Reviewerly/exHarmony huggingface dataset:
Meta Gradient Adversarial Attack

In recent years, research on adversarial attacks has be-come a hot spot. Although current literature on the transfer-based adversarial attack has achieved promising results for improving the transferability to unseen black-box models, it still leaves a long way to go. Inspired by the idea of meta-learning, this paper proposes a novel architecture called Meta Gradient Adversarial Attack (MGAA), which is plug-and-play and can be integrated with any existing gradient-based attack method for improving the cross-model transferability. Specifically, we randomly sample multiple models from a model zoo to compose different tasks and iteratively simulate a white-box attack and a black-box attack in each task. By narrowing the gap between the gradient directions in white-box and black-box attacks, the transfer-ability of adversarial examples on the black-box setting can be improved. Extensive experiments on the CIFAR10 and ImageNet datasets show that our architecture outperforms the state-of-the-art methods for both black-box and white-box attack settings.
Framework to enable pharmacist access to health care data using Blockchain technology and artificial intelligence

Decentralization and authentication are embedded in blockchain technology, which utilizes artificial intelligence (AI) to ensure seamless sharing of data among different health care providers while safeguarding data privacy. Although community pharmacists are highly accessible to patients and possess robust clinical knowledge, they are limited in the clinical services they can provide owing to their lack of access to patient health records. We proposed a blockchain- and AI-based conceptual framework by performing a scoping review of successful blockchain integration in health systems.To formulate a conceptual framework based on a scoping review to improve access to health care data in the community pharmacy setting through the adoption of blockchain technology and AI.We performed a scoping review of literature based on Preferred Reporting Items for Systematic reviews and Meta-Analyses review criteria to identify the specific areas where blockchain can be implemented in health systems. We utilized the Pharmacists' Patient Care Process (PPCP) to identify 2 critical areas for blockchain integration that can support community pharmacists to access patient electronic health records and implement patient-specific information in clinical decision-making.We included 7 articles out of 70 articles in our final review. The 2 areas in the PPCP identified for the use of blockchain on the basis of the literature review were "Assess" and "Implement." Our proposed model involves pharmacists using AI and blockchain technology to comprehensively assess any concerns with the prescribed medication through access to laboratory results for patients and then implement a plan based on a comprehensive assessment of the patient's health record.Utilizing blockchain to securely share health data with community pharmacies has the potential to improve patient outcomes, optimize medication safety, and amplify pharmacists' roles in patient care. Future research should focus on implementing the model in the real-world settings.
Estimating Egocentric 3D Human Pose in Global Space

Egocentric 3D human pose estimation using a single fisheye camera has become popular recently as it allows capturing a wide range of daily activities in unconstrained environments, which is difficult for traditional outside-in motion capture with external cameras. However, existing methods have several limitations. A prominent problem is that the estimated poses lie in the local coordinate system of the fisheye camera, rather than in the world coordinate system, which is restrictive for many applications. Furthermore, these methods suffer from limited accuracy and temporal instability due to ambiguities caused by the monocular setup and the severe occlusion in a strongly distorted egocentric perspective. To tackle these limitations, we present a new method for egocentric global 3D body pose estimation using a single head-mounted fish-eye camera. To achieve accurate and temporally stable global poses, a spatio-temporal optimization is performed over a sequence of frames by minimizing heatmap reprojection errors and enforcing local and global body motion priors learned from a mocap dataset. Experimental results show that our approach outperforms state-of-the-art methods both quantitatively and qualitatively.
Localized Simple Multiple Kernel K-means

