Reviewerly/exHarmony huggingface dataset:
Remote Sensing Object Tracking With Deep Reinforcement Learning Under Occlusion

Object tracking is an important research direction of space Earth observation in the field of remote sensing. Although the existing correlation filter-based and deep learning (DL)-based object tracking algorithms have achieved great success, they are still unsatisfactory for the problem of object occlusion. The occlusion caused by the complex change in background, and the deviation of the tracking lens, causes object information to go missing, which leads to the omission of detection. Traditionally, most methods for object tracking under occlusion adopt a complex network model, which redetects the occluded object. To address this issue, we propose a novel object tracking approach. First, an action decision-occlusion handling network (AD-OHNet) based on deep reinforcement learning (DRL) is built to achieve low computational complexity for object tracking under occlusion. Second, the temporal and spatial context, the object appearance model, and the motion vector are adopted to provide the occlusion information, which drives actions in reinforcement learning under complete occlusion and contributes to improving the accuracy of tracking while maintaining speed. Finally, the proposed AD-OHNet is evaluated on three remote sensing video datasets of Bogota, Hong Kong, and San Diego taken from Jilin-1 commercial remote sensing satellites. The video datasets all shared problems of low spatial resolution, background clutter, and small objects. Experimental results on the three video datasets validate the effectiveness and efficiency of the proposed tracker.
Deep Discriminative Domain Adaptation

Domain adaptation studies the problem of how to transfer knowledge across different domains where the source domain with rich labeled source samples and target domain with less or even no labeled target samples are drawn from different probability distribution. A prevailing strategy is to generate transferable features cross-domain, which induces deficient knowledge transfer in learning process, especially close to the input end. Moreover, when target samples distributed far their corresponding class centers, or near the edge of the clusters, only using learned source samples features to predict target samples class which may easily brings about misclassification. Targeting to deal with these issues, we propose Deep Discriminative Domain Adaptation (DDDA) method, which jointly minimizes the supervised classification loss of annotated source examples, the unsupervised center alignment and correlation alignment losses measured on both convolutional layers and fully connected layers with help of attention mechanism. The multi-layer transfer mechanism complementary strengthens each individual transfer component, and markedly improves the generalization ability of transfer models. A series of experiments conducted on several standard datasets validate that the proposed method consistently outperforms contemporary adaptation approaches.
Visually Navigated Bronchoscopy using three cycle-Consistent generative adversarial network for depth estimation

[Background] Electromagnetically Navigated Bronchoscopy (ENB) is currently the state-of-the art diagnostic and interventional bronchoscopy. CT-to-body divergence is a critical hurdle in ENB, causing navigation error and ultimately limiting the clinical efficacy of diagnosis and treatment. In this study, Visually Navigated Bronchoscopy (VNB) is proposed to address the aforementioned issue of CT-to-body divergence. [Materials and Methods] We extended and validated an unsupervised learning method to generate a depth map directly from bronchoscopic images using a Three Cycle-Consistent Generative Adversarial Network (3cGAN) and registering the depth map to preprocedural CTs. We tested the working hypothesis that the proposed VNB can be integrated to the navigated bronchoscopic system based on 3D Slicer, and accurately register bronchoscopic images to pre-procedural CTs to navigate transbronchial biopsies. The quantitative metrics to asses the hypothesis we set was Absolute Tracking Error (ATE) of the tracking and the Target Registration Error (TRE) of the total navigation system. We validated our method on phantoms produced from the pre-procedural CTs of five patients who underwent ENB and on two ex-vivo pig lung specimens. [Results] The ATE using 3cGAN was 6.2 +/- 2.9 [mm]. The ATE of 3cGAN was statistically significantly lower than that of cGAN, particularly in the trachea and lobar bronchus (p < 0.001). The TRE of the proposed method had a range of 11.7 to 40.5 [mm]. The TRE computed by 3cGAN was statistically significantly smaller than those computed by cGAN in two of the five cases enrolled (p < 0.05). [Conclusion] VNB, using 3cGAN to generate the depth maps was technically and clinically feasible. While the accuracy of tracking by cGAN was acceptable, the TRE warrants further investigation and improvement.
Unifying theory of quantum state estimation using past and future information

