Grasping of Real-World Objects Is Not Biased by Ensemble Perception

The visual system is known to extract summary representations of visually similar objects which bias the perception of individual objects toward the ensemble average. Although vision plays a large role in guiding action, less is known about whether ensemble representation is informative for action. Motor behavior is tuned to the veridical dimensions of objects and generally considered resistant to perceptual biases. However, when the relevant grasp dimension is not available or is unconstrained, ensemble perception may be informative to behavior by providing gist information about surrounding objects. In the present study, we examined if summary representations of a surrounding ensemble display influenced grip aperture and orientation when participants reached-to-grasp a central circular target which had an explicit size but importantly no explicit orientation that the visuomotor system could selectively attend to. Maximum grip aperture and grip orientation were not biased by ensemble statistics during grasping, although participants were able to perceive and provide manual estimations of the average size and orientation of the ensemble display. Support vector machine classification of ensemble statistics achieved above-chance classification accuracy when trained on kinematic and electromyography data of the perceptual but not grasping conditions, supporting our univariate findings. These results suggest that even along unconstrained grasping dimensions, visually-guided behaviors toward real-world objects are not biased by ensemble processing.

Download Full-text

Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features

Military Medicine ◽

10.1093/milmed/usaa418 ◽

2021 ◽

Vol 186 (Supplement_1) ◽

pp. 445-451

Author(s):

Yifei Sun ◽

Navid Rashedi ◽

Vikrant Vaze ◽

Parikshit Shah ◽

Ryan Halter ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Real World ◽

Short Term Memory ◽

Model Performance ◽

Learning Technologies ◽

Machine Learning Algorithms ◽

Support Vector ◽

K Nearest Neighbor ◽

Continuous Map

ABSTRACT Introduction Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. Materials and Methods Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. Results Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. Conclusion We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

Download Full-text

Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles

Advances in Mechanical Engineering ◽

10.1177/16878140211027735 ◽

2021 ◽

Vol 13 (7) ◽

pp. 168781402110277

Author(s):

Yankai Hou ◽

Zhaosheng Zhang ◽

Peng Liu ◽

Chunbao Song ◽

Zhenpo Wang

Keyword(s):

Electric Vehicles ◽

Real World ◽

Regression Models ◽

Estimation Method ◽

Recursive Least Squares ◽

Data Driven ◽

Accurate Estimation ◽

Support Vector ◽

Battery Degradation ◽

Operational Data

Accurate estimation of the degree of battery aging is essential to ensure safe operation of electric vehicles. In this paper, using real-world vehicles and their operational data, a battery aging estimation method is proposed based on a dual-polarization equivalent circuit (DPEC) model and multiple data-driven models. The DPEC model and the forgetting factor recursive least-squares method are used to determine the battery system’s ohmic internal resistance, with outliers being filtered using boxplots. Furthermore, eight common data-driven models are used to describe the relationship between battery degradation and the factors influencing this degradation, and these models are analyzed and compared in terms of both estimation accuracy and computational requirements. The results show that the gradient descent tree regression, XGBoost regression, and light GBM regression models are more accurate than the other methods, with root mean square errors of less than 6.9 mΩ. The AdaBoost and random forest regression models are regarded as alternative groups because of their relative instability. The linear regression, support vector machine regression, and k-nearest neighbor regression models are not recommended because of poor accuracy or excessively high computational requirements. This work can serve as a reference for subsequent battery degradation studies based on real-time operational data.

Download Full-text

HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

10.36227/techrxiv.14714508.v2 ◽

2021 ◽

Author(s):

Leila Zahedi ◽

Farid Ghareh Mohammadi ◽

M. Hadi Amini

Keyword(s):

Parameter Optimization ◽

Real World ◽

Optimization Problems ◽

State Of The Art ◽

Parameter Tuning ◽

Gradient Boosting ◽

Support Vector ◽

Wide Range ◽

Extreme Gradient Boosting ◽

Art Techniques

Machine learning techniques lend themselves as promising decision-making and analytic tools in a wide range of applications. Different ML algorithms have various hyper-parameters. In order to tailor an ML model towards a specific application, a large number of hyper-parameters should be tuned. Tuning the hyper-parameters directly affects the performance (accuracy and run-time). However, for large-scale search spaces, efficiently exploring the ample number of combinations of hyper-parameters is computationally challenging. Existing automated hyper-parameter tuning techniques suffer from high time complexity. In this paper, we propose HyP-ABC, an automatic innovative hybrid hyper-parameter optimization algorithm using the modified artificial bee colony approach, to measure the classification accuracy of three ML algorithms, namely random forest, extreme gradient boosting, and support vector machine. Compared to the state-of-the-art techniques, HyP-ABC is more efficient and has a limited number of parameters to be tuned, making it worthwhile for real-world hyper-parameter optimization problems. We further compare our proposed HyP-ABC algorithm with state-of-the-art techniques. In order to ensure the robustness of the proposed method, the algorithm takes a wide range of feasible hyper-parameter values, and is tested using a real-world educational dataset.

