic diversity and both the diversity and community-weighted mean of functional traits representing the ‘fast–slow’ leaf economics spectrum in driving the diversity–stability relationship. We found that high species richness and phylogenetic diversity stabilize biomass production via enhanced asynchrony in the performance of co-occurring species. Contrary to expectations, low phylogenetic diversity enhances ecosystem stability directly, albeit weakly. While the diversity of fast–slow functional traits has a weak effect on ecosystem stability, communities dominated by slow species enhance ecosystem stability by increasing mean biomass production relative to the standard deviation of biomass over time. Our in-depth, integrative assessment of factors influencing the diversity–stability relationship demonstrates a more multicausal relationship than has been previously acknowledged.
  • Computer Algorithms, Market Manipulation and the Institutionalization of High Frequency Trading (adversarial herding?)
    • The article discusses the use of algorithmic models in finance (algo or high frequency trading). Algo trading is widespread but also somewhat controversial in modern financial markets. It is a form of automated trading technology, which critics claim can, among other things, lead to market manipulation. Drawing on three cases, this article shows that manipulation also can happen in the reverse way, meaning that human traders attempt to make algorithms ‘make mistakes’ by ‘misleading’ them. These attempts to manipulate are very simple and immediately transparent to humans. Nevertheless, financial regulators increasingly penalize such attempts to manipulate algos. The article explains this as an institutionalization of algo trading, a trading practice which is vulnerable enough to need regulatory protection.
  • Karin Knorr Cetina is interested in financial markets, knowledge and information, as well as in globalization, theory and culture. Her current projects include a book on global foreign exchange markets and on post-social knowledge societies. She continues to do research on the information architecture of financial markets, on their “global microstructures” (the global social and cultural form these markets take) and on trader markets in contrast to producer markets. She also studies globalization from a microsociological perspective, using an ethnographic approach, and she continues to be interested in “laboratory studies,” the study of science, technology and information at the site of knowledge production – particularly in the life sciences and in particle physics.
  • Reading A Sociology of Algorithms: High-Frequency Trading and the Shaping of Markets
    • Markets are politics,” (pg 8). I’d reverse that and say that politics are a market for power/influence, though that may be too glib.
    • three main types of algorithm discussed here (trading venues’ matching engines, which consummate trades; execution algorithms used by institutional investors to buy or sell large blocks of shares; and HFT algorithms), (pg 11)
    • a “lit” venue is one in which the electronic order book is visible to the humans and algorithms that trade on the venue; in a “dark” venue it is not visible.  (pg 11)
  • Meeting with USPTO folks. I went over their heads, but Aaron found the right level.

Phil 9.26.18

7:00 – 5:00 ASRC MKT

  • The Publisher’s Patron: How Google’s News Initiative Is Re-Defining Journalism
    • Facebook, Twitter, Amazon and Google – many tech companies are involved in journalism. A major force, however, is Google’s News Initiative. But where does Google’s money go? We can reveal that the typical recipient of Google funding is a commercial legacy institution in Western Europe. Meanwhile, non-profit news organisations and public-service media rarely receive funding. The only question is: what is Google trying to achieve with its sponsorship?
  • Introduction to Machine Learning for Coders: Launch
    • Today we’re launching our newest (and biggest!) course, Introduction to Machine Learning for Coders. The course, recorded at the University of San Francisco as part of the Masters of Science in Data Science curriculum, covers the most important practical foundations for modern machine learning. There are 12 lessons, each of which is around two hours long—a list of all the lessons along with a screenshot from each is at the end of this post.
    • There are some excellent machine learning courses already, most notably the wonderful Coursera course from Andrew Ng. But that course is showing its age now, particularly since it uses Matlab for coursework. This new course uses modern tools and libraries, including python, pandas, scikit-learn, and pytorch. Unlike many educational materials in the field, our approach is “code first” rather than “math first”. It’s well suited to people who are writing code every day, but perhaps aren’t practicing their math chops quite as often (although we do cover all the necessary theory when appropriate). Perhaps most importantly, this course is very opinionated—rather than being a complete survey of every type of model, we focus on those that really matter in practice.
  • Been thinking about libraries being a marker for production code, and it strikes me that GitHub could be a set of “conversations”. There are markers for popularity (pulls), and markers for quality (pushes). We know how many people are contributing (plus followers and following), and there are tags. There is also a marketplace now.  There is also an API. My sense is that it should be possible to build maps of:
    • Language relationships and use (X Y Z + color?)
    • Relationships within languages?
    • Cross-linked projects across languages
    • NLP analysis of README
  • There are also other types of measures of consensus like dependency graphs (who’s actually using) and releases (more info here)
  • More iConf paper – it’s the right length. Now tweaking
  • Put Zach on JuryRoom until he is moved to A2P?
  • Reading A Sociology of Algorithms: High-Frequency Trading and the Shaping of Markets
  • Reading Antonio’s ACM Paper. Surprising alignment with our work

