Reviewerly/exHarmony huggingface dataset:
UAV image detection algorithm based on improved YOLOv5

The target detection algorithm based on deep learning usually has a good effect on traditional target detection, but has a low detection accuracy on small targets. To resolve the problem, this paper proposes an improved YOLOv5 algorithm for small object detection by research on the images captured by UAVs. First, aiming at the problem of high sampling frequency and large image receptive field, upsampling was added to further expand the feature map. Then, to solve the problem of insufficient semantic information of shallow features, feature fusion method was used and one 160*160 output detection layer was added. Finally, since the above steps would increase the amount of computation, the Mobilenet-V2lightweight network was added to improve the detection speed. The VisDrone UAV image data set was used for training and verification and the self-built data set was used for testing. The experiment results show that that improved YOLOv5 algorithm can effectively detect and identify small objects. Compared with traditional detection methods, the small objects detection accuracy and speed is also improved.
Text similarity detection method of power customer service work order based on TFIDF algorithm

The increase in the number of customer complaints has led to an increase in the number of power customer service work orders, resulting in the failure to solve the reasonable demands of some customers on time, affecting the customer service quality of Power Supply Bureau. To solve the above problems, a text similarity detection method of power customer service work order based on TFIDF algorithm is proposed. This method uses web scraper technology to obtain the power customer service work order text, and preprocesses the text, including Chinese word segmentation, desktop words and text representation. TFIDF algorithm is used to extract the preprocessed text keywords and obtain the keyword weight. Based on the weight, cosine distance method is used to calculate the text similarity of text keywords of different power customer service work orders. The results show that compared with the detection method based on weighted word and sentence vector, the detection method based on hybrid model, the detection method based on coupling distance discrimination and strong category features, the macro average F1 measurement value is higher, the method takes less time and has lower complexity.
Exploring Price Accuracy on Uniswap V3 in Times of Distress

Financial markets have evolved over centuries, and exchanges have converged to rely on the order book mechanism for market making. Latency on the blockchain, however, has prevented decentralized exchanges (DEXes) from utilizing the order book mechanism and instead gave rise to the development of market designs that are better suited to a blockchain. Although the first widely popularized DEX, Uniswap V2, stood out through its astonishing simplicity, a recent design overhaul introduced with Uniswap V3 has introduced increasing levels of complexity aiming to increase capital efficiency. In this work, we empirically study the ability of Unsiwap V3 to handle unexpected price shocks. Our analysis finds that the prices on Uniswap V3 were inaccurate during the recent abrupt price drops of two stablecoins: UST and USDT. We identify the lack of agility required of Unsiwap V3 liquidity providers as the root cause of these worrying price inaccuracies. Additionally, we outline that there are too few incentives for liquidity providers to enter liquidity pools, given the elevated volatility in such market conditions.
On Matrix Multiplication with Homomorphic Encryption

Homomorphic Encryption (HE) is one of the main cryptographic tools used to enable secure computation outsourcing. Data is outsourced encrypted to an untrusted service provider and remain encrypted during processing. In the last decade, the performance of HE schemes has impressively improved up to several orders of magnitude thanks to advances in the theory and to more efficient implementations. However, it is still significantly slower than plaintext computations, while realizing HE-based computations is complex for the non-expert. Matrix multiplication is a fundamental computation for a variety of applications that are offered as a service, like machine learning model inference. The matrices are HE encrypted and they are outsourced to an untrusted computation environment. In order to improve the performance of HE schemes, several matrices are encoded in a single ciphertext, known also as message packing. However, a single ciphertext usually has several thousands of slots, and, it is common many of these slots to remain empty due to lack of data. In this work, we introduce a secure matrix multiplication outsourcing method that takes advantage of the message packing, when the available matrix entries are very few, i.e. when several ciphertext slots remain empty. We evaluate the complexity of our proposal in terms of basic homomorphic encryption operations and we compute the multiplicative depth of the corresponding arithmetic circuit. Finally, we implement our multiplication algorithm using the CKKS HE scheme, as it is supported in the MS SEAL library.
Hybrid Seq2Seq Architecture for 3D Co-Speech Gesture Generation

This paper describes the co-speech gesture generation system developed by DSI team for the GENEA challenge 2022. The proposed framework features a unique hybrid encoder-decoder architecture based on transformer networks and recurrent neural networks. The proposed framework has been trained using only the official training data split of the challenge and its performance has been evaluated on the testing split. The framework has achieved promising results on both the subjective (specially the human-likeness) and objective evaluation metrics.
Game Theory Approaches for Evaluating the Deception-based Moving Target Defense

