Reviewerly/exHarmony huggingface dataset:
ABP: Adaptive Body Partition Model For Visible Infrared Person Re-Identification

Person re-identification (Re-ID) aims to match pedestrian images cross multiple cameras. Most Re-ID studies focus on visible pedestrian images, without considering the images obtained by infrared cameras in the dark. To solve the cross-modality person Re-ID problem, current methods usually exploit global feature descriptors to obtain discriminative representations. However, they ignore the fine-grained information of heterogeneous images. In this paper, we propose an adaptive body partition (ABP) model to automatically detect and distinguish effective part representations. Instead of utilizing a two-stream convolutional neural network (CNN) to extract modality-specific information, we directly design an end-to-end one-stream CNN to simultaneously learn multi-modality sharable features and map them on a common space. Global loss, part losses and threefold triplet loss are integrated to enhance the feature discriminability and minimize the cross-modality gap. Extensive experimental results on two cross-modality Re-ID datasets exhibit the superiority of the proposed method compared with the state-of-the-art solutions.
Deep Neural Networks for Earthquake Detection and Source Region Estimation in North-Central Venezuela

ABSTRACT Reliable earthquake detection algorithms are necessary to properly analyze and catalog the continuously growing seismic records. We report the results of applying a deep convolutional neural network, called UPC-UCV (Universitat Politecnica de Catalunya - Universidad Central de Venezuela), over single-station three-channel signal windows for P-wave earthquake detection and source region estimation in north-central Venezuela. The analysis is performed on a new dataset of handpicked arrivals of P waves from local events, named CARABOBO, built and made public for reproducibility and benchmarking purposes. The CARABOBO dataset consists of three-channel continuous data recorded by the broadband stations of the Venezuelan Foundation for Seismological Research in the region of 9.5°–11.5°N and 67.0°–69.0°W during the time period from April 2018 to April 2019. During this period, 949 earthquakes were recorded in that area, corresponding to earthquakes with magnitudes in the range from Mw 1.1 to 5.2. To estimate the epicentral source region of a detected event, the proposed network employs geographical distribution of the CARABOBO dataset into K clusters as a basis. This geographical partitioning is automatically performed by the k-means algorithm, and the optimality of the K-values for our dataset has been assessed using the elbow (K=5) and silhouette (K=3) methods. For target seismicity, the proposed network achieves 95.27% detection accuracy and 93.36% source region estimation accuracy, when using K=5 geographic clusters. The location accuracy slightly increases to 95.68% in the case of K=3 geographic partitions. The detection capability of this network has also been tested on the OKLAHOMA dataset, which compiles more than 2000 local earthquakes that occurred in this U.S. state. Without any modification, the proposed network yields excellent detection results when trained and evaluated on that dataset (98.21% accuracy; ConvNetQuake, fine-tuned for this dataset, achieves a 97.32% accuracy), corresponding to a totally different geographical region.
Ultra Low Bitrate Learned Image Compression by Selective Detail Decoding

Neural network-based learned image compression has a special feature in that a differentiable image quality index can be used as a loss function directly, and a decoder and an encoder can be optimized by the quality index through end-to-end learning. From a perceptual view, we hypothesized that there were detailed important parts in pictures. For those parts, we applied an additional decoder and weighted loss function to achieve both low bitrate image compression and perceptual quality. Furthermore, our approach can automatically determine which region an additional decoder will take for an input image. Experiments visually showed that the proposed method can recognize important parts, such as text and faces, and we show that our method can decode images more clearly than the simple MS-SSIM training model.
Learning Augmentation Network via Influence Functions

Data augmentation can impact the generalization performance of an image classification model in a significant way. However, it is currently conducted on the basis of trial and error, and its impact on the generalization performance cannot be predicted during training. This paper considers an influence function that predicts how generalization performance, in terms of validation loss, is affected by a particular augmented training sample. The influence function provides an approximation of the change in validation loss without actually comparing the performances that include and exclude the sample in the training process. Based on this function, a differentiable augmentation network is learned to augment an input training sample to reduce validation loss. The augmented sample is fed into the classification network, and its influence is approximated as a function of the parameters of the last fully-connected layer of the classification network. By backpropagating the influence to the augmentation network, the augmentation network parameters are learned. Experimental results on CIFAR-10, CIFAR-100, and ImageNet show that the proposed method provides better generalization performance than conventional data augmentation methods do.
Fast Dimensional Analysis for Root Cause Investigation in a Large-Scale Service Environment