Phil 9.25.18

7:00 – 5:00 ASRC MKT

  • Wayne’s notes from yesterday:
    • Part of the wrapper for this will be why these issues might matter for the iSchool’s research future. I can help with the framing there.
      Yikes, 4 pages in this format? That is nothing!
      Will really have to shave this down to the absolute minimum.
      To that end I think the scenarios get fleshed out in their fullest now to capture all of the ideas and then hacked brutally into 1-2 paragraphs.
      The abstract probably goes to 4 sentences.
      Images stay, but no larger.
      We’ll work this out, but, man, that is barely 1500 words. Who was thinking when they put this together? 😉
  • Want to redo the designed system chart so that the complexity zone is concave – done.
  • More writing. Figured out that cars would be crashing at a rate of 3-4/sec based on 2016 data. Yikes!
  • Worked with Aaron on response to Antonio’s proposal. IEEE Software is a “production” magazine. And a nice marker for production is what kind of libraries are available, because then articles can be written on how to use them.
  • Kate Starbird this Friday! 10:00am – 12:00pm 2119 Hornbake Library South Wing
  • There is a world nomad games

Phil 9.24.18

7:00 – 6:00 ASRC MKT

  • It’s fall and dark in the morning
  • Change the “Designed systems” diagram to be more of a bathtub curve, reflecting that there is very little activity in the complex regime – done
  • Working on the “Second middle part” (discussion? results?).
  • This from the New Yorker book review of Network Propaganda: Manipulation, Disinformation, and Radicalization in American Politics
    • The Clinton orgy-island story met a very different fate in the right-wing media, which pushed versions of it over the course of the campaign. (Fox News initially ran several segments that raised the topic of the “Lolita Express.”) The dynamic on the right, the authors found, “rewards the most popular and widely viewed channels at the very top of the media ecosystem for delivering stories, whether true or false, that protect the team, reinforce its beliefs, attack opponents, and refute any claims that might threaten ‘our’ team from outsiders.” Referring to the orgy-island story, the authors note that “not one right-wing outlet came out to criticize and expose this blatant lie for what it was. In the grip of the propaganda feedback loop, the right-wing media ecosystem had no mechanism for self-correction, and instead exhibited dynamics of self-reinforcement, confirmation, and repetition so that readers, viewers and listeners encountered multiple versions of the same story, over months, to the point that both recall and credibility were enhanced.”
  • Transdisciplinary PhD Journeys: Reflecting on the challenge of the ‘transdisciplinary triple jump’
    • Responding to calls to ‘be transdisciplinary4’, we have committed to applying and critically reflecting on the principles of TD in our PhD research. However, in current institutional structures and cultures of academia, this adds an additional challenge to the existing demands of PhD research5,6. Not only are we expected to navigate the terrain of interdisciplinarity described as an ‘undisciplinary journey’6 which requires ‘epistemological agility’, but we are also confronted with the task of engaging meaningfully with societal actors beyond our academic comfort zones. All of this means we are constantly trying to ‘be everything to everyone’ and risk burning ourselves out in the process.

