scholarly journals Adaptive Floor Cleaning Strategy by Human Density Surveillance Mapping with a Reconfigurable Multi-Purpose Service Robot

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2965
Author(s):  
Vinu Sivanantham ◽  
Anh Vu Le ◽  
Yuyao Shi ◽  
Mohan Rajesh Elara ◽  
Bing J. Sheu
Keyword(s):  
Social Distance ◽  
Large Population ◽  
Human Interaction ◽  
Superior Performance ◽  
Service Robot ◽  
Shopping Malls ◽  
Heat Map ◽  
Coverage Path Planning ◽  
Cleaning Methods ◽  
Multi Robot

Professional cleaning and safe social distance monitoring are often considered as demanding, time-consuming, repetitive, and labor-intensive tasks with the risk of getting exposed to the virus. Safe social distance monitoring and cleaning are emerging problems solved through robotics solutions. This research aims to develop a safe social distance surveillance system on an intra-reconfigurable robot with a multi-robot cleaning system for large population environments, like office buildings, hospitals, or shopping malls. We propose an adaptive multi-robot cleaning strategy based on zig-zag-based coverage path planning that works in synergy with the human interaction heat map generated by safe social distance monitoring systems. We further validate the proposed adaptive velocity model’s efficiency for the multi-robot cleaning systems regarding time consumption and energy saved. The proposed method using sigmoid-based non-linear function has shown superior performance with 14.1 percent faster and energy consumption of 11.8 percent less than conventional cleaning methods.

Context-Driven Proactive Decision Support for Hybrid Teams

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 41-57
Author(s):  
Manisha Mishra ◽  
Pujitha Mannaru ◽  
David Sidoti ◽  
Adam Bienkowski ◽  
Lingyi Zhang ◽  
...  
Keyword(s):  
Decision Support ◽  
Situational Awareness ◽  
Feature Space ◽  
Human Interaction ◽  
Superior Performance ◽  
Uncertain Environments ◽  
Cognitive States ◽  
Contextual Elements ◽  
Human Operators ◽  
Smart Machine

A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances auto­nomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

scholarly journals A novel causality-centrality-based method for the analysis of the impacts of air pollutants on PM2.5 concentrations in China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bocheng Wang
Keyword(s):  
Air Pollutants ◽  
Regression Models ◽  
Large Population ◽  
Vital Role ◽  
Superior Performance ◽  
Causal Direction ◽  
Northern Cities ◽  
Main Components ◽  
Surrounding Areas ◽  
Centrality Analysis

AbstractIn this paper, we analyzed the spatial and temporal causality and graph-based centrality relationship between air pollutants and PM2.5 concentrations in China from 2013 to 2017. NO2, SO2, CO and O3 were considered the main components of pollution that affected the health of people; thus, various joint regression models were built to reveal the causal direction from these individual pollutants to PM2.5 concentrations. In this causal centrality analysis, Beijing was the most important area in the Jing-Jin-Ji region because of its developed economy and large population. Pollutants in Beijing and peripheral cities were studied. The results showed that NO2 pollutants play a vital role in the PM2.5 concentrations in Beijing and its surrounding areas. An obvious causality direction and betweenness centrality were observed in the northern cities compared with others, demonstrating the fact that the more developed cities were most seriously polluted. Superior performance with causal centrality characteristics in the recognition of PM2.5 concentrations has been achieved.

Assessing the Unacquainted: Inferred Reviewer Personality and Review Helpfulness

MIS Quarterly ◽  
2021 ◽  
Vol 45 (3) ◽  
pp. 1113-1148
Author(s):  
Angela Xia Liu ◽  
◽  
Yilin Li ◽  
Sean Xu ◽  
◽  
...  
Keyword(s):  
Personality Traits ◽  
Supervised Classification ◽  
Large Population ◽  
Superior Performance ◽  
Product Reviews ◽  
Review Helpfulness ◽  
Personality Theories ◽  
Deep Learning Model ◽  
Practical Implications

This work examines the question of who is more likely to provide future helpful reviews in the context of online product reviews by synergistically using personality theories and data analytics. It trains a deep learning model to infer a reviewer’s personality traits. This enables analyses to reveal the role of personality traits in review helpfulness among a large population of reviewers. We develop hypotheses on how personality traits are associated with review helpfulness, followed by hypotheses testing that confirms that higher review helpfulness is related to higher openness, conscientiousness, extraversion, and agreeableness and to lower emotional stability. These results suggest the appropriateness of using these five personality traits as inputs for developing a model for predicting future review helpfulness. Based on an ensemble model using supervised classification algorithms, we develop a predictive model and demonstrate its superior performance. Theoretical and practical implications are discussed.

