Reviewerly/exHarmony huggingface dataset:
Pedestrian Origin–Destination Estimation Based on Multi-Camera Person Re-Identification

Pedestrian origin-destination (O-D) estimates that record traffic flows between origins and destinations, are essential for the management of pedestrian facilities including pedestrian flow simulation in the planning phase and crowd control in the operation phase. However, current O-D data collection techniques such as surveys, mobile sensing using GPS, Wi-Fi, and Bluetooth, and smart card data have the disadvantage that they are either time consuming and costly, or cannot provide complete O-D information for pedestrian facilities without entrances and exits or pedestrian flow inside the facilities. Due to the full coverage of CCTV cameras and the huge potential of image processing techniques, we address the challenges of pedestrian O-D estimation and propose an image-based O-D estimation framework. By identifying the same person in disjoint camera views, the O-D trajectory of each identity can be accurately generated. Then, state-of-the-art deep neural networks (DNNs) for person re-ID at different congestion levels were compared and improved. Finally, an O-D matrix based on trajectories was generated and the resident time was calculated, which provides recommendations for pedestrian facility improvement. The factors that affect the accuracy of the framework are discussed in this paper, which we believe could provide new insights and stimulate further research into the application of the Internet of cameras to intelligent transport infrastructure management.
Globally Optimal Distributed Fusion Filter for Descriptor Systems with Time-Correlated Measurement Noises

This paper concerns the distributed fusion filtering problem for descriptor systems with time-correlated measurement noises. The original descriptor is transformed into two reduced-order subsystems (ROSs) based on singular value decomposition. For the first ROS, a new measurement is obtained using measurement difference technology. Each sensor produces a local filter based on the fusion predictor from the fusion center and its own new measurement and then sends it to the fusion center. In the fusion center, based on local filters, a distributed fusion filter with feedback (DFFWF) in the linear minimum variance (LMV) sense is proposed by applying an innovative approach. The DFFWF for the second ROS is also obtained based on the DFFWF for the first ROS. Then, the DFFWF for the original descriptor is obtained. The proposed DFFWF can achieve the same estimation accuracy as the centralized fusion filter (CFF) under the condition that all local filter gain matrices are of full column rank. Its optimality is strictly proved. Moreover, it has robustness and reliability due to the parallel processing of local filters. Two simulation examples demonstrate the effectiveness of the developed fusion algorithm.
Kinematic Graph for Motion Planning of Robotic Manipulators

We introduce a kinematic graph in this article. A kinematic graph results from structuring the data obtained from the sampling method for sampling-based motion planning algorithms in robotics with the motivation to adapt the method to the positioning problem of robotic manipulators. The term kinematic graph emphasises the fact that any path computed by sampling-based motion planning algorithms using a kinematic graph is guaranteed to correspond to a feasible motion for the positioning of the robotic manipulator. We propose methods to combine the information from the configuration and task spaces of the robotic manipulators to cluster the samples. The kinematic graph is the result of this systematic clustering and a tremendous reduction in the size of the problem. Hence, using a kinematic graph, it is possible to effectively employ sampling-based motion planning algorithms for robotic manipulators, where the problem is defined in higher dimensions than those for which these algorithms were developed. Other barriers that hindered adequate utilisation of such algorithms for robotic manipulators with articulated arms, such as the non-injective surjection of the forward kinematic function, are also addressed in the structure of the kinematic graph.
Multibranch Unsupervised Domain Adaptation Network for Cross Multidomain Orchard Area Segmentation

Although unsupervised domain adaptation (UDA) has been extensively studied in remote sensing image segmentation tasks, most UDA models are designed based on single-target domain settings. Large-scale remote sensing images often have multiple target domains in practical applications, and the simple extension of single-target UDA models to multiple target domains is unstable and costly. Multi-target unsupervised domain adaptation (MTUDA) is a more practical scenario that has great potential for solving the problem of crossing multiple domains in remote sensing images. However, existing MTUDA models neglect to learn and control the private features of the target domain, leading to missing information and negative migration. To solve these problems, this paper proposes a multibranch unsupervised domain adaptation network (MBUDA) for orchard area segmentation. The multibranch framework aligns multiple domain features, while preventing private features from interfering with training. We introduce multiple ancillary classifiers to help the model learn more robust latent target domain data representations. Additionally, we propose an adaptation enhanced learning strategy to reduce the distribution gaps further and enhance the adaptation effect. To evaluate the proposed method, this paper utilizes two settings with different numbers of target domains. On average, the proposed method achieves a high IoU gain of 7.47% over the baseline (single-target UDA), reducing costs and ensuring segmentation model performance in multiple target domains.
Instance-Level Contrastive Learning for Weakly Supervised Object Detection