Fika – Sy’s talk. Better this time

Meeting with Wayne

  • Went over SASO, which we all agree went very well
  • Talked about ASRC funding conferences. Will try to see if we can do iConf if accepted
  • Went over the rough form of the iConf paper. First review pass by COB tomorrow
  • And hung the SASO poster 🙂 IMG_5490

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 9.20.18

7:00 – 5:00 ASRC MKT

  • Submit pre-approval for school – done!
  • Call bank – done!
  • Tried to do stuff on the Lufthansa site but couldn’t log in
  • Read through the USPTO RFI and realized it was a good fit for the Research Browser. Sent the RB white paper to those in the decision loop.
  • Updated the JuryRoom white paper to include an appendix on self-governance and handling hate speech, etc.
  • Introducing Cloud Inference API: uncover insights from large scale, typed time-series data
    • Today, we’re announcing the Cloud Inference API to address this need. Cloud Inference API is a simple, highly efficient and scalable system that makes it easier for businesses and developers to quickly gather insights from typed time series datasets. It’s fully integrated with Google Cloud Storage and can handle datasets as large as tens of billions of event records. If you store any time series data in Cloud Storage, you can use the Cloud Inference API to begin generating predictions.
    • Thread by Jeff Dean
  • Realized that there are additional matrices that can post-multiply the Laplacian. That way we can break down the individual components that contribute to “stiffness”. The reason for this is that only identical oscillators will synchronize. Similarity is a type of implicit coordination
    • Leave the Master matrix [M]: as degree on the diagonal, with “1” for a connection, “0” for no connection
    • =Bandwidth matrix [B]: has a value (0, 1) for each connection
    • Alignment matrix [A]: calculates the direction cosine between each connected node. Completely aligned nodes get an edge value of 1.0
    • There can also be a Weight vector W: which contains the “mass” of the node. A high mass node will be more influential in the network.
  • Had a few thoughts about JuryRoom self governance. The major social networks seem to be a mess with respect to what rights users have, and what constitutes a violation of terms of service. The solutions seem pretty brittle (Radiolab podcast on facebook rule making). JuryRoom has built in a mechanism for deliberation. Can that be used to create an online legal framework for crowdsourcing the rules and the interpretation? Roughly, I think that this requires the following:
    • A constitution – a simple document that lays out how JuryRoom will be goverened.
    • A bill of rights. What are users entitled to?
    • The concept of petition, trial, binding decisions, and precedent.
    • Is there a concept of testifying under oath?
    • The addition of “evidence” attachments that can be linked to posts. This could be existing documents, commissioned expert opinion, etc.
    • A special location for the “legal decisions”. These will become the basis for the precedent in future deliberations. Links to these prior decisions are done as attachments? Or as something else?
    • Localization. Since what is acceptable (within the bounds of the constitution and the bill of rights) changes as a function of culture, there needs to be a way that groups can split off from the main group to construct and use their own legal history. Voting/membership may need to be a part of this.
      • What is visible to non-members?
      • What are the requirements to be a member?
      • How are legal decisions implemented in software?
      • What are the duties of a “citizen”?
  • More iConf paper
  • I wanted to make figures align on the bottom. Turns out that the way that you do this is to set top alignment [t] for each minipage. Here’s my example:
    \begin{figure}[h]
    	\centering
    	\begin{minipage}[t]{.5\textwidth}
    		\centering
    		\fbox{\includegraphics[width=20em]{Nomad-Flocking-Stampede2.png}}
    		\caption{\label{fig:N-F-S} Evolved systems}
    	\end{minipage}%
    	\begin{minipage}[t]{.5\textwidth}
    		\centering
    		\fbox{\includegraphics[width=20em]{Nomad-Stampede.png}}
    		\caption{\label{fig:Monolithic_complex_nomad} Designed systems}
    	\end{minipage}%
    \end{figure}

     

