Reviewerly/exHarmony huggingface dataset:
Learning Combinatorial Solver for Graph Matching

Learning-based approaches to graph matching have been developed and explored for more than a decade, have grown rapidly in scope and popularity in recent years. However, previous learning-based algorithms, with or without deep learning strategy, mainly focus on the learning of node and/or edge affinities generation, and pay less attention on the learning of the combinatorial solver. In this paper we propose a fully trainable framework for graph matching, in which learning of affinities and solving for combinatorial optimization are not explicitly separated as in many previous arts. We firstly convert the problem of building node correspondences between two input graphs to the problem of selecting reliable nodes from a constructed assignment graph. Subsequently, the graph network block module is adopted to perform computation on the graph to form structured representations for each node. It finally predicts a label for each node that is used for node classification, and the training is performed under the supervision of both permutation differences and the one-to-one matching constraints. The proposed method is evaluated on four public benchmarks in comparison with several state-of-the-art algorithms, and the experimental results illustrate its excellent performance.
Assessing Image Quality Issues for Real-World Problems

We introduce a new large-scale dataset that links the assessment of image quality issues to two practical vision tasks: image captioning and visual question answering. First, we identify for 39,181 images taken by people who are blind whether each is sufficient quality to recognize the content as well as what quality flaws are observed from six options. These labels serve as a critical foundation for us to make the following contributions: (1) a new problem and algorithms for deciding whether an image is insufficient quality to recognize the content and so not captionable, (2) a new problem and algorithms for deciding which of six quality flaws an image contains, (3) a new problem and algorithms for deciding whether a visual question is unanswerable due to unrecognizable content versus the content of interest being missing from the field of view, and (4) a novel application of more efficiently creating a large-scale image captioning dataset by automatically deciding whether an image is insufficient quality and so should not be captioned. We publicly-share our datasets and code to facilitate future extensions of this work: https://vizwiz.org.
sureLDA: A multidisease automated phenotyping method for the electronic health record

Abstract Objective A major bottleneck hindering utilization of electronic health record data for translational research is the lack of precise phenotype labels. Chart review as well as rule-based and supervised phenotyping approaches require laborious expert input, hampering applicability to studies that require many phenotypes to be defined and labeled de novo. Though International Classification of Diseases codes are often used as surrogates for true labels in this setting, these sometimes suffer from poor specificity. We propose a fully automated topic modeling algorithm to simultaneously annotate multiple phenotypes. Materials and Methods Surrogate-guided ensemble latent Dirichlet allocation (sureLDA) is a label-free multidimensional phenotyping method. It first uses the PheNorm algorithm to initialize probabilities based on 2 surrogate features for each target phenotype, and then leverages these probabilities to constrain the LDA topic model to generate phenotype-specific topics. Finally, it combines phenotype-feature counts with surrogates via clustering ensemble to yield final phenotype probabilities. Results sureLDA achieves reliably high accuracy and precision across a range of simulated and real-world phenotypes. Its performance is robust to phenotype prevalence and relative informativeness of surogate vs nonsurrogate features. It also exhibits powerful feature selection properties. Discussion sureLDA combines attractive properties of PheNorm and LDA to achieve high accuracy and precision robust to diverse phenotype characteristics. It offers particular improvement for phenotypes insufficiently captured by a few surrogate features. Moreover, sureLDA’s feature selection ability enables it to handle high feature dimensions and produce interpretable computational phenotypes. Conclusions sureLDA is well suited toward large-scale electronic health record phenotyping for highly multiphenotype applications such as phenome-wide association studies .
Deep learning-based mobile augmented reality for task assistance using 3D spatial mapping and snapshot-based RGB-D data

This paper proposes a new deep learning-based mobile AR for intelligent task assistance by conducting 3D spatial mapping without pre-registration using AR markers, which can match virtual AR objects to their corresponding physical objects automatically and accurately using single snapshot-based RGB-D data. Firstly, the proposed approach applies a deep learning-based instance segmentation method to the snapshot-based RGB-D data to detect real object instances and to segment their surrounding regions in 3D point cloud data. Then, an iterative closest point (ICP) algorithm is used to perform a 3D spatial mapping between the segmented point cloud of the real object and its corresponding virtual model. Therefore, the virtual information can be seamlessly and automatically synchronized with its corresponding real object. To prove the effectiveness of the proposed method, we performed comparative experiments quantitatively and qualitatively, which evaluated the accuracy, basic task performance, and usability. Experimental results verify that the proposed deep learning-based 3D spatial mapping approach is more accurate and more suitable for mobile AR-based visualization and interaction than previous studies. We have also implemented several applications in actual working situations, which verifies the applicability and extensibility of the proposed approach.
Robust Movement-Specific Vehicle Counting at Crowded Intersections