Weakly supervised object detection (WSOD) has received increasing attention in object detection field, because it only requires image-level annotations to indicate the presence or absence of target objects, which greatly reduces the labeling costs. Existing methods usually focus on the current individual image to learn object instance representations, while ignoring instance correlations between different images. To address this problem, we propose an instance-level contrastive learning (ICL) framework to mine reliable instance representations from all learned images, and use the contrastive loss to guide instance representation learning for the current image. Due to the diversity of instances, with different appearances, sizes or shapes, we propose an instance-diverse memory updating (IMU) algorithm to mine different instance representations and store them in a memory bank with multiple representation vectors per class, which also considers background information to enhance foreground representations. With the help of memory bank, we further propose a memory-aware instance mining (MIM) algorithm that combines proposal confidence and instance similarity across images to mine more reliable object instances. In addition, we also propose a memory-aware proposal sampling (MPS) algorithm to sample more positive proposals and remove some negative proposals to balance the learning of positive-negative samples. We conduct extensive experiments on the PASCAL VOC2007 and VOC2012 datasets, which are widely used in WSOD, to demonstrate the effectiveness of our method. Compared to our baseline, our method brings 14.2% mAP and 13.4% CorLoc gains on PASCAL VOC2007 dataset, and 12.2% mAP and 8.3% CorLoc gains on PASCAL VOC2012 dataset.
An Improved Adaptive Median Filtering Algorithm for Radar Image Co-Channel Interference Suppression

In order to increase the accuracy of ocean monitoring, this paper proposes an improved adaptive median filtering algorithm based on the tangential interference ratio to better suppress marine radar co-channel interference. To solve the problem that co-channel interference reduces the accuracy of radar images' parameter extraction, this paper constructs a tangential interference ratio model based on the improved Laplace operator, which is used to describe the ratio of co-channel interference along the antenna rotation direction in the original radar image. Based on the idea of between-class variance, the tangential interference ratio threshold is selected to divide co-channel interference into high-ratio regions and low ones. Moreover, an improved adaptive median filter is used to process regions of high ratio based on the median of sub-windows, while that of low-ratio regions is processed by the adaptive median filter based on the median of current windows. Radar-measured data from Bohai Bay, China are used for algorithm validation and experimental results show that the proposed filtering algorithm performs better than the adaptive median filtering algorithm.
Deep Metric Learning Using Negative Sampling Probability Annealing

Multiple studies have concluded that the selection of input samples is key for deep metric learning. For triplet networks, the selection of the anchor, positive, and negative pairs is referred to as triplet mining. The selection of the negatives is considered the be the most complicated task, due to a large number of possibilities. The goal is to select a negative that results in a positive triplet loss; however, there are multiple approaches for this-semi-hard negative mining or hardest mining are well-known in addition to random selection. Since its introduction, semi-hard mining was proven to outperform other negative mining techniques; however, in recent years, the selection of the so-called hardest negative has shown promising results in different experiments. This paper introduces a novel negative sampling solution based on dynamic policy switching, referred to as negative sampling probability annealing, which aims to exploit the positives of all approaches. Results are validated on an experimental synthetic dataset using cluster-analysis methods; finally, the discriminative abilities of trained models are measured on real-life data.
Herding Behavior in the Market for Green Cryptocurrencies: Evidence from CSSD and CSAD Approaches

Green cryptocurrencies have been recently created to reduce energy consumption and environmental pollution by adopting alternative mining practices. This paper examines for the first time the market of green cryptocurrencies for indication of herding behavior in the period of January 2017–June 2022. By using two measures that capture the proximity of asset returns from the market consensus, we conclude that herding behavior among investors in green cryptocurrencies was absent in the whole sample. However, the results of a subsample analysis and rolling window regression show that herding dynamics varied significantly throughout the sample period. The recent COVID-19 pandemic amplified the observed levels of herding behavior, suggesting that opportunities for diversification for investors operating in this market may have become more limited lately. For this reason, financial regulators should focus on the market of green cryptocurrencies if they want to promote the market’s efficiency necessary to attract additional investors.
Increasing the Accuracy and Optimizing the Structure of the Scale Thickness Detection System by Extracting the Optimal Characteristics Using Wavelet Transform

