Incentivizing Evaluation with Peer Prediction and Limited Access to Ground Truth (Extended Abstract)

In many settings, an effective way of evaluating objects of interest is to collect evaluations from dispersed individuals and to aggregate these evaluations together. Some examples are categorizing online content and evaluating student assignments via peer grading. For this data science problem, one challenge is to motivate participants to conduct such evaluations carefully and to report them honestly, particularly when doing so is costly. Existing approaches, notably peer-prediction mechanisms, can incentivize truth telling in equilibrium. However, they also give rise to equilibria in which agents do not pay the costs required to evaluate accurately, and hence fail to elicit useful information. We show that this problem is unavoidable whenever agents are able to coordinate using low-cost signals about the items being evaluated (e.g., text labels or pictures). We then consider ways of circumventing this problem by comparing agents' reports to ground truth, which is available in practice when there exist trusted evaluators---such as teaching assistants in the peer grading scenario---who can perform a limited number of unbiased (but noisy) evaluations. Of course, when such ground truth is available, a simpler approach is also possible: rewarding each agent based on agreement with ground truth with some probability, and unconditionally rewarding the agent otherwise. Surprisingly, we show that the simpler mechanism achieves stronger incentive guarantees given less access to ground truth than a large set of peer-prediction mechanisms.

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An Agent-Based Crop Model Framework for Heterogeneous Soils

Agronomy ◽

10.3390/agronomy11010085 ◽

2021 ◽

Vol 11 (1) ◽

pp. 85

Author(s):

Jorge Lopez-Jimenez ◽

Nicanor Quijano ◽

Alain Vande Wouwer

Keyword(s):

Growth Models ◽

Control Strategies ◽

Low Cost ◽

Soil Heterogeneity ◽

Crop Growth ◽

Negative Effects ◽

Model Framework ◽

Agent Based ◽

Crop Growth Models ◽

Definition Of

Climate change and the efficient use of freshwater for irrigation pose a challenge for sustainable agriculture. Traditionally, the prediction of agricultural production is carried out through crop-growth models and historical records of the climatic variables. However, one of the main flaws of these models is that they do not consider the variability of the soil throughout the cultivation area. In addition, with the availability of new information sources (i.e., aerial or satellite images) and low-cost meteorological stations, it is convenient that the models incorporate prediction capabilities to enhance the representation of production scenarios. In this work, an agent-based model (ABM) that considers the soil heterogeneity and water exchanges is proposed. Soil heterogeneity is associated to the combination of individual behaviours of uniform portions of land (agents), while water fluxes are related to the topography. Each agent is characterized by an individual dynamic model, which describes the local crop growth. Moreover, this model considers positive and negative effects of water level, i.e., drought and waterlogging, on the biomass production. The development of the global ABM is oriented to the future use of control strategies and optimal irrigation policies. The model is built bottom-up starting with the definition of agents, and the Python environment Mesa is chosen for the implementation. The validation is carried out using three topographic scenarios in Colombia. Results of potential production cases are discussed, and some practical recommendations on the implementation are presented.

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Trilateration approaches for seamless out-/indoor GNSS and Wi-Fi smartphone positioning

Journal of Applied Geodesy ◽

10.1515/jag-2018-0022 ◽

2019 ◽

Vol 13 (1) ◽

pp. 47-61

Author(s):

Guenther Retscher ◽

Jonathan Kleine ◽

Lisa Whitemore

Keyword(s):

Inertial Sensors ◽

Low Cost ◽

Ground Truth ◽

Mobile User ◽

Location Based Services ◽

Raspberry Pi ◽

Indoor Environments ◽

Point Location ◽

Point Determination ◽

The Relationship

Abstract More and more sensors and receivers are found nowadays in smartphones which can enable and improve positioning for Location-based Services and other navigation applications. Apart from inertial sensors, such as accelerometers, gyroscope and magnetometer, receivers for Wireless Fidelity (Wi-Fi) and GNSS signals can be employed for positioning of a mobile user. In this study, three trilateration methods for Wi-Fi positioning are investigated whereby the influence of the derivation of the relationship between the received signal strength (RSS) and the range to an Access Points (AP) are analyzed. The first approach is a straightforward resection for point determination and the second is based on the calculation of the center of gravity in a triangle of APs while weighting the received RSS. In the third method a differential approach is employed where as in Differential GNSS (DGNSS) corrections are derived and applied to the raw RSS measurements. In this Differential Wi-Fi (DWi-Fi) method, reference stations realized by low-cost Raspberry Pi units are used to model temporal RSS variations. In the experiments in this study two different indoor environments are used, one in a laboratory and the second in the entrance of an office building. The results of the second and third approach show position deviations from the ground truth of around 2 m in dependence of the geometrical point location. Furthermore, the transition between GNSS positioning outdoors and Wi-Fi localization indoors in the entrance area of the building is studied.

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Rapid method of screening for drought stress tolerance in maize (Zea mays L.)

