An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors

Dogs and cats tend to show their conditions and desires through their behaviors. In companion animal behavior recognition, behavior data obtained by attaching a wearable device or sensor to a dog’s body are mostly used. However, differences occur in the output values of the sensor when the dog moves violently. A tightly coupled RGB time tensor network (TRT-Net) is proposed that minimizes the loss of spatiotemporal information by reflecting the three components (x-, y-, and z-axes) of the skeleton sequences in the corresponding three channels (red, green, and blue) for the behavioral classification of dogs. This paper introduces the YouTube-C7B dataset consisting of dog behaviors in various environments. Based on a method that visualizes the Conv-layer filters in analyzable feature maps, we add reliability to the results derived by the model. We can identify the joint parts, i.e., those represented as rows of input images showing behaviors, learned by the proposed model mainly for making decisions. Finally, the performance of the proposed method is compared to those of the LSTM, GRU, and RNN models. The experimental results demonstrate that the proposed TRT-Net method classifies dog behaviors more effectively, with improved accuracy and F1 scores of 7.9% and 7.3% over conventional models.

Classifying Goliath Grouper (Epinephelus itajara) Behaviors from a Novel, Multi-Sensor Tag

Inertial measurement unit sensors (IMU; i.e., accelerometer, gyroscope and magnetometer combinations) are frequently fitted to animals to better understand their activity patterns and energy expenditure. Capable of recording hundreds of data points a second, these sensors can quickly produce large datasets that require methods to automate behavioral classification. Here, we describe behaviors derived from a custom-built multi-sensor bio-logging tag attached to Atlantic Goliath grouper (Epinephelus itajara) within a simulated ecosystem. We then compared the performance of two commonly applied machine learning approaches (random forest and support vector machine) to a deep learning approach (convolutional neural network, or CNN) for classifying IMU data from this tag. CNNs are frequently used to recognize activities from IMU data obtained from humans but are less commonly considered for other animals. Thirteen behavioral classes were identified during ethogram development, nine of which were classified. For the conventional machine learning approaches, 187 summary statistics were extracted from the data, including time and frequency domain features. The CNN was fed absolute values obtained from fast Fourier transformations of the raw tri-axial accelerometer, gyroscope and magnetometer channels, with a frequency resolution of 512 data points. Five metrics were used to assess classifier performance; the deep learning approach performed better across all metrics (Sensitivity = 0.962; Specificity = 0.996; F1-score = 0.962; Matthew’s Correlation Coefficient = 0.959; Cohen’s Kappa = 0.833) than both conventional machine learning approaches. Generally, the random forest performed better than the support vector machine. In some instances, a conventional learning approach yielded a higher performance metric for particular classes (e.g., the random forest had a F1-score of 0.971 for backward swimming compared to 0.955 for the CNN). Deep learning approaches could potentially improve behavioral classification from IMU data, beyond that obtained from conventional machine learning methods.

Three-Dimensional Trajectory Construction and Observation of Group Behavior of Wild Bats During Cave Emergence

Echolocating bats perceive the surrounding environment by processing echoes of their ultrasound emissions. Echolocation enables bats to avoid colliding with external objects in complete darkness. In this study, we sought to develop a method for measuring the collective behavior of echolocating bats (Miniopterus fuliginosus) emerging from their roost cave using high-sensitivity stereo-camera recording. First, we developed an experimental system to reconstruct the three-dimensional (3D) flight trajectories of bats emerging from the roost for nightly foraging. Next, we developed a method to automatically track the 3D flight paths of individual bats so that quantitative estimation of the population in proportion to the behavioral classification could be conducted. Because the classification of behavior and the estimation of population size are ecologically important indices, the method established in this study will enable quantitative investigation of how individual bats efficiently leave the roost while avoiding colliding with each other during group movement and how the group behavior of bats changes according to weather and environmental conditions. Such high-precision detection and tracking will contribute to the elucidation of the algorithm of group behavior control in creatures that move in groups together in three dimensions, such as birds.