Loss of energy, decrement of efficiency, and decrement of the effective diameter of the oil pipe are among the consequences of scale inside oil condensate transfer pipes. To prevent these incidents and their consequences and take timely action, it is important to detect the amount of scale. One of the accurate diagnosis methods is the use of non-invasive systems based on gamma-ray attenuation. The detection method proposed in this research consists of a detector that receives the radiation sent by the gamma source with dual energy (radioisotopes 241Am and 133Ba) after passing through the test pipe with inner scale (in different thicknesses). This structure was simulated by Monte Carlo N Particle code. The simulation performed in the test pipe included a three-phase flow consisting of water, gas, and oil in a stratified flow regime in different volume percentages. The signals received by the detector were processed by wavelet transform, which provided sufficient inputs to design the radial basis function (RBF) neural network. The scale thickness value deposited in the pipe can be predicted with an MSE of 0.02. The use of a detector optimizes the structure, and its high accuracy guarantees the usefulness of its use in practical situations.
On the Classification of Low-Degree Ovoids of Q(4,q)

Ovoids of the non-degenerate quadric Q(4,q) of PG(4,q) have been studied since the end of the' 80s. They are rare objects and, beside the classical example given by an elliptic quadric, only three classes are known for q odd, one class for q even, and a sporadic example for q = 35. It is well known that to any ovoid of Q(4,q) a bivariate polynomial f (x,y) can be associated. In this paper we classify ovoids of Q(4,q) whose corresponding polynomial f (x, y) has "low degree" compared with q, in particular, $$\deg \left( f \right) < {\left( {{1 \over {6.3}}q} \right)^{{3 \over {13}}}} - 1$$ . Finally, as an application, two classes of permutation polynomials in characteristic 3 are obtained.
An <scp>expert‐in‐the‐loop</scp> method for <scp>domain‐specific</scp> document categorization based on small training data

Abstract Automated text categorization methods are of broad relevance for domain experts since they free researchers and practitioners from manual labeling, save their resources (e.g., time, labor), and enrich the data with information helpful to study substantive questions. Despite a variety of newly developed categorization methods that require substantial amounts of annotated data, little is known about how to build models when (a) labeling texts with categories requires substantial domain expertise and/or in‐depth reading, (b) only a few annotated documents are available for model training, and (c) no relevant computational resources, such as pretrained models, are available. In a collaboration with environmental scientists who study the socio‐ecological impact of funded biodiversity conservation projects, we develop a method that integrates deep domain expertise with computational models to automatically categorize project reports based on a small sample of 93 annotated documents. Our results suggest that domain expertise can improve automated categorization and that the magnitude of these improvements is influenced by the experts' understanding of categories and their confidence in their annotation, as well as data sparsity and additional category characteristics such as the portion of exclusive keywords that can identify a category.
Keyword Spotting in the Homomorphic Encrypted Domain Using Deep Complex-Valued CNN

In this paper, we propose a non-interactive scheme to achieve end-to-end keyword spotting in the homomorphic encrypted domain using deep learning techniques. We carefully designed a complex-valued convolutional neural network (CNN) structure for the encrypted domain keyword spotting to take full advantage of the limited multiplicative depth. At the same depth, the proposed complex-valued CNN can learn more speech representations than the real-valued CNN, thus achieving higher accuracy in keyword spotting. The complex activation function of the complex-valued CNN is non-arithmetic and cannot be supported by homomorphic encryption. To implement the complex activation function in the encrypted domain without interaction, we design methods to approximate complex activation functions with low-degree polynomials while preserving the keyword spotting performance. Our scheme supports single-instruction multiple-data (SIMD), which reduces the total size of ciphertexts and improves computational efficiency. We conducted extensive experiments to investigate our performance with various metrics, such as accuracy, robustness, and F1-score. The experimental results show that our approach significantly outperforms the state-of-the-art solutions on every metric.
UDoc-GAN: Unpaired Document Illumination Correction with Background Light Prior

Document images captured by mobile devices are usually degraded by uncontrollable illumination, which hampers the clarity of document content. Recently, a series of research efforts have been devoted to correcting the uneven document illumination. However, existing methods rarely consider the use of ambient light information, and usually rely on paired samples including degraded and the corrected ground-truth images which are not always accessible. To this end, we propose UDoc-GAN, the first framework to address the problem of document illumination correction under the unpaired setting. Specifically, we first predict the ambient light features of the document. Then, according to the characteristics of different level of ambient lights, we re-formulate the cycle consistency constraint to learn the underlying relationship between normal and abnormal illumination domains. To prove the effectiveness of our approach, we conduct extensive experiments on DocProj dataset under the unpaired setting. Compared with the state-of-the-art approaches, our method demonstrates promising performance in terms of character error rate (CER) and edit distance (ED), together with better qualitative results for textual detail preservation. The source code is now publicly available at \urlhttps://github.com/harrytea/UDoc-GAN.
Hierarchical Hourglass Convolutional Network for Efficient Video Classification

