Reviewerly/exHarmony huggingface dataset:
Generalized switched synchronization and dependent image encryption using dynamically rotating fractional-order chaotic systems

This paper presents a generalization, attractor control and multi-scroll generation method for fractional-order chaotic systems through rotation transformation. A novel synchronization-dependent colored image encryption and secure communication scheme is also proposed. The systems with dynamic rotation angle fit successfully in a generalized dynamic switched synchronization scheme. Dynamic control switches specify whether the system acts as a master or slave. Dynamic scaling factors determine whether the master is a single system or a combination of two or more systems. Simulation results validate successful synchronization when rotating fractional-order systems act as slave or master or both. The proposed synchronization is suitable for one-to-one, one-to-many, mutual interconnection and role switching communication applications. The proposed synchronization-dependent image encryption scheme performs rotation angle modulation using the plaintext and XOR logic operation for plaintext image substitution. The performance of the chaotic systems as Pseudo-Random Number Generators (PRNGs) is validated using National Institute of Standards & Technology (NIST). Three synchronization scenarios are tested using Lena image. The performance of the scheme is evaluated through the encrypted image, histogram and its uniformity through chi square test, pixel correlation, Mean Squared Error (MSE), entropy, Peak Signal-to-Noise Ratio (PSNR), key sensitivity, resistance to brute force, differential and other security attacks.
DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking

The increased focus on misinformation has spurred development of data and systems for detecting the veracity of a claim as well as retrieving authoritative evidence.The Fact Extraction and VERification (FEVER) dataset provides such a resource for evaluating endto-end fact-checking, requiring retrieval of evidence from Wikipedia to validate a veracity prediction.We show that current systems for FEVER are vulnerable to three categories of realistic challenges for fact-checking -multiple propositions, temporal reasoning, and ambiguity and lexical variation -and introduce a resource with these types of claims.Then we present a system designed to be resilient to these "attacks" using multiple pointer networks for document selection and jointly modeling a sequence of evidence sentences and veracity relation predictions.We find that in handling these attacks we obtain state-of-the-art results on FEVER, largely due to improved evidence retrieval.
Geometry-Aware Satellite-to-Ground Image Synthesis for Urban Areas

We present a novel method for generating panoramic street-view images which are geometrically consistent with a given satellite image. Different from existing approaches that completely rely on a deep learning architecture to generalize cross-view image distributions, our approach explicitly loops in the geometric configuration of the ground objects based on the satellite views, such that the produced ground view synthesis preserves the geometric shape and the semantics of the scene. In particular, we propose a neural network with a geo-transformation layer that turns predicted ground-height values from the satellite view to a ground view while retaining the physical satellite-to-ground relation. Our results show that the synthesized image retains well-articulated and authentic geometric shapes, as well as texture richness of the street-view in various scenarios. Both qualitative and quantitative results demonstrate that our method compares favorably to other state-of-the-art approaches that lack geometric consistency.
Novel up-scale feature aggregation for object detection in aerial images

Object detection is a pivotal task for many unmanned aerial vehicle (UAV) applications. Compared to general scenes, the objects in aerial images are typically much smaller. For this reason, most general object detectors suffer from two critical challenges while dealing with aerial images: 1) The widely exploited Feature Pyramid Network works by integrating high-level features to lower levels progressively. However, this manner does not transfer equivalent information from each level of backbone network to the generated features, and the shared detection head faces an unbalanced sources of information flow, damaging the detection accuracy. 2) Up-sampling is commonly used to expand feature resolution for feature fusion or feature aggregation. However, existing up-sampling methods are ineffective to reconstruct high resolution feature maps. To address these two challenges, two works are proposed: 1) An up-scale feature aggregation framework that fully utilizes multi-scale complementary information, and 2) a novel up-sampling method that further improve detection accuracy. These two proposals are integrated into an end-to-end single-stage object detector namely HawkNet. Extensive experiments are conducted on VisDrone-DET2018, UAVDT and DIOR datasets. Compared to the RetinaNet baseline, our HawkNet achieves absolute gains of 6.0%, 1.2% and 5.9% in average precision (AP) on VisDrone-DET2018, UAVDT and DIOR datasets, respectively. For a 800 × 1333 input on the UAVDT dataset, HawkNet with ResNet-50 backbone surpasses existing methods for single-scale inference and achieves the best performance (37.4 AP), while operating at 10.6 frames per second on a single Nvidia GTX 1080Ti GPU.
Encoding History with Context-aware Representation Learning for Personalized Search