Hidden Markov models identify major movement modes in accelerometer and magnetometer data from four albatross species

BackgroundInertial measurement units (IMUs) with high-resolution sensors such as accelerometers are now used extensively to study fine-scale behavior in a wide range of marine and terrestrial animals. Robust and practical methods are required for the computationally-demanding analysis of the resulting large datasets, particularly for automating classification routines that construct behavioral time series and time-activity budgets. Magnetometers are used increasingly to study behavior, but it is not clear how these sensors contribute to the accuracy of behavioral classification methods. Development of effective classification methodology is key to understanding energetic and life-history implications of foraging and other behaviors.MethodsWe deployed accelerometers and magnetometers on four species of free-ranging albatrosses and evaluated the ability of unsupervised hidden Markov models (HMMs) to identify three major modalities in their behavior: ‘flapping flight’, ‘soaring flight’, and ‘on-water’. The relative contribution of each sensor to classification accuracy was measured by comparing HMM-inferred states with expert classifications identified from stereotypic patterns observed in sensor data.ResultsHMMs provided a flexible and easily interpretable means of classifying behavior from sensor data. Model accuracy was high overall (92%), but varied across behavioral states (87.6, 93.1 and 91.7% for ‘flapping flight’, ‘soaring flight’ and ‘on-water’, respectively). Models built on accelerometer data alone were as accurate as those that also included magnetometer data; however, the latter were useful for investigating slow and periodic behaviors such as dynamic soaring at a fine scale.ConclusionsThe use of IMUs in behavioral studies produces large data sets, necessitating the development of computationally-efficient methods to automate behavioral classification in order to synthesize and interpret underlying patterns. HMMs provide an accessible and robust framework for analyzing complex IMU datasets and comparing behavioral variation among taxa across habitats, time and space.

Missed by Filter Lists: Detecting Unknown Third-Party Trackers with Invisible Pixels

Web tracking has been extensively studied over the last decade. To detect tracking, previous studies and user tools rely on filter lists. However, it has been shown that filter lists miss trackers. In this paper, we propose an alternative method to detect trackers inspired by analyzing behavior of invisible pixels. By crawling 84,658 webpages from 8,744 domains, we detect that third-party invisible pixels are widely deployed: they are present on more than 94.51% of domains and constitute 35.66% of all third-party images. We propose a fine-grained behavioral classification of tracking based on the analysis of invisible pixels. We use this classification to detect new categories of tracking and uncover new collaborations between domains on the full dataset of 4, 216, 454 third-party requests. We demonstrate that two popular methods to detect tracking, based on EasyList&EasyPrivacy and on Disconnect lists respectively miss 25.22% and 30.34% of the trackers that we detect. Moreover, we find that if we combine all three lists, 379, 245 requests originated from 8,744 domains still track users on 68.70% of websites.

Implementing a Distance Estimator for a Wildlife Tracking System Based on 802.15.4

In this work, a novel distance estimation mechanism using received signal strength indication (RSSI) signals with ZigBee modules is designed, implemented and tested in several scenarios. This estimator was used for a research project focused on a wildlife behavioral classification system deployed in Doñana’s National Park. As a supporting feature for that project, this work was implemented for locating animal’s collars acting as wireless nodes in order to find those who went outside of the coverage area of the network or that were accidentally detached from animals. This work describes the system architecture and the implementation of a mobile assistant capable of recovering devices located beyond the coverage of the network. The analytical model needed for distance estimation and the signal filtering are described, as well as the difficulties that the researchers must deal when building robust location estimators. This theoretical model was applied to three different scenarios and tested with two validation experiments.

The Object Space Task reveals a dissociation between semantic-like and episodic-like memory in a mouse model of Kleefstra Syndrome

Kleefstra syndrome is a disorder caused by a mutation in the EHMT1 gene characterized in humans by general developmental delay, mild to severe intellectual disability and autism. Here, we characterized semantic- and episodic-like memory in the Ehmt1+/- mouse model using the Object Space Task. We combined conventional behavioral analysis with automated analysis by deep-learning networks, a session-based computational learning model and a trial-based classifier. Ehmt1+/- mice showed more anxiety-like features and generally explored objects less, but the difference decreased over time. Interestingly, when analyzing memory-specific exploration, Ehmt1+/- show increased expression of semantic-like memory, but a deficit in episodic-like memory. A similar dissociation of semantic and episodic memory performance has been previously reported in humans with autism. Using our automatic classifier to differentiate between genotypes, we found that semantic-like memory features are better suited for classification than general exploration differences. Thus, detailed behavioral classification with the Object Space Task produced a more detailed behavioral phenotype of the Ehmt1+/- mouse model.One Sentence SummaryEhmt1+/- mice show decreased exploration and episodic-like memory but increased semantic-like memory In the Object Space Task. (143 of 150)