Reviewerly/exHarmony huggingface dataset:
Real-time Global Illumination Decomposition of Videos

We propose the first approach for the decomposition of a monocular color video into direct and indirect illumination components in real time. We retrieve, in separate layers, the contribution made to the scene appearance by the scene reflectance, the light sources, and the reflections from various coherent scene regions to one another. Existing techniques that invert global light transport require image capture under multiplexed controlled lighting or only enable the decomposition of a single image at slow off-line frame rates. In contrast, our approach works for regular videos and produces temporally coherent decomposition layers at real-time frame rates. At the core of our approach are several sparsity priors that enable the estimation of the per-pixel direct and indirect illumination layers based on a small set of jointly estimated base reflectance colors. The resulting variational decomposition problem uses a new formulation based on sparse and dense sets of non-linear equations that we solve efficiently using a novel alternating data-parallel optimization strategy. We evaluate our approach qualitatively and quantitatively and show improvements over the state-of-the-art in this field, in both quality and runtime. In addition, we demonstrate various real-time appearance editing applications for videos with consistent illumination.
Optimization of identification of non-stationary objects due to information properties and features of models

Abstract Constructive approaches, principles, methods of optimal identification of random time series of non-stationary objects with mechanisms of using statistical parameters, dynamic characteristics, specific features, and data patterns are proposed. Models and algorithms for hybrid identification have been developed that combine the capabilities of statistical, dynamic models, and a three-layer neural network. Mechanisms for adjusting model variables are implemented, taking into account the structural complexity, stochasticity of links between the elements of the random time series, ambiguity of dynamics, a large number of variables, the impact of the external environment. In the solutions of optimization problems, probabilistic and soft computing, self-organizing, predictive, adaptive properties of neural networks and dynamic modeling are used. A generalized algorithm for the identification of non-stationary objects is built on the basis of the mechanisms for identifying segments, boundaries, the general interval of element values, the selection of informative elements, and the formation of a training set. A simplified computational scheme for identifying non-stationary objects is implemented on the basis of 4-order orthogonal polynomials, a cubic extrapolation spline function, a linear Kalman filter, and a three-layer neural network. A software complex for identification in the C ++ language in the parallel computing environment “CUDA” has been developed and implemented.
An improved YOLO V3 for small vehicles detection in aerial images

Small vehicle detection in aerial images is a challenge in computer vision because small vehicles occupy less pixels and the environment around the small vehicles is complex. To improve the detection performance for the vehicles in aerial images, we propose an improved YOLO V3. The main contributions of our work include: (1) We redesign the backbone of YOLO V3 to select suitable scales for small vehicle detection in aerial images; (2) To make the improved YOLO V3 much stronger, we redesign the loss function of original YOLO V3 by GIOU loss and Focal loss; (3) To verify the performance of improved YOLO V3, we do the comparative experiments on VEDAI dataset. The experimental results show that the proposed method has obtained better performance than original YOLO V3 for small vehicle detection in aerial image.
Noise-Aware Merging of High Dynamic Range Image Stacks Without Camera Calibration

A near-optimal reconstruction of the radiance of a High Dynamic Range scene from an exposure stack can be obtained by modeling the camera noise distribution. The latent radiance is then estimated using Maximum Likelihood Estimation. But this requires a well-calibrated noise model of the camera, which is difficult to obtain in practice. We show that an unbiased estimation of comparable variance can be obtained with a simpler Poisson noise estimator, which does not require the knowledge of camera-specific noise parameters. We demonstrate this empirically for four different cameras, ranging from a smartphone camera to a full-frame mirrorless camera. Our experimental results are consistent for simulated as well as real images, and across different camera settings.
Relation Regularized Scene Graph Generation

Scene graph generation (SGG) is built on top of detected objects to predict object pairwise visual relations for describing the image content abstraction.Existing works have revealed that if the links between objects are given as prior knowledge, the performance of SGG is significantly improved.Inspired by this observation, in this article, we propose a relation regularized network (R2-Net), which can predict whether there is a relationship between two objects and encode this relation into object feature refinement and better SGG.Specifically, we first construct an affinity matrix among detected objects to represent the probability of a relationship between two objects.Graph convolution networks (GCNs) over this relation affinity matrix are then used as object encoders, producing relation-regularized representations of objects.With these relation-regularized features, our R2-Net can effectively refine object labels and generate scene graphs.Extensive experiments are conducted on the visual genome dataset for three SGG tasks (i.e., predicate classification, scene graph classification, and scene graph detection), demonstrating the effectiveness of our proposed method.Ablation studies also verify the key roles of our proposed components in performance improvement.
Consistency Regularization for Adversarial Robustness

