Reviewerly/exHarmony huggingface dataset:
Spectral-depth imaging with deep learning based reconstruction

We develop a compact imaging system to enable simultaneous acquisition of the spectral and depth information in real time. Our system consists of a spectral camera with low spatial resolution and an RGB camera with high spatial resolution, which captures two measurements from two different views of the same scene at the same time. Relying on an elaborate computational reconstruction algorithm with deep learning, our system can eventually obtain a spectral cube with a spatial resolution of 1920 × 1080 and a total of 16 spectral bands in the visible light section, as well as the corresponding depth map with the same spatial resolution. Quantitative and qualitative results on benchmark datasets and real-world scenes show that our reconstruction results are accurate and reliable. To the best of our knowledge, this is the first attempt to capture 5D information (3D space + 1D spectrum + 1D time) with a miniaturized apparatus and without active illumination.
HDT-UD: A very large Universal Dependencies Treebank for German

We report on the conversion of the Hamburg Dependency Treebank (Foth et al., 2014) to Universal Dependencies.The HDT consists of more than 200.000 sentences annotated with dependency structure, making every attempt at manual conversion or manual post-processing extremely costly.The conversion employs an unranked tree transducer.This formalism allows to express transformation rules in a concise way, guarantees the well-formedness of the output and is predictable to the rule writers.Together with the release of a converted subset of the HDT spanning 3 million tokens, we release an interactive workbench for writing and refining tree transducer rules.Our conversion achieves a very high labeled accuracy with respect to a manually converted gold standard of 97.3%.Up to now, the conversion effort took about 1000 hours of work.
Bi-Objective Continual Learning: Learning ‘New’ While Consolidating ‘Known’

In this paper, we propose a novel single-task continual learning framework named Bi-Objective Continual Learning (BOCL). BOCL aims at both consolidating historical knowledge and learning from new data. On one hand, we propose to preserve the old knowledge using a small set of pillars, and develop the pillar consolidation (PLC) loss to preserve the old knowledge and to alleviate the catastrophic forgetting problem. On the other hand, we develop the contrastive pillar (CPL) loss term to improve the classification performance, and examine several data sampling strategies for efficient onsite learning from ‘new’ with a reasonable amount of computational resources. Comprehensive experiments on CIFAR10/100, CORe50 and a subset of ImageNet validate the BOCL framework. We also reveal the performance accuracy of different sampling strategies when used to finetune a given CNN model. The code will be released.
Joint Adversarial Learning for Domain Adaptation in Semantic Segmentation

Unsupervised domain adaptation in semantic segmentation is to exploit the pixel-level annotated samples in the source domain to aid the segmentation of unlabeled samples in the target domain. For such a task, the key point is to learn domain-invariant representations and adversarial learning is usually used, in which the discriminator is to distinguish which domain the input comes from, and the segmentation model targets to deceive the domain discriminator. In this work, we first propose a novel joint adversarial learning (JAL) to boost the domain discriminator in output space by introducing the information of domain discriminator from low-level features. Consequently, the training of the high-level decoder would be enhanced. Then we propose a weight transfer module (WTM) to alleviate the inherent bias of the trained decoder towards source domain. Specifically, WTM changes the original decoder into a new decoder, which is learned only under the supervision of adversarial loss and thus mainly focuses on reducing domain divergence. The extensive experiments on two widely used benchmarks show that our method can bring considerable performance improvement over different baseline methods, which well demonstrates the effectiveness of our method in the output space adaptation.
Image Retrieval Using the Intensity Variation Descriptor

Variations between image pixel characteristics contain a wealth of information. Extraction of such cues can be used to describe image content. In this paper, we propose a novel descriptor, called the intensity variation descriptor (IVD), to represent variations in colour, edges, and intensity and apply it to image retrieval. The highlights of the proposed method are as follows. (1) The IVD combines the advantages of the HSV and RGB colour spaces. (2) It can simulate the lateral inhibition mechanism and orientation-selective mechanism to determine an optimal direction and spatial layout. (3) An extended weighted L1 distance metric is proposed to calculate the similarity of images. It does not require complex operations such as square or square root and leads to good performance. Comparative experiments on two Corel datasets containing 15,000 images show that the proposed method performs better than the SoC-GMM, CPV-THF, and STH methods and provides good matching of texture, colour, and shape.
Recursive partitioning improves paleosol proxies for rainfall

