Sensing and Human Factors Research: A Review

2022 ◽  
Vol 88 (1) ◽  
pp. 55-64
Author(s):  
Raechel A. Portelli ◽  
Paul Pope
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Earth Observation ◽  
Machine Learning Algorithms ◽  
Analog To Digital ◽  
Human In The Loop ◽  
History Of ◽  
The Military ◽  
Human Factors Research ◽  
And Training

Human experts are integral to the success of computational earth observation. They perform various visual decision-making tasks, from selecting data and training machine-learning algorithms to interpreting accuracy and credibility. Research concerning the various human factors which affect performance has a long history within the fields of earth observation and the military. Shifts in the analytical environment from analog to digital workspaces necessitate continued research, focusing on human-in-the-loop processing. This article reviews the history of human-factors research within the field of remote sensing and suggests a framework for refocusing the discipline's efforts to understand the role that humans play in earth observation.

Machine Learning and Human Factors: Status, Applications, and Future Directions

2018 ◽  
Vol 62 (1) ◽  
pp. 135-138 ◽  
Author(s):  
Nathan Lau ◽  
Lex Fridman ◽  
Brett J. Borghetti ◽  
John D. Lee
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Interaction Design ◽  
Intelligent Systems ◽  
Consumer Products ◽  
Learning Approaches ◽  
Future Directions ◽  
Industrial Systems ◽  
Human Factors Research ◽  
And Training

As machine learning approaches ubiquity in industrial systems and consumer products, human factors research must attend to machine learning, specifically on how intelligent systems built on machine learning are different from early generations of automated systems, and what these differences mean for human-system interaction, design, evaluation and training. This panel invites five researchers in different domains to discuss how human factors can contribute to machine learning research and applications, as well as how machine learning presents both challenges and contributions for human factors.

scholarly journals Machine Learning for the Dynamic Positioning of UAVs for Extended Connectivity

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4618
Author(s):  
Francisco Oliveira ◽  
Miguel Luís ◽  
Susana Sargento
Keyword(s):  
Machine Learning ◽  
Cellular Networks ◽  
Real Data ◽  
Emerging Technology ◽  
Machine Learning Algorithms ◽  
Base Stations ◽  
Aerial Vehicle ◽  
Positioning Algorithm ◽  
The Military ◽  
Better Than

Unmanned Aerial Vehicle (UAV) networks are an emerging technology, useful not only for the military, but also for public and civil purposes. Their versatility provides advantages in situations where an existing network cannot support all requirements of its users, either because of an exceptionally big number of users, or because of the failure of one or more ground base stations. Networks of UAVs can reinforce these cellular networks where needed, redirecting the traffic to available ground stations. Using machine learning algorithms to predict overloaded traffic areas, we propose a UAV positioning algorithm responsible for determining suitable positions for the UAVs, with the objective of a more balanced redistribution of traffic, to avoid saturated base stations and decrease the number of users without a connection. The tests performed with real data of user connections through base stations show that, in less restrictive network conditions, the algorithm to dynamically place the UAVs performs significantly better than in more restrictive conditions, reducing significantly the number of users without a connection. We also conclude that the accuracy of the prediction is a very important factor, not only in the reduction of users without a connection, but also on the number of UAVs deployed.

How Signs, Markings, and Hazards Impact Motorist Assessment of Cyclist Lane Placement

2020 ◽  
Vol 64 (1) ◽  
pp. 1948-1949
Author(s):  
Mary L. Still ◽  
Jeremiah D. Still
Keyword(s):  
Human Factors ◽  
The Road ◽  
On The Road ◽  
Lane Position ◽  
The Impact ◽  
Human Factors Research ◽  
And Training

Human factors research has led to safer interactions between motorists through redesigned signage, roadway designs, and training. Similar efforts are needed to understand and improve interactions between cyclists and motorists. One challenge to safe motorist-cyclist interactions are expectations about where cyclists should be on the road. In this study, we utilize more directive signage and additional lane markings to clarify where cyclists should ride in the travel lane. The impact of these signifiers was examined by having motorists indicate where cyclists should ride in the lane, how difficult it was to determine the correct lane position, and how safe they would feel if they were in that lane position. Results indicate that more directive signage – “bicycles take the lane”-and painted hazard signifiers can change motorists’ expectations, so they are more aligned with safer cyclist positioning in the lane.

