Reviewerly/exHarmony huggingface dataset:
Video Logo Retrieval Based on Local Features

Estimation of the frequency and duration of logos in videos is important and challenging in the advertisement industry as a way of estimating the impact of ad purchases. Since logos occupy only a small area in the videos, the popular methods of image retrieval could fail. This paper develops an algorithm called Video Logo Retrieval (VLR), which is an image-to-video retrieval algorithm based on the spatial distribution of local image descriptors that measure the distance between the query image (the logo) and a collection of video images. VLR uses local features to overcome the weakness of global feature based models such as convolutional neural networks (CNN). Meanwhile, VLR is flexible and does not require training after setting some hyper-parameters. The performance of VLR is evaluated on two challenging open benchmark tasks (SoccerNet and Standford I2V), and compared with other state-of-the-art logo retrieval or detection algorithms. Overall, VLR shows significantly higher accuracy compared with the existing methods.
Event Recognition with Automatic Album Detection based on Sequential Grouping of Confidence Scores and Neural Attention

In this paper a new formulation of event recognition task is examined: it is required to predict event categories given a gallery of images, for which albums (groups of photos corresponding to a single event) are unknown. The novel two-stage approach is proposed. At first, features are extracted in each photo using the pre-trained convolutional neural network (CNN). These features are classified individually. The normalized scores of the classifier are used to group sequential photos into several clusters. Finally, the features of photos in each group are aggregated into a single descriptor using neural attention mechanism. This algorithm is implemented in Android mobile application. Experimental study with features extracted by contemporary convolutional neural networks including EfficientNets for Photo Event Collection and Multi-Label Curation of Flickr Events Dataset demonstrates that the proposed approach is 9-23% more accurate than conventional event recognition on single photos. Moreover, proposed method has 13-16% lower error rate when compared to classification of groups of photos obtained with hierarchical clustering of CNN-based embeddings.
Saliency Detection in Hyperspectral Images Using Autoencoder-Based Data Reconstruction

Saliency detection extracts objects attractive to a human vision system from an image. Although saliency detection methodologies were originally investigated on RGB color images, recent developments in imaging technologies have aroused the interest in saliency detection methodologies for data captured with high spectral resolution using multispectral and hyperspectral imaging (MSI/HSI) sensors. In this paper, we propose a saliency detection methodology that elaborates HSI data reconstructed through an autoencoder architecture. It resorts to (spectral-spatial) distance measures to quantify the salience degree in the data represented through the autoencoder. Finally, it performs a clustering stage in order to separate the salient information from the background. The effectiveness of the proposed methodology is evaluated with benchmark HSI and MSI data.
O-Net: An Overall Convolutional Network for Segmentation Tasks

Convolutional neural networks (CNNs) have recently been popular for classification and segmentation through numerous network architectures offering a substantial performance improvement. Their value has been particularly appreciated in the domain of biomedical applications, where even a small improvement in the predicted segmented region (e.g., a malignancy) compared to the ground truth can potentially lead to better diagnosis or treatment planning. Here, we introduce a novel architecture, namely the Overall Convolutional Network (O-Net), which takes advantage of different pooling levels and convolutional layers to extract more deeper local and containing global context. Our quantitative results on 2D images from two distinct datasets show that O-Net can achieve a higher dice coefficient when compared to either a U-Net or a Pyramid Scene Parsing Net. We also look into the stability of results for training and validation sets which can show the robustness of model compared with new datasets. In addition to comparison to the decoder, we use different encoders including simple, VGG Net, and ResNet. The ResNet encoder could help to improve the results in most of the cases.
Context-Aware Hierarchical Feature Attention Network For Multi-Scale Object Detection

Multi-scale object detection involves classification and regression assignments of objects with variable scales from an image. How to extract discriminative features is a key point for multi-scale object detection. Recent detectors simply fuse pyramidal features extracted from ConvNets, which does not take full advantage of useful features and drop out redundant features. To address this problem, we propose Context-Aware Hierarchical Feature Attention Network (CHFANet) to focus on effective multi-scale feature extraction for object detection. Based on single shot multibox detector (SSD) framework, the CHFANet consists of two components: the context-aware feature extraction (CFE) module to capture rich multi-scale context features and the hierarchical feature fusion (HFF) module followed with the channel-wise attention model to generate deeply fused attentive features. On the Pascal VOC benchmark, our CHFANet can achieve 82.6% mAP. Extensive experiments demonstrate that the CHFANet outperforms a lot of state-of-the-art object detectors in accuracy without any bells and whistles.
A Hybrid Model for Nonlinear Regression with Missing Data Using <scp>Quasilinear</scp> Kernel

