Analysis Of Collective Behavior Research Articles

Collective Conditioned Reflex: A Bio-Inspired Fast Emergency Reaction Mechanism for Designing Safe Multi-Robot Systems

A multi-robot system (MRS) is a group of coordinated robots designed to cooperate with each other and accomplish given tasks. Due to the uncertainties in operating environments, the system may encounter emergencies, such as unobserved obstacles, moving vehicles, and extreme weather. Animal groups such as bee colonies initiate collective emergency reaction behaviors such as bypassing obstacles and avoiding predators, similar to muscle conditioned reflex which organizes local muscles to avoid hazards in the first response without delaying passage through the brain. Inspired by this, we develop a similar collective conditioned reflex mechanism for multi-robot systems to respond to emergencies. In this study, Collective Conditioned Reflex (CCR), a bio-inspired emergency reaction mechanism, is developed based on animal collective behavior analysis and multi-agent reinforcement learning (MARL). The algorithm uses a physical model to determine if the robots are experiencing an emergency; then, rewards for robots involved in the emergency are augmented with corresponding heuristic rewards, which evaluate emergency magnitudes and consequences and decide local robots' participation. CCR is validated on three typical emergency scenarios: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">turbulence, strong wind, and hidden obstacle</i> . Simulation results demonstrate that CCR improves robot teams' emergency reaction capability with faster reaction speed and safer trajectory adjustment compared with baseline methods.

Levy geometric graphs.

We present a family of graphs with remarkable properties. They are obtained by connecting the points of a random walk when their distance is smaller than a given scale. Their degree (number of neighbors) does not depend on the graph's size but only on the considered scale. It follows a gamma distribution and thus presents an exponential decay. Levy flights are particular random walks with some power-law increments of infinite variance. When building the geometric graphs from them, we show from dimensional arguments that the number of connected components (clusters) follows an inverse power of the scale. The distribution of the size of their components, properly normalized, is scale invariant, which reflects the self-similar nature of the underlying process. This allows to test if a graph (including nonspatial ones) could possibly result from an underlying Levy process. When the scale increases, these graphs never tend towards a single cluster, the giant component. In other words, while the autocorrelation of the process scales as a power of the distance, they never undergo a phase transition of percolation type. The Levy graphs may find applications in community detection and in the analysis of collective behaviors as in face-to-face interaction networks.

Vector-Controlled Wheel-Like Magnetic Swarms With Multimodal Locomotion and Reconfigurable Capabilities.

Inspired by the biological collective behaviors of nature, artificial microrobotic swarms have exhibited environmental adaptability and tasking capabilities for biomedicine and micromanipulation. Complex environments are extremely relevant to the applications of microswarms, which are expected to travel in blood vessels, reproductive and digestive tracts, and microfluidic chips. Here we present a strategy that reconfigures paramagnetic nanoparticles into a vector-controlled microswarm with 3D collective motions by programming sawtooth magnetic fields. Horizontal swarms can be manipulated to stand vertically and swim like a wheel by adjusting the direction of magnetic-field plane. Compared with horizontal swarms, vertical wheel-like swarms were evaluated to be of approximately 15-fold speed increase and enhanced maneuverability, which was exhibited by striding across complex 3D confinements. Based on analysis of collective behavior of magnetic particles in flow field using molecular dynamics methods, a rotary stepping mechanism was proposed to address the formation and locomotion mechanisms of wheel-like swarm. we present a strategy that actuates swarms to stand and hover in situ under a programming swing magnetic fields, which provides suitable solutions to travel across confined space with unexpected changes, such as stepped pipes. By biomimetic design from fin motion of fish, wheel-like swarms were endowed with multi-modal locomotion and load-carrying capabilities. This design of intelligent microswarms that adapt to complicated biological environments can promote the applications ranging from the construction of smart and multifunctional materials to biomedical engineering.

Collective Behavior Analysis and Graph Mining in Social Networks 2021

Collective Behavior Analysis and Graph Mining in Social Networks 2021

A holistic analysis of collective behaviour and team performance in Australian Football via structural equation modelling