The bulk elemental composition of soil subsurface (B) horizons is influenced by environmental, biological, geological, and climatic factors. Because fossil soils (paleosols) are common in the geologic record, quantitative models that link climate to paleosol geochemistry are highly desirable in the paleoclimate community. Error associated with these models is typically reported as the root mean square error (RMSE) of a regression analysis and reflects the variability imparted by non-climatic influences on soil formation and the uncertainty associated with model fitting. However, for prediction purposes, the RMSE is well known to underestimate model uncertainty. In this work we re-evaluate a widely used transfer function for mean annual precipitation (MAP) based on the chemical index of alteration minus potassium (CIA-K) using data science best practices on two continental-scale soil data sets. Data set inter-comparisons and cross-validation of exponential regression models indicate that the root mean square prediction error (RMSPE) between CIA-K and MAP for soils representative of climates across the continental United States is around 299 mm, significantly higher than the currently accepted 182 mm RMSE. Further, CIA-K is unable to predict perhumid (\>2000 mm MAP) climate zones. We show that transitioning from a simple regression framework to one of recursive partitioning via random forests can significantly increase prediction accuracy while automating variable selection. We introduce two new, widely applicable random forest models for MAP (RF-MAP) that use 10 elemental oxides as input variables and were calibrated on the Baylor University Soil Informatics (BU-SI) data set. RF-MAP version 1.0 (RF-MAP~1.0~) was generated using the entire BU-SI data set (n = 685) and can predict MAP values up to 6865 mm with a RMSPE of 395 mm. RF-MAP version 2.0 (RF-MAP~2.0~) was generated using a modification of the BU-SI data set (n = 642) and can predict MAP values up to ∼1600 mm with a RMSPE of 209 mm. Pruned regression trees provide insight into the mechanisms driving the random forest models and demonstrate the first empirical confirmation of the sensitivity of soil elemental responses to global climate zones. The RF-MAP~1.0~ and RF-MAP~2.0~ models predict MAP values comparable to independent proxy estimates for a range of deep-time paleosols. We advocate for application of RF-MAP~1.0~ in settings where no *a priori* information on paleoclimate is available, and encourage the use of either RF-MAP~1.0~ or RF-MAP~2.0~ if users have independent constraints that paleo-MAP was below 1600 mm.
Multi-Player Tracking for Multi-View Sports Videos with Improved K-Shortest Path Algorithm

Sports analysis has recently attracted increasing research efforts in computer vision. Among them, basketball video analysis is very challenging due to severe occlusions and fast motions. As a typical tracking-by-detection method, k-shortest paths (KSP) tracking framework has been well used for multiple-person tracking. While effective and fast, the neglect of the appearance model would easily lead to identity switches, especially when two or more players are intertwined with each other. This paper addresses this problem by taking the appearance features into account based on the KSP framework. Furthermore, we also introduce a similarity measurement method that can fuse multiple appearance features together. In this paper, we select jersey color and jersey number as two example features. Experiments indicate that about 70% of jersey color and 50% of jersey number over a whole sequence would ensure our proposed method preserve the player identity better than the existing KSP tracking method.
Real-time high-speed three-dimensional surface imaging using band-limited illumination profilometry with a CoaXPress interface

High-speed three-dimensional (3D) surface imaging by structured-light profilometry is currently driven by numerous applications. However, the limited speeds in fringe pattern projection, image acquisition, and data transmission have strained the existing methods from reaching kilohertz-level acquisition, processing, and display of 3D information during the occurrence of dynamic events (i.e., in real time). To overcome these limitations, we have developed band-limited illumination profilometry (BLIP) with a CoaXPress interface (CI), which enables real-time high-speed 3D surface imaging. We have demonstrated the system’s performance by imaging various static and fast-moving 3D objects in real time. We have also applied this system in fluid mechanics by imaging dynamics of a flag, which allowed observation of the wave propagation, gravity-induced phase mismatch, and asymmetric flapping motion. We expect CI-BLIP to find diverse scientific and industrial applications.
Tree Skeletonization for Raw Point Cloud Exploiting Cylindrical Shape Prior