In both the research and engineering fields, missing data is a serious problem that cannot be overlooked. Therefore, available datasets with missing data are a challenge to be modeled by conventional global prediction models. In this paper, we propose a hybrid model consisting of an autoencoder and a gated linear network for solving the regression problem under missing value scenario. A sophisticated modeling and identifying algorithm is developed. First, an extended affinity propagation (AP) clustering algorithm is applied to obtain a self‐organized competitive net dividing the datasets into several clusters. Second, a multiple imputation tool with top p % winner‐take‐all denoising autoencoders (DAE) is introduced to realize better predictions of missing values, in which rough estimates of missing values by using the mean imputation and similarity method within the clusters are used as teacher signals of DAE. Finally, a gated linear network is designed to construct a piecewise linear regression model with interpolations in the exact same way as a support vector regression with a quasilinear kernel composed using the cluster information obtained in the AP clustering step. Based on the experiments of five datasets, our proposed method demonstrates its effectiveness and robustness compared with other traditional kernels and state‐of‐the‐art methods, even on datasets with a large percentage of missing values. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Optimized Color Contrast Enhancement For Dichromats Using Local And Global Contrast

We propose an optimized color contrast enhancement algorithm for dichromats with color vision deficiency using local and global information. Based on the fact that dichromats perceive colors projected onto a 2D plane, we first measure the color differences on the plane. Then, we formulate an optimization problem to minimize the perceived color differences subject to constraints on the projected plane. Finally, by solving the optimization problem, we obtain the optimal plane and perform color conversion. Simulation results show that the proposed algorithm outperforms the state-of-the-art algorithms in preserving both local details and naturalness ofimages.
A Simple Yet Effective Pipeline For Radial Distortion Correction

Eliminating the radial lens distortion of an image is a crucial preprocessing step for many computer vision applications. This paper explores a simple yet effective pipeline for radial distortion correction. Different from existing state-of-the-art methods that design complex network structure and concatenate multi-branch features. Our model uses a single network without any additional supervision. We design two differentiable layers to synthesize and rectify distorted images efficiently. Based on these layers, an online data synthesis strategy, a sampling grid loss, and an image reprojection loss are proposed to improve the distortion correction accuracy. Compared with the state-of-the-art methods, our model achieves the best rectification quality on both the synthetic and real distorted images with dozens of times faster inference speed. The training data and codes will be released. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/MccreeZhao/RDCPipeline
Entanglement of formation and quantum discord in multipartite j-spin coherent states

We study the entanglement of formation and the quantum discord contained in even and odd multipartite [Formula: see text]-spin coherent states. The key element of this investigation is the fact that a single [Formula: see text]-spin coherent state is viewed as comprising [Formula: see text] qubit states. We compute the quantum correlations present in the n even and odd [Formula: see text]-spin coherent states by considering all possible bipartite splits of the multipartite system. We discuss the different bi-partition schemes of quantum systems and we examine in detail the conservation rules governing the distribution of quantum correlations between the different qubits of the multipartite system. Finally, we derive the explicit expressions of quantum correlations present in even and odd spin coherent states decomposed in four spin sub-systems. We also analyze the properties of monogamy and we show in particular that the entanglement of the formation and the quantum discord obey the relation of monogamy only for even multipartite [Formula: see text]-spin coherent states.
What Factors Determine the Value of an Online Teacher Education Experience from a Teacher’s Perspective?

E-learning is currently at the center of interest in the educational community, in a situation of global pandemics that forces us to reduce attendance in all aspects of our lives. Online courses have had a bad reputation due to the distance between the tutor and the apprentice and the high dropout rates. The main purpose is to analyze the evaluations made by teachers on different features of a course, by examining variables like content, technology, activities, final work, and format. The analysis covers the 50 online courses that form part of the continuous training offer of the Regional Ministry of Education of the Community of Madrid in courses 17–18 and 18–19 in which 7501 teachers have participated. The opinion of each participant is collected from a questionnaire at the end of the training activity. This has been validated by a group of experts from the Regional Centre for Innovation and Training, belonging to the Community of Madrid, using the Delphi method. To develop the methodology, a linear multivariate model was calculated on the independent variables and the dependent variable Net Promoter Score (NPS). Most of the findings are related to two central variables in the educational approaches that occur in digital scenarios. What the group of teachers’ values most is the content of the proposal, while the factor that they consider least important is the technology that supports the development of the course. The rest of the variables analyzed have little impact on the recommendation of the courses. Nevertheless, conclusions suggest that combining factors such as content, technology, and pedagogy are essential in experiences like these.
Handheld Ultrasound Video High-Quality Reconstruction Using a Low-Rank Representation Multipathway Generative Adversarial Network

