Reviewerly/exHarmony huggingface dataset:
Witnessing the nonclassical nature of gravity in the presence of unknown interactions

General relativity as a classical field theory does not predict gravitationally induced entanglement, as such, recent proposals seek an empirical demonstration of this feature which would represent a significant milestone for physics. We introduce improvements to a spin witness protocol that reduce the highly challenging experimental requirements. After rigorously assessing approximations from the original proposal [S. Bose et al. Phys. Rev. Lett. 119, 240401 (2017)], we focus on entanglement witnessing. We propose a new witness which greatly reduces the required interaction time, thereby making the experiment feasible for higher decoherence rates, and we show how statistical analysis can separate the gravitational contribution from other possibly dominant and ill-known interactions. We point out a potential loophole and show how it can be closed using state tomography.
Augmented Reality Training for Industrial Assembly Work - Are Projection-based AR Assistive Systems an Appropriate Tool for Assembly Training?

Augmented Reality (AR) systems are on their way to industrial application, e.g. projection-based AR is used to enhance assembly work. Previous studies showed advantages of the systems in permanent-use scenarios, such as faster assembly times. In this paper, we investigate whether such systems are suitable for training purposes. Within an experiment, we observed the training with a projection-based AR system over multiple sessions and compared it with a personal training and a paper manual training. Our study shows that projection-based AR systems offer only small benefits in the training scenario. While a systematic mislearning of content is prevented through immediate feedback, our results show that the AR training does not reach the personal training in terms of speed and recall precision after 24 hours. Furthermore, we show that once an assembly task is properly trained, there are no differences in the long-term recall precision, regardless of the training method.
Helpful or harmful? A double-edged sword of emoticons in online review helpfulness

Emoticons are graphic demonstrations of facial expressions. They have become a common and easy-to-use tool for online users to express ideas or feelings. Earlier research highlights the importance of emoticons in computer-mediated communications. However, limited attention has been given to the use of emoticons in online reviews. Using the Cognitive Tuning Theory as the backbone, we attempt to examine how emoticons in online reviews influence review helpfulness. One lab experiment with restaurants as the context and one empirical study with data of 4870 hotel reviews from Facebook business pages were conducted. The hypotheses were assessed via multilevel regression analysis using R programming language. Results indicate that positive emoticons enhance review helpfulness when the review is narrative-based and negative emoticons increase review helpfulness when the review is list-based. Processing fluency serves as the underlying mechanism. We discuss theoretical and practical implications.
Unpaired Portrait Drawing Generation via Asymmetric Cycle Mapping

Portrait drawing is a common form of art with high abstraction and expressiveness. Due to its unique characteristics, existing methods achieve decent results only with paired training data, which is costly and time-consuming to obtain.In this paper, we address the problem of automatic transfer from face photos to portrait drawings with unpaired training data. We observe that due to the significant imbalance of information richness between photos and drawings, existing unpaired transfer methods such as CycleGAN tends to embed invisible reconstruction information indiscriminately in the whole drawings, leading to important facial features partially missing in drawings. To address this problem, we propose a novel asymmetric cycle mapping that enforces the reconstruction information to be visible (by a truncation loss) and only embedded in selective facial regions (by a relaxed forward cycle-consistency loss). Along with localized discriminators for the eyes, nose and lips, our method well preserves all important facial features in the generated portrait drawings. By introducing a style classifier and taking the style vector into account, our method can learn to generate portrait drawings in multiple styles using a single network. Extensive experiments show that our model outperforms state-of-the-art methods.
Blockchain-Based Solution for the Traceability of Spare Parts in Manufacturing

Spare parts are essential assets to maintain productivity and operations. A spare part is used for the repair or replacement of failed units. Tracing and tracking of spare parts ownership can help to ensure reliable outcomes in terms of manufacturing operations and service delivery. However, today's spare part inventory systems fall short of providing reliable tracing and tracking of spare parts ownership which poses serious threats to their authenticity. Also, current approaches and systems leveraged for spare part inventory management are vulnerable to the single point of failures. In this paper, we propose a blockchain-based smart contract to trace and track the spare parts ownership details from the original equipment manufacturer to the supplier and end-users. We exploit blockchain features to build a secure and trusted spare part inventory system that is tamper-proof, traceable, trackable, accessible immutable, resilient, and reliable. The proposed system integrates decentralized storage of interplanetary file systems (IPFS) to store and share spare parts data. We present algorithms along with their full implementation details. We perform testing and validation of the smart contract. Furthermore, we provide security and cost analysis, and show how the proposed system ensures reliable tracing and tracking of spare parts ownership. We make the smart contract source code publicly available on Github.
Unfabricate: Designing Smart Textiles for Disassembly

