Formalizing Parameter Constraints to Support Intelligent Geoprocessing: A SHACL-Based Method

Intelligent geoprocessing relies heavily on formalized parameter constraints of geoprocessing tools to validate the input data and to further ensure the robustness and reliability of geoprocessing. However, existing methods developed to formalize parameter constraints are either designed based on ill-suited assumptions, which may not correctly identify the invalid parameter inputs situation, or are inefficient to use. This paper proposes a novel method to formalize the parameter constraints of geoprocessing tools, based on a high-level and standard constraint language (i.e., SHACL) and geoprocessing ontologies, under the guidance of a systematic classification of parameter constraints. An application case and a heuristic evaluation were conducted to demonstrate and evaluate the effectiveness and usability of the proposed method. The results show that the proposed method is not only comparatively easier and more efficient than existing methods but also covers more types of parameter constraints, for example, the application-context-matching constraints that have been ignored by existing methods.

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Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom

Scientific Reports ◽

10.1038/s41598-021-88661-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yersultan Mirasbekov ◽

Adina Zhumakhanova ◽

Almira Zhantuyakova ◽

Kuanysh Sarkytbayev ◽

Dmitry V. Malashenkov ◽

...

Keyword(s):

Machine Learning ◽

Flow Cytometry ◽

Spatial Resolution ◽

Mesocosm Experiment ◽

Imaging Flow Cytometry ◽

Leibler Divergence ◽

Temporal And Spatial ◽

High Level ◽

Training Sets

AbstractA machine learning approach was employed to detect and quantify Microcystis colonial morphospecies using FlowCAM-based imaging flow cytometry. The system was trained and tested using samples from a long-term mesocosm experiment (LMWE, Central Jutland, Denmark). The statistical validation of the classification approaches was performed using Hellinger distances, Bray–Curtis dissimilarity, and Kullback–Leibler divergence. The semi-automatic classification based on well-balanced training sets from Microcystis seasonal bloom provided a high level of intergeneric accuracy (96–100%) but relatively low intrageneric accuracy (67–78%). Our results provide a proof-of-concept of how machine learning approaches can be applied to analyze the colonial microalgae. This approach allowed to evaluate Microcystis seasonal bloom in individual mesocosms with high level of temporal and spatial resolution. The observation that some Microcystis morphotypes completely disappeared and re-appeared along the mesocosm experiment timeline supports the hypothesis of the main transition pathways of colonial Microcystis morphoforms. We demonstrated that significant changes in the training sets with colonial images required for accurate classification of Microcystis spp. from time points differed by only two weeks due to Microcystis high phenotypic heterogeneity during the bloom. We conclude that automatic methods not only allow a performance level of human taxonomist, and thus be a valuable time-saving tool in the routine-like identification of colonial phytoplankton taxa, but also can be applied to increase temporal and spatial resolution of the study.

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AI-based localization and classification of skin disease with erythema

Scientific Reports ◽

10.1038/s41598-021-84593-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ha Min Son ◽

Wooho Jeon ◽

Jinhyun Kim ◽

Chan Yeong Heo ◽

Hye Jin Yoon ◽

...

Keyword(s):

Neural Network ◽

Skin Diseases ◽

Classification Model ◽

Screening Tests ◽

Sensitivity Score ◽

Common Skin ◽

Novel Method ◽

Improved Performance ◽

High Level

AbstractAlthough computer-aided diagnosis (CAD) is used to improve the quality of diagnosis in various medical fields such as mammography and colonography, it is not used in dermatology, where noninvasive screening tests are performed only with the naked eye, and avoidable inaccuracies may exist. This study shows that CAD may also be a viable option in dermatology by presenting a novel method to sequentially combine accurate segmentation and classification models. Given an image of the skin, we decompose the image to normalize and extract high-level features. Using a neural network-based segmentation model to create a segmented map of the image, we then cluster sections of abnormal skin and pass this information to a classification model. We classify each cluster into different common skin diseases using another neural network model. Our segmentation model achieves better performance compared to previous studies, and also achieves a near-perfect sensitivity score in unfavorable conditions. Our classification model is more accurate than a baseline model trained without segmentation, while also being able to classify multiple diseases within a single image. This improved performance may be sufficient to use CAD in the field of dermatology.

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Human Hand Anatomy-Based Prosthetic Hand

Sensors ◽

10.3390/s21010137 ◽

2020 ◽

Vol 21 (1) ◽

pp. 137

Author(s):

Larisa Dunai ◽

Martin Novak ◽

Carmen García Espert

Keyword(s):

3D Printing ◽

Degrees Of Freedom ◽

Use Of Force ◽

Hand Movements ◽

Human Hand ◽

Prosthetic Hand ◽

Object Surface ◽

Hand Phalanges ◽

High Level

The present paper describes the development of a prosthetic hand based on human hand anatomy. The hand phalanges are printed with 3D printing with Polylactic Acid material. One of the main contributions is the investigation on the prosthetic hand joins; the proposed design enables one to create personalized joins that provide the prosthetic hand a high level of movement by increasing the degrees of freedom of the fingers. Moreover, the driven wire tendons show a progressive grasping movement, being the friction of the tendons with the phalanges very low. Another important point is the use of force sensitive resistors (FSR) for simulating the hand touch pressure. These are used for the grasping stop simulating touch pressure of the fingers. Surface Electromyogram (EMG) sensors allow the user to control the prosthetic hand-grasping start. Their use may provide the prosthetic hand the possibility of the classification of the hand movements. The practical results included in the paper prove the importance of the soft joins for the object manipulation and to get adapted to the object surface. Finally, the force sensitive sensors allow the prosthesis to actuate more naturally by adding conditions and classifications to the Electromyogram sensor.