Despite awareness of the importance of quantifying technical, tactical, and physical characteristics of match play, few studies have examined the structural relationship of these aspects in professional sport. Accordingly, this study concurrently examined these components in relation to quarter outcome (n = 272) in Australian Football. The study followed a retrospective longitudinal case study design where one teams’ cooperative passing network, skill counts, physical loads, and spatiotemporal behaviours during official Australian Football League games were collected from a period spanning four seasons (2016-2019). A principal components analysis (PCA) and structural equation modelling were used to explore the structural relationships between components and examine the influence on quarter outcome as determined by the point differential (quarter margin). Scoring opportunity and ball movement had direct associations with quarter margin, while unpredictability, uncontested behaviour and physical behaviour did not. Negative associations between uncontested behaviour and scoring opportunity suggest that elevated high-pressure success and a lack of synchrony may positively influence scoring opportunity, a determinant of quarter margin. Further, negative associations between physical behaviour and ball movement suggest that with less physical work, a team’s collective ability to transfer possession between teammates is facilitated, offering an interesting dichotomy between skill and physical demands of Australian Football. While hundreds of different metrics are available, the present study was the first to concurrently examine the influence of a variety of match play components on performance outcomes in Australian Football. These results may provide direction for coaches and practitioners when contemplating practice design, tactical strategies, or the development of behaviour through specific training exercises. Game plans and training drills that focus on optimising attacking and low-pressure ball movement coupled with high levels of mutual interaction between teammates may be beneficial for performance.

Assessing behavioral data science privacy issues in government artificial intelligence deployment

In today's global culture where the Internet has established itself as the main tool for communication and commerce, the capability to massively analyze and predict citizens' behavior has become a priority for governments in terms of collective intelligence and security. At the same time, in the context of novel possibilities that artificial intelligence (AI) brings to governments in terms of understanding and developing collective behavior analysis, important concerns related to citizens' privacy have emerged. In order to identify the main uses that governments make of AI and to define citizens' concerns about their privacy, in the present study, we undertook a systematic review of the literature, conducted in-depth interviews, and applied data-mining techniques. Based on our results, we classified and discussed the risks to citizens' privacy according to the types of AI strategies used by governments that may affect collective behavior and cause massive behavior modification. Our results revealed 11 uses of AI strategies used by the government to improve their interaction with citizens, organizations in cities, services provided by public institutions or the economy, among other areas. In relation to citizens' privacy when AI is used by governments, we identified 8 topics related to human behavior predictions, intelligence decision making, decision automation, digital surveillance, data privacy law and regulation, and the risk of behavior modification. The paper concludes with a discussion of the development of regulations focused on the ethical design of citizen data collection, where implications for governments are presented aimed at regulating security, ethics, and data privacy. Additionally, we propose a research agenda composed by 16 research questions to be investigated in further research.

Characterizing user behavior in online social networks: Analysis of the regular use of Facebook

The analysis of user behaviour in online social networks (OSNs) is one of the important research interests related to human-computer interactions. OSNs gives a large space to share news with no limits around the world and allows user to benefit from properties of this interactive and dynamic system. The study of user behaviour on a social and popular platform characterized by the use of new technologies requires to understand and the analysis of collective behaviour on Facebook. This paper aims to analyse the usage patterns in OSNs using the visible interactions of Facebook, by studying the time of activity and the evolution of human behaviour through a process of detection of visible and non-volatile interactions. In the first step, we perform a data collection process based on breadth first search algorithm (BFS) and semi-supervised crawler agent. In the second step, we build an interaction quantification process to measure users’ activities and analysis related time series. The study of the frequency of periodic use has shown that the communities monitored follow a weekly rhythm that decreases over time to reach a frequency of daily use, which reflects a stability of activities and a case of dependency of use.

Pose Estimation of Swimming Fish Using NACA Airfoil Model for Collective Behavior Analysis

We propose a pose estimation method using a National Advisory Committee for Aeronautics (NACA) airfoil model for fish schools. This method allows one to understand the state in which fish are swimming based on their posture and dynamic variations. Moreover, their collective behavior can be understood based on their posture changes. Therefore, fish pose is a crucial indicator for collective behavior analysis. We use the NACA model to represent the fish posture; this enables more accurate tracking and movement prediction owing to the capability of the model in describing posture dynamics. To fit the model to video data, we first adopt the DeepLabCut toolbox to detect body parts (i.e., head, center, and tail fin) in an image sequence. Subsequently, we apply a particle filter to fit a set of parameters from the NACA model. The results from DeepLabCut, i.e., three points on a fish body, are used to adjust the components of the state vector. This enables more reliable estimation results to be obtained when the speed and direction of the fish change abruptly. Experimental results using both simulation data and real video data demonstrate that the proposed method provides good results, including when rapid changes occur in the swimming direction.

Constants of motion for the finite-dimensional Lohe type models with frustration and applications to emergent dynamics

We present constants of motion for the finite-dimensional Lohe type aggregation models with frustration, and apply them to the analysis of collective behaviors. The Lohe type models have been proposed as possible non-abelian and higher-dimensional generalizations of the Kuramoto model, which is a prototype phase model for synchronization. In this paper, we study the emergent collective dynamics of these models under the effect of (interaction) frustration, which generalizes phase-shift frustrations in the Kuramoto model. To this end, we provide constants of motion, i.e., conserved quantities along the flow generated by the models under consideration. From the perspective of the low-dimensional dynamics, we derive several results on the emergent asymptotic patterns of the Kuramoto and Lohe sphere models.

Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection

This paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can be split into two categories. First, algorithms based on data mining and knowledge discovery, which study the different correlation among human behavioral data, and identify the collective abnormal human behavior from knowledge extracted. Secondly, algorithms exploring convolution deep neural networks, which learn different features of historical data to determine the collective abnormal human behaviors. Experiments on an actual human behaviors database have been carried out to demonstrate the usefulness of the proposed algorithms. The results show that the deep learning solution outperforms both data mining as well as the state-of-the-art solutions in terms of runtime and accuracy performance. In particular, for large datasets, the accuracy of the deep learning solution reaches 88%, however other solutions do not exceed 81%. Additionally, the runtime of the deep learning solution is below 50 seconds, whereas other solutions need more than 80 seconds for analyzing the same database.

Behavioral And Perceptual Models for Secure Data Analysis and Management

The ability to monitor and forecast citizen behavior on a large scale developed to be a top target for governments subject to security and intelligence that are collective in the current worldwide culture where the web has become the primary medium for commerce and communication. Meanwhile, significant privacy-related issues have surfaced considering the innovative opportunities that artificial intelligence (AI) generates for collective behavior analysis when presented to governments as a way in which the government will comprehend. In the current study, we conducted an extensive literature analysis using techniques such as data mining and interviews such as in-depth to determine the primary uses of Artificial Intelligence that governments use and describe citizens' privacy issues. Our findings showed that the government employed 11 AI initiatives to enhance interactions with residents, local organizations, services offered by government agencies, and the economy, among other things. Issues are identified relating to the risk of behavior modification, intelligent decision-making, data privacy regulation and law, digital surveillance, and decision automation as they pertain to people's privacy when governments deploy AI. Finally, the report concluded that the debate of developing rules centered on the moral citizen data architecture gathering, with consequences for governments aiming to regulate security, morality, and data privacy. We also suggest a study schedule with 16 research questions to be investigated in future studies.

Structural features of complex hydrochemical systems

The set of non-conservative hydrochemical parameters is considered as a complex system, which displays collective behavior. It is found that the collective behavior is described by the power relation between the time variability (the standard deviations) and the average concentrations of different hydrochemical parameters in the scale range 100 – 0:0001 mg/kg. The exponent can be 0:7 – 0:9. Power law scaling is the mathematical expression of self similarity and fractality. The complex systems of nonconservative chemical parameters have a structure that can be characterized by exponent, normalization coefficient, standard error, correlation coefficient, and by sharp deviations of the individual parameters from the regression line and from the most probable average and standard deviation values, if any. It is shown with specific examples that changes in the hydrochemical systems structure are the result of the manifestation of biogeochemical processes and the dynamics of water. Regression analysis of collective behavior of complex hydrochemical systems is one of the examples of the use of modern information technologies based on the methods of system analysis.

Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence.

Computational approaches to the analysis of collective behavior in social insects increasingly rely on motion paths as an intermediate data layer from which one can infer individual behaviors or social interactions. Honey bees are a popular model for learning and memory. Previous experience has been shown to affect and modulate future social interactions. So far, no lifetime history observations have been reported for all bees of a colony. In a previous work we introduced a recording setup customized to track up to 4,000 marked bees over several weeks. Due to detection and decoding errors of the bee markers, linking the correct correspondences through time is non-trivial. In this contribution we present an in-depth description of the underlying multi-step algorithm which produces motion paths, and also improves the marker decoding accuracy significantly. The proposed solution employs two classifiers to predict the correspondence of two consecutive detections in the first step, and two tracklets in the second. We automatically tracked ~2,000 marked honey bees over 10 weeks with inexpensive recording hardware using markers without any error correction bits. We found that the proposed two-step tracking reduced incorrect ID decodings from initially ~13% to around 2% post-tracking. Alongside this paper, we publish the first trajectory dataset for all bees in a colony, extracted from ~3 million images covering 3 days. We invite researchers to join the collective scientific effort to investigate this intriguing animal system. All components of our system are open-source.

