Reviewerly/exHarmony huggingface dataset:
What determines bitcoin exchange prices? A network VAR approach

We aim to understand the dynamics of cryptocurrency prices and, specifically, how price information is transmitted between different crypto market exchanges, and between crypto markets and traditional ones. To this aim, we propose an extended Vector Autoregressive model, aimed at explaining the evolution of bitcoin prices. The extension is based on network models, which improve over pure autoregressive models, as they introduce a contemporaneous contagion component, that describes contagion effects between prices. Our empirical findings show that the proposed model is able to well describe the correlation structure between bitcoin prices in different exchange markets, which appear rather strong, whereas the correlation of bitcoin prices with traditional assets is low. The model is also able to improve bitcoin price predictions, with respect to a simpler autoregressive model.
Geochemical and micro-textural fingerprints of boiling in pyrite

The chemical composition, textures and mineral associations of pyrite provide key information that help elucidate the evolution of hydrothermal systems. However, linking the compositional and micro-textural features of pyrite with a specific physico-chemical process, e.g., boiling versus non-boiling, remains elusive and challenging. In this study we examine pyrite geochemical and micro-textural features and relate these results to pyrite-forming processes at the active Cerro Pabellón Geothermal System (CPGS) in the Altiplano of the northern Chile. We integrate electron microprobe analysis (EMPA) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) data with micro-textural observations of pyrite and associated gangue minerals recovered from a ∼500 m long drill core that crosscuts the argillic, sub-propylitic and propylitic alteration zones of the CPGS. Additionally, we carried out a Principal Component Analysis (PCA) in order to inspect and understand the main data structure of the pyrite geochemical dataset. The concentrations of precious metals (Au and Ag), metalloids (As, Sb, Se, Bi and Tl), and base and heavy metals (Cu, Co, Ni and Pb) in pyrite from the CPGS are significant. Among the elements analyzed, As, Cu and Pb are the most abundant with concentrations that vary from a few parts per million (ppm) to wt% levels (up to 4.4 wt% of As, 0.5 wt% of Cu and 0.2 wt% of Pb). Based on contemporaneous gangue mineral associations and textures, the mechanisms of pyrite precipitation in the CPGS were inferred. Pyrite formed during vigorous boiling is characterized by relatively high concentrations of As, Cu, Pb, Ag and Au and lower concentrations of Co and Ni compared to pyrite formed under different conditions. These anhedral to euhedral pyrite grains display zones with a porous texture and abundant mineral micro- to nano-inclusions (mainly galena and chalcopyrite) indicating a formation by rapid crystallization. In contrast, pyrite formed under gentle boiling (more gradual cooling and less abrupt physico-chemical variations than in vigorous boiling) to non-boiling conditions is characterized by a higher concentration of Co and Ni, and relatively low concentrations of As, Cu, Pb, Ag and Au. Texturally, these pyrites form aggregates of euhedral and pristine pyrite crystals with scarce pores and mineral inclusions suggesting formation under steadier physico-chemical conditions. Our results show that pyrite can not only record the chemical evolution of hydrothermal fluids, but can also provide critical information related to physico-chemical process such as boiling and phase separation. Since boiling of aqueous fluids is a common phenomenon occurring in a variety of pyrite-forming environments, e.g., active continental and seafloor hydrothermal systems, and porphyry Cu-epithermal Au-Ag deposits, pyrite compositional and textural features are a valuable complement for discriminating and tracking boiling events in modern and fossil hydrothermal systems.
Quick-RRT*: Triangular inequality-based implementation of RRT* with improved initial solution and convergence rate

The Rapidly-exploring Random Tree (RRT) algorithm is a popular algorithm in motion planning problems. The optimal RRT (RRT*) is an extended algorithm of RRT, which provides asymptotic optimality. This paper proposes Quick-RRT* (Q-RRT*), a modified RRT* algorithm that generates a better initial solution and converges to the optimal faster than RRT*. Q-RRT* enlarges the set of possible parent vertices by considering not only a set of vertices contained in a hypersphere, as in RRT*, but also their ancestry up to a user-defined parameter, thus, resulting in paths with less cost than those of RRT*. It also applies a similar technique to the rewiring procedure resulting in acceleration of the tendency that near vertices share common parents. Since the algorithm proposed in this paper is a tree extending algorithm, it can be combined with other sampling strategies and graph-pruning algorithms. The effectiveness of Q-RRT* is demonstrated by comparing the algorithm with existing algorithms through numerical simulations. It is also verified that the performance can be further enhanced when combined with other sampling strategies.
GANobfuscator: Mitigating Information Leakage Under GAN via Differential Privacy

