Reviewerly/exHarmony huggingface dataset:
Consistency-based Semi-supervised Learning for Object detection

Making a precise annotation in a large dataset is crucial to the performance of object detection. While the object detection task requires a huge number of annotated samples to guarantee its performance, placing bounding boxes for every object in each sample is time-consuming and costs a lot. To alleviate this problem, we propose a Consistency-based Semi-supervised learning method for object Detection (CSD), which is a way of using consistency constraints as a tool for enhancing detection performance by making full use of available unlabeled data. Specifically, the consistency constraint is applied not only for object classification but also for the localization. We also proposed Background Elimination (BE) to avoid the negative effect of the predominant backgrounds on the detection performance. We have evaluated the proposed CSD both in single-stage and two-stage detectors and the results show the effectiveness of our method.
Pretrained Transformers for Simple Question Answering over Knowledge Graphs

Answering simple questions over knowledge graphs is a well-studied problem in question answering. Previous approaches for this task built on recurrent and convolutional neural network based architectures that use pretrained word embeddings. It was recently shown that finetuning pretrained transformer networks (e.g. BERT) can outperform previous approaches on various natural language processing tasks. In this work, we investigate how well BERT performs on SimpleQuestions and provide an evaluation of both BERT and BiLSTM-based models in limited-data scenarios.
Multi-Feature Fusion in Particle Filter Framework for Visual Tracking

In this article, a particle filter based tracking algorithm is proposed to track a target in video with vivid and complex environments. The target is represented in feature space by both color distribution and KAZE features. Color distribution is selected for its robustness to target's scale variation and partial occlusion. KAZE features are chosen for their ability to represent the target structure and also for their superior performance in feature matching. Fusion of these two features will lead to effective tracking as compared to other features due to their better representational abilities, under challenging conditions. The trajectory of the target is established using the particle filter algorithm based on similarity between the extracted features from the target and the probable candidates in the consecutive frames. For the color distribution model, Bhattacharya coefficient is used as a similarity metric whereas Nearest Neighbor Distance Ratio is used for matching of corresponding feature points in KAZE algorithm. The particle filter update model is based on kinematic motion equations and the weights on particles are governed by an equation fusing both the color and KAZE features. Centre Location Error, Average Tracking Accuracy and Tracking Success Rate are the performance metrics considered in the evaluation process. Also, the overlap success plot and precision plot is considered for performance evaluation. On the basis of these metrics and visual results obtained under different environment conditions: outdoor, occluding and underwater ones, the proposed tracking scheme performs significantly better than the contemporary feature-based iterative object tracking methods and even few of the learning-based algorithms.
Fast Structured Decoding for Sequence Models

Autoregressive sequence models achieve state-of-the-art performance in domains like machine translation. However, due to the autoregressive factorization nature, these models suffer from heavy latency during inference. Recently, non-autoregressive sequence models were proposed to speed up the inference time. However, these models assume that the decoding process of each token is conditionally independent of others. Such a generation process sometimes makes the output sentence inconsistent, and thus the learned non-autoregressive models could only achieve inferior accuracy compared to their autoregressive counterparts. To improve then decoding consistency and reduce the inference cost at the same time, we propose to incorporate a structured inference module into the non-autoregressive models. Specifically, we design an efficient approximation for Conditional Random Fields (CRF) for non-autoregressive sequence models, and further propose a dynamic transition technique to model positional contexts in the CRF. Experiments in machine translation show that while increasing little latency (8~14ms, our model could achieve significantly better translation performance than previous non-autoregressive models on different translation datasets. In particular, for the WMT14 En-De dataset, our model obtains a BLEU score of 26.80, which largely outperforms the previous non-autoregressive baselines and is only 0.61 lower in BLEU than purely autoregressive models.
Non-interactive zero-knowledge and its applications

research-article Share on Non-interactive zero-knowledge and its applications Authors: Manuel Blum Univ. of Calif. Univ. of Calif.View Profile , Paul Feldman MIT MITView Profile , Silvio Micali MIT MITView Profile Authors Info & Claims Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio MicaliOctober 2019 Pages 329–349https://doi.org/10.1145/3335741.3335757Published:04 October 2019Publication History 29citation576DownloadsMetricsTotal Citations29Total Downloads576Last 12 Months44Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Duplicate Questions Pair Detection Using Siamese MaLSTM

