Miracle-dz/UniFAR-FTUs huggingface dataset:
Domain Optimization for Hierarchical Planning based on Set-Theory

The design of planning domains for autonomous systems is a hard task, especially when different parties are involved. We present a domain optimization algorithm for hierarchical planners that uses a set-based formulation. Due to an automatic alignment we can compose models from different sources to a larger domain for efficient planning. The combination of domain optimization and hierarchical planning can handle large scale domains very efficiently. Our algorithm reduces the effects of the non-optimality that comes with the hierarchical approach. We demonstrate the scalability with a task and motion planning problem. In the scenario of a robotic assembly with up to 62 parts and plan lengths of over 1000 steps the planning times are kept within 15 minutes. We show the execution of our plans on a real-world dual-robot setup.
Improved IMU-based Human Activity Recognition using Hierarchical HMM Dissimilarity

: Although there are many classiﬁcation approaches in IMU-based Human Activity Recognition, they are in general not explicitly designed to consider the particular nature of human actions. These actions may be extremely complex and subtle and the performance of such approaches may degrade signiﬁcantly in such scenarios. However, techniques like Hidden Markov Models (HMMs) have shown promising performance on this task, due to their ability to model the dynamics of such activities. In this work, we propose a novel classiﬁcation technique for human activity recognition. Our technique involves the use of HMMs to char-acterize samples and subsequent classiﬁcation based on the dissimilarity between HMMs generated from unseen samples and previously-generated HMMs from training/template samples. We apply our method to two publicly-available activity recognition datasets and also compare it against an extant approach utilizing feature extraction and another technique utilizing a deep Long Short-Term Memory (LSTM) classiﬁer. Our experimental results indicate that our proposed method outperforms both of these baselines in terms of several standard metrics.
Nearly Non-Expansive Bounds for Mahalanobis Hard Thresholding

Given a vector w ∈ R and a positive semi-definite matrix A ∈ Rp×p, we study the expansion ratio bound for the following defined Mahalanobis hard thresholding operator of w: HA,k(w) := arg min ‖θ‖0≤k 1 2 ‖θ − w‖A, where k ≤ p is the desired sparsity level. The core contribution of this paper is to prove that for any k̄-sparse vector w̄ with k̄ < k, the estimation error ‖HA,k(w)− w̄‖A satisfies ‖HA,k(w)− w̄‖A ≤ 1 +O κ(A, 2k) √ k̄ k − k̄  ‖w − w̄‖A, where κ(A, 2k) is the restricted strong condition number of A over (2k)-sparse subspace. This estimation error bound is nearly non-expansive when k is sufficiently larger than k̄. Specially when A is the identity matrix such that κ(A, 2k) ≡ 1, our bound recovers the previously known nearly non-expansive bounds for Euclidean hard thresholding operator. We further show that such a bound extends to an approximate version of HA,k(w) estimated by Hard Thresholding Pursuit (HTP) algorithm. We demonstrate the applicability of these bounds to the mean squared error analysis of HTP and its novel extension based on preconditioning method. Numerical evidence is provided to support our theory and demonstrate the superiority of the proposed preconditioning HTP algorithm.
Mapping of Eucalyptus in Natura 2000 Areas Using Sentinel 2 Imagery and Artificial Neural Networks

Plantations of fast-growing Eucalyptus trees have become a common sight in the western Iberian peninsula where they are planted to exploit their economic potential. Negative side-effects of large scale plantations including the invasive behavior of Eucalyptus trees outside of regular plantations have become apparent. This study uses medium resolution, multi-spectral imagery of the Sentinel 2 satellites to map Eucalyptus across Portugal and parts of Spain with a focus on Natura 2000 areas inside Portugal, that are protected under the European birds and habitats directives. This method enables the detection of small incipient as well as mixed populations outside of regular plantations. Ground truth maps were compiled using field surveys as well as high resolution satellite imagery and were used to train Feedforward Neural Networks. These models predict Eucalyptus tree cover with a sensitivity of up to 75.7% as well as a specificity of up to 95.8%. The overall accuracy of the prediction is 92.5%. A qualitative assessment of Natura 2000 areas in Portugal has been performed and 15 areas have been found to be affected by Eucalyptus of which 9 are strongly affected. This study demonstrates the applicability of multi-spectral imagery for tree-species classification and invasive species control. It provides a probability-map of Eucalyptus tree cover for the western Iberian peninsula with 10 m spatial resolution and shows the need for monitoring of Eucalyptus in protected areas.
Radar-Based Non-Contact Continuous Identity Authentication