Download Full-text

A Measure Optimized Cost-Sensitive Learning Framework for Imbalanced Data Classification

Advances in Data Mining and Database Management - Biologically-Inspired Techniques for Knowledge Discovery and Data Mining ◽

10.4018/978-1-4666-6078-6.ch003 ◽

2014 ◽

pp. 48-75 ◽

Cited By ~ 2

Author(s):

Peng Cao ◽

Osmar Zaiane ◽

Dazhe Zhao

Keyword(s):

Real World ◽

Class Imbalance ◽

Imbalanced Data ◽

Support Vector ◽

Feature Subset ◽

Cost Sensitive Learning ◽

Intrinsic Parameters ◽

Real World Problem ◽

Benchmark Datasets ◽

Feed Forward Neural Networks

Class imbalance is one of the challenging problems for machine-learning in many real-world applications. Many methods have been proposed to address and attempt to solve the problem, including sampling and cost-sensitive learning. The latter has attracted significant attention in recent years to solve the problem, but it is difficult to determine the precise misclassification costs in practice. There are also other factors that influence the performance of the classification including the input feature subset and the intrinsic parameters of the classifier. This chapter presents an effective wrapper framework incorporating the evaluation measure (AUC and G-mean) into the objective function of cost sensitive learning directly to improve the performance of classification by simultaneously optimizing the best pair of feature subset, intrinsic parameters, and misclassification cost parameter. The optimization is based on Particle Swarm Optimization (PSO). The authors use two different common methods, support vector machine and feed forward neural networks, to evaluate the proposed framework. Experimental results on various standard benchmark datasets with different ratios of imbalance and a real-world problem show that the proposed method is effective in comparison with commonly used sampling techniques.

Download Full-text

Predictive Analytics with Strategically Missing Data

INFORMS Journal on Computing ◽

10.1287/ijoc.2019.0947 ◽

2020 ◽

Author(s):

Juheng Zhang ◽

Xiaoping Liu ◽

Xiao-Bai Li

Keyword(s):

Missing Data ◽

Financial Reporting ◽

Real World ◽

Missing Values ◽

Predictive Analytics ◽

Support Vector ◽

Real World Data ◽

Novel Approach ◽

Strategic Behaviors ◽

Job Application

We study strategically missing data problems in predictive analytics with regression. In many real-world situations, such as financial reporting, college admission, job application, and marketing advertisement, data providers often conceal certain information on purpose in order to gain a favorable outcome. It is important for the decision-maker to have a mechanism to deal with such strategic behaviors. We propose a novel approach to handle strategically missing data in regression prediction. The proposed method derives imputation values of strategically missing data based on the Support Vector Regression models. It provides incentives for the data providers to disclose their true information. We show that with the proposed method imputation errors for the missing values are minimized under some reasonable conditions. An experimental study on real-world data demonstrates the effectiveness of the proposed approach.

Download Full-text

An Adaptive Data-Driven Approach to Solve Real-World Vehicle Routing Problems in Logistics

Complexity ◽

10.1155/2020/7386701 ◽

2020 ◽

Vol 2020 ◽

pp. 1-24

Author(s):

Emir Žunić ◽

Dženana Đonko ◽

Emir Buza

Keyword(s):

Vehicle Routing ◽

Real World ◽

Prediction Method ◽

Transportation Systems ◽

Data Driven ◽

Support Vector ◽

Control Parameters ◽

Vehicle Routing Problems ◽

Routing Problems ◽

Benchmark Instances

Transportation occupies one-third of the amount in the logistics costs, and accordingly transportation systems largely influence the performance of the logistics system. This work presents an adaptive data-driven innovative modular approach for solving the real-world vehicle routing problems (VRPs) in the field of logistics. The work consists of two basic units: (i) an innovative multistep algorithm for successful and entirely feasible solving of the VRPs in logistics and (ii) an adaptive approach for adjusting and setting up parameters and constants of the proposed algorithm. The proposed algorithm combines several data transformation approaches, heuristics, and Tabu search. Moreover, as the performance of the algorithm depends on the set of control parameters and constants, a predictive model that adaptively adjusts these parameters and constants according to historical data is proposed. A comparison of the acquired results has been made using the decision support system with predictive models: generalized linear models (GLMs) and support vector machine (SVM). The algorithm, along with the control parameters, which uses the prediction method, was acquired and was incorporated into a web-based enterprise system, which is in use in several big distribution companies in Bosnia and Herzegovina. The results of the proposed algorithm were compared with a set of benchmark instances and validated over real benchmark instances as well. The successful feasibility of the given routes, in a real environment, is also presented.