Phil 9.19.18

7:00 – 5:30 ASRC MKT

  • More iConf paper
  • GSS Meeting?
  • Meeting with Wayne? No, he’s out till Thursday
  • Pinged Don about Aaron Mannes. He’s OOO as well
  • Understanding the interplay between social and spatial behaviour
    • Laura Alessandretti
    • Sune Lehmann
    • Andrea Baronchelli
    • According to personality psychology, personality traits determine many aspects of human behaviour. However, validating this insight in large groups has been challenging so far, due to the scarcity of multi-channel data. Here, we focus on the relationship between mobility and social behaviour by analysing trajectories and mobile phone interactions of 1000 individuals from two high-resolution longitudinal datasets. We identify a connection between the way in which individuals explore new resources and exploit known assets in the social and spatial spheres. We show that different individuals balance the exploration-exploitation trade-off in different ways and we explain part of the variability in the data by the big five personality traits. We point out that, in both realms, extraversion correlates with the attitude towards exploration and routine diversity, while neuroticism and openness account for the tendency to evolve routine over long time-scales. We find no evidence for the existence of classes of individuals across the spatio-social domains. Our results bridge the fields of human geography, sociology and personality psychology and can help improve current models of mobility and tie formation.
    • This looks to be a missing link paper that I can use to connect animal behavior in physical space and human behavior in belief space
  • A Sociology of Algorithms: High-Frequency Trading and the Shaping of Markets
    • Donald MacKenzie
      • My current research is on the sociology of markets, focusing on automated trading. I’ve worked in the past on topics ranging from the sociology of nuclear weapons to the meaning of proof in the context of computer systems critical to safety or security.
    • Computer algorithms are playing an ever more important role in financial markets. This paper proposes and exemplifies a sociology of algorithms that is (i) historical, in that it demonstrates path-dependence in the development of automated markets; (ii) ecological (in Abbott’s sense), in that it shows how automated high-frequency trading (HFT) is both itself an ecology and also is shaped by other linked ecologies (especially those of trading venues and of regulation); and (iii) “Zelizerian,” in that it highlights the importance of boundary work, especially of efforts to distinguish between (in effect) “good” and “bad” actors and algorithms. Empirically, the paper draws on interviews with 43 practitioners of HFT, and on a wider historical-sociology study (including interviews with a further 44 people) of the development of trading venues. The paper investigates the practices of HFT and analyses (in historical, ecological, and “Zelizerian” terms) how these differ in three different contexts (two types of share trading and foreign exchange).
  • A2P marketing meeting in Greenbelt
  • Long discussion on networks and the stiffness of links

Phil 9.18.18

7:00 – 5:00 ASRC MKT

  • I need to use this in a paper/poster on maps: frontiers
  • Finished review of extended abstract
  • Working on iConf paper. Good progress. Here are some notes/markup from Aaron: Markup_9_18_18
  • Human stampede
    • The four planes struck nose first into the sand while practicing a ”loop and tail” maneuver, the Air Force said. ”The pilot farthest to the east hit the ground first and the other three followed within a tenth of a second, flying in formation,” said Tom Sullivan of Boulder City, Nev., who was driving to a construction job in the area at the time.

