Reviewerly/exHarmony huggingface dataset:
UIDEF: A real-world underwater image dataset and a color-contrast complementary image enhancement framework

Numerous underwater image enhancement methods have been proposed in recent years. However, these methods are mainly evaluated using synthetic datasets with similar degradation or real-world datasets with insufficient images. A benchmark dataset containing various degraded situations and real-world underwater images is needed to evaluate the performance of these methods. In this paper, we propose a Real-world Underwater Image Dataset (UIDEF), which consists of seven categories and eleven subcategories with 9200 real-world underwater images. These categories roughly cover the multiple degradation types and different shooting perspectives of common underwater imagery. Using this dataset, we conduct a qualitative and quantitative empirical comparison of eight state-of-the-art underwater image enhancement methods to evaluate their effectiveness and robustness. Considering that these methods cannot handle both color restoration and contrast enhancement of the underwater degraded images well, we present a color-contrast complementary image enhancement framework that consists of the adaptive color perception balance and multi-scale weighted fusion. The former procedure is essential to remove the color cast of original input images, while the latter defines four attentive weight maps for making the enhanced output images present a more comfortable visual perception. Extensive experiments have validated that our framework achieves relatively satisfactory performance in most cases. In addition, we demonstrate an additional application of UIDEF in reconstructing a wider-field underwater image based on multiple images with overlapping regions. The dataset is available at https://github.com/LaibinChang/UIDEF.git.
Multi-spectra synchrosqueezing transform

The synchrosqueezing transform (SST) is an effective method to obtain a clear time-frequency representation (TFR). Unfortunately, it suffers from low time-frequency (TF) resolution faced with strongly frequency-modulated signals, such as chirp signals. In this paper, we propose the generalized synchrosqueezing fractional S-transform (GSSFrST) to address this deficiency. In addition, we have also considered its second-order version to further enhance the TFR. They combine the advantages of fractional Fourier transform and synchrosqueezing generalized S-transform to improve the TF concentration. To further improve the TF resolution, we propose the multi-spectra synchrosqueezing transform (MSST) based on the GSSFrST and Hough transform. Inspired by the idea of image fusion, the MSST uses multiple TF spectra to fuse into a novel TF spectrum to represent each component of the signal in more detail. The experimental results show that MSST is a promising TF analysis tool for cross chirp signal separation and instantaneous frequency estimation.
UDAformer: Underwater image enhancement based on dual attention transformer

Underwater images suffer from color casts and low contrast degraded due to wavelength-dependent light scatter and abortion of the underwater environment, which impacts the application of high-level computer vision tasks. Considering the characteristics of uneven degradation and loss of color channel of underwater images, a novel dual attention transformer-based underwater image enhancement method, called UDAformer, is proposed. Specifically, Dual Attention Transformer Block (DATB) combining Channel Self-Attention Transformer (CSAT) with Pixel Self-Attention Transformer is proposed for efficient encoding and decoding of underwater image features. Then, the shifted window method for the pixel self-attention (SW-PSAT) is proposed to improve computational efficiency. Finally, the underwater images are recovered through the design of residual connections based on the underwater imaging model. Experimental results demonstrate the proposed UDAformer surpasses previous state-of-the-art methods, both qualitatively and quantitatively. The code is publicly available at: https://github.com/ShenZhen0502/UDAformer.
Deep learning based physical layer security for terrestrial communications in 5G and beyond networks: A survey

The key principle of physical layer security (PLS) is to permit the secure transmission of confidential data using efficient signal-processing techniques. Also, deep learning (DL) has emerged as a viable option to address various security concerns and enhance the performance of conventional PLS techniques in wireless networks. DL is a strong data exploration technique which can be used to learn normal and abnormal behavior of 5G and beyond wireless networks in an insecure channel paradigm. Also, since DL techniques can successfully predict future new instances by learning from existing ones, they can successfully predict new attacks, which frequently involve mutations of earlier attacks. Thus, motivated by the benefits of DL and PLS, this survey provides a comprehensive review that overviews how DL-based PLS techniques can be employed for solving various security concerns in 5G and beyond networks. The survey begins with an overview of physical layer threats and security concerns in 5G and beyond networks. Then, we present a detailed analysis of various DL and deep reinforcement learning (DRL) techniques that are applicable to PLS applications. We present the specific use-cases of PLS design for each type of technique, including attack detection, physical layer authentication (PLA), and other PLS techniques. Then, we present an in-depth overview of the key areas of PLS where DL can be used to enhance the security of wireless networks, such as automatic modulation classification (AMC), secure beamforming, PLA, etc. Performance evaluation metrics for DL-based PLS design are subsequently covered. Finally, we provide insights to the readers about various challenges and future research trends in the design of DL-based PLS for terrestrial communications in 5G and beyond networks.
A hybrid tuple selection pipeline for smartphone based Human Activity Recognition

