Reviewerly/exHarmony huggingface dataset:
Robust Correlation Tracking for UAV with Feature Integration and Response Map Enhancement

Recently, correlation filter (CF)-based tracking algorithms have attained extensive interest in the field of unmanned aerial vehicle (UAV) tracking. Nonetheless, existing trackers still struggle with selecting suitable features and alleviating the model drift issue for online UAV tracking. In this paper, a robust CF-based tracker with feature integration and response map enhancement is proposed. Concretely, we develop a novel feature integration method that comprehensively describes the target by leveraging auxiliary gradient information extracted from the binary representation. Subsequently, the integrated features are utilized to learn a background-aware correlation filter (BACF) for generating a response map that implies the target location. To mitigate the risk of model drift, we introduce saliency awareness in the BACF framework and further propose an adaptive response fusion strategy to enhance the discriminating capability of the response map. Moreover, a dynamic model update mechanism is designed to prevent filter contamination and maintain tracking stability. Experiments on three public benchmarks verify that the proposed tracker outperforms several state-of-the-art algorithms and achieves a real-time tracking speed, which can be applied in UAV tracking scenarios efficiently.
Using blockchain for global governance: past, present and future

Purpose This paper examines the current state of blockchain governance research. The study’s findings also propose a conceptual framework for the use of blockchain in governance and global governance and provide a global look at how public and private sectors alike are implementing new technologies. Design/methodology/approach The study is qualitative as well as quantitative in nature. The authors used Preferred Reporting Items for Systematic and Meta-Analysis (PRISMA) to gather data for the study. Furthermore, a bibliometric analysis using VOSviewer visualization tool and R Studio was carried out to attain the research objectives. Findings Many scholars and practitioners from around the world are interested in the topic, according to the analysis. This is a multidisciplinary study, so researchers have looked at how the blockchain can be used to govern countries, public utilities and global facilities, including corporations. There are numerous examples of how technology has been used in global governance, and the authors found that governments, as well as corporations around the world, have implemented technology in a variety of areas that affect the public and other stakeholders. Practical implications This study makes numerous contributions. In the first place, it presents the complex concept of blockchain in an easier to understand way. The numerous governmental and commercial initiatives that have made use of blockchain are also highlighted. As a result, the use of technology in corporate and social governance will continue to grow. Finally, the research will inform the academic community on the current state of the topic and potential future directions. Originality/value As a result of this research, academics and scholars can better understand the potential of blockchain in various governance models, ranging from developed to developing economies. The general public, as well as organizations, will benefit from the decentralized nature of the blockchain in a variety of ways related to their day-to-day governance. To the best of authors’ knowledge, this is a first kind of research on blockchain in governance using PRISMA and bibliometrics tools.
Exploring dynamic effects on classifying service quality attributes under the impacts of COVID-19 with evidence from online reviews

Purpose The purpose of this paper is exploring the effects of segment dynamic and temporal dynamic triggered by the COVID-19 pandemic on classifying service quality attributes, thereby formulating improvement strategies to satisfy customers and respond to threats. Design/methodology/approach Given the dynamics of the attractive quality theory, this paper designs a framework with four phases by embedding techniques of text mining and deep learning based on evidence from online reviews. Findings This paper figures out dynamics of service quality attributes for distinct segments and their dynamic proportion along with different stages of the pandemic. Another finding demonstrates segment dynamic and temporal dynamic effects of sentiments toward service quality attributes on customer satisfaction under the impacts of pandemic. Classification results and improvement strategies are derived for varying segments at different pandemic situations. Practical implications This paper reveals dynamic effects on classifying service quality attributes, which contributes to assisting hospitality practitioners from different segments in improving service quality when facing with the challenges of crisis and potential risks. Originality/value Given hospitality industry is time- and segment-sensitive, the authors achieve the quantification of dynamics of attractive quality theory and extend it into hospitality marketing and crisis management from the perspective of dynamics with evidence from online reviews.
SeNPIS: Sequential Network Pruning by class-wise Importance Score

