Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis \& Application

Spontaneous subtle emotions are expressed through micro-expressions, which are tiny, sudden and short-lived dynamics of facial muscles; thus poses a great challenge for visual recognition. The abrupt but significant dynamics for the recognition task are temporally sparse while the rest, irrelevant dynamics, are temporally redundant. In this work, we analyze and enforce sparsity constrains to learn significant temporal and spectral structures while eliminate irrelevant facial dynamics of micro-expressions, which would ease the challenge in the visual recognition of spontaneous subtle emotions. The hypothesis is confirmed through experimental results of automatic spontaneous subtle emotion recognition with several sparsity levels on CASME II and SMIC, the only two publicly available spontaneous subtle emotion databases. The overall performances of the automatic subtle emotion recognition are boosted when only significant dynamics are preserved from the original sequences.Comment: IEEE Transaction of Affective Computing (2016

Spontaneous Subtle Expression Detection and Recognition based on Facial Strain

Optical strain is an extension of optical flow that is capable of quantifying subtle changes on faces and representing the minute facial motion intensities at the pixel level. This is computationally essential for the relatively new field of spontaneous micro-expression, where subtle expressions can be technically challenging to pinpoint. In this paper, we present a novel method for detecting and recognizing micro-expressions by utilizing facial optical strain magnitudes to construct optical strain features and optical strain weighted features. The two sets of features are then concatenated to form the resultant feature histogram. Experiments were performed on the CASME II and SMIC databases. We demonstrate on both databases, the usefulness of optical strain information and more importantly, that our best approaches are able to outperform the original baseline results for both detection and recognition tasks. A comparison of the proposed method with other existing spatio-temporal feature extraction approaches is also presented.Comment: 21 pages (including references), single column format, accepted to Signal Processing: Image Communication journa

Spatial assessment of pollutant loads for surface water quality management: a case study in Lai Chau city, Vietnam

The aim of this study is to present a method for estimating the pollutant load from different sources in an effort to provide improved information regarding water pollution and help control the surface water pollution, using Lai Chau city as a case study. The pollutant load was calculated in accordance with the Vietnam Environment Administration Decree No.154/2019 on the guidance for calculating the total pollutant load of river water. The pollutant sources include point sources (domestic wastewater, animal husbandry, industrial complexes and economic services) and surface sources (run-off from agricultural land uses) that generate wastes that potentially contaminate water bodies. The source locations were mapped and spatially joined with the drainage-basin map delineated from a Digital Elevation Model (DEM) to calculate the loads for the sub-basin units. Multivariate analysis then showed that the farming and domestic sources had the strongest positive loading factors for the sub-basins located in the city center and its fringe areas. Of these waste from animal husbandry account for up to 75.1% of total pollutant load. The main conclusion from the study's results is that the management approach should be changed from the total controlling mode, which is currently applied in the city, to a source specific approach based on the pollutant discharge loads and the allocated capacities

Calibration and validation of agent-based models of land-cover change

This chapter considers two important issues in the development of agent-based models, i.e. calibration and validation. These terms are defined and framed into a step-by-step process. Each step is then explained in further detail and illustrated using an agent-based model of shifting cultivation developed by Ngo (2009) as part of his PhD research project. Although the process of model validation presented here is applicable to agent-based models in general, some of the finer details are more relevant to agent-based models of land use and land cover change

Improving inertial navigation systems with pedestrian locomotion classifiers

Researches on inertial navigation systems (INS) have formulated complex step detection algorithms and stride length estimations. But for current systems to work, INSs have to correctly identify negative pedestrian locomotion. Negative pedestrian locomotion are movements that a user can naturally make without any real position displacement, but has sensor signals that might be misidentified as steps. As the INS\u27s modules have a cascading nature, it is important that these false movements are identified beforehand. This research aims to provide a solution by studying patterns exhibited by positive and negative pedestrian locomotion when sensors are placed on a user\u27s front pocket. A model was then built to classify negative from positive pedestrian locomotion, and to improve the INS\u27s accuracy overall

An agent-based modelling application of shifting cultivation

This paper outlines an agent-based modeling application of shifting cultivation for an upland village in Vietnam, which was developed to improve the management of shifting cultivation and aid forest protection. The model consists of household and land agents situated in a dynamic social, economic and political environment. Adaptation of the agents to changes in policy is incorporated through a trade-off between economic gains and social responsibility, which affect the subsequent decision-making process. The basics of the model are described including the validation process and the results in a business as usual scenario

Simulating the spatial distribution of pollutant loads from pig farming using an agent-based modeling approach

This research developed an agent-based model (ABM) for simulating pollutant loads from pig farming. The behavior of farmer agents was captured using concepts from the theory of planned behavior. The ABM has three basic components: the household or farmer agent, the land patches, and global parameters that capture the environmental context. The model was evaluated using a sensitivity analysis and then validated using data from a household survey, which showed that the predictive ability of the model was good. The ABM was then used in three scenarios: a baseline scenario, a positive scenario in which the number of pigs was assumed to remain stable but supporting policies for environmental management were increased, and a negative scenario, which assumed the number of pigs increases but management measures did not improve relative to the baseline. The positive scenario showed reductions in the discharged loads for many sub-basins of the study area while the negative scenario indicated that increased loads will be discharged to the environment. The scenario results suggest that to maintain the development of pig production while ensuring environmental protection for the district, financial, and technical support must be provided to the pig producers. The experience and education level of the farmers were significant factors influencing behaviors related to manure reuse and treatment, so awareness raising through environmental communication is needed in addition to technical measures