scholarly journals Earth Observation Data for Agricultural Drought Monitoring in the Pannonian Basin

2020 ◽  
Author(s):  
Laura Crocetti ◽  
Milan Fischer ◽  
Matthias Forkel ◽  
Aleš Grlj ◽  
Wai-Tim Ng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Pannonian Basin ◽  
Spatial Scales ◽  
European Space Agency ◽  
Earth Observation ◽  
Machine Learning Algorithms ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Observation Data

<p>The Pannonian Basin is a region in the southeastern part of Central Europe that is heavily used for agricultural purposes. It is geomorphological defined as the plain area that is surrounded by the Alps in the west, the Dinaric Alps in the Southwest, and the Carpathian mountains in the North, East and Southeast. In recent decades, the Pannonian Basin has experienced several drought episodes, leading to severe impacts on the environment, society, and economy. Ongoing human-induced climate change, characterised by increasing temperature and potential evapotranspiration as well as changes in precipitation distribution will further exacerbate the frequency and intensity of extreme events. Therefore, it is important to monitor, model, and forecast droughts and their impact on the environment for a better adaption to the changing weather and climate extremes. The increasing availability of long-term Earth observation (EO) data with high-resolution, combined with the progress in machine learning algorithms and artificial intelligence, are expected to improve the drought monitoring and impact prediction capacities.</p><p>Here, we assess novel EO-based products with respect to drought processes in the Pannonian Basin. To identify meteorological and agricultural drought, the Standardized Precipitation-Evapotranspiration Index was computed from the ERA5 meteorological reanalysis and compared with drought indicators based on EO time series of soil moisture and vegetation like the Soil Water Index or the Normalized Difference Vegetation Index. We suggest that at resolution representing the ERA5 reanalysis (~0.25°) or coarser, both meteorological as well as EO data can identify drought events similarly well. However, at finer spatial scales (e.g. 1 km) the variability of biophysical properties between fields cannot be represented by meteorological data but can be captured by EO data. Furthermore, we analyse historical drought events and how they occur in different EO datasets. It is planned to enhance the forecasting of agricultural drought and estimating drought impacts on agriculture through exploiting the potential of EO soil moisture and vegetation data in a data-driven machine learning framework.</p><p>This study is funded by the DryPan project of the European Space Agency (https://www.eodc.eu/esa-drypan/).</p>

scholarly journals Machine Learning Algorithms as a Computer-Assisted Decision Tool for Oral Cancer Prognosis and Management Decisions: A Systematic Review

ORL ◽  
2022 ◽  
pp. 1-11
Author(s):  
Carlos M. Chiesa-Estomba ◽  
Manuel Graña ◽  
Alfonso Medela ◽  
Jon A. Sistiaga-Suarez ◽  
Jerome R. Lechien ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Final Analysis ◽  
Machine Learning Algorithms ◽  
Cancer Prognosis ◽  
Prognostic Indicators ◽  
Computer Assisted ◽  
Advance Research ◽  
And Training ◽  
Potential Tool

<b><i>Introduction:</i></b> Despite multiple prognostic indicators described for oral cavity squamous cell carcinoma (OCSCC), its management still continues to be a matter of debate. Machine learning is a subset of artificial intelligence that enables computers to learn from historical data, gather insights, and make predictions about new data using the model learned. Therefore, it can be a potential tool in the field of head and neck cancer. <b><i>Methods:</i></b> We conducted a systematic review. <b><i>Results:</i></b> A total of 81 manuscripts were revised, and 46 studies met the inclusion criteria. Of these, 38 were excluded for the following reasons: use of a classical statistical method (<i>N</i> = 16), nonspecific for OCSCC (<i>N</i> = 15), and not being related to OCSCC survival (<i>N</i> = 7). In total, 8 studies were included in the final analysis. <b><i>Conclusions:</i></b> ML has the potential to significantly advance research in the field of OCSCC. Advantages are related to the use and training of ML models because of their capability to continue training continuously when more data become available. Future ML research will allow us to improve and democratize the application of algorithms to improve the prediction of cancer prognosis and its management worldwide.