Phil 9.17.18

7:00 – ASRC MKT

  • Dan Ariely Professor of psychology and behavioral economics, Duke University (Scholar)
    • Controlling the Information Flow: Effects on Consumers’ Decision Making and Preferences
      • One of the main objectives facing marketers is to present consumers with information on which to base their decisions. In doing so, marketers have to select the type of information system they want to utilize in order to deliver the most appropriate information to their consumers. One of the most interesting and distinguishing dimensions of such information systems is the level of control the consumer has over the information system. The current work presents and tests a general model for understanding the advantages and disadvantages of information control on consumers’ decision quality, memory, knowledge, and confidence. The results show that controlling the information flow can help consumers better match their preferences, have better memory and knowledge about the domain they are examining, and be more confident in their judgments. However, it is also shown that controlling the information flow creates demands on processing resources and therefore under some circumstances can have detrimental effects on consumers’ ability to utilize information. The article concludes with a summary of the findings, discussion of their application for electronic commerce, and suggestions for future research avenues.
      • This may be a good example of work that relates to socio-cultural interfaces.
  • Democracy’s Wisdom: An Aristotelian Middle Way for Collective Judgment
    • Josiah Ober (Scholar)
    •  The Greeks had experts determine choices, and the public vote between the expert choices
    • A satisfactory model of decision-making in an epistemic democracy must respect democratic values, while advancing citizens’ interests, by taking account of relevant knowledge about the world. Analysis of passages in Aristotle and legislative process in classical Athens points to a “middle way” between independent-guess aggregation and deliberation: an epistemic approach to decision-making that offers a satisfactory model of collective judgment that is both time-sensitive and capable of setting agendas endogenously. By aggregating expertise across multiple domains, Relevant Expertise Aggregation (REA) enables a body of minimally competent voters to make superior choices among multiple options, on matters of common interest. REA differs from a standard Condorcet jury in combining deliberation with voting based on judgments about the reputations and arguments of domain-experts.
  • NESTA Center for Collective Intelligence Design
    • The Centre for Collective Intelligence Design will explore how human and machine intelligence can be combined to make the most of our collective knowledge and develop innovative and effective solutions to social challenges.
    • Call for ideas (JuryRoom!)
      • Nesta is offering grants of up to £20,000 for projects that generate new knowledge on how to advance collective intelligence (combining human and machine intelligence) to solve social problems.
  • Synchronize gdrive, subversion
  • Finish abstract review
  • Organize iConf paper into something more coherent
    • Created folder for lit review
  • Start putting together notes on At Home in the Universe?
  • Ping folks from SASO
    • Graph Laplacian paper
    • Cycling stuff
  • Fika?
  • Meeting with Wayne?

Phil 9.8.18

How intermittent breaks in interaction improve collective intelligence

  • Many human endeavors—from teams and organizations to crowds and democracies—rely on solving problems collectively. Prior research has shown that when people interact and influence each other while solving complex problems, the average problem-solving performance of the group increases, but the best solution of the group actually decreases in quality. We find that when such influence is intermittent it improves the average while maintaining a high maximum performance. We also show that storing solutions for quick recall is similar to constant social influence. Instead of supporting more transparency, the results imply that technologies and organizations should be redesigned to intermittently isolate people from each other’s work for best collective performance in solving complex problems.

Will Foreign Agents Rig the U.S. Midterm Elections Through Social Media?

  • Samantha Bradshaw, an expert on computational propaganda, weighs in on whether Facebook, Twitter, and others are doing enough to curb political social media bots.

Detecting signs of dementia using word vector representations

  • Recent approaches to word vector representations, e.g., ‘w2vec’ and ‘GloVe’, have been shown to be powerful methods for capturing the semantics and syntax of words in a text. The approaches model the co-occurrences of words and recent successful applications on written text have shown how the vector representations and their interrelations represent the meaning or sentiment in the text. Most applications have targeted written language, however, in this paper, we investigate how these models port to the spoken language domain where the text is the result of (erroneous) automatic speech transcription. In particular, we are interested in the task of detecting signs of dementia in a person’s spoken language. This is motivated by the fact that early signs of dementia are known to affect a person’s ability to express meaning articulately for example when they engage in a conversation – something which is known to be cognitively very demanding. We analyse conversations designed to probe people’s short and long-term memory and propose three different methods for how word vectors may be used in a classification setup. We show that it is possible to identify dementia from the output of a speech recognizer despite a high occurrence of recognition errors.

Phil 7.9.18

SASO 2018

Trust in Organizations: Frontiers of Theory and Research

  • Although its importance is readily apparent, the contours of trust in collective contexts are much less obvious. The decision to trust in collective settings is different from, and in many respects more problematic than, decisions about trust that arise in other social contexts. Because of the size and structural complexity of large organizations, for example, individuals do not have the opportunity to engage in the sort of incremental and repeated exchanges that have been shown to facilitate the development of trust in more intimate settings, such as dyadic relationships

Keynote 4 – Heiko Hamann (chair R.P. Würtz, room: FBK – Sala Stringa)

