Unchaining Collective Intelligence for Science, Research and Technology Development by Blockchain-Boosted Community Participation

Since its launch just over a decade ago by the cryptocurrency Bitcoin, the distributed ledger technology (DLT) blockchain has followed a breathtaking trajectory into manifold application spaces. This paper analyses how key factors underpinning the success of this ground-breaking “internet of value” technology, such as staking of collateral (“skin in the game”), competitive crowdsourcing, crowdfunding, and prediction markets, can be applied to substantially innovate the legacy organization of science, research and technology development (RTD). Here, we elaborate a highly integrative, community-based strategy where a token-based crypto-economy supports finding best possible consensus, trust and truth through adding unconventional elements known from reputation systems, betting, secondary markets and social networking. These tokens support the holder’s formalized reputation, and are used in liquid-democracy style governance and arbitration within projects or community-driven initiatives. This participatory research model serves as a solid basis for comprehensively leveraging collective intelligence by effectively incentivizing contributions from the crowd, such as intellectual property (IP), work, validation, assessment, infrastructure, education, assessment, governance, publication, and promotion of projects. On the analogy of its current blockbusters like peer-to-peer structured decentralized finance (“DeFi”), blockchain technology can seminally enhance the efficiency of science and RTD initiatives, even permitting to fully stage operations as a chiefless Decentralised Autonomous Organization (DAOs)

A large-scale study across the avian clade identifies ecological drivers of neophobia

Neophobia, or aversion to novelty, is important for adaptability and survival as it influences the ways in which animals navigate risk and interact with their environments. Across individuals, species and other taxonomic levels, neophobia is known to vary considerably, but our understanding of the wider ecological drivers of neophobia is hampered by a lack of comparative multispecies studies using standardized methods. Here, we utilized the ManyBirds Project, a Big Team Science large-scale collaborative open science framework, to pool efforts and resources of 129 collaborators at 77 institutions from 24 countries worldwide across six continents. We examined both difference scores (between novel object test and control conditions) and raw data of latency to touch familiar food in the presence (test) and absence (control) of a novel object among 1,439 subjects from 136 bird species across 25 taxonomic orders incorporating lab, field, and zoo sites. We first demonstrated that consistent differences in neophobia existed among individuals, among species, and among other taxonomic levels in our dataset, rejecting the null hypothesis that neophobia is highly plastic at all taxonomic levels with no evidence for evolutionary divergence. We then tested for effects of ecological factors on neophobia, including diet, sociality, habitat, and range, while accounting for phylogeny. We found that (i) species with more specialist diets were more neophobic than those with more generalist diets, providing support for the Neophobia Threshold Hypothesis; (ii) migratory species were also more neophobic than nonmigratory species, which supports the Dangerous Niche Hypothesis. Our study shows that the evolution of avian neophobia has been shaped by ecological drivers and demonstrates the potential of Big Team Science to advance our understanding of animal behavior

Winkie - time series behavioral data classification

Supplementary videos and used data of the paper "Supervised Machine Learning Aided Behavior Classification in Pigeons

Winkie - time series behavioral data classification

Supplementary videos and used data of the paper "Supervised Machine Learning Aided Behavior Classification in Pigeons

Perceptual decision making and visual perception in pigeons

In Studie 1 habe ich untersucht, ob die Wahrnehmungsentscheidungen von Tauben eine vergleichbare Verhaltens- und Berechnungsdynamik aufweisen wie die von Säugetieren. Die Ergebnisse zeigten, dass die Dynamik der Wahrnehmungsentscheidung sowohl bei Tauben als auch bei Säugetieren sehr ähnlich ist. Studie 2 untersuchte, ob Tauben ihr Spiegelbild als unheimliches Individuum und nicht als Artgenossen wahrnehmen. Zusammen mit den vorangegangenen Ergebnissen unterstützen die Ergebnisse eine gradualistische Sichtweise der Spiegelselbsterkennung: Arten können entlang eines Kontinuums vom vollständigen Fehlen der Selbsterkennung bis zur vollständigen Spiegelselbsterkennung klassifiziert werden. Die Verhaltensweisen in Studie 2 ermutigten mich, die automatische Verhaltensanalyse bei Vögeln zu erforschen (Studie 3). Ich entwickelte eine Open-Source-Software als Ausgangspunkt für die automatische Klassifizierung von Vogelverhalten