Moving target defense (MTD) is a proactive defensive mechanism proposed to disrupt and disable potential attacks, thus reversing the defender's disadvantages. Cyber deception is a complementary technique that is often used to enhance MTD by utilizing misinformation to deceive and mislead attackers. Deception elements, such as honeypot, honey bait, honey token, breadcrumb, and well-constructed deception scenes, can significantly increase the uncertainties for attackers. Deception-based MTD techniques can change the asymmetry situation between defenders and attackers through affecting the attacker's perception of the system. However, there is still a lack of understanding about the role of cyber deception in MTD, and few research works have evaluated the effectiveness of cyber deception.
UEA Digital Humans entry to the GENEA Challenge 2022

This paper describes our entry to the GENEA (Generation and Evaluation of Non-verbal Behaviour for Embodied Agents) challenge 2022. The challenge aims to further the scientific knowledge using a large-scale, joint subjective evaluation of many gesture generation systems. We present two models to the challenge. A Bi-Directional LSTM for the full-body tier and a BDLSTM multi-decoder to produce body-section specific experts. We develop a loss function using both rotations and positions for training our models. We also introduce PASE+ features to the task of pose prediction, along with FastText word embeddings. Our models performed competitively regarding human likeness, and our multiple decoder system performed in the top two submissions for appropriateness of gesture.
Semi-Supervised 3D Medical Image Segmentation Via Boundary-Aware Consistent Hidden Representation Learning

This paper proposes a novel Boundary-aware Consistent Hidden Representation Learning Network (BA-CHRLN), which contains two branches for semi-supervised 3D medical image segmentation. Inspired by the contrastive learning, the two branches share the same encoder and each has its individual decoder, namely supervised decoder and unsupervised one. A stop-gradient operation is also utilized to prevent collapsing of solutions. Taking the unlabeled images as references, BA-CHRLN imposes the consistency by applying a perturbation on the high-level hidden feature representations, which significantly improves the encoder’s representation and the network’s robustness. A boundary-aware map is further introduced to capture the organ’s boundary without any prior knowledge and additional parameters. Experiments on the Left Atrium (LA) benchmark dataset demonstrate the effectiveness of the BA-CHRLN.
Automatic Fuzzy Graph Construction For Interpretable Image Classification

Interpretable machine learning models have recently received a considerable attention for their capability to provide understandable explanations of predictions evoked from complex systems. In this paper a novel automatic interpretable classification scheme is introduced based on a Fuzzy Cognitive Map (FCM). The proposed approach aims to address the problem of image classification using high-level features, extracted from a Convolutional Neural Network (CNN). The proposed FCM constitutes a fuzzy-graph structure for representing causal reasoning concerning semantic concepts of the real world, as these are depicted within different images. An advantage over current classifiers is that the proposed FCM embeds a mechanism for automatic determination of its structure from the datasets used in each experiment. More importantly, it provides interpretable classification results, whereas it is simple to implement. Experimental results using publicly available datasets show that the proposed approach efficiently extends the application of the traditional FCMs into image classification problem.
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration

Image restoration schemes based on the pre-trained deep models have received great attention due to their unique flexibility for solving various inverse problems. In particular, the Plug-and-Play (PnP) framework is a popular and powerful tool that can integrate an off-the-shelf deep denoiser for different image restoration tasks with known observation models. However, obtaining the observation model that exactly matches the actual one can be challenging in practice. Thus, the PnP schemes with conventional deep denoisers may fail to generate satisfying results in some real-world image restoration tasks. We argue that the robustness of the PnP framework is largely limited by using the off-the-shelf deep denoisers that are trained by deterministic optimization. To this end, we propose a novel deep reinforcement learning (DRL) based PnP framework, dubbed RePNP, by leveraging a light-weight DRL-based denoiser for robust image restoration tasks. Experimental results demonstrate that the proposed RePNP is robust to the observation model used in the PnP scheme deviating from the actual one. Thus, RePNP can generate more reliable restoration results for image deblurring and super resolution tasks. Compared with several state-of-the-art deep image restoration baselines, RePNP achieves better results subjective to model deviation with fewer model parameters.
HARP: Autoregressive Latent Video Prediction with High-Fidelity Image Generator

Video prediction is an important yet challenging problem; burdened with the tasks of generating future frames and learning environment dynamics. Recently, autoregressive latent video models have proved to be a powerful video prediction tool, by separating the video prediction into two sub-problems: pre-training an image generator model, followed by learning an autoregressive prediction model in the latent space of the image generator. However, successfully generating high-fidelity and high-resolution videos has yet to be seen. In this work, we investigate how to train an autoregressive latent video prediction model capable of predicting high-fidelity future frames with minimal modification to existing models, and produce high-resolution (256x256) videos. Specifically, we scale up prior models by employing a high-fidelity image generator (VQ-GAN) with a causal transformer model, and introduce additional techniques of top-k sampling and data augmentation to further improve video prediction quality. Despite the simplicity, the proposed method achieves competitive performance to state-of-the-art approaches on standard video prediction benchmarks with fewer parameters, and enables high-resolution video prediction on complex and large-scale datasets.
Deep Unfolding of Image Denoising by Quantum Interactive Patches