Quantum state estimation for continuously monitored dynamical systems involves assigning a quantum state to an individual system at some time, conditioned on the results of continuous observations. The quality of the estimation depends on how much observed information is used and on how optimality is defined for the estimate. In this work, we consider problems of quantum state estimation where some of the measurement records are not available, but where the available records come from both before (past) and after (future) the estimation time, enabling better estimates than is possible using the past information alone. Past-future information for quantum systems has been used in various ways in the literature, in particular, the quantum state smoothing, the most-likely path, and the two-state vector and related formalisms. To unify these seemingly unrelated approaches, we propose a framework for partially-observed quantum system with continuous monitoring, wherein the first two existing formalisms can be accommodated, with some generalization. The unifying framework is based on state estimation with expected cost minimization, where the cost can be defined either in the space of the unknown record or in the space of the unknown true state. Moreover, we connect all three existing approaches conceptually by defining five new cost functions, and thus new types of estimators, which bridge the gaps between them. We illustrate the applicability of our method by calculating all seven estimators we consider for the example of a driven two-level system dissipatively coupled to bosonic baths. Our theory also allows connections to classical state estimation, which create further conceptual links between our quantum state estimators.
Data Augmentation for Sentiment Analysis Using Sentence Compression-Based SeqGAN With Data Screening

Sentiment analysis refers to the process of automatically identifying the emotions expressed by people. Its accuracy is highly dependent on the amount of training data. However, it takes time and cost for humans to collect a large number of data. Many research works used generative models to generate a large amount of data based on a small amount of data for sentiment analysis. However, training on long texts and inaccurate sentiment information that might be generated are two severe challenges. It is difficult to improve the sentiment analysis accuracy effectively. In this paper, we propose a novel data augmentation framework based on Sequence generative adversarial networks (SeqGAN) to improve the sentiment analysis accuracy when the dataset already has a certain amount of data and contains long texts. Penalty-based SeqGAN is used to generate high-quality and diversified text data. Long short-term memory (LSTM) networks with attention mechanisms are used to conduct sentence compression for the training data of SeqGAN. A sentiment dictionary is used to retain the sentiment words for compressed data. We also propose a data screening method to obtain more accurate data from the generated data. The results of the usability, novelty, and diversity of the generated data show that the proposed sentence compression method can help SeqGAN learn more information from the long text data. The data generated by the proposed framework improve the classification accuracy of four classifiers applied on two distinct text datasets.
Augmented reality as a product presentation tool: focusing on the role of product information and presence in AR

Abstract As augmented reality (AR) technology advances, marketers are eager to adopt the technology for communication to persuade consumers to develop favorable attitudes and behaviors toward their products and services. This study aims to investigate the effect of product information (utilitarian vs. hedonic attributes) and presence on consumers’ product evaluation in AR. Through a quasi experiment, this study demonstrates how product attribute information and presence in AR affect product evaluation by mediating imagery, information fulfillment, and psychological ownership. At the same time, this study identifies the moderating role of consumers’ technological innovativeness in the effect of presence on consumers’ imagery. This research offers new insights into the role of product information in AR, which previous studies lack, to explore and highlight the predictors of positive product experiences in AR. Innovative marketers are likely to benefit from this study in developing product presentation tactics with AR technology.
Fast Complex-Valued CNN for Radar Jamming Signal Recognition

Jamming is a big threat to the survival of a radar system. Therefore, the recognition of radar jamming signal type is a part of radar countermeasure. Recently, convolutional neural networks (CNNs) have shown their effectiveness in radar signal processing, including jamming signal recognition. However, most of existing CNN methods do not regard radar jamming as a complex value signal. In this study, a complex-valued CNN (CV-CNN) is investigated to fully explore the inherent characteristics of a radar jamming signal, and we find that we can obtain better recognition accuracy using this method compared with a real-valued CNN (RV-CNN). CV-CNNs contain more parameters, which need more inference time. To reduce the parameter redundancy and speed up the recognition time, a fast CV-CNN (F-CV-CNN), which is based on pruning, is proposed for radar jamming signal fast recognition. The experimental results show that the CV-CNN and F-CV-CNN methods obtain good recognition performance in terms of accuracy and speed. The proposed methods open a new window for future research, which shows a huge potential of CV-CNN-based methods for radar signal processing.
An Efficient Approach for Anomaly Detection in Traffic Videos

Due to its relevance in intelligent transportation systems, anomaly detection in traffic videos has recently received much interest. It remains a difficult problem due to a variety of factors influencing the video quality of a real-time traffic feed, such as temperature, perspective, lighting conditions, and so on. Even though state-of-the-art methods perform well on the available benchmark datasets, they need a large amount of external training data as well as substantial computational resources. In this paper, we propose an efficient approach for a video anomaly detection system which is capable of running at the edge devices, e.g., on a roadside camera. The proposed approach comprises a preprocessing module that detects changes in the scene and removes the corrupted frames, a two-stage background modelling module and a two-stage object detector. Finally, a backtracking anomaly detection algorithm computes a similarity statistic and decides on the onset time of the anomaly. We also propose a sequential change detection algorithm that can quickly adapt to a new scene and detect changes in the similarity statistic. Experimental results on the Track 4 test set of the 2021 AI City Challenge show the efficacy of the proposed framework as we achieve an F1-score of 0. 9157 along with 8.4027 root mean square error (RMSE) and are ranked fourth in the competition.
Selecting an optimal architecture of neural network using genetic algorithm