Non-contact vital signs monitoring using microwave Doppler radar has shown great promise in healthcare applications. Recently, this unobtrusive form of physiological sensing has also been gaining attention for its potential for continuous identity authentication, which can reduce the vulnerability of traditional one-pass validation authentication systems. Physiological Doppler radar is an attractive approach for continuous identity authentication as it requires neither contact nor line-of-sight and does not give rise to privacy concerns associated with video imaging. This paper presents a review of recent advances in radar-based identity authentication systems. It includes an evaluation of the applicability of different research efforts in authentication using respiratory patterns and heart-based dynamics. It also identifies aspects of future research required to address remaining challenges in applying unobtrusive respiration-based or heart-based identity authentication to practical systems. With the advancement of machine learning and artificial intelligence, radar-based continuous authentication can grow to serve a wide range of valuable functions in society.
Position Control of Cable-Driven Robotic Soft Arm Based on Deep Reinforcement Learning

The cable-driven soft arm is mostly made of soft material; it is difficult to control because of the material characteristics, so the traditional robot arm modeling and control methods cannot be directly applied to the soft robot arm. In this paper, we combine the data-driven modeling method with the reinforcement learning control method to realize the position control task of robotic soft arm, the method of control strategy based on deep Q learning. In order to solve slow convergence and unstable effect in the process of simulation and migration when deep reinforcement learning is applied to the actual robot control task, a control strategy learning method is designed, which is based on the experimental data, to establish a simulation environment for control strategy training, and then applied to the real environment. Finally, it is proved by experiment that the method can effectively complete the control of the soft robot arm, which has better robustness than the traditional method.
Real-time Implementation and Evaluation of Magnetometerless Tracking System for Human and Humanoid Posture Control Benchmarking based on Inertial Sensors

: This work describes a tracking system designed for humanoid robots, exoskeletons and humans oriented to posture control and balance experiments. The system aims to provide a tool that allows for repeatability of balance experiments across different robotics platforms and control algorithms with the ultimate aim of providing a standardized framework for performance benchmarking. To make the system suitable for different geometries and materials, it relies just on inertial sensors. The system is evaluated with a marker-based optical tracking, performing a trial of a typical posture control and balance experiment. In particular the frequency response function of the body segments respect to the support surface tilt is evaluated.
Damage Detection and Diagnosis for Offshore Wind Foundations

: Structural health monitoring for wind turbines (WT) in remote locations, as offshore, is crucial (Presencia and Shaﬁee, 2018). Offshore wind farms are increasingly realized in water depths beyond 30 meters, where lattice foundations (as jacket-type) are a highly competitive substructure type (Moulas et al., 2017). In this work, a methodology for the diagnosis of structural damage in jacket-type foundations is stated by means of a small-scale structure -an experimental laboratory tower modeling an offshore-ﬁxed jacket-type WT. In the literature, a lot of methodologies for damage detection can be found (Li et al., 2015). Among them, the vibration-based methods are one of the most proliﬁc ones. However, they are, primarily, focused on the case of measurable input excitation and vibration response signals, with only few recent studies focused on the vibration–response–only case, the importance of which stems from the fact that in some applications the excitation cannot be imposed and often is not measurable. This work aims to contribute in this area, as the vibration excitation is given by the wind and analyzed by a convolutional neural network (CNN), with a classiﬁcation accuracy result of 93 %.
Does hearing loss lead to dementia? A review of the literature