Multilevel Regression and Poststratification Versus Survey Sample Weighting for Estimating Population Quantities in Large Population Health Studies

2020 ◽  
Vol 189 (7) ◽  
pp. 717-725 ◽  
Author(s):  
Marnie Downes ◽  
John B Carlin
Keyword(s):  
Population Health ◽  
Large Scale ◽  
Census Data ◽  
Large Population ◽  
Population Data ◽  
Superior Performance ◽  
Population Parameter ◽  
Multilevel Regression ◽  
Sample Weighting ◽  
Survey Weighting

Abstract Multilevel regression and poststratification (MRP) is a model-based approach for estimating a population parameter of interest, generally from large-scale surveys. It has been shown to be effective in highly selected samples, which is particularly relevant to investigators of large-scale population health and epidemiologic surveys facing increasing difficulties in recruiting representative samples of participants. We aimed to further examine the accuracy and precision of MRP in a context where census data provided reasonable proxies for true population quantities of interest. We considered 2 outcomes from the baseline wave of the Ten to Men study (Australia, 2013–2014) and obtained relevant population data from the 2011 Australian Census. MRP was found to achieve generally superior performance relative to conventional survey weighting methods for the population as a whole and for population subsets of varying sizes. MRP resulted in less variability among estimates across population subsets relative to sample weighting, and there was some evidence of small gains in precision when using MRP, particularly for smaller population subsets. These findings offer further support for MRP as a promising analytical approach for addressing participation bias in the estimation of population descriptive quantities from large-scale health surveys and cohort studies.

scholarly journals Online Multilingual Hate Speech Detection: Experimenting with Hindi and English Social Media

2020 ◽  
Author(s):  
Neeraj Vashistha ◽  
Arkaitz Zubiaga
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Model Performance ◽  
Academic Community ◽  
Human Interaction ◽  
Superior Performance ◽  
Competitive Performance ◽  
Speech Detection ◽  
Improve Model ◽  
Use Of The Internet

The exponential increase in the use of the Internet and social media over the last two decades has changed human interaction. This has led to many positive outcomes, but at the same time it has brought risks and harms. While the volume of harmful content online, such as hate speech, is not manageable by humans, interest in the academic community to investigate automated means for hate speech detection has increased. In this study, we analyse six publicly available datasets by combining them into a single homogeneous dataset and classify them into three classes, abusive, hateful or neither. We create a baseline model and we improve model performance scores using various optimisation techniques. After attaining a competitive performance score, we create a tool which identifies and scores a page with effective metric in near-real time and uses the same as feedback to re-train our model. We prove the competitive performance of our multilingual model on two langauges, English and Hindi, leading to comparable or superior performance to most monolingual models.

scholarly journals A bibliometric study of Video Retrieval Evaluation Benchmarking (TRECVid): A methodological analysis

2011 ◽  
Vol 37 (6) ◽  
pp. 577-593 ◽  
Author(s):  
Clare V. Thornley ◽  
Shane J. McLoughlin ◽  
Andrea C. Johnson ◽  
Alan F. Smeaton
Keyword(s):  
Video Retrieval ◽  
Citation Count ◽  
Detailed Comparison ◽  
Bibliometric Study ◽  
Google Scholar ◽  
Academic Disciplines ◽  
Superior Performance ◽  
Publish Or Perish ◽  
Bibliographic Data ◽  
Cleaning Methods

This paper provides a discussion and analysis of methodological issues encountered during a scholarly impact and bibliometric study within the field of Computer Science (TRECVid Text Retrieval and Evaluation Conference, Video Retrieval Evaluation). The purpose of this paper is to provide a reflection and analysis of the methods used to provide useful information and guidance for those who may wish to undertake similar studies, and is of particular relevance for the academic disciplines which have publication and citation norms that may not perform well using traditional tools. Scopus and Google Scholar are discussed and a detailed comparison of the effects of different search methods and cleaning methods within and between these tools for subject and author analysis is provided. The additional database capabilities and usefulness of ‘Scopus More’ in addition to ‘Scopus General’ are discussed and evaluated. Scopus paper coverage is found to favourably compare with Google Scholar but Scholar consistently has superior performance at finding citations to those papers. These additional citations significantly increase the citation totals and also change the relative ranking of papers. Publish or Perish, a software wrapper for Google Scholar, is also examined and its limitations and some possible solutions are described. Data cleaning methods, including duplicate checks, expert domain checking of bibliographic data, and content checking of retrieved papers, are compared and their relative effects on paper and citation count discussed. Google Scholar and Scopus are also compared as tools for collecting bibliographic data for visualizations of developing trends and, owing to the comparative ease of collecting abstracts, Scopus is found far more effective.

