scholarly journals Tracking Footprints in a Swarm: Information-Theoretic and Spatial Centre of Influence Measures

2020 ◽  
Author(s):  
Adam Hepworth ◽  
Kate yaxley ◽  
Daniel Baxter ◽  
Keith Joiner ◽  
Hussein Abbass
Keyword(s):  
Learning Algorithm ◽  
Control Strategies ◽  
Repulsive Force ◽  
Future Research ◽  
Information Theoretic ◽  
Efficient Control ◽  
Aerial Vehicle ◽  
Flock Dynamics ◽  
Spatial Indicators ◽  
Synthetic Simulation

<p>Boids (bird-oids) is a widely used model to mimic the behaviour of birds. Shoids (sheep-oids) rely on the same boids rules with the addition of a repulsive force away from a sheepdog (a herding agent). Previous work assumed homogeneous shoids. Real-world observations of sheep show non-homogeneous responses to the presence of a herding agent. We present a portfolio of information-theoretic and spatial indicators to track the footprints of shoids with different parameters within the shoid flock. The portfolio is named the Centre of Influence to indicate that the aim is to identify the influential shoids with the highest impact on flock dynamics. We use both synthetic simulation-driven data and measurements collected from live sheep herding trials by an unmanned aerial vehicle (UAV) to validate the proposed measures. The resultant measures will allow us in our future research to design more efficient control strategies for the UAV, by polarising the attention of the machine learning algorithm on those shoids with influence footprints, to drive the flock to improve the herding of sheep.<br></p>

Tracking Footprints in a Swarm: Information-Theoretic and Spatial Centre of Influence Measures

2020 ◽  
Author(s):  
Adam Hepworth ◽  
Kate yaxley ◽  
Daniel Baxter ◽  
Keith Joiner ◽  
Hussein Abbass
Keyword(s):  
Learning Algorithm ◽  
Control Strategies ◽  
Repulsive Force ◽  
Future Research ◽  
Information Theoretic ◽  
Efficient Control ◽  
Aerial Vehicle ◽  
Flock Dynamics ◽  
Spatial Indicators ◽  
Synthetic Simulation

<p>Boids (bird-oids) is a widely used model to mimic the behaviour of birds. Shoids (sheep-oids) rely on the same boids rules with the addition of a repulsive force away from a sheepdog (a herding agent). Previous work assumed homogeneous shoids. Real-world observations of sheep show non-homogeneous responses to the presence of a herding agent. We present a portfolio of information-theoretic and spatial indicators to track the footprints of shoids with different parameters within the shoid flock. The portfolio is named the Centre of Influence to indicate that the aim is to identify the influential shoids with the highest impact on flock dynamics. We use both synthetic simulation-driven data and measurements collected from live sheep herding trials by an unmanned aerial vehicle (UAV) to validate the proposed measures. The resultant measures will allow us in our future research to design more efficient control strategies for the UAV, by polarising the attention of the machine learning algorithm on those shoids with influence footprints, to drive the flock to improve the herding of sheep.<br></p>

scholarly journals Tracking Footprints in a Swarm: Information-Theoretic and Spatial Centre of Influence Measures

2020 ◽  
Author(s):  
Adam Hepworth ◽  
Kate yaxley ◽  
Daniel Baxter ◽  
Keith Joiner ◽  
Hussein Abbass
Keyword(s):  
Learning Algorithm ◽  
Control Strategies ◽  
Repulsive Force ◽  
Future Research ◽  
Information Theoretic ◽  
Predator Prey ◽  
Efficient Control ◽  
Aerial Vehicle ◽  
Flock Dynamics ◽  
Synthetic Simulation