Adversarial training (AT) is currently one of the most successful methods to obtain the adversarial robustness of deep neural networks. However, the phenomenon of robust overfitting, i.e., the robustness starts to decrease significantly during AT, has been problematic, not only making practitioners consider a bag of tricks for a successful training, e.g., early stopping, but also incurring a significant generalization gap in the robustness. In this paper, we propose an effective regularization technique that prevents robust overfitting by optimizing an auxiliary `consistency' regularization loss during AT. Specifically, we discover that data augmentation is a quite effective tool to mitigate the overfitting in AT, and develop a regularization that forces the predictive distributions after attacking from two different augmentations of the same instance to be similar with each other. Our experimental results demonstrate that such a simple regularization technique brings significant improvements in the test robust accuracy of a wide range of AT methods. More remarkably, we also show that our method could significantly help the model to generalize its robustness against unseen adversaries, e.g., other types or larger perturbations compared to those used during training. Code is available at https://github.com/alinlab/consistency-adversarial.
Training, Validation, and Test of Deep Learning Models for Classification of Receptor Expressions in Breast Cancers From Mammograms

PURPOSE The molecular subtype of breast cancer is an important component of establishing the appropriate treatment strategy. In clinical practice, molecular subtypes are determined by receptor expressions. In this study, we developed a model using deep learning to determine receptor expressions from mammograms. METHODS A developing data set and a test data set were generated from mammograms from the affected side of patients who were pathologically diagnosed with breast cancer from January 2006 through December 2016 and from January 2017 through December 2017, respectively. The developing data sets were used to train and validate the DL-based model with five-fold cross-validation for classifying expression of estrogen receptor (ER), progesterone receptor (PgR), and human epidermal growth factor receptor 2-neu (HER2). The area under the curves (AUCs) for each receptor were evaluated with the independent test data set. RESULTS The developing data set and the test data set included 1,448 images (997 ER-positive and 386 ER-negative, 641 PgR-positive and 695 PgR-negative, and 220 HER2-enriched and 1,109 non–HER2-enriched) and 225 images (176 ER-positive and 40 ER-negative, 101 PgR-positive and 117 PgR-negative, and 53 HER2-enriched and 165 non–HER2-enriched), respectively. The AUC of ER-positive or -negative in the test data set was 0.67 (0.58-0.76), the AUC of PgR-positive or -negative was 0.61 (0.53-0.68), and the AUC of HER2-enriched or non–HER2-enriched was 0.75 (0.68-0.82). CONCLUSION The DL-based model effectively classified the receptor expressions from the mammograms. Applying the DL-based model to predict breast cancer classification with a noninvasive approach would have additive value to patients.
Flatter Is Better

It is well known that explicit user ratings in recommender systems are biased toward high ratings and that users differ significantly in their usage of the rating scale. Implementers usually compensate for these issues through rating normalization or the inclusion of a user bias term in factorization models. However, these methods adjust only for the central tendency of users’ distributions. In this work, we demonstrate that a lack of flatness in rating distributions is negatively correlated with recommendation performance. We propose a rating transformation model that compensates for skew in the rating distribution as well as its central tendency by converting ratings into percentile values as a pre-processing step before recommendation generation. This transformation flattens the rating distribution, better compensates for differences in rating distributions, and improves recommendation performance. We also show that a smoothed version of this transformation can yield more intuitive results for users with very narrow rating distributions. A comprehensive set of experiments, with state-of-the-art recommendation algorithms in four real-world datasets, show improved ranking performance for these percentile transformations.
COVID-19 Postage Stamps—Messages in a Message

In living memory, no epidemic has generated a medical, scientific, social, and political response as massive, rapid, and global as that of COVID-19, which quickly became a global public health emergency recognized by the World Health Organization in January 2020 The commemoration of major events takes many forms, including the issuing of postage stamps It is a mass medium used to commemorate events or raise public awareness about topics of wide social interest or influence Educational and financial messages about infectious diseases on stamps appeared in 1904 when Danish postal clerk Einar Holboll developed the idea of raising money for tuberculosis by adding a surcharge to the sale of a Christmas seal stamp With the rise of email the popular cultural significance of postage stamps has diminished, but they still remain a way for governments and nongovernment organizations to communicate on a broad scale and to raise funds
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy

Neural network pruning is a popular technique used to reduce the inference costs of modern, potentially overparameterized, networks. Starting from a pre-trained network, the process is as follows: remove redundant parameters, retrain, and repeat while maintaining the same test accuracy. The result is a model that is a fraction of the size of the original with comparable predictive performance (test accuracy). Here, we reassess and evaluate whether the use of test accuracy alone in the terminating condition is sufficient to ensure that the resulting model performs well across a wide spectrum of harder metrics such as generalization to out-of-distribution data and resilience to noise. Across evaluations on varying architectures and data sets, we find that pruned networks effectively approximate the unpruned model, however, the prune ratio at which pruned networks achieve commensurate performance varies significantly across tasks. These results call into question the extent of \emph{genuine} overparameterization in deep learning and raise concerns about the practicability of deploying pruned networks, specifically in the context of safety-critical systems, unless they are widely evaluated beyond test accuracy to reliably predict their performance. Our code is available at this https URL.
Classification with ensembles and case study on functional magnetic resonance imaging

The ensemble is a technique that strategically combines basic models to achieve better accuracy rates. Diversity, combination methods, and selection topology are the main factors determining ensemble performance. Consequently, it is a challenging task to design an efficient ensemble scheme. Even though numerous paradigms have been proposed to classify ensemble schemes, there is still much room for improvement. This paper proposes a general framework for creating ensembles in the context of classification. Specifically, the ensemble framework consists of four stages: objectives, data preparing, model training, and model testing. It is comprehensive to design diverse ensembles. The proposed ensemble approach can be used for a wide variety of machine learning tasks. We validate our approach on real-world datasets. The experimental results show the efficiency of the proposed approach.
Wasserstein Generative Adversarial Networks for Realistic Traffic Sign Image Generation

Recently, Convolutional neural networks (CNN) with properly annotated training data and results will obtain the best traffic sign detection (TSD) and traffic sign recognition (TSR). The efficiency of the whole system depends on the data collection, based on neural networks. The traffic sign datasets in most countries around the world are therefore difficult to identify because they are so different from one to others. To solve this issue, we need to generate a synthetic image to enlarge our dataset. We employ Wasserstein generative adversarial networks (Wasserstein GAN, WGAN) to synthesize realistic and various added training images to supply the data deficiency in the original image distribution. This research explores primarily how the WGAN images with different parameters are generated in terms of consistency. For training, we use a real image with a different number and scale. Moreover, the Image quality was measured using the Structural Similarity Index (SSIM) and the Mean Square Error (MSE). The SSIM values between images and their respective actual images were calculated in our work. The images generated exhibit high similarity to the original image when using more training images. Our experiment results find the most leading SSIM values reached when using 200 total images as input, images size 32 × 32, and epoch 2000.
Software Development Sistem Informasi Kursus Mengemudi (Kasus: Kursus Mengemudi Widi Mandiri)

Widi Mandiri is an institution that provides training courses for driving four-wheeled vehicles. In managing data, Widi Mandiri uses records in certain forms or books from participant registration, scheduling and all business processes. With the existing system, there are several obstacles that hinder the conduct of business at Widi Mandiri. For that we need a driving course information system that helps in the management of all business activities at Widi Mandiri. The system development is carried out by using the extreme programming (XP) system development approach. The research has produced a system that can manage driving course data from registration of course participants, booking courses and cars to scheduling according to the schedule of course participants and instructors. Based on usability testing, the average value was 85.8%, and was in the good category
Local Anomaly Detection in Videos Using Object-Centric Adversarial Learning

We propose a novel unsupervised approach based on a two-stage object-centric adversarial framework that only needs object regions for detecting frame-level local anomalies in videos. The first stage consists in learning the correspondence between the current appearance and past gradient images of objects in scenes deemed normal, allowing us to either generate the past gradient from current appearance or the reverse. The second stage extracts the partial reconstruction errors between real and generated images (appearance and past gradient) with normal object behaviour, and trains a discriminator in an adversarial fashion. In inference mode, we employ the trained image generators with the adversarially learned binary classifier for outputting region-level anomaly detection scores. We tested our method on four public benchmarks, UMN, UCSD, Avenue and ShanghaiTech and our proposed object-centric adversarial approach yields competitive or even superior results compared to state-of-the-art methods.
interacCircos: an R package based on JavaScript libraries for the generation of interactive circos plots

Abstract Summary JavaScript-based Circos libraries have been widely implemented to generate interactive Circos plots in web applications. However, these libraries require either local installation, which requires the compilation of extra libraries, or extra data processing procedures to prepare input and configuration for each track of plot, which limits the utility and capability of integration with powerful R packages. In this report, we present interacCircos, an R package for creating interactive Circos plots through the integration of JavaScript-based libraries. interacCircos can simply and flexibly implement 14 track-plot functions and 7 auxiliary functions for presenting large-scale genomic data in interactive Circos plots. Availability and implementation InteracCircos and its manual are freely available at https://github.com/mrcuizhe/interacCircos under the GPL license. The online documentation is available at https://mrcuizhe.github.io/interacCircos_documentation/index.html. Supplementary information Supplementary data are available at Bioinformatics online.
SIMPLE: SIngle-network with Mimicking and Point Learning for Bottom-up Human Pose Estimation