Root cause analysis in a large-scale production environment is challenging due to the complexity and scale of the services running across global data centers. It is often difficult to review the logs jointly for understanding production issues given the distributed nature of the system. Additionally, there could easily be millions of entities, each described by hundreds of features. In this paper we present a fast dimensional analysis framework that automates the root cause analysis on structured logs with improved scalability.
NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks

Deciding the amount of neurons during the design of a deep neural network to maximize performance is not intuitive. In this work, we attempt to search for the neuron (filter) configuration of a fixed network architecture that maximizes accuracy. Using iterative pruning methods as a proxy, we parametrize the change of the neuron (filter) number of each layer with respect to the change in parameters, allowing us to efficiently scale an architecture across arbitrary sizes. We also introduce architecture descent which iteratively refines the parametrized function used for model scaling. The combination of both proposed methods is coined as NeuralScale. To prove the efficiency of NeuralScale in terms of parameters, we show empirical simulations on VGG11, MobileNetV2 and ResNet18 using CIFAR10, CIFAR100 and TinyImageNet as benchmark datasets. Our results show an increase in accuracy of 3.04%, 8.56% and 3.41% for VGG11, MobileNetV2 and ResNet18 on CIFAR10, CIFAR100 and TinyImageNet respectively under a parameter-constrained setting (output neurons (filters) of default configuration with scaling factor of 0.25).
Action Modifiers: Learning From Adverbs in Instructional Videos

We present a method to learn a representation for adverbs from instructional videos using weak supervision from the accompanying narrations.Key to our method is the fact that the visual representation of the adverb is highly dependant on the action to which it applies, although the same adverb will modify multiple actions in a similar way.For instance, while 'spread quickly' and 'mix quickly' will look dissimilar, we can learn a common representation that allows us to recognize both, among other actions.We formulate this as an embedding problem, and use scaled dot-product attention to learn from weaklysupervised video narrations.We jointly learn adverbs as invertible transformations operating on the embedding space, so as to add or remove the effect of the adverb.As there is no prior work on weakly supervised learning of adverbs, we gather paired action-adverb annotations from a subset of the HowTo100M dataset for 6 adverbs: quickly/slowly, finely/coarsely, and partially/completely.Our method outperforms all baselines for video-to-adverb retrieval with a performance of 0.719 mAP.We also demonstrate our model's ability to attend to the relevant video parts in order to determine the adverb for a given action.
uavAP: A Modular Autopilot Framework for UAVs

Being applied to many fields of research and industry, UAVs require reliable but modular autopilot software. An autopilot task can range from simple waypoint following to complex maneuvering or adaptive mission tracking. The developed and presented autopilot, uavAP, aims to be fully modular in a decentralized manner, embracing an object-oriented design in C++. It implements a typical control stack comprising of a mission planner, global planner, local planner, and controller. To facilitate its modularity, uavAP makes use of its core, cpsCore, for module management as well as core utilities. cpsCore administers the configuration, aggregation, and synchronization of all the modules in uavAP. With the emulation environment uavEE, uavAP forms an ecosystem for rapid prototyping and testing of modules for various research directions, ranging from scheduling and memory management, through planning and control system design, to flight profile and configuration optimization. The uavAP-uavEE ecosystem has facilitated the design of an accurate UAV power model based on the aircraft's physical model, flight maneuver automation for aircraft system identification and dynamics parametrization, and an algorithm for geo-fencing of fixed-wing UAVs. This paper describes the control stack of uavAP, its core, cpsCore, as well as application examples highlighting the framework's modularity and flexibility.
Variational Label Enhancement

Multi-label learning focuses on the ambiguity at the label side, i.e., one instance is associated with multiple class labels, where the logical labels are always adopted to partition class labels into relevant labels and irrelevant labels rigidly. However, the relevance or irrelevance of each label corresponding to one instance is essentially relative in real-world tasks and the label distribution is more fine-grained than the logical labels by denoting one instance with a certain number of the description degrees of all class labels. As the label distribution is not explicitly available in most training sets, a process named label enhancement emerges to recover the label distributions in training datasets. By inducing the generative model of the label distribution and adopting the variational inference technique, the approximate posterior density of the label distributions should maximize the variational lower bound. Following the above consideration, LEVI is proposed to recover the label distributions from the training examples. In addition, the multi-label predictive model is induced for multi-label learning by leveraging the recovered label distributions along with a specialized objective function. The recovery experiments on fourteen label distribution datasets and the predictive experiments on fourteen multi-label learning datasets validate the advantage of our approach over the state-of-the-art approaches.
Rddan: A Residual Dense Dilated Aggregated Network For Single Image Deraining