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Testing Todd: family types and development

Journal of Institutional Economics ◽

10.1017/s1744137421000175 ◽

2021 ◽

pp. 1-18

Author(s):

Jerg Gutmann ◽

Stefan Voigt

Keyword(s):

Rule Of Law ◽

Civil Society Organizations ◽

The Rule Of Law ◽

State Fragility ◽

Family Types ◽

Low Levels ◽

Parsimonious Model ◽

The One ◽

High Level

Abstract Many years ago, Emmanuel Todd came up with a classification of family types and argued that the historically prevalent family types in a society have important consequences for its economic, political, and social development. Here, we evaluate Todd's most important predictions empirically. Relying on a parsimonious model with exogenous covariates, we find mixed results. On the one hand, authoritarian family types are, in stark contrast to Todd's predictions, associated with increased levels of the rule of law and innovation. On the other hand, and in line with Todd's expectations, communitarian family types are linked to racism, low levels of the rule of law, and late industrialization. Countries in which endogamy is frequently practiced also display an expectedly high level of state fragility and weak civil society organizations.

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Space Precession Target Classification Based on Radar High-Resolution Range Profiles

International Journal of Antennas and Propagation ◽

10.1155/2019/8151620 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9

Author(s):

Yizhe Wang ◽

Cunqian Feng ◽

Yongshun Zhang ◽

Sisan He

Keyword(s):

Parameter Extraction ◽

Classification Performance ◽

Support Vector ◽

Electromagnetic Data ◽

Feature Extractor ◽

Different Types ◽

Radar Echo ◽

High Level ◽

Cone Target

Precession is a common micromotion form of space targets, introducing additional micro-Doppler (m-D) modulation into the radar echo. Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. This paper presents two methods for classifying different types of space precession targets from the HRRPs. We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. Experimental results demonstrate that the support vector machine (SVM) using histograms of oriented gradient (HOG) features achieves a good result, whereas the deep convolutional neural network (DCNN) obtains a higher classification accuracy. DCNN combines the feature extractor and the classifier itself to automatically mine the high-level signatures of HRRPs through a training process. Besides, the efficiency of the two classification processes are compared using the same dataset.

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Classification of a Granular Product using High-Level Fusion of Vision Features

Journal of Agricultural Engineering Research ◽

10.1006/jaer.1997.0189 ◽

1997 ◽

Vol 68 (2) ◽

pp. 115-124 ◽

Cited By ~ 2

Author(s):

F. Ros ◽

S. Guillaume ◽

V. Bellon-Maurel

Keyword(s):

High Level ◽

Level Fusion

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Analysis of ancient DNA from a prehistoric Amerindian cemetery

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.1999.0368 ◽

1999 ◽

Vol 354 (1379) ◽

pp. 153-159 ◽

Cited By ~ 41

Author(s):

Anne C. Stone ◽

Mark Stoneking

Keyword(s):

Ancient Dna ◽

Dna Analysis ◽

Nuclear Dna ◽

Genetic Research ◽

Hypervariable Region ◽

Dna Preservation ◽

European Contact ◽

Novel Method ◽

High Level ◽

Central Illinois

The Norris Farms No. 36 cemetery in central Illinois has been the subject of considerable archaeological and genetic research. Both mitochondrial DNA (mtDNA) and nuclear DNA have been examined in this 700–year–old population. DNA preservation at the site was good, with about 70% of the samples producing mtDNA results and approximately 15% yielding nuclear DNA data. All four of the major Amerindian mtDNA haplogroups were found, in addition to a fifth haplogroup. Sequences of the first hypervariable region of the mtDNA control region revealed a high level of diversity in the Norris Farms population and confirmed that the fifth haplogroup associates with Mongolian sequences and hence is probably authentic. Other than a possible reduction in the number of rare mtDNA lineages in many populations, it does not appear as if European contact significantly altered patterns of Amerindian mtDNA variation, despite the large decrease in population size that occurred. For nuclear DNA analysis, a novel method for DNA–based sex identification that uses nucleotide differences between the X and Y copies of the amelogenin gene was developed and applied successfully in approximately 20 individuals. Despite the well–known problems of poor DNA preservation and the ever–present possibility of contamination with modern DNA, genetic analysis of the Norris Farms No. 36 population demonstrates that ancient DNA can be a fruitful source of new insights into prehistoric populations.

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