Smart textiles development is combining computing and textile technologies to create tactile, functional objects such as smart garments, soft medical devices, and space suits. However, the field also combines the massive waste streams of both the digital electronics and textiles industries. The following work explores how HCI researchers might be poised to address sustainability and waste in future smart textiles development through interventions at design time. Specifically, we perform a design inquiry into techniques and practices for reclaiming and reusing smart textiles materials and explore how such techniques can be integrated into smart textiles design tools. Beginning with a practice in sustainable or "slow" fashion, unravelling a garment into yarn, the suite of explorations titled "Unfabricate" probes values of time and labor in crafting a garment; speculates how a smart textile garment may be designed with reuse in mind; and imagines how electronic and textile components may be given new life in novel uses.
SpotNet: Self-Attention Multi-Task Network for Object Detection

Humans are very good at directing their visual attention toward relevant areas when they search for different types of objects. For instance, when we search for cars, we will look at the streets, not at the top of buildings. The motivation of this paper is to train a network to do the same via a multi-task learning approach. To train visual attention, we produce foreground/background segmentation labels in a semi-supervised way, using background subtraction or optical flow. Using these labels, we train an object detection model to produce foreground/background segmentation maps as well as bounding boxes while sharing most model parameters. We use those segmentation maps inside the network as a self-attention mechanism to weight the feature map used to produce the bounding boxes, decreasing the signal of non-relevant areas. We show that by using this method, we obtain a significant mAP improvement on two traffic surveillance datasets, with state-of-the-art results on both UA-DETRAC and UAVDT.
Phishing Website Classification and Detection Using Machine Learning

The phishing website has evolved as a major cybersecurity threat in recent times. The phishing websites host spam, malware, ransomware, drive-by exploits, etc. A phishing website many a time look-alike a very popular website and lure an unsuspecting user to fall victim to the trap. The victim of the scams incurs a monetary loss, loss of private information and loss of reputation. Hence, it is imperative to find a solution that could mitigate such security threats in a timely manner. Traditionally, the detection of phishing websites is done using blacklists. There are many popular websites which host a list of blacklisted websites, e. g. PhisTank. The blacklisting technique lack in two aspects, blacklists might not be exhaustive and do not detect a newly generated phishing website. In recent times machine learning techniques have been used in the classification and detection of phishing websites. In, this paper we have compared different machine learning techniques for the phishing URL classification task and achieved the highest accuracy of 98% for Naïve Bayes Classifier with a precision=1, recall = .95 and F1-Score= .97.
Design and Evaluation of a Real-Time Face Recognition System using Convolutional Neural Networks

The advent of high speed processors and high resolution cameras has spearheaded the research towards design of face recognition systems for various applications. Face recognition systems use either offline data or real-time input, based on the application. In this paper, design and evaluation of a real-time face recognition system using Convolutional Neural Network (CNN) is proposed. The initial evaluation of the proposed design is carried out using standard AT&T datasets and the same is later extended towards the design of a real-time system. Details about the tuning of CNN parameters to assess and enhance the recognition accuracy of the proposed system are also reported. A systematic approach to tune the parameters is also proposed to enhance the performance of the system. Maximum recognition accuracies of 98.75% and 98.00% are obtained on using the proposed system with standard datasets and real-time inputs respectively.
Video Instance Segmentation Tracking With a Modified VAE Architecture

We propose a modified variational autoencoder (VAE) architecture built on top of Mask R-CNN for instance-level video segmentation and tracking. The method builds a shared encoder and three parallel decoders, yielding three disjoint branches for predictions of future frames, object detection boxes, and instance segmentation masks. To effectively solve multiple learning tasks, we introduce a Gaussian Process model to enhance the statistical representation of VAE by relaxing the prior strong independent and identically distributed (iid) assumption of conventional VAEs and allowing potential correlations among extracted latent variables. The network learns embedded spatial interdependence and motion continuity in video data and creates a representation that is effective to produce high-quality segmentation masks and track multiple instances in diverse and unstructured videos. Evaluation on a variety of recently introduced datasets shows that our model outperforms previous methods and achieves the new best in class performance.
MTL-NAS: Task-Agnostic Neural Architecture Search Towards General-Purpose Multi-Task Learning