Tree skeleton extraction plays a fundamental role in reconstructing both biological and structural models of trees.However, traditional approaches can be ineffective and problematic in guaranteeing the topological correctness and centeredness of the tree skeleton when the tree point clouds contain noise and occlusions.To overcome this limitation, we present a tree skeletonization method to generate topologically correct and well-centered tree skeletons.We extract an initial skeleton from the tree point clouds via an octree and level set method, use cylindrical prior constraint (CPC) optimization and the estimated radii of branches to yield corrected positions of improper joints, and finally obtain updated skeletons with improved smoothness.The good centeredness of our proposed method is intrinsically achieved by (1) exploiting the cylindrical shape prior and calculating the CPC in the local neighborhood and (2) feeding the prior knowledge regarding the radii of tree branches into a topology refinement algorithm to yield near-optimal estimates of the positions of the skeleton points.To evaluate our method, we construct a novel tree point cloud data set with known ground truth and propose three quantitative assessment methods: skeleton points deviation (SPD), bifurcation points coverage (BPC) and endpoints coverage (EPC).The quantitative assessment and visual assessment show that the proposed method clearly outperforms traditional ones in terms of topology correctness and centeredness of the extracted tree skeleton.
Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound

In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in children’s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy.
Immersive Experience Model of the Elderly Welfare Centers Supporting Successful Aging

This study investigates the application of immersive experience services to leisure facilities for the successful aging of the elderly. In the past, the social image of the elderly was that of passive beings who needed care due to physical and cognitive decline associated with biological aging. However, the concept of "successful aging" actively highlighting the positive aspects of aging and trying to promote longer and healthier life has started to acquire importance in recent years. In this context, elderly welfare centers can be described as facilities that encourage learning, leisure, and social activities of the elderly with impaired physical and cognitive functions. The use of recent immersive experience technologies such as virtual reality and mixed reality (MR), in order to mitigate physical and spatial constraints and provide an immersion into the desired environment and situation, could contribute substantially to the health of the elderly. However, the application of immersive technologies is concentrated on the provision of entertainment, education, and medical facilities. The number of previous studies on the immersion experiences of the elderly is limited, and the connection between immersion experiences and various services and programs that promote successful aging at elderly welfare centers requires further research. This study analyzes the function and space of the elderly welfare centers for successful aging through the review of previous studies and classifies immersion technology categories based on the review of the relevant literature. The study analyzes the health benefits of immersive experience technologies and related products and services and proposes an immersive experience service model for the elderly welfare center. The results of the study could provide a valuable input for the spatial application of immersive experience technologies for successful aging in the future.
On the Activity Privacy of Blockchain for IoT

Blockchain has received tremendous attention as a distributed platform to enhance the security of Internet of Things (IoT). The history of communications is stored in blockchain which introduces auditability. On the flip side, new privacy risks are introduced as the entire history of IoT device communication is exposed to participants. We study the likelihood of classifying IoT devices by analyzing the temporal patterns of their transactions, which to the best of our knowledge, is the first work of its kind. We apply machine learning algorithms on blockchain data to analyze the success rate of device classification. Our results demonstrate success rates over 90% in classifying devices. We propose three timestamp obfuscation methods, namely combining multiple packets into a single transaction, merging ledgers of multiple devices, and randomly delaying transactions, to reduce the success rate in classifying devices which reduce the classification success rates to as low as 24%.
Measurement-Based Quantum Correlations for Quantum Information Processing

Abstract Measurement-based quantum correlations (MbQCs) depend on how strongly an observer perturbs the unobserved system. This distinctive property differentiates MbQCs from traditional quantum correlations such as entanglement and discord. We utilize MbQCs to elucidate quantum information processing capabilities in quantum computation and quantum state discrimination. We show that MbQCs exist more generally than entanglement and discord in optimal assisted quantum state discrimination and in a deterministic quantum computation with a single qubit. We also propose an MbQC-based dimension witness and analyze it in different noisy and noiseless scenarios.
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning

Saliency maps are frequently used to support explanations of the behavior of deep reinforcement learning (RL) agents. However, a review of how saliency maps are used in practice indicates that the derived explanations are often unfalsifiable and can be highly subjective. We introduce an empirical approach grounded in counterfactual reasoning to test the hypotheses generated from saliency maps and assess the degree to which they correspond to the semantics of RL environments. We use Atari games, a common benchmark for deep RL, to evaluate three types of saliency maps. Our results show the extent to which existing claims about Atari games can be evaluated and suggest that saliency maps are best viewed as an exploratory tool rather than an explanatory tool.
Hyperspectral Image Denoising via Combined Non-Local Self-Similarity and Local Low-Rank Regularization

