scholarly journals Embedding Computer Vision in Citizen Science

2019 ◽  
Vol 3 ◽  
Author(s):  
Tom August ◽  
J Terry ◽  
David Roy
Keyword(s):  
Decision Making ◽  
Computer Vision ◽  
Data Quality ◽  
Citizen Science ◽  
Learning Process ◽  
Contextual Information ◽  
Limited Resource ◽  
Machine Learning Techniques ◽  
Ecological Knowledge ◽  
Time Of Year

The rapid rise of Artificial Intelligence (AI) methods has presented new opportunities for those who work with biodiversity data. Computer vision, in particular where computers can be trained to identify species in digital photographs, has significant potential to address a number of existing challenges in citizen science. The Biological Records Centre (www.brc.ac.uk) has been a central focus for terrestrial and freshwater citizen science in the United Kingdom for over 50 years. We will present our research on how computer vision can be embedded in citizen science, addressing three important questions. How can contextual information, such as time of year, be included in computer vision? A naturalist will use a wealth of ecological knowledge about species in combination with information about where and when the image was taken to augment their decision making; we should emulate this in our AI. How can citizen scientists be best supported by computer vision? Our ambition is not to replace identification skills with AI but to use AI to support the learning process. How can computer vision support our limited resource of expert verifiers as data volumes increase? We receive more and more data each year, which puts a greater demand on our expert verifiers, who review all records to ensure data quality. We have been exploring how computer vision can lighten this workload. How can contextual information, such as time of year, be included in computer vision? A naturalist will use a wealth of ecological knowledge about species in combination with information about where and when the image was taken to augment their decision making; we should emulate this in our AI. How can citizen scientists be best supported by computer vision? Our ambition is not to replace identification skills with AI but to use AI to support the learning process. How can computer vision support our limited resource of expert verifiers as data volumes increase? We receive more and more data each year, which puts a greater demand on our expert verifiers, who review all records to ensure data quality. We have been exploring how computer vision can lighten this workload. We will present work that addresses these questions including: developing machine learning techniques that incorporate ecological information as well as images to arrive at a species classification; co-designing an identification tool to help farmers identify flowers beneficial to wildlife; and assessing the optimal combination of computer vision and expert verification to improve our verification systems.

Perancangan aplikasi multi criteria decision making dalam penerimaan beasiswa kepada dosen studi lanjut STMIK Balikpapan menggunakan metode SAW

2018 ◽  
Vol 1 (1) ◽  
pp. 35-42
Author(s):  
Muslimin B ◽  
Sumardi Sumardi
Keyword(s):  
Decision Making ◽  
Learning Process ◽  
Teaching And Learning ◽  
Teaching Assistants ◽  
Multi Criteria Decision Making ◽  
Scholarship Recipients ◽  
The Matrix ◽  
Application Decision ◽  
High Degree ◽  
New Student

 Interests and number of STMIK Balikpapan new student enrollments are increasing every year. The balance of the ratio of lecturers to students is one of the most important components in improving the quality and teaching and learning process of a university. Avoiding shortages in the number of lecturers can be realized by providing scholarship programs to alumni and teaching assistants. This study aims to build a multi criteria decision making application that can assist the Head of HRD in the process of receiving scholarships to advanced and effective study lecturers. The multi criteria decision making application developed in this study uses the SAW method. The implementation of the SAW method includes the process of evaluating the weighting of criteria, evaluating alternative weights, the matrix process, the results of decision making preferences, resulting in the weighting and ranking of each alternative candidate for the scholarship recipient. The results of the evaluation of multi-criteria application decision making in the study are expected to produce modeling with a high degree of accuracy. The results of the analysis carried out can provide alternative recommendations for prospective scholarship recipients to advanced study lecturers in STMIK Balikpapan.