By learning generative models of semantic-rich data distributions from samples, generative adversarial network (GAN) has recently attracted intensive research interests due to its excellent empirical performance as a generative model. The model is used to estimate the underlying distribution of a dataset and randomly generate realistic samples according to their estimated distribution. However, GANs can easily remember training samples due to the high model complexity of deep networks. When GANs are applied to private or sensitive data, the concentration of distribution may divulge some critical information. It consequently requires new technological advances to mitigate the information leakage under GANs. To address this issue, we propose GANobfuscator, a differentially private GAN, which can achieve differential privacy under GANs by adding carefully designed noise to gradients during the learning procedure. With GANobfuscator, analysts are able to generate an unlimited amount of synthetic data for arbitrary analysis tasks without disclosing the privacy of training data. Moreover, we theoretically prove that GANobfuscator can provide strict privacy guarantee with differential privacy. In addition, we develop a gradient-pruning strategy for GANobfuscator to improve the scalability and stability of data training. Through extensive experimental evaluation on benchmark datasets, we demonstrate that GANobfuscator can produce high-quality generated data and retain desirable utility under practical privacy budgets.
Know More Say Less: Image Captioning Based on Scene Graphs

Automatically describing the content of an image has been attracting considerable research attention in the multimedia field. To represent the content of an image, many approaches directly utilize convolutional neural networks (CNNs) to extract visual representations, which are fed into recurrent neural networks to generate natural language. Recently, some approaches have detected semantic concepts from images and then encoded them into high-level representations. Although substantial progress has been achieved, most of the previous methods treat entities in images individually, thus lacking structured information that provides important cues for image captioning. In this paper, we propose a framework based on scene graphs for image captioning. Scene graphs contain abundant structured information because they not only depict object entities in images but also present pairwise relationships. To leverage both visual features and semantic knowledge in structured scene graphs, we extract CNN features from the bounding box offsets of object entities for visual representations, and extract semantic relationship features from triples (e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">man riding bike</i> ) for semantic representations. After obtaining these features, we introduce a hierarchical-attention-based module to learn discriminative features for word generation at each time step. The experimental results on benchmark datasets demonstrate the superiority of our method compared with several state-of-the-art methods.
Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks

Missing data is a ubiquitous problem. It is especially challenging in medical settings because many streams of measurements are collected at different-and often irregular-times. Accurate estimation of the missing measurements is critical for many reasons, including diagnosis, prognosis, and treatment. Existing methods address this estimation problem by interpolating within data streams or imputing across data streams (both of which ignore important information) or ignoring the temporal aspect of the data and imposing strong assumptions about the nature of the data-generating process and/or the pattern of missing data (both of which are especially problematic for medical data). We propose a new approach, based on a novel deep learning architecture that we call a Multi-directional Recurrent Neural Network that interpolates within data streams and imputes across data streams. We demonstrate the power of our approach by applying it to five real-world medical datasets. We show that it provides dramatically improved estimation of missing measurements in comparison to 11 state-of-the-art benchmarks (including Spline and Cubic Interpolations, MICE, MissForest, matrix completion, and several RNN methods); typical improvements in Root Mean Squared Error are between 35%-50%. Additional experiments based on the same five datasets demonstrate that the improvements provided by our method are extremely robust.
Improving text classification with weighted word embeddings via a multi-channel TextCNN model

In recent years, convolutional neural networks (CNNs) have gained considerable attention in text classification because of the remarkable good performance they achieved in various situations. The usual practice is to first perform word embedding (i.e., mapping each word into a word vector), and then employ a CNN to perform classification. To improve classification accuracy, term weighting approaches have been proven to be quite effective. But to the best of our knowledge, almost all these methods assign only one weight to each term (word). Considering the fact that one term generally has different importance in documents with different class labels, we propose in this paper a novel term weighting scheme to be combined with word embeddings to enhance the classification performance of CNNs. In the novel method, multiple weights are assigned to each term and these weights are applied to the word embeddings of the words separately. Subsequently, the transformed features are fed into a multi-channel CNN model to predict the label of the sentence. By comparing the novel method with several other baseline methods with five benchmark data sets, the results manifest that the classification accuracy of the proposed method exceeds that of other methods by an amazing margin. Moreover, the weights assigned by different weighting schemes are also analyzed to get more insights of their working mechanism.
A Blockchain-Based Medical Data Sharing and Protection Scheme