The key to personalized search is to clarify the meaning of current query based on user's search history. Previous personalized studies tried to build user profiles on the basis of historical data to tailor the ranking. However, we argue that the user profile based methods do not really disambiguate the current query. They still retain some semantic bias when building user profiles. In this paper, we propose to encode history with context-aware representation learning to enhance the representation of current query, which is a direct way to clarify the user's information need. Specifically, endowed with the benefit from transformer on aggregating contextual information, we devise a query disambiguation model to parse the meaning of current query in multiple stages. Moreover, for covering the cases that current query is not sufficient to express the intent, we train a personalized language model to predict user intent from existing queries. Under the interaction of two sub-models, we can generate the context-aware representation of current query and re-rank the results based on it. Experimental results show the significant improvement of our model compared with previous methods.
Deep Implicit Volume Compression

We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures. To compress the TSDF, our method relies on a block-based neural network architecture trained end-to-end, achieving state-of-the-art rate-distortion trade-off. To prevent topological errors, we losslessly com- press the signs of the TSDF, which also upper bounds the reconstruction error by the voxel size. To compress the corresponding texture, we designed a fast block-based UV parameterization, generating coherent texture maps that can be effectively compressed using existing video compression algorithms. We demonstrate the performance of our algo- rithms on two 4D performance capture datasets, reducing bitrate by 66% for the same distortion, or alternatively re- ducing the distortion by 50% for the same bitrate, compared to the state-of-the-art.
Which Is Plagiarism: Fashion Image Retrieval Based on Regional Representation for Design Protection

With the rapid growth of e-commerce and the popularity of online shopping, fashion retrieval has received considerable attention in the computer vision community. Different from the existing works that mainly focus on identical or similar fashion item retrieval, in this paper, we aim to study the plagiarized clothes retrieval which is somewhat ignored in the academic community while itself has great application value. One of the key challenges is that plagiarized clothes are usually modified in a certain region on the original design to escape the supervision by traditional retrieval methods. To relieve it, we propose a novel network named Plagiarized-Search-Net (PS-Net) based on regional representation, where we utilize the landmarks to guide the learning of regional representations and compare fashion items region by region. Besides, we propose a new dataset named Plagiarized Fashion for plagiarized clothes retrieval, which provides a meaningful complement to the existing fashion retrieval field. Experiments on Plagiarized Fashion dataset verify that our approach is superior to other instance-level counterparts for plagiarized clothes retrieval, showing a promising result for original design protection. Moreover, our PS-Net can also be adapted to traditional fashion retrieval and landmark estimation tasks and achieves the state-of-the-art performance on the DeepFashion and DeepFashion2 datasets.
Three-Dimensional Reconstruction of Human Interactions

Understanding 3d human interactions is fundamental for fine grained scene analysis and behavioural modeling. However, most of the existing models focus on analyzing a single person in isolation, and those who process several people focus largely on resolving multi-person data association, rather than inferring interactions. This may lead to incorrect, lifeless 3d estimates, that miss the subtle human contact aspects--the essence of the event--and are of little use for detailed behavioral understanding. This paper addresses such issues and makes several contributions: (1) we introduce models for interaction signature estimation (ISP) encompassing contact detection, segmentation, and 3d contact signature prediction; (2) we show how such components can be leveraged in order to produce augmented losses that ensure contact consistency during 3d reconstruction; (3) we construct several large datasets for learning and evaluating 3d contact prediction and reconstruction methods; specifically, we introduce CHI3D, a lab-based accurate 3d motion capture dataset with 631 sequences containing 2,525 contact events, 728,664 ground truth 3d poses, as well as FlickrCI3D, a dataset of 11,216 images, with 14,081 processed pairs of people, and 81,233 facet-level surface correspondences within 138,213 selected contact regions. Finally, (4) we present models and baselines to illustrate how contact estimation supports meaningful 3d reconstruction where essential interactions are captured. Models and data are made available for research purposes at http://vision.imar.ro/ci3d.
Analyzing and Learning from User Interactions for Search Clarification

Asking clarifying questions in response to search queries has been recognized as a useful technique for revealing the underlying intent of the query. Clarification has applications in retrieval systems with different interfaces, from the traditional web search interfaces to the limited bandwidth interfaces as in speech-only and small screen devices. Generation and evaluation of clarifying questions have been recently studied in the literature. However, user interaction with clarifying questions is relatively unexplored. In this paper, we conduct a comprehensive study by analyzing large-scale user interactions with clarifying questions in a major web search engine. In more detail, we analyze the user engagements received by clarifying questions based on different properties of search queries, clarifying questions, and their candidate answers. We further study click bias in the data, and show that even though reading clarifying questions and candidate answers does not take significant efforts, there still exist some position and presentation biases in the data. We also propose a model for learning representation for clarifying questions based on the user interaction data as implicit feedback. The model is used for re-ranking a number of automatically generated clarifying questions for a given query. Evaluation on both click data and human labeled data demonstrates the high quality of the proposed method.
Density-Aware Feature Embedding for Face Clustering