As a representative of multiple kernel clustering (MKC), simple multiple kernel k-means (SimpleMKKM) is recently put forward to boosting the clustering performance by optimally fusing a group of pre-specified kernel matrices. Despite achieving significant improvement in a variety of applications, we find out that SimpleMKKM could indiscriminately force all sample pairs to be equally aligned with the same ideal similarity. As a result, it does not sufficiently take the variation of samples into consideration, leading to unsatisfying clustering performance. To address these issues, this paper proposes a novel MKC algorithm with a "local" kernel alignment, which only requires that the similarity of a sample to its k-nearest neighbours be aligned with the ideal similarity matrix. Such an alignment helps the clustering algorithm to focus on closer sample pairs that shall stay together and avoids involving unreliable similarity evaluation for farther sample pairs. After that, we theoretically show that the objective of SimpleMKKM is a special case of this local kernel alignment criterion with normalizing each base kernel matrix. Based on this observation, the proposed localized SimpleMKKM can be readily implemented by existing SimpleMKKM package. Moreover, we conduct extensive experiments on several widely used benchmark datasets to evaluate the clustering performance of localized SimpleMKKM. The experimental results have demonstrated that our algorithm consistently outperforms the state-of-the-art ones, verifying the effectiveness of the proposed local kernel alignment criterion. The code of Localized SimpleMKKM is publicly available at: https://github.com/xinwangliu/LocalizedSMKKM.
Meta captioning: A meta learning based remote sensing image captioning framework

Remote sensing image captioning models require large amounts of caption-labeled training data. Though image classification models are normally trained with sufficient training data, they cannot be straightforwardly applied to remote sensing image captioning, because the labels for classification and captioning arise from different task domains. Additionally, remote sensing images with caption labels are not as sufficient as images with class labels. Such limitations render difficulty to effective remote sensing image captioning. To address these limitations, we develop a meta captioning framework that conducts remote sensing image captioning with meta learning. The meta captioning framework extracts meta features from two support tasks, i.e., natural image classification and remote sensing image classification, and transfers the meta features to one target task, i.e., remote sensing image captioning. The two support tasks train classification models with big amounts of class-labeled data such that they extract meta features that comprehensively represent image visual features from the perspective of classification. The target task, equipped by the meta features, just requires a relatively small amount of caption-labeled training data for achieving effective remote sensing image captioning. Experimental evaluations on three public datasets validate that the meta captioning framework achieves state-of-the-art performance on remote sensing image captioning. We release the code for our work at: https://github.com/QiaoqiaoYang/MetaCaptioning.
A Ship Detection Method via Redesigned FCOS in Large-Scale SAR Images

Ship detection in large-scale synthetic aperture radar (SAR) images has achieved breakthroughs as a result of the improvement of SAR imaging technology. However, there still exist some issues due to the scattering interference, sparsity of ships, and dim and small ships. To address these issues, an anchor-free method is proposed for dim and small ship detection in large-scale SAR images. First, fully convolutional one-stage object detection (FCOS) as the baseline is applied to detecting ships pixel by pixel, which can eliminate the effect of anchors and avoid the missing detection of small ships. Then, considering the particularity of SAR ships, the sample definition is redesigned based on the statistical characteristics of ships. Next, the feature extraction is redesigned to improve the feature representation for dim and small ships. Finally, the classification and regression are redesigned by introducing an improved focal loss and regression refinement with complete intersection over union (CIoU) loss. Experimental simulation results show that the proposed R-FCOS method can detect dim and small ships in large-scale SAR images with higher accuracy compared with other methods.
A Unified 3D Human Motion Synthesis Model via Conditional Variational Auto-Encoder

We present a unified and flexible framework to address the generalized problem of 3D motion synthesis that covers the tasks of motion prediction, completion, interpolation, and spatial-temporal recovery. Since these tasks have different input constraints and various fidelity and diversity requirements, most existing approaches only cater to a specific task or use different architectures to address various tasks. Here we propose a unified framework based on Conditional Variational Auto-Encoder (CVAE), where we treat any arbitrary input as a masked motion series. Notably, by considering this problem as a conditional generation process, we estimate a parametric distribution of the missing regions based on the input conditions, from which to sample and synthesize the full motion series. To further allow the flexibility of manipulating the motion style of the generated series, we design an Action-Adaptive Modulation (AAM) to propagate the given semantic guidance through the whole sequence. We also introduce a cross-attention mechanism to exploit distant relations among decoder and encoder features for better realism and global consistency. We conducted extensive experiments on Human 3.6M and CMU-Mocap. The results show that our method produces coherent and realistic results for various motion synthesis tasks, with the synthesized motions distinctly adapted by the given action labels.
DepthTrack: Unveiling the Power of RGBD Tracking