Hyperspectral images (HSIs) are usually corrupted by various noises during the image acquisition process, e.g., Gaussian noise, impulse noise, stripes, deadlines and many others. Such complex noise severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSI applications. In this paper, a spatial non-local and local rank-constrained low-rank regularized Plug-and-Play (NLRPnP) model is presented for mixed noise removal in HSIs. Specifically, we first divide HSIs into local overlapping patches. Local rank-constrained low-rank matrix recovery is adopted to effectively separate the low-rank clean HSI patches from the sparse noise and a part of Gaussian noise, and to significantly preserve local structure and detail information in HSIs. Then the spatial non-local based denoiser is introduced to promote the non-local self-similarity and obviously depress the Gaussian noise. Without increasing the difficulty of solving optimization problems, we combine the local and non-local based methods into the Plug-and-Play framework, and develop an efficient algorithm for solving the proposed NLRPnP model by using the alternating direction method of multipliers method. Finally, several experiments are conducted in both simulated and real data conditions to illustrate the better performance of the proposed NLRPnP model than the existing state-of-the-art denoising models.
Enhancing random projection with independent and cumulative additive noise for privacy-preserving data stream mining

The sensitive nature of many data streams necessitates data mining techniques that are privacy-preserving. This paper proposes two data perturbation methods for privacy-preserving stream mining based on a combination of random projection, random translation, and two alternative forms of additive noise: noise generated independently for each record and noise that accumulates over the lifetime of a stream. Variations of the known input-output Maximum A Posteriori (MAP) attack that can account for the combinations of perturbation techniques are proposed as a means of evaluating the privacy guarantees of the proposed perturbation methods. The capabilities of the proposed methods to resist privacy-breaching recovery attacks and retain accuracy in models trained on perturbed data are experimentally evaluated. Experimentation revealed that the cumulative noise injection scheme outperformed other schemes by achieving a superior trade-off between privacy and classification.
Generalized Polarimetric Dehazing Method Based on Low-Pass Filtering in Frequency Domain

Polarimetric dehazing methods can significantly enhance the quality of hazy images. However, current methods are not robust enough under different imaging conditions. In this paper, we propose a generalized polarimetric dehazing method based on low-pass filtering in the frequency domain. This method can accurately estimate the polarized state of the scattering light automatically without adjusting bias parameters. Experimental results show the effectiveness and robustness of our proposed method in different hazy weather and scattering underwater environments with different densities. Furthermore, computational efficiency is enhanced more than 70% compared to the polarimetric dehazing method we proposed previously.
Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

This article proposes the design of an automatic classifier using the empirical mode decomposition (EMD) along with machine learning techniques for identifying the five most important types of events of the Ubinas volcano, the most active volcano in Peru. The proposed method uses attributes from temporal, spectral, and cepstral domains, extracted from the EMD of the signals, as well as a set of preprocessing and instrument correction techniques. Due to the fact that multichannel sensors are currently being installed in seismic networks worldwide, the proposed approach uses a multichannel sensor to perform the classification, contrary to the usual approach of the literature of using a single channel. The presented method is scalable to use data from multiple stations with one or more channels. The principal component analysis method is applied to reduce the dimensionality of the feature vector and the supervised classification is carried out by means of several machine learning algorithms, the support vector machine providing the best results. The presented investigation was tested with a large database that has a considerable number of explosion events, measured at the Ubinas volcano, located in Arequipa, Peru. The proposed classification system achieved a success rate of more than 90%.
Classification of Malaria Cell Images with Deep Learning Architectures

Malaria is a contagious disease caused by the infection of erythrocytes by Plasmodium parasites, which are transmitted to human by parasitic female anopheles' mosquitoes during feeding.Malaria is a type of infection that can be fatal if left untreated.It is very important to classify malaria virus images quickly and accurately using computer-aided systems.Because there are not enough personnel in each health unit to perform this procedure, traditional methods are both time consuming and open to errors.Once malaria images have been classified, it will be easier to diagnose malaria virus related diseases.Multiple methods have been developed to process large amounts of data.In particular, deep learning methods are frequently used for classification.In this paper, Convolutional Neural Networks (CNN) have been used to classify malaria images as healthy and parasited.Then, medium filter and gauss filter are applied to the original dataset.When classifying malaria data, the highest accuracy rate is achieved in the DenseNet201 architecture with gaussian filtered data of 97.83%.It is observed that the result obtained with the preprocessed data are higher.The application is implemented in the Matlab environment and works independently of the size of the images in the data set.