Recent studies have revealed a correlation between aging-related hearing loss and the likelihood of developing Alzheimer Disease. However, it is not yet known if the correlation simply reflects the fact that these two disorders share common risk factors or whether there is a causal link between them. The answer to this question carries therapeutic implications. Unfortunately, it is not possible to study the question of causality between aging-related hearing loss and dementia in human subjects. Here, we evaluate the research surrounding induced-hearing loss in animal models on non-auditory cognition to help infer if there is any causal evidence linking hearing loss and a more general dementia. We find ample evidence that induction of hearing loss in animals produces cognitive decline, particularly hippocampal dysfunction. The data suggest that noise-exposure produces a toxic milieu in the hippocampus consisting of a spike in glucocorticoid levels, elevations of mediators of oxidative stress and excitotoxicity, which as a consequence induce cessation of neurogenesis, synaptic loss and tau hyperphosphorylation. These data suggest that hearing loss can lead to pathological hallmarks similar to those seen in Alzheimer's Disease and other dementias. However, the rodent data do not establish that hearing loss on its own can induce a progressive degenerative dementing illness. Therefore, we conclude that an additional "hit", such as aging, APOE genotype, microvascular disease or others, may be necessary to trigger an ongoing degenerative process such as Alzheimer Disease.
An Integrated Object Detection and Tracking Framework for Mobile Robots

: In this paper, we propose an end-to-end-solution to the problem of multi-object tracking on a mobile robot. The tracking system consists of a process where we project 2D multi-object detections to the robots base frame, using RGB-D sensor data. These detections are then transformed to the map frame using a localization algorithm. This system predicts trajectories of humans and objects in the environment of the robot and can be adapted to work with any detector and track from multiple cameras. The system can then be used to build a temporally consistent costmap to improve navigation strategies.
AndroDFA: Android Malware Classification Based on Resource Consumption

The vast majority of today’s mobile malware targets Android devices. An important task of malware analysis is the classification of malicious samples into known families. In this paper, we propose AndroDFA (DFA, detrended fluctuation analysis): an approach to Android malware family classification based on dynamic analysis of resource consumption metrics available from the proc file system. These metrics can be easily measured during sample execution. From each malware, we extract features through detrended fluctuation analysis (DFA) and Pearson’s correlation, then a support vector machine is employed to classify malware into families. We provide an experimental evaluation based on malware samples from two datasets, namely Drebin and AMD. With the Drebin dataset, we obtained a classification accuracy of 82%, comparable with works from the state-of-the-art like DroidScribe. However, compared to DroidScribe, our approach is easier to reproduce because it is based on publicly available tools only, does not require any modification to the emulated environment or Android OS, and by design, can also be used on physical devices rather than exclusively on emulators. The latter is a key factor because modern mobile malware can detect the emulated environment and hide its malicious behavior. The experiments on the AMD dataset gave similar results, with an overall mean accuracy of 78%. Furthermore, we made the software we developed publicly available, to ease the reproducibility of our results.
Sentinel-2 Data for Land Cover/Use Mapping: A Review

The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel-2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.
Are temperate mature forests buffered from invasive lianas?1

Abstract Mature and old-growth forests are often thought to be buffered against invasive species due to low levels of light and infrequent disturbance. Lianas (woody vines) and other climbing plants are also known to exhibit lower densities in older forests. As part of a larger survey of the lianas of the southern Lake Michigan region in mature and old-growth forests, the level of infestation by invasive lianas was evaluated. The only invasive liana detected in these surveys was Celastrus orbiculatus Thunb. (Celastraceae). Although this species had only attached to trees and reached the canopy in a few instances, it was present in 30% of transects surveyed, mostly as a component of the ground layer. Transects with C. orbiculatus had higher levels of soil potassium and higher liana richness than transects without. In contrast, transects with the native C. scandens had higher pH, sand content, and soil magnesium and lower organic matter compared to transects where it was absent. Celastrus orbiculatus appears to be a generalist liana since it often occurs with native lianas. Celastrus orbiculatus poses a substantial threat to mature forests as it will persist in the understory until a canopy gap or other disturbance provides the light and supports necessary for it to ascend to the canopy and damage tree species. As a result, these forests should be monitored by land managers so that C. orbiculatus eradication can occur while invasions are at low densities and restricted to the ground layer.
Soil Moisture Estimate Uncertainties from the Effect of Soil Texture on Dielectric Semiempirical Models