The article presents the results of applying a genetic algorithm to find the most optimal architecture of the neural network that would solve classification problem with minimal errors. The stages of the genetic algorithm are considered and the rule for encoding the parameters of the neural network is determined. A genetic algorithm for constructing the optimal architecture of a multilayer perceptron for solving classification problems has been developed. The algorithm independently creates a random population, evolves, creating new generations with more adapted individuals, i.e., neural networks with better architectures than previous generations. The paper describes the process of population formation, substantiates the choice of the fitness function and the method of selecting parents. Modifications of the crossover and mutation operators are proposed in order to ensure the operability of the algorithm on variable size individuals. A software tool that generates a neural network with the best parameters for solving classification problems has been developed on Python3 programming language. The architecture of a neural network for detecting fraudulent transactions has been built by using the developed software.
Sentence Semantic Matching Based on 3D CNN for Human–Robot Language Interaction

The development of cognitive robotics brings an attractive scenario where humans and robots cooperate to accomplish specific tasks. To facilitate this scenario, cognitive robots are expected to have the ability to interact with humans with natural language, which depends on natural language understanding ( NLU ) technologies. As one core task in NLU, sentence semantic matching ( SSM ) has widely existed in various interaction scenarios. Recently, deep learning–based methods for SSM have become predominant due to their outstanding performance. However, each sentence consists of a sequence of words, and it is usually viewed as one-dimensional ( 1D ) text, leading to the existing available neural models being restricted into 1D sequential networks. A few researches attempt to explore the potential of 2D or 3D neural models in text representation. However, it is hard for their works to capture the complex features in texts, and thus the achieved performance improvement is quite limited. To tackle this challenge, we devise a novel 3D CNN-based SSM ( 3DSSM ) method for human–robot language interaction. Specifically, first, a specific architecture called feature cube network is designed to transform a 1D sentence into a multi-dimensional representation named as semantic feature cube. Then, a 3D CNN module is employed to learn a semantic representation for the semantic feature cube by capturing both the local features embedded in word representations and the sequential information among successive words in a sentence. Given a pair of sentences, their representations are concatenated together to feed into another 3D CNN to capture the interactive features between them to generate the final matching representation. Finally, the semantic matching degree is judged with the sigmoid function by taking the learned matching representation as the input. Extensive experiments on two real-world datasets demonstrate that 3DSSM is able to achieve comparable or even better performance over the state-of-the-art competing methods.
Distinguishing IOCG and IOA deposits via random forest algorithm based on magnetite composition

Iron oxide-copper-gold (IOCG) and iron oxide-apatite (IOA) are two significant mineral deposit types with similar tectonic settings and hydrothermal alteration characteristics. There are huge differences in the geological setting, alteration system, and ore-forming fluid composition among IOCG and IOA deposits, leading to controversial genesis. Distinguishing between these two deposit types is significant to reveal the origin of IOCG and IOA systems. In this study, random forest (RF) was employed to classify IOCG and IOA deposits based on the chemical composition of magnetite measured by the electron probe microanalyzer (EPMA) and laser-ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The obtained results show that (1) a relatively high overall classification accuracy (0.76 for EPMA data and 0.91 for LA-ICP-MS) was obtained via the RF, indicating that the elemental composition of magnetite can effectively distinguish IOCG and IOA deposits; (2) the performance of the RF model based on LA-ICP-MS data is better than that of EMPA data, indicating that the application of more geochemical variables is helpful in distinguishing IOCG and IOA deposits; and (3) the elements V, Mg, and Mn in EPMA data, and Si, Mg, and V in LA-ICP-MS data are identified as the key elements for distinguishing IOCG and IOA deposits.
Discretization-aware architecture search

The search cost of neural architecture search (NAS) has been largely reduced by differentiable architecture search and weight-sharing methods. Such methods optimize a super-network with all possible edges and operations, and determine the optimal sub-network by discretization, i.e., pruning off operations/edges of small weights. However, the discretization process performed on either operations or edges incurs significant inaccuracy and thus the quality of the architecture is not guaranteed. In this paper, we propose discretization-aware architecture search (DA2S), and target at pushing the super-network towards the configuration of desired topology. DA2S is implemented with an entropy-based loss term, which can be regularized to differentiable architecture search in a plug-and-play fashion. The regularization is controlled by elaborated continuation functions, so that discretization is adaptive to the dynamic change of edges and operations. Experiments on standard image classification benchmarks demonstrate the effectiveness of our approach, in particular, under imbalanced network configurations that were not studied before. Code is available at github.com/sunsmarterjie/DAAS.
Improving robustness against common corruptions with frequency biased models