Minimizing Bias in Forensic Handwriting Examinations

2015 ◽  
Vol 25 ◽  
pp. 17-26 ◽  
Author(s):  
L. C. Alewijnse ◽  
E.J.A.T. Mattijssen ◽  
R.D. Stoel
Keyword(s):  
Decision Making ◽  
Information Management ◽  
Contextual Information ◽  
Context Information ◽  
Decision Making Process ◽  
Potential Bias ◽  
Potential Influence ◽  
Management Procedures

The purpose of this paper is to contribute to the increasing awareness about the potential bias on the interpretation and conclusions of forensic handwriting examiners (FHEs) by contextual information. We briefly provide the reader with an overview of relevant types of bias, the difficulties associated with studying bias, the sources of bias and their potential influence on the decision making process in casework, and solutions to minimize bias in casework. We propose that the limitations of published studies on bias need to be recognized and that their conclusions must be interpreted with care. Instead of discussing whether bias is an issue in casework, the forensic handwriting community should actually focus on how bias can be minimized in practice. As some authors have already shown (e.g., Found & Ganas, 2014), it is relatively easy to implement context information management procedures in practice. By introducing appropriate procedures to minimize bias, not only forensic handwriting examination will be improved, it will also increase the acceptability of the provided evidence during court hearings. Purchase Article - $10

scholarly journals Teaching and Learning Process of Decision-Making Units in Talented Young Players From U-10 to U-14

2020 ◽  
Vol 11 ◽  
Author(s):  
Juan Carlos Pastor-Vicedo ◽  
Alejandro Prieto-Ayuso ◽  
Onofre Ricardo Contreras-Jordán ◽  
Filipe Manuel Clemente ◽  
Pantelis Theo Nikolaidis ◽  
...  
Keyword(s):  
Decision Making ◽  
Learning Process ◽  
Teaching And Learning ◽  
Decision Making Units

scholarly journals Individual differences in experienced and observational decision-making illuminate interactions between reinforcement learning and declarative memory

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Batel Yifrah ◽  
Ayelet Ramaty ◽  
Genela Morris ◽  
Avi Mendelsohn
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Declarative Memory ◽  
Contextual Information ◽  
Memory Performance ◽  
Relevant Information ◽  
Subjective Memory ◽  
Types Of Information ◽  
Reinforcement Learning Models ◽  
Implicit And Explicit

AbstractDecision making can be shaped both by trial-and-error experiences and by memory of unique contextual information. Moreover, these types of information can be acquired either by means of active experience or by observing others behave in similar situations. The interactions between reinforcement learning parameters that inform decision updating and memory formation of declarative information in experienced and observational learning settings are, however, unknown. In the current study, participants took part in a probabilistic decision-making task involving situations that either yielded similar outcomes to those of an observed player or opposed them. By fitting alternative reinforcement learning models to each subject, we discerned participants who learned similarly from experience and observation from those who assigned different weights to learning signals from these two sources. Participants who assigned different weights to their own experience versus those of others displayed enhanced memory performance as well as subjective memory strength for episodes involving significant reward prospects. Conversely, memory performance of participants who did not prioritize their own experience over others did not seem to be influenced by reinforcement learning parameters. These findings demonstrate that interactions between implicit and explicit learning systems depend on the means by which individuals weigh relevant information conveyed via experience and observation.

scholarly journals The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets

Computation ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 12
Author(s):  
Evangelos Maltezos ◽  
Athanasios Douklias ◽  
Aris Dadoukis ◽  
Fay Misichroni ◽  
Lazaros Karagiannidis ◽  
...  
Keyword(s):  
Decision Making ◽  
Object Detection ◽  
Situational Awareness ◽  
Machine Learning Techniques ◽  
Measurement Instruments ◽  
Time Data ◽  
Modular Architecture ◽  
Seamless Integration ◽  
Object Detection And Tracking ◽  
Detection And Tracking