With the increasing use of sensors in almost every device and application worldwide, the volume of data increases exponentially. Instance selection is an important data preprocessing step that can be applied to many machine learning models. It is even crucial for executing machine learning tasks in constrained environments as demanded by many Internet of Things (IoT) enabled applications today such as the Human Activity Recognition (HAR) domain of applications. In this paper, we have proposed a hybrid selection and training pipeline methodology that combines the nearest neighbor concept with evolutionary computing to address the instance selection problem of smartphone sensing-based HAR. A clustering algorithm has been proposed first followed by a Genetic Algorithm based instance selection approach. It is so far the first of its kind in the aforementioned domain of HAR based on smartphone sensing data. The proposed instance selection has been tested on two of the popular benchmark HAR datasets — UCI-HAR and WISDM. The experimented outputs report that the proposed method has effectively reduced the dataset size by around 40% for the benchmark datasets while retaining the recognition accuracy above 94%. This is a clear depiction of the removal of the outliers from the instance set. The proposed approach has also been compared with other state-of-the-art under-sampling approaches and the results show that our approach has performed better.
OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection

Monocular 3D object detection has recently made a significant leap forward thanks to the use of pre-trained depth estimators for pseudo-LiDAR recovery. Yet, such two-stage methods typically suffer from overfitting and are incapable of explicitly encapsulating the geometric relation between depth and object bounding box. To overcome this limitation, we instead propose to jointly estimate dense scene depth with depth-bounding box residuals and object bounding boxes, allowing a two-stream detection of 3D objects that harnesses both geometry and context information. Thereby, the geometry stream combines visible depth and depth-bounding box residuals to recover the object bounding box via explicit occlusion-aware optimization. In addition, a bounding box based geometry projection scheme is employed in an effort to enhance distance perception. The second stream, named as the Context Stream, directly regresses 3D object location and size. This novel two-stream representation enables us to enforce cross-stream consistency terms, which aligns the outputs of both streams, and further improves the overall performance. Extensive experiments on the public benchmark demonstrate that OPA-3D outperforms state-of-the-art methods on the main Car category, whilst keeping a real-time inference speed.
SGU-Net: Shape-Guided Ultralight Network for Abdominal Image Segmentation

Convolutional neural networks (CNNs) have achieved significant success in medical image segmentation. However, they also suffer from the requirement of a large number of parameters, leading to a difficulty of deploying CNNs to low-source hardwares, e.g., embedded systems and mobile devices. Although some compacted or small memory-hungry models have been reported, most of them may cause degradation in segmentation accuracy. To address this issue, we propose a shape-guided ultralight network (SGU-Net) with extremely low computational costs. The proposed SGU-Net includes two main contributions: it first presents an ultralight convolution that is able to implement double separable convolutions simultaneously, i.e., asymmetric convolution and depthwise separable convolution. The proposed ultralight convolution not only effectively reduces the number of parameters but also enhances the robustness of SGU-Net. Secondly, our SGUNet employs an additional adversarial shape-constraint to let the network learn shape representation of targets, which can significantly improve the segmentation accuracy for abdomen medical images using self-supervision. The SGU-Net is extensively tested on four public benchmark datasets, LiTS, CHAOS, NIH-TCIA and 3Dircbdb. Experimental results show that SGU-Net achieves higher segmentation accuracy using lower memory costs, and outperforms state-of-the-art networks. Moreover, we apply our ultralight convolution into a 3D volume segmentation network, which obtains a comparable performance with fewer parameters and memory usage. The available code of SGUNet is released at https://github.com/SUST-reynole/SGUNet.
Semantic segmentation of UAV remote sensing images based on edge feature fusing and multi-level upsampling integrated with Deeplabv3+

Deeplabv3+ currently is the most representative semantic segmentation model. However, Deeplabv3+ tends to ignore targets of small size and usually fails to identify precise segmentation boundaries in the UAV remote sensing image segmentation task. To handle these problems, this paper proposes a semantic segmentation algorithm of UAV remote sensing images based on edge feature fusing and multi-level upsampling integrated with Deeplabv3+ (EMNet). EMNet uses MobileNetV2 as its backbone and adds an edge detection branch in the encoder to provide edge information for semantic segmentation. In the decoder, a multi-level upsampling method is designed to retain high-level semantic information (e.g., the target's location and boundary information). The experimental results show that the mIoU and mPA of EMNet improved over Deeplabv3+ by 7.11% and 6.93% on the dataset UAVid, and by 0.52% and 0.22% on the dataset ISPRS Vaihingen.
Deep Gaussian Process Emulation using Stochastic Imputation