<div><div><div><p>Boids (Bird-oids) is a widely used model to mimic the behaviour of birds. Shoids (Sheep-oids) rely on the same boids rules with the addition of a repulsive force away from a sheepdog to model predation risk in predator-prey dynamic. Previous work assumed homogeneous shoids. Real-world observations on sheep show non-homogeneous responses to the presence of a herding agent. We present a portfolio of information-theoretic and spatial indicators to track the footprints of shoid with different parameters from the remainder of the shoid flock. The portfolio is named the Centre of Influence to indicate that the aim is to identify the influential shoids with the highest impact on flock dynamics. We use both synthetic simulation-driven data and measurements collected from actual sheep herding trial by an Unmanned Aerial Vehicle (UAV) to validate the proposed measures. The resultant footprints will allow us in our future research to design more efficient control strategies for the UAV to improve the herding of sheep, by polarising the attention of the machine learning algorithm on those Shoids with influence footprints to drive the flock.</p></div></div></div>

scholarly journals Information-Theoretic Generalization Bounds for Meta-Learning and Applications

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 126
Author(s):  
Sharu Theresa Jose ◽  
Osvaldo Simeone
Keyword(s):  
Learning Algorithm ◽  
Broad Class ◽  
Performance Measure ◽  
Training Data ◽  
Learning To Learn ◽  
Data Set ◽  
Information Theoretic ◽  
Meta Learning ◽  
Task Training ◽  
Test Sets

Meta-learning, or “learning to learn”, refers to techniques that infer an inductive bias from data corresponding to multiple related tasks with the goal of improving the sample efficiency for new, previously unobserved, tasks. A key performance measure for meta-learning is the meta-generalization gap, that is, the difference between the average loss measured on the meta-training data and on a new, randomly selected task. This paper presents novel information-theoretic upper bounds on the meta-generalization gap. Two broad classes of meta-learning algorithms are considered that use either separate within-task training and test sets, like model agnostic meta-learning (MAML), or joint within-task training and test sets, like reptile. Extending the existing work for conventional learning, an upper bound on the meta-generalization gap is derived for the former class that depends on the mutual information (MI) between the output of the meta-learning algorithm and its input meta-training data. For the latter, the derived bound includes an additional MI between the output of the per-task learning procedure and corresponding data set to capture within-task uncertainty. Tighter bounds are then developed for the two classes via novel individual task MI (ITMI) bounds. Applications of the derived bounds are finally discussed, including a broad class of noisy iterative algorithms for meta-learning.

scholarly journals Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 737
Author(s):  
Fengjie Sun ◽  
Xianchang Wang ◽  
Rui Zhang
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Optimal Policy ◽  
Feasible Solution ◽  
Learning Algorithm ◽  
Plant Protection ◽  
Agricultural Plant ◽  
Q Learning ◽  
Aerial Vehicle ◽  
Optimal Action

An Unmanned Aerial Vehicle (UAV) can greatly reduce manpower in the agricultural plant protection such as watering, sowing, and pesticide spraying. It is essential to develop a Decision-making Support System (DSS) for UAVs to help them choose the correct action in states according to the policy. In an unknown environment, the method of formulating rules for UAVs to help them choose actions is not applicable, and it is a feasible solution to obtain the optimal policy through reinforcement learning. However, experiments show that the existing reinforcement learning algorithms cannot get the optimal policy for a UAV in the agricultural plant protection environment. In this work we propose an improved Q-learning algorithm based on similar state matching, and we prove theoretically that there has a greater probability for UAV choosing the optimal action according to the policy learned by the algorithm we proposed than the classic Q-learning algorithm in the agricultural plant protection environment. This proposed algorithm is implemented and tested on datasets that are evenly distributed based on real UAV parameters and real farm information. The performance evaluation of the algorithm is discussed in detail. Experimental results show that the algorithm we proposed can efficiently learn the optimal policy for UAVs in the agricultural plant protection environment.

Research Advances in Potyviruses: From the Laboratory Bench to the Field

2021 ◽  
Vol 59 (1) ◽  
Author(s):  
Xiuling Yang ◽  
Yinzi Li ◽  
Aiming Wang
Keyword(s):  
Control Strategies ◽  
Genetic Resistance ◽  
Plant Viruses ◽  
Infection Process ◽  
Future Research ◽  
Annual Review ◽  
Publication Date ◽  
Viral Pathogens ◽  
Genome Expression ◽  
Amount Of Knowledge