Enabling impedance-based physical human–multi–robot collaboration: Experiments with four torque-controlled manipulators

2021 ◽  
pp. 027836492110536
Author(s):  
Niels Dehio ◽  
Joshua Smith ◽  
Dennis L. Wigand ◽  
Pouya Mohammadi ◽  
Michael Mistry ◽  
...  
Keyword(s):  
Human Robot Interaction ◽  
Contact Forces ◽  
Torque Control ◽  
Human Interaction ◽  
Physical Interaction ◽  
Complex Situation ◽  
Control Approach ◽  
Model Based ◽  
Compliant Control ◽  
Multi Robot

Robotics research into multi-robot systems so far has concentrated on implementing intelligent swarm behavior and contact-less human interaction. Studies of haptic or physical human-robot interaction, by contrast, have primarily focused on the assistance offered by a single robot. Consequently, our understanding of the physical interaction and the implicit communication through contact forces between a human and a team of multiple collaborative robots is limited. We here introduce the term Physical Human Multi-Robot Collaboration (PHMRC) to describe this more complex situation, which we consider highly relevant in future service robotics. The scenario discussed in this article covers multiple manipulators in close proximity and coupled through physical contacts. We represent this set of robots as fingers of an up-scaled agile robot hand. This perspective enables us to employ model-based grasping theory to deal with multi-contact situations. Our torque-control approach integrates dexterous multi-manipulator grasping skills, optimization of contact forces, compensation of object dynamics, and advanced impedance regulation into a coherent compliant control scheme. For this to achieve, we contribute fundamental theoretical improvements. Finally, experiments with up to four collaborative KUKA LWR IV+ manipulators performed both in simulation and real world validate the model-based control approach. As a side effect, we notice that our multi-manipulator control framework applies identically to multi-legged systems, and we execute it also on the quadruped ANYmal subject to non-coplanar contacts and human interaction.

Coverage Path Planning Using Mobile Robot Team Formations

2015 ◽  
pp. 214-247
Author(s):  
Prithviraj Dasgupta
Keyword(s):  
Path Planning ◽  
A Priori ◽  
Experimental Results ◽  
Future Research ◽  
Planning Problem ◽  
Coverage Path Planning ◽  
Preliminary Results ◽  
Time And Energy ◽  
Path Planning Problem ◽  
Multi Robot

The multi-robot coverage path-planning problem involves finding collision-free paths for a set of robots so that they can completely cover the surface of an environment. This problem is non-trivial as the geometry and location of obstacles in the environment is usually not known a priori by the robots, and they have to adapt their coverage path as they discover obstacles while moving in the environment. Additionally, the robots have to avoid repeated coverage of the same region by each other to reduce the coverage time and energy expended. This chapter discusses the research results in developing multi-robot coverage path planning techniques using mini-robots that are coordinated to move in formation. The authors present theoretical and experimental results of the proposed approach using e-puck mini-robots. Finally, they discuss some preliminary results to lay the foundation of future research for improved coverage path planning using coalition game-based, structured, robot team reconfiguration techniques.

Multi-robot coverage path planning using hexagonal segmentation for geophysical surveys

Robotica ◽  
2018 ◽  
Vol 36 (8) ◽  
pp. 1144-1166 ◽  
Author(s):  
Héctor Azpúrua ◽  
Gustavo M. Freitas ◽  
Douglas G. Macharet ◽  
Mario F. M. Campos
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Robots ◽  
Mineral Exploration ◽  
Statistical Investigation ◽  
Coverage Path Planning ◽  
Simulated Environment ◽  
Outdoor Setting ◽  
Geophysical Surveys ◽  
Multi Robot

SUMMARYThe field of robotics has received significant attention in our society due to the extensive use of robotic manipulators; however, recent advances in the research on autonomous vehicles have demonstrated a broader range of applications, such as exploration, surveillance, and environmental monitoring. In this sense, the problem of efficiently building a model of the environment using cooperative mobile robots is critical. Finding routes that are either length or time-optimized is essential for real-world applications of small autonomous robots. This paper addresses the problem of multi-robot area coverage path planning for geophysical surveys. Such surveys have many applications in mineral exploration, geology, archeology, and oceanography, among other fields. We propose a methodology that segments the environment into hexagonal cells and allocates groups of robots to different clusters of non-obstructed cells to acquire data. Cells can be covered by lawnmower, square or centroid patterns with specific configurations to address the constraints of magneto-metric surveys. Several trials were executed in a simulated environment, and a statistical investigation of the results is provided. We also report the results of experiments that were performed with real Unmanned Aerial Vehicles in an outdoor setting.

scholarly journals A Preliminary Study on Effectiveness of a Standardized Multi-Robot Therapy for Improvement in Collaborative Multi-Human Interaction of Children With ASD

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 109466-109474 ◽  
Author(s):  
Sara Ali ◽  
Faisal Mehmood ◽  
Muhammad Jawad Khan ◽  
Yasar Ayaz ◽  
Umer Asgher ◽  
...  
Keyword(s):  
Human Interaction ◽  
Children With Asd ◽  
Preliminary Study ◽  
Multi Robot
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