Electronic health record (EHR) has recorded the process of occurrence, development, and treatment of diseases.So it has high medical value.Owing to the private and sensitive nature of medical data for patients, the data sharing and privacy preservation are critical issues in EHR.Blockchain technology may be a promising solution for the problems above since it holds the features of decentralization and tamper resistance.In the paper, we propose a medical data sharing and protection scheme based on the hospital's private blockchain to improve the electronic health system of the hospital.Firstly, the scheme can satisfy various security properties such as decentralization, openness, and tamper resistance.A reliable mechanism is created for the doctors to store medical data or access the historical data of patients while meeting privacy preservation.Furthermore, a symptoms-matching mechanism is given between patients.It allows patients who get the same symptoms to conduct mutual authentication and create a session key for their future communication about the illness.The proposed scheme is implemented by using PBC and OpenSSL libraries.Finally, the security and performance evaluation of the proposed scheme is given.
Hyperspectral Image Restoration Using Weighted Group Sparsity-Regularized Low-Rank Tensor Decomposition

Mixed noise (such as Gaussian, impulse, stripe, and deadline noises) contamination is a common phenomenon in hyperspectral imagery (HSI), greatly degrading visual quality and affecting subsequent processing accuracy. By encoding sparse prior to the spatial or spectral difference images, total variation (TV) regularization is an efficient tool for removing the noises. However, the previous TV term cannot maintain the shared group sparsity pattern of the spatial difference images of different spectral bands. To address this issue, this article proposes a group sparsity regularization of the spatial difference images for HSI restoration. Instead of using l1 - or l2 -norm (sparsity) on the difference image itself, we introduce a weighted l2,1 -norm to constrain the spatial difference image cube, efficiently exploring the shared group sparse pattern. Moreover, we employ the well-known low-rank Tucker decomposition to capture the global spatial-spectral correlation from three HSI dimensions. To summarize, a weighted group sparsity-regularized low-rank tensor decomposition (LRTDGS) method is presented for HSI restoration. An efficient augmented Lagrange multiplier algorithm is employed to solve the LRTDGS model. The superiority of this method for HSI restoration is demonstrated by a series of experimental results from both simulated and real data, as compared with the other state-of-the-art TV-regularized low-rank matrix/tensor decomposition methods.
Occlusion-Aware Networks for 3D Human Pose Estimation in Video

Occlusion is a key problem in 3D human pose estimation from a monocular video. To address this problem, we introduce an occlusion-aware deep-learning framework. By employing estimated 2D confidence heatmaps of keypoints and an optical-flow consistency constraint, we filter out the unreliable estimations of occluded keypoints. When occlusion occurs, we have incomplete 2D keypoints and feed them to our 2D and 3D temporal convolutional networks (2D and 3D TCNs) that enforce temporal smoothness to produce a complete 3D pose. By using incomplete 2D keypoints, instead of complete but incorrect ones, our networks are less affected by the error-prone estimations of occluded keypoints. Training the occlusion-aware 3D TCN requires pairs of a 3D pose and a 2D pose with occlusion labels. As no such a dataset is available, we introduce a ``Cylinder Man Model'' to approximate the occupation of body parts in 3D space. By projecting the model onto a 2D plane in different viewing angles, we obtain and label the occluded keypoints, providing us plenty of training data. In addition, we use this model to create a pose regularization constraint, preferring the 2D estimations of unreliable keypoints to be occluded. Our method outperforms state-of-the-art methods on Human 3.6M and HumanEva-I datasets.
Fallout

Meltdown and Spectre enable arbitrary data leakage from memory via various side channels. Short-term software mitigations for Meltdown are only a temporary solution with a significant performance overhead. Due to hardware fixes, these mitigations are disabled on recent processors. In this paper, we show that Meltdown-like attacks are still possible on recent CPUs which are not vulnerable to Meltdown. We identify two behaviors of the store buffer, a microarchitectural resource to reduce the latency for data stores, that enable powerful attacks. The first behavior, Write Transient Forwarding forwards data from stores to subsequent loads even when the load address differs from that of the store. The second, Store-to-Leak exploits the interaction between the TLB and the store buffer to leak metadata on store addresses. Based on these, we develop multiple attacks and demonstrate data leakage, control flow recovery, and attacks on ASLR. Our paper shows that Meltdown-like attacks are still possible, and software fixes with potentially significant performance overheads are still necessary to ensure proper isolation between the kernel and user space.
DADA: Depth-Aware Domain Adaptation in Semantic Segmentation

Unsupervised domain adaptation (UDA) is important for applications where large scale annotation of representative data is challenging. For semantic segmentation in particular, it helps deploy on real “target domain” data models that are trained on annotated images from a different “source domain”, notably a virtual environment. To this end, most previous works consider semantic segmentation as the only mode of supervision for source domain data, while ignoring other, possibly available, information like depth. In this work, we aim at exploiting at best such a privileged information while training the UDA model. We propose a unified depth-aware UDA framework that leverages in several complementary ways the knowledge of dense depth in the source domain. As a result, the performance of the trained semantic segmentation model on the target domain is boosted. Our novel approach indeed achieves state-of-the-art performance on different challenging synthetic-2-real benchmarks.
An image encryption algorithm based on a hidden attractor chaos system and the Knuth–Durstenfeld algorithm