In this paper, we propose a blueprint of a new deep network unfolding a baseline quantum mechanics-based adaptive denoising scheme (De-QuIP). Relying on the theory of quantum many-body physics, the De-QuIP architecture incorporates local patch similarity measures through a term akin to interaction in quantum physics. Our proposed deep network embeds both quantum interactions and other quantum concepts, mainly the Hamiltonian operator. The integration of these quantum tools brings a nonlocal structure to the proposed deep network that harnesses the power of the convolutional layers to enhance the adaptability of the model. Thus, recasting De-QuIP in the framework of a deep learning network while preserving the essence of the baseline structure is the main contribution of this work. Experiments conducted on the Gaussian denoising problem, chosen here for illustration purpose, over a large sample set demonstrate start-of-the-art performance of the proposed deep network, dubbed as Deep-De-QuIP hereafter. Based on the properties of De-QuIP, its intrinsic adaptive structure, Deep-De-QuIP network could be easily extended to other noise models.
Knowledge Distillation for Multi-Target Domain Adaptation in Real-Time Person Re-Identification

Despite the recent success of deep learning architectures, person re-identification (ReID) remains a challenging problem in real-word applications. Several unsupervised single-target domain adaptation (STDA) methods have recently been proposed to limit the decline in ReID accuracy caused by the domain shift that typically occurs between source and target video data. Given the multimodal nature of person ReID data (due to variations across camera viewpoints and capture conditions), training a common CNN backbone to address domain shifts across multiple target domains, can provide an efficient solution for real-time ReID applications. Although multi-target domain adaptation (MTDA) has not been widely addressed in the ReID literature, a straightforward approach consists in blending different target datasets, and performing STDA on the mixture to train a common CNN. However, this approach may lead to poor generalization, especially when blending a growing number of distinct target domains to train a smaller CNN. To alleviate this problem, we introduce a new MTDA method based on knowledge distillation (KD-ReID) that is suitable for real-time person ReID applications. Our method adapts a common lightweight student backbone CNN over the target domains by alternatively distilling from multiple specialized teacher CNNs, each one adapted on data from a specific target domain. Extensive experiments <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> conducted on several challenging person ReID datasets indicate that our approach outperforms state-of-art methods for MTDA, including blending methods, particularly when training a compact CNN backbone like OSNet. Results suggest that our flexible MTDA approach can be employed to design cost-effective ReID systems for real-time video surveillance applications.
Auth-Persons: A Dataset for Detecting Humans in Crowds from Aerial Views

Recent advances in artificial intelligence, control and sensing technologies have facilitated the development of autonomous Unmanned Aerial Vehicles (UAVs). Detecting humans from video input captured on-the-fly from UAVs is a critical task for ensuring flight safety, mostly handled with lightweight Deep Neural Networks (DNNs). However the detection of individual people in the case of dense crowds and/or distribution shifts (i.e., significant visual differences between the training and the test sets) is still very challenging. This paper presents AUTH-Persons, a new, annotated, publicly available video dataset, that consists of both real and synthetic footage, suitable for training and evaluating aerial-view person detection algorithms. The synthetic data were collected from 8 visually distinct photorealistic outdoor environments and they mostly contain scenes with crowded areas, where heavy occlusions and high person densities pose challenges to common detectors. This dataset is employed to evaluate the generalization performance of various state-of-the-art detection frameworks, by testing them on environments that are visually distinct from those they have been trained on. Finally, given that Non-Maximum Suppression (NMS) methods at the end of person detection pipelines typically suffer in crowded scenes, the performance of various NMS algorithms is also compared in AUTH-Persons.
DCT-Based Residual Network for NIR Image Colorization

Colorization of Near-Infrared (NIR) image is a challenging problem due to the lack of low-level clues available in the luminance channel of visible images. Recently, deep learning has witnessed remarkable progress in the NIR image colorization approaches. However, we have observed that most research focuses on designing deeper and wider architectures to improve the quality of RGB images at the expense of computational burden and speed. Few studies have adopted lightweight but effective modules to improve the efficiency of NIR image colorization without affecting its performance. In this paper, we propose the Discrete Cosine Transform (DCT)-based Residual Network (DCT-RCAN) for NIR image colorization. Specifically, the output of our network is four coefficients generated by the Two-Dimensional (2D) 4×4 DCT of RGB images, which reduces the training difficulty of our network by explicitly separating low-frequency and high-frequency details into four subgroups. We adopt the Residual in Residual (RIR) module as a basic module in our network, which can reduce the complexity of the model. Thus, our method can focus on more crucial underlying patterns in channel dimension in a lightweight manner. Extensive experiments validate that our DCT-RCAN is computationally efficient and demonstrate competitive results against state-of-the-art NIR image colorization methods.
Depth is all you Need: Single-Stage Weakly Supervised Semantic Segmentation From Image-Level Supervision