In the last decade, pattern recognition and decision making from images has mainly focused on the development of deep learning architectures, with different types of networks such as sequential, residual and parallel. Although the depth and size varies between models, they all have in common that they can contain multiple filters or neurons that are not important for the purpose of prediction, and that do negatively impact the size of the model and their inference times. Therefore, it is advantageous to use pruning methods that, while largely maintaining the initial performance of the classifier, significantly reduce its size and FLOPs. In parameter reduction, the decision rule is generally based on mathematical criteria, e.g. the amplitude of the weights, but not on the actual impact of the filter or neuron on the classifier performance for each of the classes. Therefore, we propose SeNPIS as a method that involves both filter and neuron selection based on a class-wise importance score, and network resizing to increase parameter reduction and FLOPs in sequential CNN networks. Several tests were performed to compare SeNPIS with other representative state-of-the-art methods, for the CIFAR-10 and Scene-15 datasets. It was found that for similar values of accuracy, and even in some cases with a slight increase in accuracy, SeNPIS significantly reduces the number of parameters by up to an additional 23.5% (i.e., a 51.05% reduction with SeNPIS versus a 27.53% reduction with Gradient) and FLOPs by up to an additional 26.6% (i.e., a 74.82% reduction with SeNPIS versus a 48.16% reduction with Weight) compared to the Weight, Taylor, Gradient and LRP methods.
System: A core conceptual modeling construct for capturing complexity

The digitalization of human society continues at a relentless rate. However, to develop modern information technologies, the increasing complexity of the real-world must be modeled, suggesting the general need to reconsider how to carry out conceptual modeling. This research proposes that the often-overlooked notion of “system” should be a separate, and core, conceptual modeling construct and argues for incorporating it and related concepts, such as emergence, into existing approaches to conceptual modeling. The work conducts a synthesis of the ontology of systems and general systems theory. These modeling foundations are then used to propose a CESM+ template for conducing systems-grounded conceptual modeling. Several new conceptual modeling notations are introduced. The systemist modeling is then applied to a case study on the development of a citizen science platform. The case demonstrates the potential contributions of the systemist approach and identifies specific implications of explicit modeling with systems for theory and practice. The paper provides recommendations for how to incorporate systems into existing projects and suggests fruitful opportunities for future conceptual modeling research.
Research on Seismic Signal Analysis Based on Machine Learning

In this paper, the time series classification frontier method MiniRocket was used to classify earthquakes, blasts, and background noise. From supervised to unsupervised classification, a comprehensive analysis was carried out, and finally, the supervised method achieved excellent results. The relatively simple model, MiniRocket, is only a one-dimensional convolutional neural network structure which has achieved the best comprehensive results, and its computational efficiency is far stronger than other supervised classification methods. Through our experimental results, we found that the MiniRocket model could well-extract the decisive features of the seismic sensing signal. In order to try to eliminate the tedious work of making data labels, we proposed a novel lightweight collaborative learning for seismic sensing signals (LCL-SSS) based on the method of feature extraction in MiniRocket combined with unsupervised classification. The new method gives new vitality to the unsupervised classification method that could not be used originally and opens up a new path for the unsupervised classification of seismic sensing signals.
Covid-19 impact on Cryptocurrencies market using Multivariate Time Series Models

The ever-growing volume of cryptocurrency transactions indicates the importance to understand the new cryptocurrency market. Many research works have demonstrated the unique features of cryptocurrency market compared to other asset markets. Under the impact of Covid-19, the cryptocurrency market may display more differential features. We analyse these differential features of the cryptocurrency market by studying their return persistence, return asymmetry, interdependency, and volatility spillover. The vector autoregressive moving average model with variance gamma innovations is proposed to capture these features before and during the pandemic outbreak. We consider four cryptocurrencies, namely Bitcoin, Ripple, Dash, and Litecoin which have top market capitalisation. For model estimation, we apply the computational efficient expectation/conditional maximisation algorithm. We interpret the results concerning their technological setups. • All Bitcoin, Ripple, Litecoin, and Dash returns have anti-persistence showing some market inefficiency. • Bitcoin has higher return and lower volatility. • Bitcoin, Dash and Litecoin have higher cross-dependency but Ripple is less uncorrelated. • During the pandemic, there are higher return persistence, volatility and cross-dependency showing higher risk.
Dual Heterogeneous Complementary Networks for Single Image Deraining

Single image deraining is an extreme challenge task since it requires to not only recover the spatial detail and high-level contextualized structure of the underlying image but also remove multiple rain layers with various blurring degrees and resolutions. Despite of the great performance advance with the deep learning networks, the dominated researches devote to either constructing deeper and complicated network architecture for recovering reliable detailed texture at the original resolution of the input image or exploiting multi-scale encoder-decode structure for learning semantic context in more larger receptive field while are still far from sufficiency to capture both complementary detailed and semantic contexts. This study proposes a novel dual heterogeneous complementary networks consisting of a main original resolution learning subnet and an auxiliary encoder-decoder subnet for exploring both detailed structure and semantic contexts. Specifically, to capture more plausible intermediate features in dual subnets, we concurrently evaluate the deraining losses of both branches in training phase, and exploit an auxiliary pseudo-label supervised attention module to further guide the feature learning in the main subnet. Moreover, to reconstruct more natural and sharper images, we incorporate multiple losses for network training including an improved MSE, an edge-based loss to recover reliable shape information, and a perceptual loss by evaluating the reconstruction error on the feature map of the learned VGGNet model instead of pixel intensity. Experiments on several benchmark deraining datasets demonstrate great superiority over the state-of-the-arts methods.
Few-Shot Class Incremental Learning Leveraging Self-Supervised Features