RGBD (RGB plus depth) object tracking is gaining momentum as RGBD sensors have become popular in many application fields such as robotics. However, the best RGBD trackers are extensions of the state-of-the-art deep RGB trackers. They are trained with RGB data and the depth channel is used as a sidekick for subtleties such as occlusion detection. This can be explained by the fact that there are no sufficiently large RGBD datasets to 1) train "deep depth trackers" and to 2) challenge RGB trackers with sequences for which the depth cue is essential. This work introduces a new RGBD tracking dataset - Depth-Track - that has twice as many sequences (200) and scene types (40) than in the largest existing dataset, and three times more objects (90). In addition, the average length of the sequences (1473), the number of deformable objects (16) and the number of annotated tracking attributes (15) have been increased. Furthermore, by running the SotA RGB and RGBD trackers on DepthTrack, we propose a new RGBD tracking baseline, namely DeT, which reveals that deep RGBD tracking indeed benefits from genuine training data. The code and dataset is available at https://github.com/xiaozai/DeT.
Vi<sup>2</sup>CLR: Video and Image for Visual Contrastive Learning of Representation

In this paper, we introduce a novel self-supervised visual representation learning method which understands both images and videos in a joint learning fashion. The proposed neural network architecture and objectives are designed to obtain two different Convolutional Neural Networks for solving visual recognition tasks in the domain of videos and images. Our method called Video/Image for Visual Contrastive Learning of Representation(Vi <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CLR) uses unlabeled videos to exploit dynamic and static visual cues for self-supervised and instances similarity/dissimilarity learning. Vi <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CLR optimization pipeline consists of visual clustering part and representation learning based on groups of similar positive instances within a cluster and negative ones from other clusters and learning visual clusters and their distances. We show how a joint self-supervised visual clustering and instance similarity learning with 2D (image) and 3D (video) CovNet encoders yields such robust and near to supervised learning performance.We extensively evaluate the method on downstream tasks like large scale action recognition, image and object classification on datasets like Kinetics, ImageNet, Pascal VOC’07 and UCF101 and achieve outstanding results compared to state-of-the-art self-supervised methods.
Serverless computing on heterogeneous computers

Existing serverless computing platforms are built upon homogeneous computers, limiting the function density and restricting serverless computing to limited scenarios. We introduce Molecule, the first serverless computing system utilizing heterogeneous computers. Molecule enables both general-purpose devices (e.g., Nvidia DPU) and domain-specific accelerators (e.g., FPGA and GPU) for serverless applications that significantly improve function density (50% higher) and application performance (up to 34.6x). To achieve these results, we first propose XPU-Shim, a distributed shim to bridge the gap between underlying multi-OS systems (when using general-purpose devices) and our serverless runtime (i.e., Molecule). We further introduce vectorized sandbox, a sandbox abstraction to abstract hardware heterogeneity (when using domain-specific accelerators). Moreover, we also review state-of-the-art serverless optimizations on startup and communication latency and overcome the challenges to implement them on heterogeneous computers. We have implemented Molecule on real platforms with Nvidia DPUs and Xilinx FPGAs and evaluate it using benchmarks and real-world applications.
STRICTs: A Blockchain-enabled Smart Emission Cap Restrictive and Carbon Permit Trading System