The costly process of obtaining semantic segmentation labels has driven research towards to weakly supervised semantic segmentation (WSSS) methods, with only image-level labels available for training. The lack of dense semantic scene representation requires methods to increase complexity to obtain additional semantic information (i.e. object/stuff extent and boundary) about the scene. This is often done though increased model complexity and sophisticated multi-stage training/refinement procedures. However, the lack of 3D geometric structure of a single image makes these efforts desperate at a certain point. In this work, we propose to harness (inverse) depth maps estimated from one single image via a monocular depth estimation model to integrate the 3D geometric structure of the scene into the segmentation model. In light of this proposal, we develop an end-to-end segmentation-based network model and a self-supervised training process to train for semantic masks from only image-level annotations in a single stage. Our experiments show that our one-stage method achieves comparable segmentation performance (val: 64.32, test: 64.91) on Pascal VOC when compared with those significantly more complex pipelines and outperforms SOTA single-stage methods.
Using Vision Transformers in 3-D Medical Image Classifications

Convolutional Neural Networks (CNNs) have been the controlling deep learning approach for a decade in automated medical image diagnosis. Recently, vision transformers (ViTs) have appeared as a competitive alternative to CNNs in computer vision, yielding similar levels of performance while possessing several interesting properties that could prove to be beneficial for the explanation of deep neural networks. Since most medical images are grayscale scans of CT, MRI, etc. and in 3-dimensional (3-D) spaces, which are highly different from natural images, we explore whether it is possible to move to transformer-based models or if we should keep working with CNNs in the domain of 3-D medical image classifi-cations. If so, what are the advantages and drawbacks of switching to ViTs for medical image diagnosis? We consider these problems in a series of experiments on three 3-D medical image datasets. Our findings show that, while CNNs perform better when trained from scratch, ViTs gain strong benifit when pre-trained on ImageNet and outperform their CNN counterparts using self-supervised learning and sharpness-aware minimizer optimization method on the large datasets.
SWIS: Self-Supervised Representation Learning for Writer Independent Offline Signature Verification

Writer independent offline signature verification is one of the most challenging tasks in pattern recognition as there is often a scarcity of training data. To handle such data scarcity problem, in this paper, we propose a novel self-supervised learning (SSL) framework for writer independent offline signature verification. To our knowledge, this is the first attempt to utilize self-supervised setting for the signature verification task. The objective of self-supervised representation learning from the signature images is achieved by minimizing the cross-covariance between two random variables belonging to different feature directions and ensuring a positive cross-covariance between the random variables denoting the same feature direction. This ensures that the features are decorrelated linearly and the redundant information is discarded. Through experimental results on different data sets, we obtained encouraging results.
Dynamic Mutual Enhancement Network for Single Remote Sensing Image Dehazing

In this paper, we propose a dynamic mutual enhancement network (DMENet) for haze removal in remote sensing images. It has three major advantages compared with other dehazing algorithms: 1) The proposed DMENet is based on the U-Net architecture to extract features effectively, which is composed of three components, i.e., a multi-scale encoder, a middle transmission layer (MTL), and a dynamic mutual decoder. 2) The dynamic mutual enhancement (DME) module is designed to dynamically integrate multi-level feature maps in a mutual way, which contains the low-level detail information and high-level semantic information respectively. 3) To improve the robustness and generalization performance of the DMENet, the hybrid supervision is built for network training between the restored results and their ground-truth labels, which consists of the pixel-level supervision, patch-level supervision and image-level supervision. Experimental results on both synthetic datasets and real remote sensing hazy images demonstrate that the proposed DMENet can gain significant progresses over the competing methods.
Efficient Video Enhancement Transformer

Video Enhancement is an important computer vision task aiming at the removal of the artifacts from a lossy compressed video and the improvement of the visual properties by a photo-realistic restoration of the video contents. Decades of research produced a multitude of efficient algorithms, enabling the reduction of the memory footprint of the transferred video contents in a contiguously increasing network of video streaming services. In this work, we propose VETRAN - a low latency real-time online Video Enhancement TRANsformer based on spatial and temporal attention mechanisms. We validate our method on recent Video Enhancement NTIRE and AIM challenge benchmarks, i.e. REDS/REDS4, LDV, and IntVID. We improve over the compared state-of-the-art methods both quantitatively and qualitatively, while maintaining a low inference time.