Few-Shot Class Incremental Learning (FSCIL) is a recently introduced Class Incremental Learning (CIL) setting that operates under more constrained assumptions: only very few samples per class are available in each incremental session, and the number of samples/classes is known ahead of time. Due to limited data for class incremental learning, FSCIL suffers more from over-fitting and catastrophic forgetting than general CIL. In this paper we study leveraging the advances due to self-supervised learning to remedy overfitting and catastrophic forgetting and significantly advance the state-of-the-art FSCIL. We explore training a lightweight feature fusion plus classifier on a concatenation of features emerging from supervised and self-supervised models. The supervised model is trained on data from a base session, where a relatively larger amount of data is available in FSCIL. Whereas a self-supervised model is learned using an abundance of unlabeled data. We demonstrate a classifier trained on the fusion of such features outperforms classifiers trained independently on either of these representations. We experiment with several existing self-supervised models and provide results for three popular benchmarks for FSCIL including Caltech-UCSD Birds-200-2011 (CUB200), miniImageNet, and CIFAR100 where we advance the state-ofthe-art for each benchmark. Code is available at: https://github.com/TouqeerAhmad/FeSSSS
SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment

Image compression has raised widespread interest recently due to its significant importance for multimedia storage and transmission. Meanwhile, a reliable image quality assessment (IQA) for compressed images can not only help to verify the performance of various compression algorithms but also help to guide the compression optimization in turn. In this paper, we design a full-reference image quality assessment metric SwinIQA to measure the perceptual quality of compressed images in a learned Swin distance space. It is known that the compression artifacts are usually non-uniformly distributed with diverse distortion types and degrees. To warp the compressed images into the shared representation space while maintaining the complex distortion information, we extract the hierarchical feature representations from each stage of the Swin Transformer. Besides, we utilize cross attention operation to map the extracted feature representations into a learned Swin distance space. Experimental results show that the proposed metric achieves higher consistency with human’s perceptual judgment compared with both traditional methods and learning-based methods on CLIC datasets.
AquaGAN: Restoration of Underwater Images

In this paper, we propose a generative model to restore degraded underwater images considering attenuation coefficients as clue and name it as AquaGAN. Computing the attenuation coefficients as given in revised image formation model demands in-situ measurements. However, in-situ measurements in underwater scenario is infeasible. Towards this, we propose to estimate the attenuation coefficients using learning based methods and use these parameters as clue for restoration of degraded underwater images. Restoration of true colors in underwater scenario is challenging as intensity of light changes with distance. Preserving true colors during restoration by minimizing single objective function may affect the quality of restored image. Towards this, we propose to combine different objective functions for restoration of true colors. We demonstrate the results of restoration on benchmark dataset and compare the results of proposed methodology with state-of-the-art methods both qualitatively and quantitatively.
Nonuniformly Dehaze Network for Visible Remote Sensing Images

Nonuniform haze on remote sensing images degrades image quality and hinders many high-level tasks. In this paper, we propose a Nonuniformly Dehaze Network towards nonuniform haze on visible remote sensing images. To extract robust haze-aware features, we propose Nonuniformly Excite (NE) module. Inspired by the well-known gather-excite attention module, NE module works in a map-excite manner. In the map operation, we utilize a proposed Dual Attention Dehaze block to extract local enhanced features. In the gather operation, we utilize a strided deformable convolution to nonuniformly process features and extract nonlocal haze-aware features. In the excite operation, we employ a pixel-wise attention between local enhanced features and nonlocal haze-aware features, to gain finer haze-aware features. Moreover, we recursively embed NE modules in a multi-scale framework. It helps not only significantly reduce network’s parameters, but also recursively deliver and fuse haze-aware features from higher levels, which makes learning more efficient. Experiments demonstrate that the proposed network performs favorably against the state-of-the-art methods on both synthetic and real-world images.
RV-GAN: Recurrent GAN for Unconditional Video Generation