  • Florarobotica
  • Micro and macro behaviors. He’s talking about ants, but I’m interested in UIs. What interfaces/communication channels cause an intelligent agent to perform constructive/destructive/etc behaviors
  • Swarm Robotics: A Formal Approach
  • How do you make decisions on the macroscopic scale?
  • Fast and sloppy vs. slow and accurate. What about ‘hardware acceleration?
  • Kilobot
  • Voter models
  • Majority rule is faster diffusion than voter model, but less accurate.
  • Mixing models does affect the results. Is this explore/exploit as well?
  • Swarm performance over density is a left-skewed normal distribution? Is this because they occupy physical space?
  • Hmmm. Density is a function of dimension
  • Density adaptation. Is it true that people like density to a point? And that online always “feels” different from actual density? What does popularity ranking vs other ranking do to human behavior?
  • subCULTron
  • generic, scalable and decentralized fault detection for robot swarms
    • Robot swarms are large-scale multirobot systems with decentralized control which means that each robot acts based only on local perception and on local coordination with neighboring robots. The decentralized approach to control confers number of potential benefits. In particular, inherent scalability and robustness are often highlighted as key distinguishing features of robot swarms compared with systems that rely on traditional approaches to multirobot coordination. It has, however, been shown that swarm robotics systems are not always fault tolerant. To realize the robustness potential of robot swarms, it is thus essential to give systems the capacity to actively detect and accommodate faults. In this paper, we present a generic fault-detection system for robot swarms. We show how robots with limited and imperfect sensing capabilities are able to observe and classify the behavior of one another. In order to achieve this, the underlying classifier is an immune system-inspired algorithm that learns to distinguish between normal behavior and abnormal behavior online. Through a series of experiments, we systematically assess the performance of our approach in a detailed simulation environment. In particular, we analyze our system’s capacity to correctly detect robots with faults, false positive rates, performance in a foraging task in which each robot exhibits a composite behavior, and performance under perturbations of the task environment. Results show that our generic fault-detection system is robust, that it is able to detect faults in a timely manner, and that it achieves a low false positive rate. The developed fault-detection system has the potential to enable long-term autonomy for robust multirobot systems, thus increasing the usefulness of robots for a diverse repertoire of upcoming applications in the area of distributed intelligent automation.

SISSY

Satisfy: Towards a self-learning analyzer for time series forecasting in self-improving systems

  • Hyperparameter tuning!
  • Add a meta-learning component (Forecasting module)DSCN0629
  • AutoWEKA (CASHO) – automatically defined search space <— this works!
  • Clustering on the data to try different algorithms on clusters
  • Need to spend some time looking at random forest. It seems to be high value for lower cost
  • Sante-fe institute time-series contest (set A) (set B)

Adaptive Coordination to Complete Mission Goals
Charles Walter, Sarra Alqahtani, and Rose Gamble

  • What Reasonable Guarantees Can We Make for a SISSY System? Kirstie Bellman
    • Trustworthy and knowable
    • Guarantees

Hierarchical Self-Awareness and Authority for Scalable Self-Integrating Systems
Ada Diaconescu, Barry Porter, Roberto Rodrigues Filho and Evangelos Pournaras

  • Hierarchy: Not authority and control, Not distribution. Do mean a multilevel system of different levels of abstraction with feedback loops between them
  • Levels of abstraction allow availability through loss of irrelevant information
  • Executes at different time scales
  • Abstraction goes up, feedback goes down
  • DSCN0632
  • DSCN0633

Security Issues in Self-improving System Integration – Challenges and Solution Strategies Henner Heck, Bernhard Sick and Sven Tomforde

  • Hardly manageable system structures
  • Capabilities
    • Mutual influence detection
    • Mutual dependency detection
    • Emergence detection
    • Self-reflection
  • Am I approaching the optimum?
  • Am I degraded?
  • Additional attack vectors targeting
    • self reflection
    • trust
    • mutual influence
    • adaptation

Improving Security and Interoperability of Interwoven Systems through Rigorous Selective Encapsulation of Critical Physical Resources
Phyllis Nelson

Outriggers and Training Wheels for Cooperating Systems  – Christopher Landauer

  • Kreitman’s Theorem?
  • Training wheels are constraints on a system that prevent catastrophic failures.
  • DSCN0633
  • Efficiency and robustness are direct competitors

A Concept for Proactive Knowledge Construction in Self-Learning Autonomous Systems Anthony Stein, Sven Tomforde, Ada Diaconescu, Jörg Hähner and Christian Müller-Schloer <- nice paper?