Deep Gaussian processes (DGPs) provide a rich class of models that can better represent functions with varying regimes or sharp changes, compared to conventional GPs. In this work, we propose a novel inference method for DGPs for computer model emulation. By stochastically imputing the latent layers, our approach transforms a DGP into a linked GP: a novel emulator developed for systems of linked computer models. This transformation permits an efficient DGP training procedure that only involves optimizations of conventional GPs. In addition, predictions from DGP emulators can be made in a fast and analytically tractable manner by naturally using the closed form predictive means and variances of linked GP emulators. We demonstrate the method in a series of synthetic examples and empirical applications, and show that it is a competitive candidate for DGP surrogate inference, combining efficiency that is comparable to doubly stochastic variational inference and uncertainty quantification that is comparable to the fully-Bayesian approach. A Python package dgpsi implementing the method is also produced and available at https://github.com/mingdeyu/DGP.
A system-based view of blockchain governance

Governance is crucial in achieving the success and sustainability of blockchain systems. However, blockchain governance is multi-faceted, complex, dynamic, and challenging due to its decentralized nature and automatically enforced rules and mechanisms. This study aims to advance the theory of blockchain governance and support practitioners to deepen the researchers’ and practitioners’ understanding of blockchain governance. The study is a systematic literature review of 75 articles that applies systems theory to conceptualize blockchain governance as a system and parsimoniously organize its interrelated elements into a conceptual model. The paper proposes a holistic definition and a conceptual model of blockchain governance. Blockchain governance encompasses technical and social means to make decisions on the different levels (e.g., individual, community, organizational, national, international) related to actors, roles, rights, incentives, responsibilities, rules, and the business, technological, legal, and regulatory aspects of a blockchain system during its whole lifecycle. The system-based model of blockchain governance can serve as a reference framework and structured foundation for analyzing, discussing, and developing the governance of blockchain systems.
A Novel Multi-Factor Authentication Algorithm Based on Image Recognition and User Established Relations

Conventional authentication methods, like simple text-based passwords, have shown vulnerabilities to different types of security attacks. Indeed, 61% of all breaches involve credentials, whether stolen via social engineering or hacked using brute force. Therefore, a robust user authentication mechanism is crucial to have secure systems. Combining textual passwords with graphical passwords in a multi-factor approach can be an effective strategy. Advanced authentication systems, such as biometrics, are secure, but require additional infrastructure for efficient implementation. This paper proposes a Multi-Factor Authentication (MFA) based on a non-biometric mechanism that does not require additional hardware. The novelty of the proposed mechanism lies in a two-factor authentication algorithm which requires a user to identify specific images out of a set of randomly selected images, then the user is required to establish a self-pre-configured relation between two given images to complete authentication. A functional prototype of the proposed system was developed and deployed. The proposed system was tested by users of different backgrounds achieving 100% accuracy in identifying and authenticating users, if authentication elements and credentials were not forgotten. It was also found to be accepted by the users as being easy to use and preferable over common MFA mechanisms.
Distributed Target Tracking With Optimal Data Migration

The paper presents an Extended Kalman Filter based framework for airborne target tracking using adaptive information fusion from multi-modal multi-rate distributed sensors network. First, the tracking algorithm execution location is determined using an optimal data migration strategy, which also computes the associated delays for each sensor data to arrive at the computing location. Next, the fast (zero-delay) sensors information is dynamically fused in the filter correction procedure at the arrival instance of each valid sensor reading. Finally, the target estimation is updated based on the valid slow (delayed) data, which are grouped according to the delay-time steps before application of the Larsen's method. This approach is applied to the synthetic sensor data generated by means of the ground based radar and camera models for the simulated target flight in Reflection simulation environment.
The Implications of Food Security on Sustainability: Do Trade Facilitation, Population Growth, and Institutional Quality Make or Mar the Target for SSA?