With the demands of intelligent traffic, vehicle counting has become a vital problem, which can be used to mitigate traffic congestion and elevate the efficiency of the traffic light. Traditional vehicle counting problems focus on counting vehicles in a single frame or consecutive frames. Nevertheless, they are not expected to count vehicles by movements of interest (MOI), which can be pre-defined by all possible states of vehicles, combining different lanes and directions. In this paper, we mainly focus on movement-specific vehicle counting problem. A detection-tracking-counting (DTC) framework is applied, which detects and tracks objects in the region of interest (ROI), then counts those tracked trajectories by movements. To be specific, we propose the detection augmentation method and the Mahalanobis distance smoothness method to improve the multi-object tracking performance. For vehicle counting, a shape-based movement assignment method is carefully designed to categorize each trajectory by movements. Experiments are conducted on both the AICity 2020 Track-1 Dataset and the Vehicle-Track Dataset, which is built in this paper. Experimental results show the effectiveness and efficiency of our method.
Optical Flow in the Dark

Many successful optical flow estimation methods have been proposed, but they become invalid when tested in dark scenes because low-light scenarios are not considered when they are designed and current optical flow benchmark datasets lack low-light samples. Even if we preprocess to enhance the dark images, which achieves great visual perception, it still leads to poor optical flow results or even worse ones, because information like motion consistency may be broken while enhancing. We propose an end-to-end data-driven method that avoids error accumulation and learns optical flow directly from low-light noisy images. Specifically, we develop a method to synthesize large-scale low-light optical flow datasets by simulating the noise model on dark raw images. We also collect a new optical flow dataset in raw format with a large range of exposure to be used as a benchmark. The models trained on our synthetic dataset can relatively maintain optical flow accuracy as the image brightness descends and they outperform the existing methods greatly on low-light images.
Reinforcement Learning based Recommendation with Graph Convolutional Q-network

Reinforcement learning (RL) has been successfully applied to recommender systems. However, the existing RL-based recommendation methods are limited by their unstructured state/action representations. To address this limitation, we propose a novel way that builds high-quality graph-structured states/actions according to the user-item bipartite graph. More specifically, we develop an end-to-end RL agent, termed Graph Convolutional Q-network (GCQN), which is able to learn effective recommendation policies based on the inputs of the proposed graph-structured representations. We show that GCQN achieves significant performance margins over the existing methods, across different datasets and task settings.
Mineral–nutrient relationships in African soils assessed using cluster analysis of X-ray powder diffraction patterns and compositional methods

Soil mineral compositions are often complex and spatially diverse, with each mineral exhibiting characteristic chemical properties that determine the intrinsic total concentration of soil nutrients and their phyto-availability. Defining soil mineral-nutrient relationships is therefore important for understanding the inherent fertility of soils for sustainable nutrient management, and data-driven approaches such as cluster analysis allow for these relations to be assessed in new detail. Here the fuzzy-c-means clustering algorithm was applied to an X-ray powder diffraction (XRPD) dataset of 935 soils from sub-Saharan Africa, with each diffractogram representing a digital signature of a soil’s mineralogy. Nine mineralogically distinct clusters were objectively selected from the soil mineralogy continuum by retaining samples exceeding the 75% quantile of the membership coefficients in each cluster, yielding a dataset of 239 soils. As such, samples within each cluster represented mineralogically similar soils from different agro-ecological environments of sub-Saharan Africa. Mineral quantification based on the mean diffractogram of each cluster illustrated substantial mineralogical diversity between the nine groups with respect to quartz, K-feldspar, plagioclase, Fe/Al/Ti-(hydr)oxides, phyllosilicates (1:1 and 2:1), ferromagnesians, and calcite. Mineral–nutrient relationships were defined using the clustered XRPD patterns and corresponding measurements of total and/or extractable (Mehlich-3) nutrient concentrations (B, Mg, K, Ca, Mn, Fe, Ni, Cu and Zn) in combination with log-ratio compositional data analysis. Fe/Al/Ti/Mn-(hydr)oxides and feldspars were found to be the primary control of total nutrient concentrations, whereas 2:1 phyllosilicates were the main source of all extractable nutrients except for Fe and Zn. Kaolin minerals were the most abundant phyllosilicate group within the dataset but did not represent a nutrient source, which reflects the lack of nutrients within their chemical composition and their low cation exchange capacity. Results highlight how the mineral composition controls the total nutrient reserves and their phyto-availability in soils of sub-Saharan Africa. The typical characterisation of soils and their parent material based on the clay particle size fraction (i.e. texture) and/or the overall silica component (i.e. acid and basic rock types) alone may therefore mask the intricacies of mineral contributions to soil nutrient concentrations.
SubFlow: A Dynamic Induced-Subgraph Strategy Toward Real-Time DNN Inference and Training