Situational awareness is a critical aspect of the decision-making process in emergency response and civil protection and requires the availability of up-to-date information on the current situation. In this context, the related research should not only encompass developing innovative single solutions for (real-time) data collection, but also on the aspect of transforming data into information so that the latter can be considered as a basis for action and decision making. Unmanned systems (UxV) as data acquisition platforms and autonomous or semi-autonomous measurement instruments have become attractive for many applications in emergency operations. This paper proposes a multipurpose situational awareness platform by exploiting advanced on-board processing capabilities and efficient computer vision, image processing, and machine learning techniques. The main pillars of the proposed platform are: (1) a modular architecture that exploits unmanned aerial vehicle (UAV) and terrestrial assets; (2) deployment of on-board data capturing and processing; (3) provision of geolocalized object detection and tracking events; and (4) a user-friendly operational interface for standalone deployment and seamless integration with external systems. Experimental results are provided using RGB and thermal video datasets and applying novel object detection and tracking algorithms. The results show the utility and the potential of the proposed platform, and future directions for extension and optimization are presented.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

scholarly journals A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 875
Author(s):  
Jesus Cerquides ◽  
Mehmet Oğuz Mülâyim ◽  
Jerónimo Hernández-González ◽  
Amudha Ravi Shankar ◽  
Jose Luis Fernandez-Marquez
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Graphical Model ◽  
Real Life ◽  
Scientific Journals ◽  
Probabilistic Framework ◽  
Science Data ◽  
Label Aggregation ◽  
Evaluation Of Data ◽  
Model Formalism

Over the last decade, hundreds of thousands of volunteers have contributed to science by collecting or analyzing data. This public participation in science, also known as citizen science, has contributed to significant discoveries and led to publications in major scientific journals. However, little attention has been paid to data quality issues. In this work we argue that being able to determine the accuracy of data obtained by crowdsourcing is a fundamental question and we point out that, for many real-life scenarios, mathematical tools and processes for the evaluation of data quality are missing. We propose a probabilistic methodology for the evaluation of the accuracy of labeling data obtained by crowdsourcing in citizen science. The methodology builds on an abstract probabilistic graphical model formalism, which is shown to generalize some already existing label aggregation models. We show how to make practical use of the methodology through a comparison of data obtained from different citizen science communities analyzing the earthquake that took place in Albania in 2019.

scholarly journals The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

2021 ◽  
Vol 444 ◽  
pp. 109453
Author(s):  
Camille Van Eupen ◽  
Dirk Maes ◽  
Marc Herremans ◽  
Kristijn R.R. Swinnen ◽  
Ben Somers ◽  
...  
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Model Performance ◽  
Distribution Model ◽  
Science Data ◽  
Quality Filtering ◽  
The Impact

scholarly journals CWDAT—An Open-Source Tool for the Visualization and Analysis of Community-Generated Water Quality Data

2021 ◽  
Vol 10 (4) ◽  
pp. 207
Author(s):  
Annie Gray ◽  
Colin Robertson ◽  
Rob Feick
Keyword(s):  
Water Quality ◽  
Data Quality ◽  
Capacity Building ◽  
Open Source ◽  
Citizen Science ◽  
Water Resource ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
User Engagement ◽  
Community Based

Citizen science initiatives span a wide range of topics, designs, and research needs. Despite this heterogeneity, there are several common barriers to the uptake and sustainability of citizen science projects and the information they generate. One key barrier often cited in the citizen science literature is data quality. Open-source tools for the analysis, visualization, and reporting of citizen science data hold promise for addressing the challenge of data quality, while providing other benefits such as technical capacity-building, increased user engagement, and reinforcing data sovereignty. We developed an operational citizen science tool called the Community Water Data Analysis Tool (CWDAT)—a R/Shiny-based web application designed for community-based water quality monitoring. Surveys and facilitated user-engagement were conducted among stakeholders during the development of CWDAT. Targeted recruitment was used to gather feedback on the initial CWDAT prototype’s interface, features, and potential to support capacity building in the context of community-based water quality monitoring. Fourteen of thirty-two invited individuals (response rate 44%) contributed feedback via a survey or through facilitated interaction with CWDAT, with eight individuals interacting directly with CWDAT. Overall, CWDAT was received favourably. Participants requested updates and modifications such as water quality thresholds and indices that reflected well-known barriers to citizen science initiatives related to data quality assurance and the generation of actionable information. Our findings support calls to engage end-users directly in citizen science tool design and highlight how design can contribute to users’ understanding of data quality. Enhanced citizen participation in water resource stewardship facilitated by tools such as CWDAT may provide greater community engagement and acceptance of water resource management and policy-making.

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