Chaotic systems have been widely applied in digital image encryption due to their complex properties such as ergodicity, pseudo randomness and extreme sensitivity to their initial values and parameters. An image encryption algorithm based on a hidden attractor chaos system and Knuth–Durstenfeld algorithm is proposed. First, a hidden attractor chaos system is used to encrypt digital image. Compared to a self-excited attractor, the hidden attractor's attracting basin does not intersect with any small neighbourhoods of the equilibria. It is difficult for attackers to reconstruct the attractor by finding equilibrium points. Therefore, the hidden attractor chaotic system is difficult to decrypt. Meanwhile, the hidden attractor chaos system is very sensitive to initial values and parameters. Second, the Knuth–Durstenfeld algorithm has good randomness. In addition, the Knuth–Durstenfeld algorithm can reduce the time complexity and the space complexity of the permutation while achieving good permutation effects. Thus, Knuth–Durstenfeld algorithm is used to permutate the digital image. Finally, DNA sequence operations are used to diffuse image pixels values. Some experimental analyses have been applied to measure the new scheme, and the experimental results illustrate the scheme possesses better encryption performances. This method can be applied in secure image communication fields.
Incremental Learning Techniques for Semantic Segmentation

Deep learning architectures exhibit a critical drop of performance due to catastrophic forgetting when they are required to incrementally learn new tasks. Contemporary incremental learning frameworks focus on image classification and object detection while in this work we formally introduce the incremental learning problem for semantic segmentation in which a pixel-wise labeling is considered. To tackle this task we propose to distill the knowledge of the previous model to retain the information about previously learned classes, whilst updating the current model to learn the new ones. We propose various approaches working both on the output logits and on intermediate features. In opposition to some recent frameworks, we do not store any image from previously learned classes and only the last model is needed to preserve high accuracy on these classes. The experimental evaluation on the Pascal VOC2012 dataset shows the effectiveness of the proposed approaches.
GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking With 2D-3D Multi-Feature Learning

3D Multi-object tracking (MOT) is crucial to autonomous systems. Recent work uses a standard tracking-by-detection pipeline, where feature extraction is first performed independently for each object in order to compute an affinity matrix. Then the affinity matrix is passed to the Hungarian algorithm for data association. A key process of this standard pipeline is to learn discriminative features for different objects in order to reduce confusion during data association. In this work, we propose two techniques to improve the discriminative feature learning for MOT: (1) instead of obtaining features for each object independently, we propose a novel feature interaction mechanism by introducing the Graph Neural Network. As a result, the feature of one object is informed of the features of other objects so that the object feature can lean towards the object with similar feature (i.e., object probably with a same ID) and deviate from objects with dissimilar features (i.e., object probably with different IDs), leading to a more discriminative feature for each object; (2) instead of obtaining the feature from either 2D or 3D space in prior work, we propose a novel joint feature extractor to learn appearance and motion features from 2D and 3D space simultaneously. As features from different modalities often have complementary information, the joint feature can be more discriminate than feature from each individual modality. To ensure that the joint feature extractor does not heavily rely on one modality, we also propose an ensemble training paradigm. Through extensive evaluation, our proposed method achieves state-of-the-art performance on KITTI and nuScenes 3D MOT benchmarks. Our code will be made available at https://github.com/xinshuoweng/GNN3DMOT.
Robust Detection of Image Operator Chain With Two-Stream Convolutional Neural Network

Many forensic techniques have recently been developed to determine whether an image has undergone a specific manipulation operation. When multiple consecutive operations are applied to images, forensic analysts not only need to identify the existence of each manipulation operation, but also to distinguish the order of the involved operations. However, image operator chain detection is still a challenging problem. In this paper, an order forensics framework for detecting image operator chain based on convolutional neural network (CNN) is presented. Two-stream CNN architecture is designed to capture both tampering artifact evidence and local noise residual evidence. Specifically, the new CNN-based method is proposed for forensically detecting a chain made of two image operators, which could automatically learn manipulation detection features directly from image data. Further, we empirically investigate the robustness of our proposed method in two practical scenarios: forensic investigators have no access to the operating parameters, and manipulations are applied to a JPEG compressed image. Experimental results show that the proposed framework not only obtains significant detection performance but also can distinguish the order in some cases that previous works were unable to identify.