We introduce SubFlow-a dynamic adaptation and execution strategy for a deep neural network (DNN), which enables real-time DNN inference and training. The goal of SubFlow is to complete the execution of a DNN task within a timing constraint that may dynamically change while ensuring comparable performance to executing the full network by executing a subset of the DNN at run-time. To this end, we propose two online algorithms that enable SubFlow: 1) dynamic construction of a sub-network which constructs the best subnetwork of the DNN in terms of size and configuration, and 2) time-bound execution which executes the sub-network within a given time budget either for inference or training. We implement and open-source SubFlow by extending TensorFlow with full compatibility by adding SubFlow operations for convolutional and fully-connected layers of a DNN. We evaluate SubFlow with three popular DNN models (LeNet-5, AlexNet, and KWS), which shows that it provides flexible run-time execution and increases the utility of a DNN under dynamic timing constraints, e.g., lx-6.7x range of dynamic execution speed with average -3% of performance (inference accuracy) difference. We also implement an autonomous robot as an example system that uses SubFlow and demonstrate that its obstacle detection DNN is flexibly executed to meet a range of deadlines that varies depending on its running sped.
GAME

Group recommendation aims to suggest preferred items to a group of users rather than to an individual user. Most existing methods on group recommendation directly learn theinherent interests of groups and users orinherent features of items, i.e., independently modeling the inherent embeddings of groups, users or items. However, the independent view severely suffers from the cold-start problem when making recommendations for occasional groups that are temporally formed by a set of users and have few interactions on items. Actually, the groups, users and items are interdependent because they interact with one another. The interdependencies constitute an interaction graph that provides multiple views to model the embeddings of groups, users and items from their interacting counterparts to improve recommendation for occasional groups. To this end, we propose a model, named GAME to learn the Graphical and Attentive Multi-view Embeddings (i.e., representations) for the groups, users and items from the independent view and counterpart views based on the interaction graph. In the counterpart views, the embedding of a group, user or item is aggregated from the interacting counterparts based on an attention mechanism that derives the adaptive weight for each counterpart. For instance, a user's embedding may be aggregated from her interacting items or groups. Further, GAME applies neural collaborative filtering to investigate the interactions between the multi-view embeddings of groups (or users) and items for group recommendation. Finally, we conduct extensive experiments on two real datasets. The experimental results show that GAME outperforms other state-of-the-art models, especially on both cold-start groups (i.e., occasional groups) and cold-start items.
OASIS: A Large-Scale Dataset for Single Image 3D in the Wild

Single-view 3D is the task of recovering 3D properties such as depth and surface normals from a single image. We hypothesize that a major obstacle to single-image 3D is data. We address this issue by presenting Open Annotations of Single Image Surfaces (OASIS), a dataset for single-image 3D in the wild consisting of annotations of detailed 3D geometry for 140,000 images. We train and evaluate leading models on a variety of single-image 3D tasks. We expect OASIS to be a useful resource for 3D vision research. Project site: https://pvl.cs.princeton.edu/OASIS.
On Joint Estimation of Pose, Geometry and svBRDF From a Handheld Scanner

We propose a novel formulation for joint recovery of camera pose, object geometry and spatially-varying BRDF. The input to our approach is a sequence of RGB-D images captured by a mobile, hand-held scanner that actively illuminates the scene with point light sources. Compared to previous works that jointly estimate geometry and materials from a hand-held scanner, we formulate this problem using a single objective function that can be minimized using off-the-shelf gradient-based solvers. By integrating material clustering as a differentiable operation into the optimization process, we avoid pre-processing heuristics and demonstrate that our model is able to determine the correct number of specular materials independently. We provide a study on the importance of each component in our formulation and on the requirements of the initial geometry. We show that optimizing over the poses is crucial for accurately recovering fine details and show that our approach naturally results in a semantically meaningful material segmentation.
Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition

Few-shot, fine-grained classification requires a model to learn subtle, fine-grained distinctions between different classes (e.g., birds) based on a few images alone. This requires a remarkable degree of invariance to pose, articulation and background. A solution is to use pose-normalized representations: first localize semantic parts in each image, and then describe images by characterizing the appearance of each part. While such representations are out of favor for fully supervised classification, we show that they are extremely effective for few-shot fine-grained classification. With a minimal increase in model capacity, pose normalization improves accuracy between 10 and 20 percentage points for shallow and deep architectures, generalizes better to new domains, and is effective for multiple few-shot algorithms and network backbones. Code is available at https://github.com/Tsingularity/PoseNorm_Fewshot.
Towards Real-Time Systems for Vehicle Re-Identification, Multi-Camera Tracking, and Anomaly Detection