Quora is a growing platform comprising a user generated collection of questions and answers. The questions and answers are created, edited, and organized by the users. Enormous number of users on the Quora website makes it unavoidable to have multiple questions from different users with similar intent, which raises the issue of duplicate questions. Effectively detecting duplicate questions would make it easier to find high quality answers and help save time, which in turn would result in an improved user experience for writers and readers on Quora. In this paper, Quora Question Pairs dataset is collected from Kaggle for detection of duplicate questions. First, three types of word embeddings involving Google news vector embedding, FastText crawl embedding with 300 dimensions, and FastText crawl sub words embedding with 300 dimensions are implemented individually to vectorize all the questions and train the model. The final features used for prediction are blend of these three types of word embeddings. Then, Siamese MaLSTM (“Ma” for Manhattan distance) Neural Network model is applied for prediction of duplicate questions in the dataset. Finally, the model is tested on 100000 pairs of questions. The experiments show that the proposed model achieves 91.14% accuracy which is better than the state-of-the-art models.
Cybersickness in Virtual Reality Head-Mounted Displays: Examining the Influence of Sex Differences and Vehicle Control

Motion sickness is more common among women than among men. In vehicles, motion sickness is more common among passengers than among drivers. We asked whether these two effects might interact. In a yoked-control design using a head-mounted display, one member of each pair drove a virtual automobile, while the other member watched a recording of the driver’s performance. Overall reports of motion sickness were representative of previous research. We found no evidence that the incidence of motion sickness, or the severity of motion sickness symptoms differed between the sexes, or between drivers and passengers. However, among participants who discontinued early, the exposure time for female drivers was significantly less than for male drivers. The results confirm that motion sickness is a common effect of HMD use, and suggest that in virtual environments sex differences in motion sickness may vary with specific tasks.
Blockchain-Empowered Socially Optimal Transactive Energy System: Framework and Implementation

Transactive energy plays a key role in the operation and energy management of future power systems. However, the conventional operational mechanism, which follows a centralized design, is often less secure, vulnerable to malicious behaviors, and suffers from privacy leakage. In this article, we introduce blockchain technology in transactive energy to address these challenges. Specifically, we develop a novel blockchain-based transactive energy framework for prosumers and design a decentralized energy trading algorithm that matches the operation of the underlying blockchain system. We prove that the trading algorithm improves the individual benefit and guarantees the socially optimal performance, and thus, incentivizes prosumers to join the transactive energy platform. Moreover, we evaluate the feasibility of the transactive energy platform throughout the implementation of a small-scale network of Internet of Things devices and extensive simulations using real-world data. Our results show that this blockchain-based transactive energy platform is feasible in practice, and the decentralized trading algorithm reduces the user's individual cost by up to 77% and lowers the overall cost by 24%.
Deep convolutional neural networks in the face of caricature

Real-world face recognition requires us to perceive the uniqueness of a face across variable images. Deep convolutional neural networks (DCNNs) accomplish this feat by generating robust face representations that can be analysed in a multidimensional ‘face space’. We examined the organization of viewpoint, illumination, gender and identity in this space. We found that DCNNs create a highly organized face similarity structure in which identities and images coexist. Natural image variation is organized hierarchically, with face identity nested under gender, and illumination and viewpoint nested under identity. To examine identity, we caricatured faces and found that identification accuracy increased with the strength of identity information in a face, and caricature representations ‘resembled’ their veridical counterparts—mimicking human perception. DCNNs therefore offer a theoretical framework for reconciling decades of behavioural and neural results that emphasized either the image or the face in representations, without understanding how a neural code could seamlessly accommodate both. Human face recognition is robust to changes in viewpoint, illumination, facial expression and appearance. The authors investigated face recognition in deep convolutional neural networks by manipulating the strength of identity information in a face by caricaturing. They found that networks create a highly organized face similarity structure in which identities and images coexist.
Multi-objective Reinforced Evolution in Mobile Neural Architecture Search