  • Reactive knowledge creation = trial and error
  • Proactive behavior = new knowledge created before it is needed
  • MLOC architectures (Multi-Layer Observer/Controller)
  • Drifting distributions leads to changes in the fitness function
  • Suggested NNMF or actually tensor factorization as a way of filling in the tensor

Aspects of Measuring and Evaluating the Integration Status of a (Sub-)System at Runtime
Christian Gruhl, Sven Tomforde, and Bernhard Sick

  • Entity and system-based evaluation

Coopetitive Soft Gating Ensemble
Jens Schreiber, Maarten Bieshaar, André Gensler, Bernhard Sick and Stephan Deist

  • Ensemble estimators?
  • Combinations of weighting and gating to do optimized selection of the best estimator from a collection of estimators that work in particular situations.
  • DSCN0635

Levels of Networked Self-awareness
Lukas Esterle (chair for next years’ SASO)  and John N.A. Brown

 

Phil 9.6.18

SASO 2018

IEEE (f*) proceedings list (conference should show up here)

Keynote Gábor Vásárhelyi – CollMot Robotics, Budapest, Hungary (chair A. Montresor, room: FBK – Sala Stringa)

  • Multi-level hierarchy in pigeons (gps), wild horses (drone video)
  • Tight turns with pigeons rearrange order. Gentle turns maintain structure
  • Universal rules of collective motion
  • evolution is the best optimizer that they have found
  • Hierarchy aids emergent optimization
  • Drones are exploding in youth. Why? Fashion? Like AI in the ’70’s. Will it then crash?
  • Salable in size and speed
  • Tolerate noise, delay, and error tolerant
  • Controllable meta-unit
  • Drones need to be
    • Extensable
    • real-time
    • sensors
    • communication
    • simulation for dev and debugging
    • algorithms
  • Enemies
    • delay
    • noise
    • inertia,
    • finite acceleration
    • communication errors
    • environment?
  • Communication delays bring self-exited oscillation (repulsion-attraction temporal imbalance)
  • Evolution is the second-best optimization function for all problems
  • Solving decentralized traffic control. A sort of inverse flocking issue
  • Anisotropic repulsion
  • Selective alignment
  • Publications
  • Smaller and lighter (more maneuverable) flocks are more egalitarian. Heavier, smaller groups have hierarchies because there can be trust.

Rule-based utility-driven???
Sona Ghahremani, Christian Medeiros Adriano and Holger Giese

  • Another hyperparameter approach. This one seems quite good
  • Handles
    • Non-linearities
    • Uncertainty from dynamic archetecture
    • Black box models
  • How to select the right model for a class of utility functions prior to deployment
  • Graph grammar rules
  • mRUBiS
    • mRUBiS as an exemplar for model-based architectural self-adaptation is now available at GitHub. It provides a simulator and architectural runtime model of mRUBiS to develop, evaluate, and compare self-adaptation solutions.

Model-Driven Elasticity Control for Multi-Server Queues Under Traffic Surges in Cloud Environments
Venkat Tadakamalla and Daniel Menasce

A Temporal Model for Interactive Diagnosis of Adaptive Systems
Ludovic Mouline, Amine Benelallam, Francois Fouquet, Johann Bourcier and Olivier Barais

  • Might be a way of evaluating fitness tests?
  • At least keep track of the last N (or whole history?) of chromosome so that the principal components can be analyzed.
  • By comparing the states over time, we could find the states adjacent to problem states?

Towards Generic Adaptive Monitoring
Thomas Brand and Holger Giese

  • Goal adaptation (fitness test adaptation?)