Clustering has many applications in research and industry. However, traditional clustering methods, such as K-means, DBSCAN and HAC, impose oversimplifying assumptions and thus are not well-suited to face clustering. To adapt to the distribution of realistic problems, a natural approach is to use Graph Convolutional Networks (GCNs) to enhance features for clustering. However, GCNs can only utilize local information, which ignores the overall characterisitcs of the clusters. In this paper, we propose a Density-Aware Feature Embedding Network (DA-Net) for the task of face clustering, which utilizes both local and non-local information, to learn a robust feature embedding. Specifically, DA-Net uses GCNs to aggregate features locally, and then incorporates non-local information using a density chain, which is a chain of faces from low density to high density. This density chain exploits the non-uniform distribution of face images in the dataset. Then, an LSTM takes the density chain as input to generate the final feature embedding. Once this embedding is generated, traditional clustering methods, such as density-based clustering, can be used to obtain the final clustering results. Extensive experiments verify the effectiveness of the proposed feature embedding method, which can achieve state-of-the-art performance on public benchmarks.
Cross-View Correspondence Reasoning Based on Bipartite Graph Convolutional Network for Mammogram Mass Detection

Mammogram mass detection is of great clinical significance due to its high proportion in breast cancers. The information from cross views (i.e., mediolateral oblique and cranio-caudal) is highly related and complementary, and is helpful to make comprehensive decisions. However, unlike radiologists who are able to recognize masses with reasoning ability in cross-view images, most existing methods lack the ability to reason under the guidance of domain knowledge, thus it limits the performance. In this paper, we introduce bipartite graph convolutional network to endow existing methods with cross-view reasoning ability of radiologists in mammogram mass detection. The bipartite node sets are constructed by cross-view images respectively to represent relatively consistent regions in breasts, while the bipartite edge learns to model both inherent cross-view geometric constraints and appearance similarities between correspondences. Based on the bipartite graph, the information propagates methodically through correspondences and enables spatial visual features equipped with customized cross-view reasoning ability. Experimental results on DDSM dataset demonstrate that the proposed algorithm achieves state-of-the-art performance. Besides, visual analysis shows the model has a clear physical meaning, which is helpful for radiologists in clinical interpretation.
BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance

Pretraining deep language models has led to large performance gains in NLP.Despite this success, Schick and Schütze (2020) recently showed that these models struggle to understand rare words.For static word embeddings, this problem has been addressed by separately learning representations for rare words.In this work, we transfer this idea to pretrained language models: We introduce BERTRAM, a powerful architecture based on BERT that is capable of inferring high-quality embeddings for rare words that are suitable as input representations for deep language models.This is achieved by enabling the surface form and contexts of a word to interact with each other in a deep architecture.Integrating BERTRAM into BERT leads to large performance increases due to improved representations of rare and medium frequency words on both a rare word probing task and three downstream tasks. 1
Why Having 10,000 Parameters in Your Camera Model Is Better Than Twelve

Camera calibration is an essential first step in setting up 3D Computer Vision systems. Commonly used parametric camera models are limited to a few degrees of freedom and thus often do not optimally fit to complex real lens distortion. In contrast, generic camera models allow for very accurate calibration due to their flexibility. Despite this, they have seen little use in practice. In this paper, we argue that this should change. We propose a calibration pipeline for generic models that is fully automated, easy to use, and can act as a drop-in replacement for parametric calibration, with a focus on accuracy. We compare our results to parametric calibrations. Considering stereo depth estimation and camera pose estimation as examples, we show that the calibration error acts as a bias on the results. We thus argue that in contrast to current common practice, generic models should be preferred over parametric ones whenever possible. To facilitate this, we released our calibration pipeline at https://github.com/puzzlepaint/camera_calibration, making both easy-to-use and accurate camera calibration available to everyone.
Joint Input and Output Space Learning for Multi-Label Image Classification