Rainy images contain rain streaks with different sizes, shapes, directions, and densities. To efficiently remove rain streaks from rainy images, it is necessary to capture rich rain details. In this paper, we propose a Residual Dense Dilated Aggregated Network (RDDAN) to focus on different types of rain steaks and efficiently model rain distribution from rainy images. Specifically, a Residual Dense Dilated Aggregated Block (RDDAB) is constructed to fully extract and exploit rain details hierarchically. In RDDAB, dilated aggregated module is applied to capture multi-scale rain details, dense connection is employed to fully exploit hierarchical features extracted by dilated aggregated module, and residual connection is introduced to keep flow of rain details among different blocks. Besides, all the features extracted by each RDDAB are fused progressively which allows the network to adaptively focus on significant hierarchical features inter blocks. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods on synthetic and real-world datasets. The source code is available at https://github.com/nnUyi/RDDAN.
Video Anomaly Detection Via Predictive Autoencoder With Gradient-Based Attention

Video anomaly detection is a challenging problem due to the ambiguity and diversity of anomalies in different scenes. In this paper, we present a novel framework to detect abnormal in surveillance videos. Inspired by the common deep reconstruction methods and deep prediction ones, we propose a new two-branch predictive autoencoder, including a reconstruction decoder and a prediction decoder, in which the prediction decoder is used to generate future frame and carry out anomaly detection by comparing the difference between predicted future frame and its ground truth. And the reconstruction decoder reconstructs the current frame, which can constrains the encoder to learn video representations better. Moreover, reconstruction decoder provides a gradient-based attention, which significantly helps the prediction decoder to generate higher quality future frame. Our method unifies reconstruction and prediction methods in an end-to-end framework, and it obtains impressive results with better predicted future frame on some publicly available datasets including CUHK Avenue and UCSD Pedestrian.
MMDM: Multi-frame and Multi-scale for Image Demoiréing

The imaging characteristics of digital sensors often lead to the moiré patterns, which are widely distributed over the frequency domain and have irregular colors and shapes. The images with moiré patterns could lead to a serious decline in the visual quality. The difficulty of demoiréing lies in that the moiré patterns mix both low and high frequency information to be processed. In this paper, we propose MMDM, an effective image demoimng network, which uses multiple images as inputs and multi-scale feature encoding module as low-frequency information enhancement. Our MMDM has three key modules: the newly designed multiframe spatial transformer networks (M-STN), the multiscale feature encoding module (MSFE), and the enhanced asymmetric convolution block (EACB). Especially, the M- STN aims to align the multiple input images simultaneously. The MSFE is for multiple frequency information encoding, which is built on the efficient EACB module. Experiments prove the effectiveness of MMDM. Also, our model achieves the 2nd place on both demoiring track and demising track in the NTIRE2020 Challenge. Code is avaliable at: https://github.com/q935970314/MMDM
Photometric Stereo via Discrete Hypothesis-and-Test Search

In this paper, we consider the problem of estimating surface normals of a scene with spatially varying, general BRDFs observed by a static camera under varying, known, distant illumination. Unlike previous approaches that are mostly based on continuous local optimization, we cast the problem as a discrete hypothesis-and-test search problem over the discretized space of surface normals. While a naive search requires a significant amount of time, we show that the expensive computation block can be precomputed in a scene-independent manner, resulting in accelerated inference for new scenes. It allows us to perform a full search over the finely discretized space of surface normals to determine the globally optimal surface normal for each scene point. We show that our method can accurately estimate surface normals of scenes with spatially varying different reflectances in a reasonable amount of time.
Class-Balanced Training for Deep Face Recognition

The performance of deep face recognition depends heavily on the training data. Recently, larger and larger datasets have been developed for the training of deep models. However, most face recognition training sets suffer from the class imbalance problem, and most studies ignore the benefit of optimizing dataset structures. In this paper, we study how class-balanced training can promote face recognition performance. A medium-scale face recognition training set BUPT-CBFace is built by exploring the optimal data structure from massive data. This publicly available dataset is characterized by the uniformly distributed sample size per class, as well as the balance between the number of classes and the number of samples in one class. Experimental results show that deep models trained with BUPT-CBFace can not only achieve comparable results to larger-scale datasets such as MS-Celeb-1M but also alleviate the problem of recognition bias.
The Role of ‘Sign’ and ‘Direction’ of Gradient on the Performance of CNN