Indian Journal of Genetics and Plant Breeding (The) ◽

10.31742/ijgpb.80.1.3 ◽

2020 ◽

Vol 80 (01) ◽

Author(s):

Bhupender Kumar ◽

Krishan Kumar ◽

Shankar Lal Jat ◽

Shraddha Srivastava ◽

Tanu Tiwari ◽

...

Keyword(s):

Drought Stress ◽

Drought Tolerance ◽

Stress Tolerance ◽

Low Cost ◽

Large Set ◽

Antioxidant Genes ◽

Screening Technique ◽

Drought Stress Tolerance ◽

Hydroponic Solution ◽

Drought Screening

Drought stress is the major production constraint in rainfed maize. Screening for drought tolerance is severely affected by the lack of a simple and reliable phenotyping technique. The objective of this study was to standardize a simple hydroponic based drought screening technique in maize. In this context, one week old uniform seedlings of 55 inbreds and 5 hybrids were transferred to hydroponic solution in the glass house. The seedlings were allowed to acclimatize for next one week in hydroponic solution. The drought stress was imposed by removing seedlings from nutrient solution and exposed to air for 6 and 4 hours daily for a period of 5 and 4 consecutive days in hybrids and inbreds, respectively. Data were recorded on all shoot and root parameters, and based on stress symptoms, a drought tolerance score was given to each genotype. The percent deductions in shoot and root fresh weight from non-stress to stress ranged from 11.7 to 84.4 and 2.1 to 77.5, respectively. Six inbred lines, namely, DQL790-4, CML334, CM140, CML422, CM125 and HKI488 and three hybrids namely DMRH1306, DMRH1410 and PMH4 were found drought tolerant. The effectiveness of this screening technique was compared and confirmed using pots screening as well as by expression profiling of key antioxidant genes (Sod2, Sod4, Sod9 and Apx1) playing role in drought stress tolerance. This phenotyping technique is very short, low cost and simple which can be utilized in preliminary drought screening for large set of maize germplasm and mapping populations.

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Use of a sonification system for science learning by people who are blind

Journal of Assistive Technologies ◽

10.1108/jat-11-2015-0032 ◽

2016 ◽

Vol 10 (4) ◽

pp. 187-198 ◽

Cited By ~ 1

Author(s):

Orly Lahav ◽

Nuha Chagab ◽

Vadim Talis

Keyword(s):

Complex Systems ◽

Conceptual Knowledge ◽

Science Curriculum ◽

Low Cost ◽

Agent Based Modeling ◽

Learning System ◽

Learning Ability ◽

Content Type ◽

Agent Based ◽

K 12

Purpose The purpose of this paper is to examine a central need of students who are blind: the ability to access science curriculum content. Design/methodology/approach Agent-based modeling is a relatively new computational modeling paradigm that models complex dynamic systems. NetLogo is a widely used agent-based modeling language that enables exploration and construction of models of complex systems by programming and running the rules and behaviors. Sonification of variables and events in an agent-based NetLogo computer model of gas in a container is used to convey phenomena information. This study examined mainly two research topics: the scientific conceptual knowledge and systems reasoning that were learned as a result of interaction with the listen-to-complexity (L2C) environment as appeared in answers to the pre- and post-tests and the learning topics of kinetic molecular theory of gas in chemistry that was learned as a result of interaction with the L2C environment. The case study research focused on A., a woman who is adventitiously blind, for eight sessions. Findings The participant successfully completed all curricular assignments; her scientific conceptual knowledge and systems reasoning became more specific and aligned with scientific knowledge. Practical implications A practical implication of further studies is that they are likely to have an impact on the accessibility of learning materials, especially in science education for students who are blind, as equal access to low-cost learning environments that are equivalent to those used by sighted users would support their inclusion in the K-12 academic curriculum. Originality/value The innovative and low-cost learning system that is used in this research is based on transmittal of visual information of dynamic and complex systems, providing perceptual compensation by harnessing auditory feedback. For the first time the L2C system is based on sound that represents a dynamic rather than a static array. In this study, the authors explore how a combination of several auditory representations may affect cognitive learning ability.

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TSLAM: Tethered simultaneous localization and mapping for mobile robots

The International Journal of Robotics Research ◽

10.1177/0278364917732639 ◽

2017 ◽

Vol 36 (12) ◽

pp. 1363-1386 ◽

Cited By ~ 4

Author(s):

Patrick McGarey ◽

Kirk MacTavish ◽

François Pomerleau ◽

Timothy D Barfoot

Keyword(s):