Vehicle re-identification, multi-camera vehicle tracking, and anomaly detection are essential for city-scale intelligent transportation systems. Both vehicle re-id and multi-camera tracking are challenging due to variations in aspect-ratio, occlusion, and orientation. Robust re-id and tracking systems must consider small scale variations in a vehicle's appearance to accurately distinguish among vehicles of the same make, model, and color. Scalability is critical for multi-camera systems, as the number of objects in a scene is not known a-priori. Anomaly detection presents a unique challenge due to a dearth of annotations and varied video quality. In this paper, we address the task of vehicle re-id by introducing an unsupervised excitation layer to enhance representation learning. We propose a multi-camera tracking pipeline leveraging this re-id feature extractor to compute a distance matrix and perform clustering to obtain multi-camera vehicle trajectories. Lastly, we leverage background modeling techniques to localize anomalies such as stalled vehicles and collisions. We show the effectiveness of our proposed method on the NVIDIA AI City Challenge, where we obtain 7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> place out of 41 teams for the task of vehicle re-id, with an mAP score of 66.68% and achieve state-of-the-art results on the Vehicle-ID dataset. We also obtain an IDF1 score of 12.45% on multi-camera vehicle tracking, and an S4 score of 29.52% for task of anomaly detection, ranking in the top 5 for both tracks.
Detection of Social Network Spam Based on Improved Extreme Learning Machine

With the rapid advancement of the online social network, social media like Twitter has been increasingly critical to real life and become the prime objective of spammers. Twitter spam detection refers to a complex task for the involvement of a range of characteristics, and spam and non-spam have caused unbalanced data distribution in Twitter. To solve the mentioned problems, Twitter spam characteristics are analyzed as the user attribute, content, activity and relationship in this study, and a novel spam detection algorithm is designed based on regularized extreme learning machine, called the Improved Incremental Fuzzy-kernel-regularized Extreme Learning Machine (I2FELM), which is used to detect the Twitter spam accurately. As revealed from the experience validation results, the proposed I2FELM can efficiently identify the balanced and unbalanced dataset. Moreover, with few characteristics taken, the I2FELM can more effectively detect spam, which proves the effectiveness of the algorithm.
Kuwait University faculty’s use of electronic resources during the COVID-19 pandemic

Purpose This paper aims to examine the extent of benefits Kuwait University faculty received from the electronic resources offered by Kuwait University Libraries Administration, specifically during the COVID-19 pandemic in 2020. Design/methodology/approach An online survey was distributed using Google Forms via different social media platforms and official emails. The sample was Kuwait University faculty from all 16 colleges. Findings The results showed humble use of electronic resources during this critical time. The faculty mostly used the electronic resources for writing research papers, and databases and e-journals were their most preferred resources. The majority of nonusers indicated that they did not need these resources, and the rest referred to their unawareness of these resources. The results also showed that the majority of nonusers were willing to learn more about the electronic resources and how to use them. Originality/value The results and recommendations of this study are expected to be beneficial to Kuwait University and Kuwait University Libraries Administration, specifically, in terms of knowing what the faculty members use and prefer and the problems they face when searching electronic resources.
State Estimation using Extended Kalman Filter and Unscented Kalman Filter

In any linear system the Kalman Filter is highly used to tracking and estimation. Extended Kalman Filter is deal nonlinear system better than Kalman Filter. But the framework of Extended Kalman Filter is not easy to draw they requires some highly numerical terms in nature. So, there using a new method called Unscented Kalman Filter to provide an easy task to user with use of sigma focus points. Nonlinear approach is used to estimate the state of the System.
A New Membership Scaling Fuzzy C-Means Clustering Algorithm

Fuzzy c-means (FCM) is one of the most frequently used methods for clustering. However, with increasing amount of data, FCM suffers from slow convergence and a large amount of calculation because all samples are involved in updating the solutions per iteration without considering the current clustering results. In this article, a new membership scaling FCM (MSFCM) is proposed, based on the observation that the samples, whose nearest cluster center is <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</b> , aid the convergence of <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</b> , whereas the remaining samples prevent the convergence of <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</b> . In the new algorithm, many samples whose nearest cluster centers do not change in the next iteration are chosen by using the triangle inequality. A new scheme for scaling the membership degrees of the chosen samples is suggested to boost the effect of the in-cluster samples and to weaken the effect of the out-of-cluster samples in the clustering process. The new scheme not only accelerates the convergence of the algorithm but also maintains the high clustering quality. Many experimental results on synthetic and real-world data sets have verified the effectiveness of the proposed algorithm in improving the speed of the convergence of the fuzzy clustering. In particular, compared with FCM, MSFCM saves at least two thirds of the total rounds of iterations without significantly increasing the cost per iteration.