State-of-the-art deep learning models have achieved superlative performance across multiple computer vision applications such as object recognition, face recognition, and digits/character classification. Most of these models highly rely on the gradient information flows through the network for learning. By utilizing this gradient information, a simple gradient sign method based attack is developed to fool the deep learning models. However, the primary concern with this attack is the perceptibility of noise for large degradation in classification accuracy. This research address the question of whether an imperceptible gradient noise can be generated to fool the deep neural networks? For this, the role of sign function in the gradient attack is analyzed. The analysis shows that without-sign function, i.e. gradient magnitude, not only leads to a successful attack mechanism but the noise is also imperceptible to the human observer. Extensive quantitative experiments performed using two convolutional neural networks validate the above observation. For instance, AlexNet architecture yields 63.54% accuracy on the CIFAR-10 database which reduces to 0.0% and 26.39% when sign (i.e., perceptible) and without-sign (i.e., imperceptible) of the gradient is utilized, respectively.Further, the role of the direction of the gradient for image manipulation is studied. When an image is manipulated in the positive direction of the gradient, an adversarial image is generated. On the other hand, if the opposite direction of the gradient is utilized for image manipulation, it is observed that the classification error rate of the CNN model is reduced. On AlexNet, the error rate of 36.46% reduces to 4.29% when images of CIFAR-10 are manipulated in the negative direction of the gradient. To explore other enthusiastic results on multiple object databases, including CIFAR-100, fashion-MNIST, and SVHN, please refer to the full paper.
The Use of the Blockchain Technology and Digital Watermarking to Provide Data Authenticity on a Mining Enterprise

Prompt development of information technology has made an essential impact on many industries. There appeared a concept "Industry 4.0" symbolizing the fourth industrial revolution. The given concept is closely connected with such promising technologies as the Internet of Things, blockchain, fog computing, Big Data. In the present research, the sphere of the mining industry is examined. We discuss the possibility to increase the efficiency of mining enterprises at the expense of the development of common information space based on modern digital technologies. We analyze security problems at the level of data flow between the participants of the production process on a mining enterprise. We define the problem of providing the reliability of data on the production course on mining enterprise in the conditions of the possible connection loss between the control center and separate technological units. We offer a new approach to the solution of the given problems, based on the technology of blockchain and digital watermarking. The computing experiment is conducted presenting a possibility to implement the offered approaches on common models of microcontrollers.
Using decomposition-based multi-objective evolutionary algorithm as synthetic example optimization for self-labeling

Existing a lot of unlabeled data and few labeled data is one of the most common problems in real datasets. Semi-supervised classification methods can well handle such a problem and have a desirable performance. Among them, one of the most successful methods in dealing with shortage of labeled data is self-labeled technique. One of the difficulties of this technique is wrong data labeling in iterative process of self-labeling. The main reasons are 1) existing outlier and noisy data, 2) inappropriate distribution of labeled data in problem space, and 3) shortage of labeled data in order to make diversity in learning hypotheses. In this paper, a method is developed so as to generate synthetic labeled using decomposition-based multi-objective evolutionary algorithm as synthetic example optimization for self-labeling called DMSS. In DMSS, the synthetic labeled datasets with high diversity and high classification accuracy are generated and then added to the labeled datasets for better training of the algorithm. In already conducted researches, the diversity of the generated data and their distribution in problem space have not been well investigated. The proposed method is a data preparation method which can be employed in all self-labeled techniques. To do so, the proposed method is applied over four self-labeled algorithms having different features and their performances are then evaluated using 25 pattern datasets. The obtained results show high performance of the DMSS with regard to the classification accuracy compared to the existing methods in the literature. Also, the outcomes of conducted non-parametric statistical tests show that the proposed method significantly outperforms the other existing methods in the literature, as well.
Development and promise of a vocabulary intelligent tutoring system for Second-Grade Latinx English learners

We examine the promise, usability, and feasibility of an intelligent tutoring system (ITS) to improve the vocabulary and language proficiency in science and social studies of Latinx second grade English learners (ELs). Participants were 217 ELs attending English only programs or bilingual programs. Findings from the clustered randomized control trial using a researcher-developed proximal assessment indicated that students who used the ITS significantly improved their vocabulary knowledge of the words taught compared to students in the comparison group (ES = 0.38). Results also indicated that most students enjoyed using the ITS and learning new words. Teachers found the program feasible and beneficial to their students.