scholarly journals The Employment in Innovative Enterprises in Europe

2022 ◽  
Author(s):  
Lucio Laureti ◽  
Costantiello Alberto ◽  
Marco Maria Matarrese ◽  
Angelo Leogrande
Keyword(s):  
Machine Learning ◽  
Panel Data ◽  
Fixed Effects ◽  
Value Added ◽  
Machine Learning Algorithms ◽  
High Tech ◽  
Dynamic Panel ◽  
Silhouette Coefficient ◽  
High Tech Product ◽  
And Training

Abstract In this article we evaluate the determinants of the Employment in Innovative Enterprises in Europe. We use data from the European Innovation Scoreboard of the European Commission for 36 countries in the period 2000-2019 with Panel Data with Fixed Effects, Panel Data with Random Effects, Dynamic Panel, WLS and Pooled OLS. We found that the “Employment in Innovative Enterprises in Europe” is positively associated with “Broadband Penetration in Europe”, “Foreign Controlled Enterprises Share of Value Added”, “Innovation Index”, “Medium and High-Tech Product Exports” and negatively associated to “Basic School Entrepreneurial Education and Training”, “International Co-Publications”, and “Marketing or Organizational Innovators”. Secondly, we perform a cluster analysis with the k-Means algorithm optimized with the Silhouette Coefficient and we found the presence of four different clusters. Finally, we perform a comparison among eight different machine learning algorithms to predict the level of “Employment in Innovative Enterprises” in Europe and we found that the Linear Regression is the best predictor.

scholarly journals Understanding the Relationship between Interactions and Outcomes in Human-in-the-Loop Machine Learning

2021 ◽  
Author(s):  
Yuchen Cui ◽  
Pallavi Koppol ◽  
Henny Admoni ◽  
Scott Niekum ◽  
Reid Simmons ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Performance ◽  
Autonomous Agents ◽  
Training Data ◽  
Open Problems ◽  
Human In The Loop ◽  
Efficiency And Effectiveness ◽  
Interaction Types ◽  
The Relationship ◽  
And Training

Human-in-the-loop Machine Learning (HIL-ML) is a widely adopted paradigm for instilling human knowledge in autonomous agents. Many design choices influence the efficiency and effectiveness of such interactive learning processes, particularly the interaction type through which the human teacher may provide feedback. While different interaction types (demonstrations, preferences, etc.) have been proposed and evaluated in the HIL-ML literature, there has been little discussion of how these compare or how they should be selected to best address a particular learning problem. In this survey, we propose an organizing principle for HIL-ML that provides a way to analyze the effects of interaction types on human performance and training data. We also identify open problems in understanding the effects of interaction types.

Reconstructing Simulator Control Inputs: A Machine Learning Approach

2018 ◽  
Vol 62 (1) ◽  
pp. 54-56
Author(s):  
Eamon Caddigan
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Response Times ◽  
Control Surface ◽  
Behavioral Measure ◽  
Control Input ◽  
Flight Simulators ◽  
Machine Learning Approach ◽  
The Moment ◽  
Human Factors Research

Flight simulators are valuable tools for human factors research. However, some simulation platforms fail to record all of the information relevant to the researcher. While the data produced by most simulators includes details about the position and state of the simulated aircraft, some platforms do not record pilots’ control input. Missing control input data make it difficult to evaluate response times, a key behavioral measure in human factors research. Here we describe a technique that uses machine learning to reconstruct aircraft maneuvers using aircraft control surface information, which is typically available in simulator output files. This allows researchers to more accurately estimate the moment at which a pilot initiated a maneuver.

Comparing Statistical and Machine Learning Classifiers: Alternatives for Predictive Modeling in Human Factors Research

2003 ◽  
Vol 45 (3) ◽  
pp. 408-423 ◽  
Author(s):  
Brian Carnahan ◽  
Gérard Meyer ◽  
Lois-Ann Kuntz
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Discriminant Analysis ◽  
Human Factors ◽  
Predictive Accuracy ◽  
Performance Outcomes ◽  
Learning Approaches ◽  
Classification Models ◽  
Machine Learning Classification ◽  
Human Factors Research

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches - genetic programming and decision tree induction - were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

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