Soil texture has been shown to affect the dielectric behavior of soil over the entire frequency range. Three universally employed dielectric semiempirical models (SEMs), the Dobson model, the Wang–Schmugge model and the Mironov model, as well as a new improved SEM known as the soil semi-empirical mineralogy-related-to-water dielectric model (SSMDM), incorporate a significant soil texture effect in different ways. In this paper, soil moisture estimate uncertainties from the effect of soil texture on these four SEMs are systematically and widely investigated over all soil texture cases at different frequencies between 1.4 and 18 GHz for volumetric water content levels between 0.0 and 0.4 m3/m3 from the perspective of two aspects: soil dielectric model discordance and soil texture discordance. Firstly, the effect of soil texture on these four dielectric SEMs is analyzed. Then, soil moisture estimate uncertainties due to the effect of soil texture are carefully investigated. Finally, the applicability of these SEMs is discussed, which can supply references for their choice. The results show that soil moisture estimate uncertainties are small and satisfy the 4% volumetric water content retrieval requirement in some cases. However, in other cases, it may contribute relatively significant uncertainties to soil moisture estimates and correspond to a difference that exceeds the 4% volumetric water content requirement, with potential for the largest deviations to exceed 0.22 m3/m3.
An Analysis of Xi Feng Parties in the Northern Song Dynasty through Complex Network Algorithms

In this paper, we mainly use several methods of machine learning. These methods were previously used on social networks to model and analyze the social relations, between Xifeng Parties in the Northern Song Dynasty. To our best knowledge, no one has ever made a combination of big data analysis, social network theory and the history of factional conflict in the Northern Song Dynasty. The attempt we made is an innovation of involving the intersection of natural sciences, social sciences and the humanities. We use BFS on CBDB with constraints to establish a network of people, which can be regarded as a weighted directed multi-graph. We simplify the graph into a weighted digraph and use pagerank algorithm to obtain the influence table, the political influence table and the positive political influence table by using different weighting methods. By analyzing the data obtained from the algorithm, we confirm several conclusions about the new and old parties in the study of Song history.
Persuasive communication aimed at achieving wildlife-disease management goals

Achieving an adequate hunter harvest of game animals that meets wildlife-disease management objectives is a challenge if hunters perceive too few animals relative to expectations. Persuasive communication is a strategy commonly used to influence human perceptions, attitudes, and behaviors in public health. Research on effectiveness of persuasive communication in the context of wildlife-disease management is limited, however, which reduces the utility of communication as a management option. To gain insight into the effectiveness of persuasion in wildlife management, we experimentally evaluated a communication campaign aimed at influencing hunters' perceptions and behavioral intentions to harvest more antlerless white-tailed deer (Odocoileus virginianus). Reduction of deer density is a prevailing management technique where bovine tuberculosis (Mycobacterium bovis) exists in wild deer populations. In 2012, we conducted an experiment with pre- and post-exposure self-administered mail questionnaires to survey deer hunters in Michigan, USA. Campaign materials were direct mailed with the post-exposure survey. Paired change score analysis was used to evaluate effects of exposure to campaign materials for an intervention relative to control group. Respondents exposed to persuasive communication materials (n = 480) reported changes in perceptions of what others are doing, perceptions of risks from bovine tuberculosis, and behavioral intentions toward hunting and harvesting more antlerless deer. Changes in perceptions of risks from disease management policies negatively affecting deer hunting also were reported in the exposure group. Our results reveal communication can be most persuasive when it focuses on the activity of interest (deer hunting), communicates goals of management (wildlife-disease management), and communicates actions individuals (deer hunters) can take to reduce risks. © 2014 The Wildlife Society.
Combined bending–torsion testing device for characterization of shape memory alloy endodontic files

Design has an important influence on mechanical response of endodontic instruments made of shape memory alloys. The experimental and numerical prediction of their thermomechanical response is necessary to improve their behavior during operating inside root canal. Due to the curved and tapered shape of the dental canal, endodontic files are subjected to rotating bending during the root canal preparation phase. These rotative bending could ever be combined with torsion when the instruments are engined in the root canal. Bending and torsion tests available in the standard ISO 3630-1 do not take into account this combined loading leading to a response different from the one obtained by superposition of separated bending and torsion loadings. This article details the design and the realization of bending–torsion testing device particularly adapted to shape memory alloy endodontic files. It allows to control the torsion and the bending rotations in a separate or a combined way. Qualification tests using this bending–torsion testing device on NiTi wires showed a good agreement between the experimental and the simulated responses. Finally, this bending–torsion testing device allowed to analyze the response of various NiTi endodontic files, currently used in endodontics, subjected to bending, torsion, and combined bending–torsion loadings. Obtained results showed clearly that combined bending–torsion loading changes significantly the shape memory alloy file response.