Potyviruses (viruses in the genus Potyvirus, family Potyviridae) constitute the largest group of known plant-infecting RNA viruses and include many agriculturally important viruses that cause devastating epidemics and significant yield losses in many crops worldwide. Several potyviruses are recognized as the most economically important viral pathogens. Therefore, potyviruses are more studied than other groups of plant viruses. In the past decade, a large amount of knowledge has been generated to better understand potyviruses and their infection process. In this review, we list the top 10 economically important potyviruses and present a brief profile of each. We highlight recent exciting findings on the novel genome expression strategy and the biological functions of potyviral proteins and discuss recent advances in molecular plant–potyvirus interactions, particularly regarding the coevolutionary arms race. Finally, we summarize current disease control strategies, with a focus on biotechnology-based genetic resistance, and point out future research directions. Expected final online publication date for the Annual Review of Phytopathology, Volume 59 is August 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Virus infections of honeybees Apis Mellifera

2015 ◽  
Vol 4 (3) ◽  
Author(s):  
Giuseppina Tantillo ◽  
Marilisa Bottaro ◽  
Angela Di Pinto ◽  
Vito Martella ◽  
Pietro Di Pinto ◽  
...  
Keyword(s):  
Viral Infections ◽  
Control Strategies ◽  
Future Research ◽  
Virus Infections ◽  
Deformed Wing Virus ◽  
Starting Point ◽  
Invasive Organisms ◽  
Queen Cell ◽  
Kashmir Bee Virus ◽  
Paralysis Virus

The health and vigour of honeybee colonies are threatened by numerous parasites (such as <em>Varroa destructor</em> and <em>Nosema</em> spp.) and pathogens, including viruses, bacteria, protozoa. Among honeybee pathogens, viruses are one of the major threats to the health and wellbeing of honeybees and cause serious concern for researchers and beekeepers. To tone down the threats posed by these invasive organisms, a better understanding of bee viral infections will be of crucial importance in developing effective and environmentally benign disease control strategies. Here we summarize recent progress in the understanding of the morphology, genome organization, transmission, epidemiology and pathogenesis of eight honeybee viruses: Deformed wing virus (DWV) and Kakugo virus (KV); Sacbrood virus (SBV); Black Queen cell virus (BQCV); Acute bee paralysis virus (ABPV); Kashmir bee virus (KBV); Israeli Acute Paralysis Virus (IAPV); Chronic bee paralysis virus (CBPV). The review has been designed to provide researchers in the field with updated information about honeybee viruses and to serve as a starting point for future research.

Control of flexible manipulators: A survey

Robotica ◽  
2004 ◽  
Vol 22 (5) ◽  
pp. 533-545 ◽  
Author(s):  
M. Benosman ◽  
G. Le Vey
Keyword(s):  
Linear Models ◽  
Control Strategies ◽  
Research Area ◽  
Future Research ◽  
Complex Nature ◽  
Open Problems ◽  
Complex Models ◽  
The Many ◽  
The One ◽  
Flexible Arms

A survey of the field of control for flexible multi-link robots is presented. This research area has drawn great attention during the last two decades, and seems to be somewhat less “attractive” now, due to the many satisfactory results already obtained, but also because of the complex nature of the remaining open problems. Thus it seems that the time has come to try to deliver a sort of “state of the art” on this subject, although an exhaustive one is out of scope here, because of the great amount of publications. Instead, we survey the most salient progresses – in our opinion – approximately during the last decade, that are representative of the essential different ideas in the field. We proceed along with the exposition of material coming from about 119 included references. We do not pretend to deeply present each of the methods quoted hereafter; however, our goal is to briefly introduce most of the existing methods and to refer the interested reader to more detailed presentations for each scheme. To begin with, a now well-established classification of the flexible arms control goals is given. It is followed by a presentation of different control strategies, indicating in each case whether the approach deals with the one-link case, which can be successfully treated via linear models, or with the multi-link case which necessitates nonlinear, more complex, models. Some possible issues for future research are given in conclusion.

scholarly journals Detection System of HTTP DDoS Attacks in a Cloud Environment Based on Information Theoretic Entropy and Random Forest