Download Full-text

Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls

Sensors ◽

10.3390/s20226479 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6479

Author(s):

Luca Palmerini ◽

Jochen Klenk ◽

Clemens Becker ◽

Lorenzo Chiari

Keyword(s):

Machine Learning ◽

Real World ◽

Inertial Sensor ◽

Fall Detection ◽

Medical Attention ◽

Support Vector ◽

Detection Algorithms ◽

Detection Systems ◽

Machine Learning Approach ◽

Extensive Collection

Falling is a significant health problem. Fall detection, to alert for medical attention, has been gaining increasing attention. Still, most of the existing studies use falls simulated in a laboratory environment to test the obtained performance. We analyzed the acceleration signals recorded by an inertial sensor on the lower back during 143 real-world falls (the most extensive collection to date) from the FARSEEING repository. Such data were obtained from continuous real-world monitoring of subjects with a moderate-to-high risk of falling. We designed and tested fall detection algorithms using features inspired by a multiphase fall model and a machine learning approach. The obtained results suggest that algorithms can learn effectively from features extracted from a multiphase fall model, consistently overperforming more conventional features. The most promising method (support vector machines and features from the multiphase fall model) obtained a sensitivity higher than 80%, a false alarm rate per hour of 0.56, and an F-measure of 64.6%. The reported results and methodologies represent an advancement of knowledge on real-world fall detection and suggest useful metrics for characterizing fall detection systems for real-world use.

Download Full-text

Using Random Forests on Real-World City Data for Urban Planning in a Visual Semantic Decision Support System

Sensors ◽

10.3390/s19102266 ◽

2019 ◽

Vol 19 (10) ◽

pp. 2266 ◽

Cited By ~ 1

Author(s):

Nikolaos Sideris ◽

Georgios Bardis ◽

Athanasios Voulodimos ◽

Georgios Miaoulis ◽

Djamchid Ghazanfarpour

Keyword(s):

Machine Learning ◽

Urban Planning ◽

Random Forests ◽

Real World ◽

Performance Metrics ◽

World City ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Real World Data

The constantly increasing amount and availability of urban data derived from varying sources leads to an assortment of challenges that include, among others, the consolidation, visualization, and maximal exploitation prospects of the aforementioned data. A preeminent problem affecting urban planning is the appropriate choice of location to host a particular activity (either commercial or common welfare service) or the correct use of an existing building or empty space. In this paper, we propose an approach to address these challenges availed with machine learning techniques. The proposed system combines, fuses, and merges various types of data from different sources, encodes them using a novel semantic model that can capture and utilize both low-level geometric information and higher level semantic information and subsequently feeds them to the random forests classifier, as well as other supervised machine learning models for comparisons. Our experimental evaluation on multiple real-world data sets comparing the performance of several classifiers (including Feedforward Neural Networks, Support Vector Machines, Bag of Decision Trees, k-Nearest Neighbors and Naïve Bayes), indicated the superiority of Random Forests in terms of the examined performance metrics (Accuracy, Specificity, Precision, Recall, F-measure and G-mean).

Download Full-text

On the Relation Between Object Shape and Grasping Kinematics

Journal of Neurophysiology ◽

10.1152/jn.00644.2003 ◽

2004 ◽

Vol 91 (6) ◽

pp. 2598-2606 ◽

Cited By ~ 83

Author(s):

Raymond H. Cuijpers ◽

Jeroen B. J. Smeets ◽

Eli Brenner

Keyword(s):

Principal Axis ◽

Grip Aperture ◽

Object Shape ◽

Hand Orientation ◽

Principal Axes ◽

Movement Distance ◽

Maximum Grip Aperture ◽

The Many ◽

Elliptical Cylinders ◽

Maximum Grip

Despite the many studies on the visual control of grasping, little is known about how and when small variations in shape affect grasping kinematics. In the present study we asked subjects to grasp elliptical cylinders that were placed 30 and 60 cm in front of them. The cylinders' aspect ratio was varied systematically between 0.4 and 1.6, and their orientation was varied in steps of 30°. Subjects picked up all noncircular cylinders with a hand orientation that approximately coincided with one of the principal axes. The probability of selecting a given principal axis was the highest when its orientation was equal to the preferred orientation for picking up a circular cylinder at the same location. The maximum grip aperture was scaled to the length of the selected principal axis, but the maximum grip aperture was also larger when the length of the axis orthogonal to the grip axis was longer than that of the grip axis. The correlation between the grip aperture— or the hand orientation—at a given instant, and its final value, increased monotonically with the traversed distance. The final hand orientation could already be inferred from its value after 30% of the movement distance with a reliability that explains 50% of the variance. For the final grip aperture, this was only so after 80% of the movement distance. The results indicate that the perceived shape of the cylinder is used for selecting appropriate grasping locations before or early in the movement and that the grip aperture and orientation are gradually attuned to these locations during the movement.

Download Full-text