Fabricating neural models for a wide range of mobile devices is a challenging task due to highly constrained resources. Recent trends favor neural architecture search involving evolutionary algorithms (EA) and reinforcement learning (RL), however, they are separately used. In this paper, we present a novel multi-objective algorithm called MoreMNAS (Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search) by leveraging good virtues from both sides. Particularly, we devise a variant of multi-objective genetic algorithm NSGA-II, where mutations are performed either by reinforcement learning or a natural mutating process. It maintains a delicate balance between exploration and exploitation. Not only does it prevent the search degradation, but it also makes use of the learned knowledge. Our experiments conducted in Super-resolution domain (SR) deliver rivaling models compared with some state-of-the-art methods at fewer FLOPS (Evaluation code can be found at https://github.com/xiaomi-automl/MoreMNAS ).
Topology-Aware Graph Pooling Networks

Pooling operations have shown to be effective on computer vision and natural language processing tasks. One challenge of performing pooling operations on graph data is the lack of locality that is not well-defined on graphs. Previous studies used global ranking methods to sample some of the important nodes, but most of them are not able to incorporate graph topology. In this work, we propose the topology-aware pooling (TAP) layer that explicitly considers graph topology. Our TAP layer is a two-stage voting process that selects more important nodes in a graph. It first performs local voting to generate scores for each node by attending each node to its neighboring nodes. The scores are generated locally such that topology information is explicitly considered. In addition, graph topology is incorporated in global voting to compute the importance score of each node globally in the entire graph. Altogether, the final ranking score for each node is computed by combining its local and global voting scores. To encourage better graph connectivity in the sampled graph, we propose to add a graph connectivity term to the computation of ranking scores. Results on graph classification tasks demonstrate that our methods achieve consistently better performance than previous methods.
On Self-Contact and Human Pose

People touch their face 23 times an hour, they cross their arms and legs, put their hands on their hips, etc. While many images of people contain some form of self-contact, current 3D human pose and shape (HPS) regression methods typically fail to estimate this contact. To address this, we develop new datasets and methods that significantly improve human pose estimation with self-contact. First, we create a dataset of 3D Contact Poses (3DCP) containing SMPL-X bodies fit to 3D scans as well as poses from AMASS, which we refine to ensure good contact. Second, we leverage this to create the Mimic-The-Pose (MTP) dataset of images, collected via Amazon Mechanical Turk, containing people mimicking the 3DCP poses with self-contact. Third, we develop a novel HPS optimization method, SMPLify-XMC, that includes contact constraints and uses the known 3DCP body pose during fitting to create near ground-truth poses for MTP images. Fourth, for more image variety, we label a dataset of in-the-wild images with Discrete Self-Contact (DSC) information and use another new optimization method, SMPLify-DC, that exploits discrete contacts during pose optimization. Finally, we use our datasets during SPIN training to learn a new 3D human pose regressor, called TUCH (Towards Understanding Contact in Humans). We show that the new self-contact training data significantly improves 3D human pose estimates on withheld test data and existing datasets like 3DPW. Not only does our method improve results for self-contact poses, but it also improves accuracy for non-contact poses. The code and data are available for research purposes at https://tuch.is.tue.mpg.de.
NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions

We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. Based on the dataset, we set up multi-choice and open-ended QA tasks targeting causal action reasoning, temporal action reasoning, and common scene comprehension. Through extensive analysis of baselines and established VideoQA techniques, we find that top-performing methods excel at shallow scene descriptions but are weak in causal and temporal action reasoning. Furthermore, the models that are effective on multi-choice QA, when adapted to open-ended QA, still struggle in generalizing the answers. This raises doubt on the ability of these models to reason and highlights possibilities for improvement. With detailed results for different question types and heuristic observations for future works, we hope NExT-QA will guide the next generation of VQA research to go beyond superficial description towards a deeper understanding of videos. (The dataset and related resources are available at https://github.com/doc-doc/NExT-QA.git).
Pose-Guided Feature Disentangling for Occluded Person Re-identification Based on Transformer