A multi-granularity collective behavior analysis approach for online social networks

Online social networks play more and more important roles in the modern society in terms of the rapid and large scale information spread. Many efforts have been made to understand these phenomena in the computer science communities and other relative fields, ranging from popular topic detection to information diffusion modeling. In this article, a multi-granularity collective behavior modeling approach has been proposed, which describes the collective behavior at a node level, a neighbor level, a community level, and a social level. How collective behaviors on the largest online social network Weibo are formed is analyzed by this model. The results demonstrate that individuals can rationally make their decisions when they are isolated from others. However, when neighbors exist, their actions will be irrational; the phenomena will be more obvious when social reinforcement is taken into consideration. Members in the same community can easily reach a group consensus while holding different opinions in different communities.

Coarse analysis of collective behaviors: Bifurcation analysis of the optimal velocity model for traffic jam formation

We present a general method for analyzing macroscopic collective phenomena observed in many-body systems. For this purpose, we employ diffusion maps, which are one of the dimensionality-reduction techniques, and systematically define a few relevant coarse-grained variables for describing macroscopic phenomena. The time evolution of macroscopic behavior is described as a trajectory in the low-dimensional space constructed by these coarse variables. We apply this method to the analysis of the traffic model, called the optimal velocity model, and reveal a bifurcation structure, which features a transition to the emergence of a moving cluster as a traffic jam.

Collective behavior quantification on human odor effects against female Aedes aegypti mosquitoes-Open source development.

Classifying and quantifying mosquito activity includes a plethora of categories, ranging from measuring flight speeds, repellency, feeding rates, and specific behaviors such as home entry, swooping and resting, among others. Entomologists have been progressing more toward using machine vision for efficiency for this endeavor. Digital methods have been used to study the behavior of insects in labs, for instance via three-dimensional tracking with specialized cameras to observe the reaction of mosquitoes towards human odor, heat and CO2, although virtually none was reported for several important fields, such as repellency studies which have a significant need for a proper response quantification. However, tracking mosquitoes individually is a challenge and only limited number of specimens can be studied. Although tracking large numbers of individual insects is hailed as one of the characteristics of an ideal automated image-based tracking system especially in 3D, it also is a costly method, often requiring specialized hardware and limited access to the algorithms used for mapping the specimens. The method proposed contributes towards (a) unlimited open source use, (b) a low-cost setup, (c) complete guide for any entomologist to adapt in terms of hardware and software, (d) simple to use, and (e) a lightweight data output for collective behavior analysis of mosquitoes. The setup is demonstrated by testing a simple response of mosquitoes in the presence of human odor versus control, one session with continuous human presence as a stimuli and the other with periodic presence. A group of female Aedes aegypti (Linnaeus) mosquitoes are released into a white-background chamber with a transparent acrylic panel on one side. The video feed of the mosquitoes are processed using filtered contours in a threshold-adjustable video. The mosquitoes in the chamber are mapped on the raster where the coordinates of each mosquito are recorded with the corresponding timestamp. The average distance of the blobs within the frames against time forms a spectra where behavioral patterns can be observed directly, whether any collective effect is observed. With this method, 3D tracking will not be required and a more straightforward data output can be obtained.