This study examines the impactful role played by trade facilitation (TF) in promoting or hindering food security in a panel of 34 sub-Saharan countries for the period 2005–2019. The empirical evidence is based on the Two-Step Dynamic System Generalized Method of Moments estimator, employed to account for econometric concerns bothering on unobserved heterogeneity and potential endogeneity inherent in the variables used. The empirical findings show that the nature of TF procedures, which are inefficient, negatively impact food security in SSA. These effects are evident on the availability and accessibility dimensions of food security as well as their composite index. While it is noted that this result runs counter to the established a priori of positive signs on the one hand, it however portrays the reality of the economic phenomenon in SSA on the other/hand. In balance, the present TF regime can best be described as anti-food security as suggested by the prevailing burdensome procedures involved in exporting and importing staple food items. The functional roles of population growth and institutional quality are empirically enhanced divergently. Going forward, we recommend that for food sufficiency and Sustainable Development Goals to be achieved quickly, governments within the region would need to finetune the underlying modalities of the present TF regime
On p-Values and Statistical Significance

At the beginning of our research training, we learned about hypothesis testing, p-values, and statistical inference [...].
Training a neural network to predict dynamics it has never seen

Neural networks have proven to be remarkably successful for a wide range of complicated tasks, from image recognition and object detection to speech recognition and machine translation. One of their successes lies in their ability to predict future dynamics given a suitable training data set. Previous studies have shown how echo state networks (ESNs), a type of recurrent neural networks, can successfully predict both short-term and long-term dynamics of even chaotic systems. This study shows that, remarkably, ESNs can successfully predict dynamical behavior that is qualitatively different from any behavior contained in their training set. Evidence is provided for a fluid dynamics problem where the flow can transition between laminar (ordered) and turbulent (seemingly disordered) regimes. Despite being trained on the turbulent regime only, ESNs are found to predict the existence of laminar behavior. Moreover, the statistics of turbulent-to-laminar and laminar-to-turbulent transitions are also predicted successfully. The utility of ESNs in acting as early-warning generators for transition is discussed. These results are expected to be widely applicable to data-driven modeling of temporal behavior in a range of physical, climate, biological, ecological, and finance models characterized by the presence of tipping points and sudden transitions between several competing states.
Optimizing Video Analytics with Declarative Model Relationships

The availability of vast video collections and the accuracy of ML models has generated significant interest in video analytics systems. Since naively processing all frames using expensive models is impractical, researchers have proposed optimizations such as selectively using faster but less accurate models to replace or filter frames for expensive models. However, these optimizations are difficult to apply on queries with multiple predicates and models, as users must manually explore a large optimization space. Without significant systems expertise or time investment, an analyst may manually create an execution plan that is unnecessarily expensive and/or terribly inaccurate. We propose Relational Hints , a declarative interface that allows users to suggest ML model relationships based on domain knowledge. Users can express two key relationships: when a model can replace another (CAN REPLACE) and when a model can be used to filter frames for another (CAN FILTER). We aim to design an interface to express model relationships informed by domain specific knowledge and define the constraints by which these relationships hold. We then present the VIVA video analytics system that uses relational hints to optimize SQL queries on video datasets. VIVA automatically selects and validates the hints applicable to the query, generates possible query plans using a formal set of transformations, and finds the best performance plan that meets a user's accuracy requirements. VIVA relieves users from rewriting and manually optimizing video queries as new models become available and execution environments evolve. We evaluate VIVA implemented on top of Spark and show that hints improve performance up to 16.6X without sacrificing accuracy.
PILE: Robust Privacy-Preserving Federated Learning Via Verifiable Perturbations

Federated learning (FL) protects training data in clients by collaboratively training local machine learning models of clients for a global model, instead of directly feeding the training data to the server. However, existing studies show that FL is vulnerable to various attacks, resulting in training data leakage or interfering with the model training. Specifically, an adversary can analyze local gradients and the global model to infer clients’ data, and poison local gradients to generate an inaccurate global model. It is extremely challenging to guarantee strong privacy protection of training data while ensuring the robustness of model training. None of the existing studies can achieve the goal. In this paper, we propose a robust privacy-preserving federated learning framework (PILE), which protects the privacy of local gradients and global models, while ensuring their correctness by gradient verification where the server verifies the computation process of local gradients. In PILE, we develop a verifiable perturbation scheme that makes confidential local gradients verifiable for gradient verification. In particular, we build two building blocks of zero-knowledge proofs for the gradient verification without revealing both local gradients and global models. We perform rigorous theoretical analysis that proves the security of PILE and evaluate PILE on both passive and active membership inference attacks. The experiment results show that the attack accuracy under PILE is between <inline-formula><tex-math notation="LaTeX">$[50.3\%,50.9\%]$</tex-math></inline-formula> , which is close to the random guesses. Particularly, compared to prior defenses that incur the accuracy losses ranging from 2% to 13%, the accuracy loss of PILE is negligible, i.e., only <inline-formula><tex-math notation="LaTeX">$\pm 0.3\%$</tex-math></inline-formula> accuracy loss.