Generative models aiming to generate content from noise have achieved high-fidelity synthesis for image data. However, obtaining comparable performance in the field of unconditional video generation still remains challenging. In this work, we propose a recurrent GAN architecture to model the high-dimensional video data distribution. Recurrent networks by design are able to generate complex, long sequences in an autoregressive fashion. However, the standard LSTM unit for videos (ConvLSTM) is not ideally suited for the task of unconditional video generation. Therefore, we propose a simple yet effective LSTM variant called as TransConv LSTM (TC-LSTM) by modulating the conventional ConvLSTM to have a transpose convolutional structure in input-to-state transitions. This enables the network to model both spatial and temporal relationships across layers simultaneously inside the TC-LSTM unit. TC-LSTM unit acts as a building block of our generator. Extensive quantitative and qualitative analysis shows that RV-GAN outperforms state-of-the-art methods by a significant margin on Moving MNIST, MUG, Weizmann and UCF101 datasets. Additionally, owing to the recurrent structure, our method is able to generate high-quality videos, up to 2 times longer (32 frames) than training videos at inference time. Further analysis confirms that the proposed architecture is generic and can be easily adapted to other tasks like class-conditional video synthesis and text-to-video synthesis.
Boundary-aware Image Inpainting with Multiple Auxiliary Cues

Image inpainting (a.k.a. image completion) allows us to remove unexpected foreground objects from an observed image and to restore the removed region with background pixels. The performance of image inpainting is improved by auxiliary cues such as edge boundaries and segmentation regions. As a new auxiliary cue, this paper focuses on a depth image that is estimated from an input RGB image by monocular depth estimation. In the depth image, boundaries between different objects (e.g., objects located in different distances) with similar pixel values might be available, while those boundaries are difficult to be detected by edge detection and segmentation. Our proposed method employs those boundaries in the edge and depth images as auxiliary cues. Experiments demonstrate that our proposed method augmented by the depth image outperforms its baseline quantitatively (i.e., 1.17dB and 0.74dB PSNR gains on the Paris-StreetView and Places datasets, respectively) and qualitatively.
Continual Hippocampus Segmentation with Transformers

In clinical settings, where acquisition conditions and patient populations change over time, continual learning is key for ensuring the safe use of deep neural networks. Yet most existing work focuses on convolutional architectures and image classification. Instead, radiologists prefer to work with segmentation models that outline specific regions-of-interest, for which Transformer-based architectures are gaining traction. The self-attention mechanism of Transformers could potentially mitigate catastrophic forgetting, opening the way for more robust medical image segmentation. In this work, we explore how recently-proposed Transformer mechanisms for semantic segmentation behave in sequential learning scenarios, and analyse how best to adapt continual learning strategies for this setting. Our evaluation on hippocampus segmentation shows that Transformer mechanisms mitigate catastrophic forgetting for medical image segmentation compared to purely convolutional architectures, and demonstrates that regularising ViT modules should be done with caution.
Deep Scale-space Mining Network for Single Image Deraining

Images captured by outdoor vision systems can often be affected by rain weather, resulting in severe degradation of the visual quality of the captured images. Therefore, image deraining has attracted attention as urgent and challenging research. Many current data-driven approaches achieve better performance but are limited in recovering image details. This is because these methods do not fully mine the correlation of scale-space, which are beneficial for rain removal. In this paper, we design an end-to-end Deep Scalespace Mining Network (DSM-Net) for single image deraining to solve these problems. The proposed network with multi-scale extraction, concurrent attention distillation, and hierarchical information fusion accurately captures scale-space features and learns richer information for better deraining. For better feature extraction, a Multi-scale Attention Block (MAB) is introduced to obtain multi-scale rain streak features by different dilated convolutions. Besides, a Concurrent Attention Distillation Block (CADB) is developed which combined channel attention and subspace attention to calibrate the image features obtained from multi-scale acquisition and hierarchical learning, then eliminate redundant features. Importantly, the overall architecture of DSM-Net is inspired by the HourglassNet and DenseNet, which progressively explores and fuses local and global features at different scales in a hierarchical manner instead of direct concatenation. Extensive experiments on synthetic and real datasets show that the proposed DSM-Net outperforms recent state-of-the-art deraining algorithms in terms of both performance and preservation of image details.
DooDLeNet: Double DeepLab Enhanced Feature Fusion for Thermal-color Semantic Segmentation

In this paper we present a new approach for feature fusion between RGB and LWIR Thermal images for the task of semantic segmentation for driving perception. We propose DooDLeNet, a double DeepLab architecture with specialized encoder-decoders for thermal and color modalities and a shared decoder for final segmentation. We combine two strategies for feature fusion: confidence weighting and correlation weighting. We report state-of-the-art mean IoU results on the MF dataset [1].