Recently, the use of portable equipment has attracted much attention in the medical ultrasound field. Handheld ultrasound devices have great potential for improving the convenience of diagnosis, but noise-induced artifacts and low resolution limit their application. To enhance the video quality of handheld ultrasound devices, we propose a low-rank representation multipathway generative adversarial network (LRR MPGAN) with a cascade training strategy. This method can directly generate sequential, high-quality ultrasound video with clear tissue structures and details. In the cascade training process, the network is first trained with plane wave (PW) single-/multiangle video pairs to capture dynamic information and then fine-tuned with handheld/high-end image pairs to extract high-quality single-frame information. In the proposed GAN structure, a multipathway generator is applied to implement the cascade training strategy, which can simultaneously extract dynamic information and synthesize multiframe features. The LRR decomposition channel approach guarantees the fine reconstruction of both global features and local details. In addition, a novel ultrasound loss is added to the conventional mean square error (MSE) loss to acquire ultrasound-specific perceptual features. A comprehensive evaluation is conducted in the experiments, and the results confirm that the proposed method can effectively reconstruct high-quality ultrasound videos for handheld devices. With the aid of the proposed method, handheld ultrasound devices can be used to obtain convincing and convenient diagnoses.
Temporal Fusion Pointer network-based Reinforcement Learning algorithm for Multi-Objective Workflow Scheduling in the cloud

Cloud computing is emerging as a deployment promising environment for hosting exponentially increasing scientific and social media applications, but how to manage and execute these applications efficiently depends mainly on workflow scheduling. However, scheduling workflows in the cloud is an NP-hard problem and its existing solutions have certain limitations when applied to real-world scenarios. In this paper, a Temporal Fusion Pointer network-based Reinforcement Learning algorithm for multi-objective workflow scheduling (TFP-RL) is proposed. Through adopting reinforcement learning, our algorithm can discover its heuristics over time by continuous learning according to the rewards resulting from good scheduling solutions. To make more comprehensive scheduling decisions as the influence of historical actions, a novel temporal fusion pointer network (TFP) is designed for the reinforcement learning agent, which can improve the quality of our resulting solutions and the ability of our algorithm in dealing with versatile workflow applications. To decrease convergence time, we train the proposed TFP-RL model independently by the Asynchronous Advantage Actor-Critic method and use its resulting model for scheduling workflows. Finally, under a multi-agent reinforcement learning framework, a Pareto dominance-oriented criterion for reasonable action selection is established for a multi-objective optimization scenario. We first train our TFP-RL model by taking randomly generated workflows as inputs to validate its effectiveness in scheduling, then compare our trained model with other existing scheduling approaches through practical compute- and data-intensive workflows. Experimental results demonstrate that our proposed algorithm outperforms the benchmarking ones in terms of different metrics.
Decoherence dynamics estimation for superconducting gate-model quantum computers

Abstract Superconducting gate-model quantum computer architectures provide an implementable model for practical quantum computations in the NISQ (noisy intermediate scale quantum) technology era. Due to hardware restrictions and decoherence, generating the physical layout of the quantum circuits of a gate-model quantum computer is a challenge. Here, we define a method for layout generation with a decoherence dynamics estimation in superconducting gate-model quantum computers. We propose an algorithm for the optimal placement of the quantum computational blocks of gate-model quantum circuits. We study the effects of capacitance interference on the distribution of the Gaussian noise in the Josephson energy.
Bounds for Complete Arcs in $$\mathrm {PG}(3,q)$$ and Covering Codes of Radius 3, Codimension 4, Under a Certain Probabilistic Conjecture

Let t(N, q) be the smallest size of a complete arc in the N-dimensional projective space \(\mathrm {PG}(N,q)\) over the Galois field of order q. The d-length function \(\ell _q(r,R,d)\) is the smallest length of a q-ary linear code of codimension (redundancy) r, covering radius R, and minimum distance d; in particular, \(\ell _q(4,3,5)\) is the smallest length n of an \([n,n-4,5]_q3\) quasi-perfect MDS code. By the definitions, \(\ell _q(4,3,5)=t(3,q)\). In this paper, a step-by-step construction of complete arcs in \(\mathrm {PG}(3,q)\) is considered. It is proved that uncovered points are uniformly distributed in the space. A natural conjecture on quantitative estimations of the construction is presented. Under this conjecture, new upper bounds on t(3, q) are obtained, in particular, \(t(3,q)<2.93\root 3 \of {q\ln q}\).
Dynamic planning of redundant robots within a set-based task-priority inverse kinematics framework