Implementing Feedback for ABM Meta-Modeling
Karan Budhraja and Tim Oates

PINCH: Self-Organized Context Neighborhoods for Smart Environments
Chenguang Liu, Christine Julien and Amy L. Murphy

  • Neighborhood mesh networks
  • everything is stored in a small beacon that is cooperatively shared in the neighborhood

A QoS-aware Adaptive Mobility Handling Approach for LoRa-based IoT Systems
Robbe Berrevoets and Danny Weyns

Reins-MAC: Firefly Inspired Communication Scheduling for Reliable Low-Power Wireless
Matteo Ceriotti and Amy L. Murphy

Phil 9.5.18

Marco Barbina – Leonardo SpA – Italy (chair E. Di Nitto, room: FBK – Sala Stringa)

  • Sensors on UAVs
    • Electro-optic
    • Radar-based
    • Hyper-spectral(?)
  • Sensors are not the problem, bandwidth is
  • Selecting the relevant information
  • Attention – which parts of the signal are important
    • Autoencoder NN – it’s like dropout?
    • Split domain stacked cnn. The front half (encoder) NN encodes the data and transmits the “compressed” data from the hidden layer to the back half of the NN.

Goal-aware Team Affiliation in Collectives of Autonomous Robots
Lukas Esterle

  • Teamwork in nature
  • Online multi-task k-assignment
  • stampedes!
  • Observer/follower == explore/exploit
  • They did use the multi-armed bandit framework as a learning system.
  • Real world implementation is intruder detection and response

Optimizing Transitions Between Abstract ABM Demonstrations (video presentation)
Brian Seipp, Karan Budhraja and Tim Oates

  • What is the optimal path that connects two behaviors
  • Traverse a behavior coordinate space
  • Getting from a start image to a goal image. The image can “mean” many things. Agents provide the mapping?
  • Intermediate states are shown
  • Noise added to guarantee convergence?

Self-organized Resource Allocation for Reconfigurable Robot Ensembles
Julian Hanke, Oliver Kosak, Alexander Schiendorfer and Wolfgang Reif

  • Search – determine dangers
  • Continual observe
  • React
  • This is a fitness space analysis based on optimal configuration of robots for a given task. It’s kind of a distributed traveling salesman problem combined with a market and a bidder as a way of merging the distributed solution. I wonder if it is computationally better than a GA solution. It could be a way of looking at hyperparameter tuning?
  • Not clear that it can handle a floating point value, like fuel level
  • The goal is for the allocation to be fast

Panel: The Future of Autonomic Computing and Self-* Systems:

  • Kristie Bellman – some success but in isolated, hard to define areas. Build foundation of principals that can be used to frame development
  • Ada Diaconescu – Efficiency-oriented or creativity-oriented self* systems
  • Simon Dobson – We have turned a lot of fields into computation. But – AI told us nothing about how people play Go. We’re trying to build, not describe
  • Xiaohui (Helen) Gu – AIOps, bridging the gap between academia and industry
  • Arif Merchant – (Google) Missing holistic, end-to-end solutions. First and follow-on principals.
  • Danny Weyns – Be less ad-hoc and more scientific. Address threats to validity. Solid foundations.
  • I talked a bit about the continuum from the low-hanging fruit of autonomic systems to the difficult task of creating self-aware systems.

A Macro-Level Order Metric for Self-Organizing Adaptive Systems
David King and Gilbert Peterson

  • critical and stable states, based on local and global entropy
  • I think that he is talking about “personalized” entropy rather than “local” entropy.

A Self-Organized Learning Model for Anomalies Detection: Application to Elderly People
Nicolas Verstaevel, Carole Bernon, Jean-Pierre Georgé and Marie-Pierre Gleizes

  • Real time detection of abnormal behaviors by using feedback from the medical staff.
  • Detect anomalies through a linear regression of disparity values
  • Is this another hyperparameter system? It could be looking for sensitivity of the system to hyperparameter

Risk-based Testing of Self-Adaptive Systems using Run-Time Predictions
André Reichstaller and Alexander Knapp

  • Uses machine learning to explore the space that includes errors. It is a way of finding major(?) features in the state space

Play with Generated Adversarial Networks (GANs) in your browser!

  • First, we’re not visualizing anything as complex as generating realistic images. Instead, we’re showing a GAN that learns a distribution of points in just two dimensions. There’s no real application of something this simple, but it’s much easier to show the system’s mechanics. For one thing, probability distributions in plain old 2D (x,y) space are much easier to visualize than distributions in the space of high-resolution images.