Decreased Key Pecking in Response to Reward Uncertainty Followed by Surprising Delay Extension in Pigeons

The Pavlovian autoshaping paradigm has often been used to assess the behavioral effects of reward omission on behavior. We trained pigeons to receive a food reward (unconditioned stimulus or UCS) following illumination of a response key (conditioned stimulus or CS). In Experiment 1, one group of pigeons was trained with two 100% predictive CS-UCS associations (reward certainty) and another group with two 25% predictive CS-UCS associations (reward uncertainty) for 12 sessions. In both groups, the two CS durations were 8 s. Then, in each group, the duration of one CS remained unchanged and that of the other CS was suddenly extended from 8 to 24 s for 6 sessions. In Experiment 2, some experienced individuals (from Experiment 1) and naïve individuals formed two groups trained with a 24-s CS throughout for 18 sessions. Our results show that pigeons (a) pecked less at the uncertain than the certain CS, (b) decreased and then increased CS-pecking after extending CS duration, especially in the certainty condition, (c) were unresponsive to the 24-s CS in the absence of previous experience, and (d) decreased their response rate close to the end of a trial irrespective of the reinforcement condition, CS duration, and amount of training. These results are discussed in relation to several theoretical frameworks

Supervised machine learning aided behavior classification in pigeons

AbstractManual behavioral observations have been applied in both environment and laboratory experiments in order to analyze and quantify animal movement and behavior. Although these observations contributed tremendously to ecological and neuroscientific disciplines, there have been challenges and disadvantages following in their footsteps. They are not only time-consuming, labor-intensive, and error-prone but they can also be subjective, which induces further difficulties in reproducing the results. Therefore, there is an ongoing endeavor towards automated behavioral analysis, which has also paved the way for open-source software approaches. Even though these approaches theoretically can be applied to different animal groups, the current applications are mostly focused on mammals, especially rodents. However, extending those applications to other vertebrates, such as birds, is advisable not only for extending species-specific knowledge but also for contributing to the larger evolutionary picture and the role of behavior within. Here we present an open-source software package as a possible initiation of bird behavior classification. It can analyze pose-estimation data generated by established deep-learning-based pose-estimation tools such as DeepLabCut for building supervised machine learning predictive classifiers for pigeon behaviors, which can be broadened to support other bird species as well. We show that by training different machine learning and deep learning architectures using multivariate time series data as input, an F1 score of 0.874 can be achieved for a set of seven distinct behaviors. In addition, an algorithm for further tuning the bias of the predictions towards either precision or recall is introduced, which allows tailoring the classifier to specific needs.</jats:p

Statistical anomaly detection in ethereum transaction graphs

The set of transactions that occurs on the public ledger of an Ethereum network in a specific time frame can be represented as a directed graph, with vertices representing addresses and an edge indicating the interaction between two addresses. While there exists preliminary research on analyzing an Ethereum network by the means of graph analysis, most existing work is focused on either the public Ethereum Mainnet or on analyzing the different semantic transaction layers using static graph analysis in order to carve out the different network properties (such as interconnectivity, degrees of centrality, etc.) needed to characterize a blockchain network. By analyzing the consortium-run bloxberg Proof-of-Authority (PoA) Ethereum network, we show that we can identify suspicious and potentially malicious behaviour of network participants by employing statistical graph analysis. We thereby show that it is possible to identify the potentially malicious exploitation of an unmetered and weakly secured blockchain network resource. In addition, we show that Temporal Network Analysis is a promising technique to identify the occurrence of anomalies in a PoA Ethereum network