Mobile Robots ◽

Particle Filter ◽

Low Cost ◽

Simultaneous Localization And Mapping ◽

Ground Truth ◽

Anchor Point ◽

Outlier Rejection ◽

Cluttered Environments ◽

Localization And Mapping ◽

Anchor Points

Tethered mobile robots are useful for exploration in steep, rugged, and dangerous terrain. A tether can provide a robot with robust communications, power, and mechanical support, but also constrains motion. In cluttered environments, the tether will wrap around a number of intermediate ‘anchor points’, complicating navigation. We show that by measuring the length of tether deployed and the bearing to the most recent anchor point, we can formulate a tethered simultaneous localization and mapping (TSLAM) problem that allows us to estimate the pose of the robot and the positions of the anchor points, using only low-cost, nonvisual sensors. This information is used by the robot to safely return along an outgoing trajectory while avoiding tether entanglement. We are motivated by TSLAM as a building block to aid conventional, camera, and laser-based approaches to simultaneous localization and mapping (SLAM), which tend to fail in dark and or dusty environments. Unlike conventional range-bearing SLAM, the TSLAM problem must account for the fact that the tether-length measurements are a function of the robot’s pose and all the intermediate anchor-point positions. While this fact has implications on the sparsity that can be exploited in our method, we show that a solution to the TSLAM problem can still be found and formulate two approaches: (i) an online particle filter based on FastSLAM and (ii) an efficient, offline batch solution. We demonstrate that either method outperforms odometry alone, both in simulation and in experiments using our TReX (Tethered Robotic eXplorer) mobile robot operating in flat-indoor and steep-outdoor environments. For the indoor experiment, we compare each method using the same dataset with ground truth, showing that batch TSLAM outperforms particle-filter TSLAM in localization and mapping accuracy, owing to superior anchor-point detection, data association, and outlier rejection.

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Low-Cost, Accessible Fabrication Methods for Microfluidics Research in Low-Resource Settings

Micromachines ◽

10.3390/mi9090461 ◽

2018 ◽

Vol 9 (9) ◽

pp. 461 ◽

Cited By ~ 5

Author(s):

Hoang-Tuan Nguyen ◽

Ha Thach ◽

Emmanuel Roy ◽

Khon Huynh ◽

Cecile Perrault

Keyword(s):

Developing Countries ◽

Scientific Community ◽

Low Cost ◽

Microfluidic Devices ◽

Research Community ◽

Access To Healthcare ◽

Healthcare Industry ◽

Low Resource Settings ◽

Limited Access ◽

Low Resource

Microfluidics are expected to revolutionize the healthcare industry especially in developing countries since it would bring portable, easy-to-use, self-contained diagnostic devices to places with limited access to healthcare. To date, however, microfluidics has not yet been able to live up to these expectations. One non-negligible factor can be attributed to inaccessible prototyping methods for researchers in low-resource settings who are unable to afford expensive equipment and/or obtain critical reagents and, therefore, unable to engage and contribute to microfluidics research. In this paper, we present methods to create microfluidic devices that reduce initial costs from hundreds of thousands of dollars to about $6000 by using readily accessible consumables and inexpensive equipment. By including the scientific community most embedded and aware of the requirements of healthcare in developing countries, microfluidics will be able to increase its reach in the research community and be better informed to provide relevant solutions to global healthcare challenges.

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The Model Selection Curse

American Economic Review: Insights ◽

10.1257/aeri.20180485 ◽

2019 ◽

Vol 1 (2) ◽

pp. 127-140 ◽

Cited By ~ 1

Author(s):

Kfir Eliaz ◽

Ran Spiegler

Keyword(s):

Machine Learning ◽

Model Selection ◽

Learning Algorithms ◽

Personal Data ◽

Estimation Procedure ◽

Machine Learning Algorithms ◽

Human Interaction ◽

Truth Telling ◽

Agent Based

A statistician takes an action on behalf of an agent, based on the agent’s self-reported personal data and a sample involving other people. The action that he takes is an estimated function of the agent’s report. The estimation procedure involves model selection. We ask the following question: Is truth-telling optimal for the agent given the statistician’s procedure? We analyze this question in the context of a simple example that highlights the role of model selection. We suggest that our simple exercise may have implications for the broader issue of human interaction with machine learning algorithms. (JEL C52)

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Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery

Remote Sensing ◽

10.3390/rs10121907 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1907 ◽

Cited By ~ 18

Author(s):

Luís Pádua ◽

Pedro Marques ◽

Jonáš Hruška ◽

Telmo Adão ◽

Emanuel Peres ◽

...

Keyword(s):

Accuracy Assessment ◽

Low Cost ◽

Ground Truth ◽

Volume Estimation ◽

Height Range ◽

Ground Truth Data ◽

Operational Technique ◽

Digital Elevation ◽

Multi Temporal ◽

Aerial Vehicle

This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to compute crop surface models. By filtering vegetation within a given height-range, it was possible to separate grapevine vegetation from other vegetation present in a specific vineyard plot, enabling the estimation of grapevine area and volume. The results showed high accuracy in grapevine detection (94.40%) and low error in grapevine volume estimation (root mean square error of 0.13 m and correlation coefficient of 0.78 for height estimation). The accuracy assessment showed that the proposed method based on UAV-based RGB imagery is effective and has potential to become an operational technique. The proposed method also allows the estimation of grapevine areas that can potentially benefit from canopy management operations.

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