Videos naturally contain dynamic variation over the temporal axis, which will result in the same visual clues (e.g., semantics, objects) changing their scale, position, and perspective patterns between adjacent frames. A primary trend in video CNN is adopting spatial-2D convolution for spatial semantics and temporal-1D convolution for temporal dynamics. Though the direction achieves a favorable balance between efficiency and efficacy, it suffers from misalignment of visual clues with large displacements. Particularly, rigid temporal convolution would fail to capture correct motions when a specific target moves out of the reception field of temporal convolution between adjacent frames.
Consistency-Contrast Learning for Conceptual Coding

As an emerging compression scheme, conceptual coding usually encodes images into structural and textural representations and decodes them in a deep synthesis fashion. However, existing conceptual coding schemes ignore the structure of deep texture representation space, leading to a challenge of establishing efficient and faithful conceptual representations. In this paper, we firstly introduce contrastive learning into conceptual coding and propose Consistency-Contrast Learning (CCL) which optimizes the representation space by a consistency-contrast regularization. By modeling the original images and reconstructed images as "positive'' pairs and random images in a batch as "negative'' samples, CCL aims to align texture representation space with source images space relatively. Extensive experiments on diverse datasets demonstrate that: (1) the proposed CCL can achieve the best compression performance on the conceptual coding task; (2) CCL is superior to other popular regularization methods towards improving reconstruction quality; (3) CCL is general and can be applied to other tasks related to representation optimization and image reconstruction, such as GAN inversion.
Self-Supervised Multi-view Stereo via Adjacent Geometry Guided Volume Completion

Existing self-supervised multi-view stereo (MVS) approaches largely rely on photometric consistency for geometry inference, and hence suffer from low-texture or non-Lambertian appearances. In this paper, we observe that adjacent geometry shares certain commonality that can help to infer the correct geometry of the challenging or low-confident regions. Yet exploiting such property in a non-supervised MVS approach remains challenging for the lacking of training data and necessity of ensuring consistency between views. To address the issues, we propose a novel geometry inference training scheme by selectively masking regions with rich textures, where geometry can be well recovered and used for supervisory signal, and then lead a deliberately designed cost volume completion network to learn how to recover geometry of the masked regions. During inference, we then mask the low-confident regions instead and use the cost volume completion network for geometry correction. To deal with the different depth hypotheses of the cost volume pyramid, we design a three-branch volume inference structure for the completion network. Further, by considering plane as a special geometry, we first identify planar regions from pseudo labels and then correct the low-confident pixels by high-confident labels through plane normal consistency. Extensive experiments on DTU and Tanks & Temples demonstrate the effectiveness of the proposed framework and the state-of-the-art performance.
Multimodal Analysis for Deep Video Understanding with Video Language Transformer

The Deep Video Understanding Challenge (DVUC) is aimed to use multiple modality information to build high-level understanding of video, involving tasks such as relationship recognition and interaction detection. In this paper, we use a joint learning framework to simultaneously predict multiple tasks with visual, text, audio and pose features. In addition, to answer the queries of DVUC, we design multiple answering strategies and use video language transformer which learns cross-modal information for matching videos with text choices. The final DVUC result shows that our method ranks first for group one of movie-level queries, and ranks third for both of group one and group two of scene-level queries.
Multi-directional Knowledge Transfer for Few-Shot Learning

Knowledge transfer-based few-shot learning (FSL) aims at improving the recognition ability of a novel object under limited training samples by transferring relevant potential knowledge from other data. Most related methods calculate such knowledge to refine the representation of a novel sample or enrich the supervision to a classifier during a transfer procedure. However, it is easy to introduce new noise during the transfer calculations since: (1) the unbalanced quantity of samples between the known (base) and the novel categories biases the contents capturing of the novel objects, and (2) the semantic gaps existing in different modalities weakens the knowledge interaction during the training.
Benign Adversarial Attack

In spite of the successful application in many fields, machine learning models today suffer from notorious problems like vulnerability to adversarial examples. Beyond falling into the cat-and-mouse game between adversarial attack and defense, this paper provides alternative perspective to consider adversarial example and explore whether we can exploit it in benign applications. We first attribute adversarial example to the human-model disparity on employing non-semantic features. While largely ignored in classical machine learning mechanisms, non-semantic feature enjoys three interesting characteristics as (1) exclusive to model, (2) critical to affect inference, and (3) utilizable as features. Inspired by this, we present brave new idea of benign adversarial attack to exploit adversarial examples for goodness in three directions: (1) adversarial Turing test, (2) rejecting malicious model application, and (3) adversarial data augmentation. Each direction is positioned with motivation elaboration, justification analysis and prototype applications to showcase its potential.