Occluded person re-identification is a challenging task as human body parts could be occluded by some obstacles (e.g. trees, cars, and pedestrians) in certain scenes. Some existing pose-guided methods solve this problem by aligning body parts according to graph matching, but these graph-based methods are not intuitive and complicated. Therefore, we propose a transformer-based Pose-guided Feature Disentangling (PFD) method by utilizing pose information to clearly disentangle semantic components (e.g. human body or joint parts) and selectively match non-occluded parts correspondingly. First, Vision Transformer (ViT) is used to extract the patch features with its strong capability. Second, to preliminarily disentangle the pose information from patch information, the matching and distributing mechanism is leveraged in Pose-guided Feature Aggregation (PFA) module. Third, a set of learnable semantic views are introduced in transformer decoder to implicitly enhance the disentangled body part features. However, those semantic views are not guaranteed to be related to the body without additional supervision. Therefore, Pose-View Matching (PVM) module is proposed to explicitly match visible body parts and automatically separate occlusion features. Fourth, to better prevent the interference of occlusions, we design a Pose-guided Push Loss to emphasize the features of visible body parts. Extensive experiments over five challenging datasets for two tasks (occluded and holistic Re-ID) demonstrate that our proposed PFD is superior promising, which performs favorably against state-of-the-art methods. Code is available at https://github.com/WangTaoAs/PFD_Net
Camouflaged Object Detection via Context-Aware Cross-Level Fusion

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{C}^{2}\text{F}$ </tex-math></inline-formula> -Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Module (ACFM), which further integrates the features under the guidance of attention coefficients. We then propose a Dual-branch Global Context Module (DGCM) to refine the fused features for informative feature representations by exploiting rich global context information. Multiple ACFMs and DGCMs are integrated in a cascaded manner for generating a coarse prediction from high-level features. The coarse prediction acts as an attention map to refine the low-level features before passing them to our Camouflage Inference Module (CIM) to generate the final prediction. We perform extensive experiments on three widely used benchmark datasets and compare <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{C}^{2}\text{F}$ </tex-math></inline-formula> -Net with state-of-the-art (SOTA) models. The results show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{C}^{2}\text{F}$ </tex-math></inline-formula> -Net is an effective COD model and outperforms SOTA models remarkably. Further, an evaluation on polyp segmentation datasets demonstrates the promising potentials of our <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{C}^{2}\text{F}$ </tex-math></inline-formula> -Net in COD downstream applications. Our code is publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Ben57882/C2FNet-TSCVT</uri>
Challenges and Opportunities for Using Big Health Care Data to Advance Medical Science and Public Health

Methodological advancements in epidemiology, biostatistics, and data science have strengthened the research world’s ability to use data captured from electronic health records (EHRs) to address pressing medical questions, but gaps remain. We describe methods investments that are needed to curate EHR data toward research quality and to integrate complementary data sources when EHR data alone are insufficient for research goals. We highlight new methods and directions for improving the integrity of medical evidence generated from pragmatic trials, observational studies, and predictive modeling. We also discuss needed methods contributions to further ease data sharing across multisite EHR data networks. Throughout, we identify opportunities for training and for bolstering collaboration among subject matter experts, methodologists, practicing clinicians, and health system leaders to help ensure that methods problems are identified and resulting advances are translated into mainstream research practice more quickly.
Inequality thresholds, governance and gender economic inclusion in sub-Saharan Africa

Inequality and gender economic exclusion are major policy concerns facing sub-Saharan Africa in the post-2015 development agenda. This paper identifies ‘thresholds’ of inequality that should not be exceeded if governance is to promote gender economic participation. The research focuses on 42 countries in sub-Saharan Africa using annual data from 2004 to 2014. The empirical work utilises the Generalized Method of Moments. The following findings are established. First, inequality (i.e. the Gini coefficient) levels that completely nullify the positive effect of governance on female labour force participation are 0.708 for political stability, 0.601 for voice & accountability, 0.588 for government effectiveness, 0.631 for regulatory quality, 0.612 for the rule of law, and 0.550 for corruption-control. Second, inequality thresholds at which female unemployment can no longer be mitigated by governance channels include: 0.561 for political stability and 0.465 for the rule of law. Third, inequality levels that completely dampen the positive impact of governance on female employment are 0.608 for political stability, 0.580 for voice & accountability, 0.581 for government effectiveness, and 0.557 for the rule of law. As the main policy implication, for good governance to promote gender economic inclusion, inequality levels should not exceed such thresholds.
TARGER: Neural Argument Mining at Your Fingertips

We present TARGER, an open source neural argument mining framework for tagging arguments in free input texts and for keyword-based retrieval of arguments from an argument-tagged web-scale corpus.The currently available models are pre-trained on three recent argument mining datasets and enable the use of neural argument mining without any reproducibility effort on the user's side.The open source code ensures portability to other domains and use cases, such as an application to search engine ranking that we also describe shortly.