Assemblages: (Pre)Political, Ethical and Ontological Perspectives

Assemblages: (Pre)Political, Ethical and Ontological Perspectives Razvan Amironesei (bio) and Jon Bialecki1 (bio) Our work offers a new answer to a growing theoretical and practical demand within diverse domains of investigation by redefining the concept of political action. It grounds and elucidates some of the manifestations of a distinctive mode of the political. Its regulative idea is that of the assemblage. The overall aim of this volume is to show that the political assemblage should be productively distinguished from both the limiting concepts of proceduralist and identitarian politics. One can broadly define proceduralist politics as being grounded in autonomous individuals pursuing their own self-interests under the standardized rules of the political game. Identitarian politics is a mode of communitarian politics, the expression of a material experience of domination in which self-identified groups engage in different forms resistance and advocacy in order to acquire public recognition and empowerment. In both cases, the field of action is the political arena conventionally defined—at least in Western liberal democracies. We argue that political assemblages articulate beyond the problematization and the critical treatment of self-interested actions and communitarian self-realization. Our contribution situates assemblages within the dynamic of the New Social Movements (NSMs). It is well known that NSMs have been the object of theoretical interest for at least the past four decades (Buechler). A few scholars doubt that the NSMs are so new, but the majority of analysts agree that they have lineaments that the activist—often revolutionary—movements of the 19th and 20th centuries lacked. Among these features are: 1) the focus on issues of the quality of life rather than issues of class standing, 2) the tendency of their constituencies to come from the middle and upper-middle classes rather than the working classes, 3) the blurring of any straightforward divide between “right” and “left,” and 4) a similar blurring of the divide between a politics of recognition (usually cast as “identity politics”) and a politics of interests (often cast as “resource-allocation” politics). If not in so many words, many analysts have recognized that the NSMs are in fact assemblages—an intersection [End Page 3] of heterogeneous attitudes and commitments, and an assemblage that in the first instance is not “political” as the term has been conventionally understood. Hence the label: “social” rather than political movements. Analysts as early as Touraine have further recognized that the signature that has marked what have become one or another NSM is not the competitive pursuit of specific worldly ends but instead the championing of abstract values which “concern the grammar of forms of life” (Habermas 33). The distinctive features of the NSMs are thus clear enough. Feminist movements, environmentalist movements, anti-globalization movements and—worthy of special attention—the recent Occupy Movement are cases in point. While this journal issue does not theorize NSMs per se, they serve as a powerful exemplar that supplements the analytic tenor of our treatment of assemblages. At this juncture, is it important to note, however, that our philosophical treatment of assemblages as manifestation of specific forms of collective action is normatively distinct from a sociological analysis of collective behavior grounded in the belief of actors (Melucci; Cohen). If NSMs are one of the best exemplars thought of as assemblages, it seems fair enough to ask what the idea of the assemblage entails. The concept of the assemblage as we deploy it derives chiefly from Deleuze and Guattari’s A Thousand Plateaus. For Deleuze and Guattari, the assemblage stands in contrast to homeostatic systems, autonomous structures, homogenous interests and intentions. It is a distinctive, emergent composite, systemically open, substantively heterogeneous and of uncertain duration. We should note parenthetically that the assemblage is a concept that is perhaps not always treated with the technical care that it deserves.2 This is not necessarily a problem. Part of the life a concept relies on its acknowledged and unacknowledged misuse. Despite the fact that the assemblage is indissociably linked to Deleuze and Guattari, they of course have no ownership over the concept, and the term itself has a long history of lexical ‘prior art’ under both its English and its French names. But this also means...

Multiple fish tracking with an NACA airfoil model for collective behavior analysis

Abstract We propose a visual tracking method with an NACA airfoil model for dense fish schools in which occlusions occur frequently. Although much progress has been made for tracking multiple objects, it remains a challenging task to track individuals due to factors such as occlusion and target appearance variation. In this paper, we first introduce a NACA airfoil model as a deformable appearance model of fish. For occluded fish, we estimate their positions, angles, and postures with template matching and simulated annealing algorithms to effectively optimize their parameters. To improve performance of tracking, we repeatedly track fish with the parameter estimation algorithm forwards and backwards. We prepared two real fish scenes in which the average number of fish is over 25 in each frame and multiple fish superimpose over 50 times. Experimental results for the scenes show that fish are practically tracked with our method compared to a tracking method based on a mixture particle filter. Over 75 % of fish in each scene have been tracked throughout the scene, and the average difference is less than 4 % of the mean body length of the school.

Methods for measuring collective behaviour in evacuees

Emergency evacuations often depend on social interactions among individuals. However, very little is still known about how and when they do so. This paper proposes quantitative methods for the analysis of collective behaviour in evacuees. The methods are applied to evacuation experiments in a multimodal transport station. In total 110 potential evacuation groups of 2–3 members and 30 potential evacuation groups of 6–9 members are analysed. Results show that the proposed methods provide (1) quantitative evidences of collective behaviour and (2) measurements of the behavioural cohesion in individuals during response and movement phases of evacuation.

Model-free information-theoretic approach to infer leadership in pairs of zebrafish.

Collective behavior affords several advantages to fish in avoiding predators, foraging, mating, and swimming. Although fish schools have been traditionally considered egalitarian superorganisms, a number of empirical observations suggest the emergence of leadership in gregarious groups. Detecting and classifying leader-follower relationships is central to elucidate the behavioral and physiological causes of leadership and understand its consequences. Here, we demonstrate an information-theoretic approach to infer leadership from positional data of fish swimming. In this framework, we measure social interactions between fish pairs through the mathematical construct of transfer entropy, which quantifies the predictive power of a time series to anticipate another, possibly coupled, time series. We focus on the zebrafish model organism, which is rapidly emerging as a species of choice in preclinical research for its genetic similarity to humans and reduced neurobiological complexity with respect to mammals. To overcome experimental confounds and generate test data sets on which we can thoroughly assess our approach, we adapt and calibrate a data-driven stochastic model of zebrafish motion for the simulation of a coupled dynamical system of zebrafish pairs. In this synthetic data set, the extent and direction of the coupling between the fish are systematically varied across a wide parameter range to demonstrate the accuracy and reliability of transfer entropy in inferring leadership. Our approach is expected to aid in the analysis of collective behavior, providing a data-driven perspective to understand social interactions.