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Mohamed Idhammad ◽  
Karim Afdel ◽  
Mustapha Belouch
Keyword(s):  
Random Forest ◽  
Network Traffic ◽  
Learning Algorithm ◽  
Detection System ◽  
Cloud Services ◽  
Detection Accuracy ◽  
Cloud Environment ◽  
Ddos Attacks ◽  
Information Theoretic ◽  
Computing Services

Cloud Computing services are often delivered through HTTP protocol. This facilitates access to services and reduces costs for both providers and end-users. However, this increases the vulnerabilities of the Cloud services face to HTTP DDoS attacks. HTTP request methods are often used to address web servers’ vulnerabilities and create multiple scenarios of HTTP DDoS attack such as Low and Slow or Flooding attacks. Existing HTTP DDoS detection systems are challenged by the big amounts of network traffic generated by these attacks, low detection accuracy, and high false positive rates. In this paper we present a detection system of HTTP DDoS attacks in a Cloud environment based on Information Theoretic Entropy and Random Forest ensemble learning algorithm. A time-based sliding window algorithm is used to estimate the entropy of the network header features of the incoming network traffic. When the estimated entropy exceeds its normal range the preprocessing and the classification tasks are triggered. To assess the proposed approach various experiments were performed on the CIDDS-001 public dataset. The proposed approach achieves satisfactory results with an accuracy of 99.54%, a FPR of 0.4%, and a running time of 18.5s.

scholarly journals Biological Bases of Beauty Revisited: The Effect of Symmetry, Averageness, and Sexual Dimorphism on Female Facial Attractiveness

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 279 ◽  
Author(s):  
Alex L. Jones ◽  
Bastian Jaeger
Keyword(s):  
Sexual Dimorphism ◽  
Learning Algorithm ◽  
Facial Attractiveness ◽  
Choice Task ◽  
Future Research ◽  
Human Female ◽  
Literature Study ◽  
Skin Texture ◽  
Femininity And Masculinity ◽  
Forced Choice Task

The factors influencing human female facial attractiveness—symmetry, averageness, and sexual dimorphism—have been extensively studied. However, recent studies, using improved methodologies, have called into question their evolutionary utility and links with life history. The current studies use a range of approaches to quantify how important these factors actually are in perceiving attractiveness, through the use of novel statistical analyses and by addressing methodological weaknesses in the literature. Study One examines how manipulations of symmetry, averageness, femininity, and masculinity affect attractiveness using a two-alternative forced choice task, finding that increased masculinity and also femininity decrease attractiveness, compared to unmanipulated faces. Symmetry and averageness yielded a small and large effect, respectively. Study Two utilises a naturalistic ratings paradigm, finding similar effects of averageness and masculinity as Study One but no effects of symmetry and femininity on attractiveness. Study Three applies geometric face measurements of the factors and a random forest machine learning algorithm to predict perceived attractiveness, finding that shape averageness, dimorphism, and skin texture symmetry are useful features capable of relatively accurate predictions, while shape symmetry is uninformative. However, the factors do not explain as much variance in attractiveness as the literature suggests. The implications for future research on attractiveness are discussed.

scholarly journals Control Strategies of DC Microgrids Cluster: A Comprehensive Review

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7569
Author(s):  
Zaid Hamid Abdulabbas Al-Tameemi ◽  
Tek Tjing Lie ◽  
Gilbert Foo ◽  
Frede Blaabjerg
Keyword(s):  
Power Flow ◽  
Control Strategies ◽  
Future Research ◽  
Consensus Protocol ◽  
Power Flow Control ◽  
Dc Microgrids ◽  
Significant Control ◽  
Comparative Review ◽  
Cluster A ◽  
Research Recommendations

Multiple microgrids (MGs) close to each other can be interconnected to construct a cluster to enhance reliability and flexibility. This paper presents a comprehensive and comparative review of recent studies on DC MG clusters’ control strategies. Different schemes regarding the two significant control aspects of networked DC MGs, namely DC-link voltage control and power flow control between MGs, are investigated. A discussion about the architecture configuration of DC MG clusters is also provided. All advantages and limitations of various control strategies of recent studies are discussed in this paper. Furthermore, this paper discusses three types of consensus protocol with different time boundaries, including linear, finite, and fixed. Based on the main findings from the reviewed studies, future research recommendations are proposed.

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