CNNs perform remarkably well when the training and test distributions are i.i.d, but unseen image corruptions can cause a surprisingly large drop in performance. In various real scenarios, unexpected distortions, such as random noise, compression artefacts or weather distortions are common phenomena. Improving performance on corrupted images must not result in degraded i.i.d performance – a challenge faced by many state-of-the-art robust approaches. Image corruption types have different characteristics in the frequency spectrum and would benefit from a targeted type of data augmentation, which, however, is often unknown during training. In this paper, we introduce a mixture of two expert models specializing in high and low-frequency robustness, respectively. Moreover, we propose a new regularization scheme that minimizes the total variation (TV) of convolution feature-maps to increase high-frequency robustness. The approach improves on corrupted images without degrading in-distribution performance. We demonstrate this on ImageNet-C and also for real-world corruptions on an automotive dataset, both for object classification and object detection.
A Lightweight Attribute-Based Access Control Scheme for Intelligent Transportation System With Full Privacy Protection

The Intelligent Transportation System (ITS) provides more possibilities for the realization of smart cities by integrating the Internet of Things (IoT) and cloud computing. However, how to ensure security of IoT data stored in the cloud has become one of the biggest challenges at present. As a promising solution for realizing fine-grained access control, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) can be used to ensure data security. However, the traditional CP-ABE schemes may leak privacy of ITS users. Moreover, due to their high computational overheads, the current privacy-preserving techniques are not suitable for IoT lightweight devices. To fill this gap, this article presents ABE-FPP, a lightweight attribute-based access control scheme with full privacy protection (FPP), which can achieve full privacy protection in the three key stages (i.e., key generation, access control, and partial decryption), while reducing consumption overhead on the user side. Specifically, to protect privacy during key generation, a lightweight two-party secure computing protocol between the user and the authority is designed to generate secret keys; to protect privacy during the access control policy setting, we present an efficient policy hidden strategy, which only reveals attribute names and efficiently hides attribute values; to protect privacy during partial decryption, we propose a hybrid authentication method that does not need to submit attribute values to the cloud. Moreover, to achieve lightweight computation for IoT devices, online/offline encryption and outsourced decryption are employed in ABE-FPP. Finally, formal security proofs show that our scheme is secure in the standard model. The asymptotic complexity analyses and experimental results demonstrate that the presented scheme achieves higher computation efficiency than the state-of-the-art ones.
Feature Pyramid Nonlocal Network With Transform Modal Ensemble Learning for Breast Tumor Segmentation in Ultrasound Images

Automated breast ultrasound image segmentation is essential in a computer-aided diagnosis (CAD) system for breast tumors. In this article, we present a feature pyramid nonlocal network (FPNN) with transform modal ensemble learning (TMEL) for accurate breast tumor segmentation in ultrasound images. Specifically, the FPNN fuses multilevel features under special consideration of long-range dependencies by combining the nonlocal module and feature pyramid network. Additionally, the TMEL is introduced to guide two iFPNNs to extract different tumor details. Two publicly available datasets, i.e., the Dataset-Cairo University and Dataset-Merge, were used for evaluation. The proposed FPNN-TMEL achieves a Dice score of 84.70% ± 0.53%, Jaccard Index (Jac) of 78.10% ± 0.48% and Hausdorff distance (HD) of 2.815 ± 0.016 mm on the Dataset-Cairo University, and Dice of 87.00% ± 0.41%, Jac of 79.16% ± 0.56%, and HD of 2.781±0.035 mm on the Dataset-Merge. Qualitative and quantitative experiments show that our method outperforms other state-of-the-art methods for breast tumor segmentation in ultrasound images. Our code is available at https://github.com/pixixiaonaogou/FPNN-TMEL.
Multiple channels local binary pattern for color texture representation and classification

Image texture description and analysis technology are the basis of many practical applications in pattern recognition. This paper presents a novel and simple, yet powerful method, namely multiple channels local binary pattern (MCLBP), which is the natural extension and development of local binary pattern (LBP) algorithm for color texture representation and classification. MCLBP combines single-channel texture characteristics with multi-channel color information, which reflects the correlations and dependency among different channels. Furthermore, we decompose local color differences into color-difference signs and color-difference magnitudes and MCLBP is extended to MCLBP+M. Then, the resulted image descriptor is a histogram representation, which fuses rich features including color difference sign and color difference magnitude. Comprehensive experiments conducted on five benchmark databases, including Outex, KTH-TIPS, CUReT, STex and KTH-TIPS2-b clearly demonstrate that our proposed method outperforms most of the existed color texture features in terms of classification accuracy. Particularly, our method achieves the best classification performance in CUReT and STex databases.