This work presents the dynamic planning of redundant robots by merging a global and local planner. The global planner is implemented as a sampling-based algorithm which works in the reduced-dimensionality of the robot workspace applying the Cartesian constraints only. The output trajectory is then checked within a framework of set-based task priority inverse kinematics verifying the fulfillment of the other task constraints. The inverse kinematics framework is used also in real-time as local motion control to ensure a reactive behaviour to address, e.g., mismatch between the apriori information and on-line perception acquisition. During the movement, the motion planner runs in background to adapt to changes in the environment or, in general, to continuously optimize the path. The proposed method is experimentally validated with a Kinova Jaco2 7 degrees of freedom manipulator.
Accelerating Deep Neural Network Computation on a Low Power Reconfigurable Architecture

Recent work on neural network architectures has focused on bridging the gap between performance/efficiency and programmability. We consider implementations of three popular neural networks, ResNet, AlexNet and ASGD weight-dropped Recurrent Neural Network (AWD RNN) on a low power programmable architecture, Transformer. The architecture consists of light-weight cores interconnected by caches and crossbars that support run-time reconfiguration between shared and private cache mode operations. We present efficient implementations of key neural network kernels and evaluate the performance of each kernel when operating in different cache modes. The best-performing cache modes are then used in the implementation of the end-to-end network. Simulation results show superior performance with ResNet, AlexNet and AWD RNN achieving 188.19 GOPS/W, 150.53 GOPS/W and 120.68 GOPS/W, respectively, in the 14 nm technology node.
A New Path Planning Architecture to Consider Motion Uncertainty in Natural Environment

This paper proposes a new path planning algorithm to consider motion uncertainty for wheeled robots in rough environments. The proposed method uses particles to express the uncertainty propagation in complicated environments constructed with various types of terrain. Also, RRT (Rapidly-exploring Random Tree) is expanded based on the uncertainty of each node in order to prevent increasing the accumulated position uncertainty. As a result, the generated path reduces the times of path-following and re-planning based on inaccurate localization information. The effectiveness of the proposed method is evaluated in simulation using motion uncertainty models obtained by experiments. The results show that the proposed method decreases the position uncertainty while keeping the probability to avoid collisions and to reach the goal area compared with conventional approaches.
Privacy-Aware Edge Computing System For People Tracking

This work studies the practical implementation of a distributed computer vision system for people tracking. A particular focus is on improved data privacy when compared to the traditional surveillance approaches. This is achieved by extracting a feature vector from each detected person by a neural network in real-time in the edge device and transmitting only the feature vector to the cloud, eliminating privacy-sensitive image data transmission and storage. The proposed solution is implemented in a network of Raspberry Pi single-board computers and Intel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Neural Compute Stick accelerators. The system is tested in an environment where multiple edge devices are sending data to the cloud server for further analysis. In this context, we consider the spectrum of design and implementation aspects of real-time execution of multiple neural networks in a capacity limited edge computing environment.
Augmented Behavioral Cloning from Observation

Imitation from observation is a computational technique that teaches an agent on how to mimic the behavior of an expert by observing only the sequence of states from the expert demonstrations. Recent approaches learn the inverse dynamics of the environment and an imitation policy by interleaving epochs of both models while changing the demonstration data. However, such approaches often get stuck into sub-optimal solutions that are distant from the expert, limiting their imitation effectiveness. We address this problem with a novel approach that overcomes the problem of reaching bad local minima by exploring: (i) a self-attention mechanism that better captures global features of the states; and (ii) a sampling strategy that regulates the observations that are used for learning. We show empirically that our approach outperforms the state-of-the-art approaches in four different environments by a large margin.
Blind Image Deblurring With Joint Extreme Channels And L<sub>0</sub>-Regularized Intensity And Gradient Priors

The extreme channels prior (ECP) relies on the bright and dark channels of an image, and the corresponding ECP-based methods perform well in blind image deblurring. However, we experimentally observe that the pixel values of dark and bright channels in some images are not concentratedly distributed on 0 and 1 respectively. Based on this observation, we develop a model with a joint prior which combines the extreme channels prior and the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -regularized intensity and gradient prior for blind image deblurring, and previous image deblurring approaches based on dark channel prior, L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> - regularized intensity and gradient, and extreme channels prior can be seen as a particular case of our model. Then we derive an efficient optimization algorithm using the half-quadratic splitting method to address the non-convex L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -minimization problem. A large number of experiments are finally performed to demonstrate the superiority of the proposed model in details restoration and artifacts removal, and our model outperforms several leading deblurring approaches in terms of subjective results and objective assessments. In addition, our method is more applicable for deblurring natural, text and face images which do not contain many bright or dark pixels.