The practical application requests both accuracy and efficiency on multi-person pose estimation algorithms. But the high accuracy and fast inference speed are dominated by top-down methods and bottom-up methods respectively. To make a better trade-off between accuracy and efficiency, we propose a novel multi-person pose estimation framework, SIngle-network with Mimicking and Point Learning for Bottom-up Human Pose Estimation (SIMPLE). Specifically, in the training process, we enable SIMPLE to mimic the pose knowledge from the high-performance top-down pipeline, which significantly promotes SIMPLE's accuracy while maintaining its high efficiency during inference. Besides, SIMPLE formulates human detection and pose estimation as a unified point learning framework to complement each other in single-network. This is quite different from previous works where the two tasks may interfere with each other. To the best of our knowledge, both mimicking strategy between different method types and unified point learning are firstly proposed in pose estimation. In experiments, our approach achieves the new state-of-the-art performance among bottom-up methods on the COCO, MPII and PoseTrack datasets. Compared with the top-down approaches, SIMPLE has comparable accuracy and faster inference speed.
A comparative analysis of system features used in the TREC-COVID information retrieval challenge

The COVID-19 pandemic has resulted in a rapidly growing quantity of scientific publications from journal articles, preprints, and other sources. The TREC-COVID Challenge was created to evaluate information retrieval (IR) methods and systems for this quickly expanding corpus. Using the COVID-19 Open Research Dataset (CORD-19), several dozen research teams participated in over 5 rounds of the TREC-COVID Challenge. While previous work has compared IR techniques used on other test collections, there are no studies that have analyzed the methods used by participants in the TREC-COVID Challenge. We manually reviewed team run reports from Rounds 2 and 5, extracted features from the documented methodologies, and used a univariate and multivariate regression-based analysis to identify features associated with higher retrieval performance. We observed that fine-tuning datasets with relevance judgments, MS-MARCO, and CORD-19 document vectors was associated with improved performance in Round 2 but not in Round 5. Though the relatively decreased heterogeneity of runs in Round 5 may explain the lack of significance in that round, fine-tuning has been found to improve search performance in previous challenge evaluations by improving a system’s ability to map relevant queries and phrases to documents. Furthermore, term expansion was associated with improvement in system performance, and the use of the narrative field in the TREC-COVID topics was associated with decreased system performance in both rounds. These findings emphasize the need for clear queries in search. While our study has some limitations in its generalizability and scope of techniques analyzed, we identified some IR techniques that may be useful in building search systems for COVID-19 using the TREC-COVID test collections.
End-to-end joint spectral–spatial compression and reconstruction of hyperspectral images using a 3D convolutional autoencoder

Hyperspectral images (HSIs) must be preprocessed by a compression model, which reduces the pressure of storing and transmitting huge data in applications. Whereas most of the existing methods consider only the compression or reconstruction requirements, an end-to-end optimization would simultaneously improve the performance of both requirements. We propose a three-dimensional convolutional autoencoder that precisely achieves end-to-end joint spectral–spatial compression and reconstruction of HSIs. In an experimental evaluation, the proposed method improved the spectral angle mapper, peak signal-to-noise ratio, and structural similarity index measurement of the reconstructed HSIs by 20.8% to 33.1%, 0.9% to 11.5%, and 0.5% to 2.2%, respectively, relative to competitive methods.
The Development and Evaluation of Interactional Competence Elicitor for Oral Language Assessments

This paper describes the development and evaluation of Interaction Competence Elicitor (ICE), a spoken dialog system (SDS) for the delivery of a paired oral discussion task in the context of language assessment. The purpose of ICE is to sustain a topic‐specific conversation with a test taker in order to elicit discourse that can be later judged to assess the test taker's oral language ability, including interactional competence. The development of ICE is reported in detail to provide guidance for future developers of similar systems. The performance of ICE is evaluated on two aspects: (a) by analyzing system errors that occur at different stages in the natural language processing (NLP) pipeline in terms of both their preventability and their impact on the downstream stages of the pipeline, and (b) by analyzing questionnaire and semistructured interview data to establish the test takers' experience with the system. Findings suggest that ICE was robust in 90% of the dialog turns it produced, and test takers noted both positive and negative aspects of communicating with the system as opposed to a human interlocutor. We conclude that this prototype system lays important groundwork for the development and use of specialized SDSs in the assessment of oral communication, which includes interactional competence.