Multi-label image classification aims to predict the labels associated with a given image. While most existing methods utilize unified image representations, extracting label-specific features through input space learning would improve the discriminative power of the learned features. On the other hand, most feature learning studies often ignore the learning in the output label space, although taking advantage of label correlations can boost the classification performance. In this paper, we propose a deep learning framework that incorporates flexible modules which can learn from both input and output spaces for multi-label image classification. For the input space learning, we devise a label-specific feature pooling method to refine convolutional features for obtaining features specific to each label. For the output space learning, we design a Two-Stream Graph Convolutional Network (TSGCN) to learn multi-label classifiers by mapping spatial object relationships and semantic label correlations. More specifically, we build object spatial graphs to characterize the spatial relationships among objects in an image, which supplements the label semantic graphs modelling the semantic label correlations. Experimental results on two popular benchmark datasets (i.e., Pascal VOC and MS-COCO) show that our proposed method achieves superior performance over the state-of-the-arts.
Learning Oracle Attention for High-Fidelity Face Completion

High-fidelity face completion is a challenging task due to the rich and subtle facial textures involved. What makes it more complicated is the correlations between different facial components, for example, the symmetry in texture and structure between both eyes. While recent works adopted the attention mechanism to learn the contextual relations among elements of the face, they have largely overlooked the disastrous impacts of inaccurate attention scores; in addition, they fail to pay sufficient attention to key facial components, the completion results of which largely determine the authenticity of a face image. Accordingly, in this paper, we design a comprehensive framework for face completion based on the U-Net structure. Specifically, we propose a dual spatial attention module to efficiently learn the correlations between facial textures at multiple scales; moreover, we provide an oracle supervision signal to the attention module to ensure that the obtained attention scores are reasonable. Furthermore, we take the location of the facial components as prior knowledge and impose a multi-discriminator on these regions, with which the fidelity of facial components is significantly promoted. Extensive experiments on two high-resolution face datasets including CelebA-HQ and Flickr-Faces-HQ demonstrate that the proposed approach outperforms state-of-the-art methods by large margins.
Probabilistic solar forecasting benchmarks on a standardized dataset at Folsom, California

The present paper echos a recent data article, “A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods” [J. Renewable Sustainable Energy 11, 036102 (2019)]. The carefully composed dataset by Pedro, Larson, and Coimbra (PLC) presents a rare opportunity for solar forecasters to develop transparent and reproducible algorithms that can bring incremental contributions to the field. In their original paper, data from four different sources, namely, ground-based measurements, sky-camera images, satellite-imagery features, and numerical weather prediction outputs, were arranged in a machine-learning-ready setup. Subsequently, several benchmarks for deterministic forecasting were set forth, for intra-hour, intra-day, and day-ahead scenarios. Nonetheless, a weather forecast is intrinsically five-dimensional, spanning space, time, and probability. In this regard, five reference methods for probabilistic forecasting: (1) complete-history persistence ensemble, (2) Markov-chain mixture model, (3) ordinary least squares, (4) analog ensemble, and (5) quantile regression, are applied to the PLC dataset. The R code provided in this paper follows the structure of the original Python code precisely, facilitating those solar forecasters who are not familiar with Python but have a statistics background.
DesnowGAN: An Efficient Single Image Snow Removal Framework Using Cross-Resolution Lateral Connection and GANs

In this paper, we present a simple, efficient, and highly modularized network architecture for single-image snow-removal. To address the challenging snow-removal problem in terms of network interpretability and computational complexity, we employ a pyramidal hierarchical design with lateral connections across different resolutions. This design enables us to incorporate high-level semantic features with other feature maps at different scales to enrich location information and reduce computational time. In addition, a refinement stage based on recently introduced generative adversarial networks (GANs) is proposed to further improve the visual quality of the resulting snow-removed images and make a refined image and a clean image indistinguishable by a computer vision algorithm to avoid the potential perturbations of machine interpretation. Finally, atrous spatial pyramid pooling (ASPP) is adopted to probe features at multiple scales and further boost the performance. The proposed DesnowGAN (DS-GAN) performs significantly better than state-of-the-art methods quantitatively and qualitatively on the Snow100K dataset.
Application of Blockchain in Asset-Backed Securitization

one of the most important emerging technologies in the recent years, blockchain has been well developed and widely used in many domains, including financial business systems. One of such financial business systems, traditionally called AssetBacked securitization (ABS), suffers from several shortcomings such as information asymmetry, credit rating inaccuracy and poor product liquidity. In this paper, we propose to use the blockchain technology to improve ABS financial business system. First, we analyze the current situation and existing issues of ABS business in China to summarize the problems to be solved. Then, we propose our designs of an ABS business system based on the blockchain technology.