We propose to incorporate neural architecture search (NAS) into general-purpose multi-task learning (GP-MTL). Existing NAS methods typically define different search spaces according to different tasks. In order to adapt to different task combinations (i.e., task sets), we disentangle the GP-MTL networks into single-task backbones (optionally encode the task priors), and a hierarchical and layerwise features sharing/fusing scheme across them. This enables us to design a novel and general task-agnostic search space, which inserts cross-task edges (i.e., feature fusion connections) into fixed single-task network backbones. Moreover, we also propose a novel single-shot gradient-based search algorithm that closes the performance gap between the searched architectures and the final evaluation architecture. This is realized with a minimum entropy regularization on the architecture weights during the search phase, which makes the architecture weights converge to near-discrete values and therefore achieves a single model. As a result, our searched model can be directly used for evaluation without (re-)training from scratch. We perform extensive experiments using different single-task backbones on various task sets, demonstrating the promising performance obtained by exploiting the hierarchical and layerwise features, as well as the desirable generalizability to different i) task sets and ii) single-task backbones. The code of our paper is available at https://github.com/bhpfelix/MTLNAS.
Image Steganography With Symmetric Embedding Using Gaussian Markov Random Field Model

Recent advances on adaptive steganography show that the performance of image steganographic communication can be improved by incorporating the non-additive models that capture the dependencies among adjacent pixels. In this paper, a Gaussian Markov Random Field model (GMRF) with four-element cross neighborhood is proposed to characterize the interactions among local elements of cover images, and the problem of secure image steganography is formulated as the one of minimization of KL-divergence in terms of a series of low-dimensional clique structures associated with GMRF by taking advantages of the conditional independence of GMRF. The adoption of the proposed GMRF tessellates the cover image into two disjoint subimages, and an alternating iterative optimization scheme is developed to effectively embed the given payload while minimizing the total KL-divergence between cover and stego, i.e., the statistical detectability. Experimental results demonstrate that the proposed GMRF outperforms the prior arts of model based schemes, e.g., MiPOD, and rivals the state-of-the-art HiLL for practical steganography, where the selection channel knowledges are unavailable to steganalyzers.
Learning to Evaluate Perception Models Using Planner-Centric Metrics

Variants of accuracy and precision are the gold-standard by which the computer vision community measures progress of perception algorithms. One reason for the ubiquity of these metrics is that they are largely task-agnostic; we in general seek to detect zero false negatives or positives. The downside of these metrics is that, at worst, they penalize all incorrect detections equally without conditioning on the task or scene, and at best, heuristics need to be chosen to ensure that different mistakes count differently. In this paper, we propose a principled metric for 3D object detection specifically for the task of self-driving. The core idea behind our metric is to isolate the task of object detection and measure the impact the produced detections would induce on the downstream task of driving. Without hand-designing it to, we find that our metric penalizes many of the mistakes that other metrics penalize by design. In addition, our metric downweighs detections based on additional factors such as distance from a detection to the ego car and the speed of the detection in intuitive ways that other detection metrics do not. For human evaluation, we generate scenes in which standard metrics and our metric disagree and find that humans side with our metric 79% of the time. Our project page including an evaluation server can be found at https://nv-tlabs.github.io/detection-relevance.
Robust multi-label feature selection with dual-graph regularization

Multi-label learning is facing great challenges due to high-dimensional feature space, complex label correlations and noises in multi-label data. Feature selection techniques have attracted considerable attention to address the problems. In this paper, we design our method based on dual-graph regularization, i.e., feature graph regularization and label graph regularization. The feature graph regularization is used to preserve the geometric structure of features, while label graph regularization intends to explore the correlations of labels. Furthermore, the l2,1-norm is imposed on the loss function to enhance the robust of feature selection methods. As a result, a new feature selection method termed Robust Multi-label Feature Selection based on Dual-graph (DRMFS) is proposed. Particularly, only one unknown variable, feature weight matrix, is incorporated in our proposed method, which can reach global optimum. Additionally, we impose both l2,1-norm and non-negative constraints onto the feature weight matrix to enhance the property of row-sparse. Finally, we design an optimization scheme to solve the proposed method, and offer the convergence proof of the optimization scheme. Extensive experimental results demonstrate the superiority of the proposed method in comparison to the-state-of-art multi-label feature selection methods. Finally, some insightful discussions with respect to the convergence analysis, complexity analysis and parameter sensitivity analysis are presented.
Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimation

The best performing methods for 3D human pose estimation from monocular images require large amounts of in-the-wild 2D and controlled 3D pose annotated datasets which are costly and require sophisticated systems to acquire. To reduce this annotation dependency, we propose Multiview-Consistent Semi Supervised Learning (MCSS) framework that utilizes similarity in pose information from unannotated, uncalibrated but synchronized multi-view videos of human motions as additional weak supervision signal to guide 3D human pose regression. Our framework applies hard-negative mining based on temporal relations in multi-view videos to arrive at a multi-view consistent pose embedding and when jointly trained with limited 3D pose annotations, our approach improves the baseline by 25% and state-of-the-art by 8.7%, whilst using substantially smaller networks. Lastly, but importantly, we demonstrate the advantages of the learned embedding and establish view-invariant pose retrieval benchmarks on two popular, publicly available multi-view human pose datasets, Human 3.6M and MPI-INF-3DHP, to facilitate future research.
Spanet: Spatial Pyramid Attention Network for Enhanced Image Recognition

Attention mechanism has shown great success in computer vision. In this paper, we introduce Spatial Pyramid Attention Network (SPANet) to investigate the role of attention block for image recognition. Our SPANet is conceptually simple but practically powerful. It enhances the base network by adding Spatial Pyramid Attention (SPA) Blocks laterally. In contrast to other attention based networks that leverage global average pooling, our proposed SPANet considers both structural regularization and structural information. Furthermore, we investigate the topology structure of attention path connection and present three SPANet structures. SPA block is flexible to be deployed to various convolutional neural network (CNN) architectures. The experimental results show that our SPANet significantly improves the recognition accuracy without introducing much computation overhead compared with other CNN models. Codes are made publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> https://github.com/13952522076/SPANet.
Learning Multi-Object Tracking and Segmentation From Automatic Annotations

In this work we contribute a novel pipeline to automatically generate training data, and to improve over state-of-the-art multi-object tracking and segmentation (MOTS) methods. Our proposed track mining algorithm turns raw street-level videos into high-fidelity MOTS training data, is scalable and overcomes the need of expensive and time-consuming manual annotation approaches. We leverage state-of-the-art instance segmentation results in combination with optical flow predictions, also trained on automatically harvested training data. Our second major contribution is MOTSNet - a deep learning, tracking-by-detection architecture for MOTS - deploying a novel mask-pooling layer for improved object association over time. Training MOTSNet with our automatically extracted data leads to significantly improved sMOTSA scores on the novel KITTI MOTS dataset (+1.9%/+7.5% on cars/pedestrians), and MOTSNet improves by +4.1% over previously best methods on the MOTSChallenge dataset. Our most impressive finding is that we can improve over previous best-performing works, even in complete absence of manually annotated MOTS training data.
L<sup>2</sup>UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion

Images captured underwater often suffer from suboptimal illumination settings that can hide important visual features, reducing their quality. We present a novel single-image low-light underwater image enhancer, L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> UWE, that builds on our observation that an efficient model of atmospheric lighting can be derived from local contrast information. We create two distinct models and generate two enhanced images from them: one that highlights finer details, the other focused on darkness removal. A multi-scale fusion process is employed to combine these images while emphasizing regions of higher luminance, saliency and local contrast. We demonstrate the performance of L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> UWE by using seven metrics to test it against seven state-of-the-art enhancement methods specific to underwater and low-light scenes.
A Novel Privacy Approach of Digital Aerial Images Based on Mersenne Twister Method with DNA Genetic Encoding and Chaos

Aerial photography involves capturing images from aircraft and other flying objects, including Unmanned Aerial Vehicles (UAV). Aerial images are used in many fields and can contain sensitive information that requires secure processing. We proposed an innovative new cryptosystem for the processing of aerial images utilizing a chaos-based private key block cipher method so that the images are secure even on untrusted cloud servers. The proposed cryptosystem is based on a hybrid technique combining the Mersenne Twister (MT), Deoxyribonucleic Acid (DNA), and Chaotic Dynamical Rossler System (MT-DNA-Chaos) methods. The combination of MT with the four nucleotides and chaos sequencing creates an enhanced level of security for the proposed algorithm. The system is tested at three separate phases. The combined effects of the three levels improve the overall efficiency of the randomness of data. The proposed method is computationally agile, and offered more security than existing cryptosystems. To assess, this new system is examined against different statistical tests such as adjacent pixels correlation analysis, histogram consistency analyses and its variance, visual strength analysis, information randomness and uncertainty analysis, pixel inconsistency analysis, pixels similitude analyses, average difference, and maximum difference. These tests confirmed its validity for real-time communication purposes.