As the first legally binding global agreement on climate change, the Paris Agreement aims to limit long-term global warming to less than 2 °C, preferably below 1.5 °C compared to pre-industrial levels. Road transport, as one of the primary sources of energy consumption and carbon emissions, has great potential for energy conservation and emission reduction in achieving the climate goals. However, current approaches cannot efficiently reduce road travel, making it challenging to decarbonize in the transport sector. Therefore, in this paper, we propose STRICTs, a blockchain-enabled motor vehicle restrictive and trading system based on the carbon emissions cap. STRICTs allows automated carbon emission auditing and carbon emission violation punishment without relying on third-party escrow. Through a decentralized enforcer, STRICTs also enables carbon permit trading reliably and transparently. Finally, we implement a proof-of-concept prototype of the system based on Hyperledger Fabric and conduct experiments for comprehensive performance evaluation through CO2 Emission by Vehicles dataset. The experiment results show the practically affordable performance of STRICTs.
Remote Sensing Image Denoising Based on Deep and Shallow Feature Fusion and Attention Mechanism

Optical remote sensing images are widely used in the fields of feature recognition, scene semantic segmentation, and others. However, the quality of remote sensing images is degraded due to the influence of various noises, which seriously affects the practical use of remote sensing images. As remote sensing images have more complex texture features than ordinary images, this will lead to the previous denoising algorithm failing to achieve the desired result. Therefore, we propose a novel remote sensing image denoising network (RSIDNet) based on a deep learning approach, which mainly consists of a multi-scale feature extraction module (MFE), multiple local skip-connected enhanced attention blocks (ECA), a global feature fusion block (GFF), and a noisy image reconstruction block (NR). The combination of these modules greatly improves the model’s use of the extracted features and increases the model’s denoising capability. Extensive experiments on synthetic Gaussian noise datasets and real noise datasets have shown that RSIDNet achieves satisfactory results. RSIDNet can improve the loss of detail information in denoised images in traditional denoising methods, retaining more of the higher-frequency components, which can have performance improvements for subsequent image processing.
Improved Sine-Tangent chaotic map with application in medical images encryption

The attractive properties of ergodicity, unpredictability, and initial state sensitivity have made chaotic maps the go-to tools in many applications, including cryptography and cyber–physical systems. Despite this, two challenges arise when using chaos systems in cryptography: (i) some one-dimensional (1D) chaotic maps do not satisfy the unpredictability property, and (ii) although they exhibit a complex and chaotic behavior, high-dimensional (HD) chaotic maps incur higher computational complexity. To address these issues, this paper proposes a new 1D chaotic map dubbed the improved Sine-Tangent map (IST map) that is derived from of the Sine map, a tangent function, and a Chebyshev polynomial of the first kind. Relative to chaotic maps, the proposed IST map provides better unpredictability and ergodicity, a vast chaotic range, enhanced complex behavior, and competitive computational complexity. Based on the IST map, we also introduced an encryption scheme for securing medical images in telemedicine. It consists of two diffusion phases, i.e., a bitwise XOR operation and a bitwise expanded XOR (eXOR) operation, with an intermediated confusion one, i.e., random circular-shift. An overriding step is foremost first completed before performing these cryptography phases, i.e., key generation. The secret key of the IST map is updated using the sine and cosine values of the sum of pixels of the input image. This leads to a unique secret key for each image. That is, one-time chaotic sequences are produced for each input image. The cyclic pattern of the sine and cosine values of the sum of pixels provides a prominent sensibility to small changes in the input image. Thus, the proposed algorithm is capable of resisting any chosen/known plaintext attacks. A performance analysis shows that the proposed algorithm outperforms a set of state-of-the-art comparison algorithms and its variants based on Sine, SE, and ST maps since it allows the best performance/complexity trade-off.
Sampling scheme-based classification rule mining method using decision tree in big data environment

Obtaining comprehensible classification rules may be extremely important in many real applications such as data-driven decision-making and classification tasks. Decision-tree methods are powerful and popular tools for acquiring classification rules. However, they do not show good performance, and the base data processing methods lack strong theoretical support in big data scenarios. This study introduces a sampling scheme with and without the replacement of the implementations of decision-tree methods. This method, called sampling-based classification rule mining (SCRM), is designed to improve the adaptation and generalization ability of classification rules in a big-data environment. Sampling without replacement is conducted to refine classification rules using the concept of conflict and coverage rules, while sampling with replacement is applied to determine rule reliability; the reliability approximation property of classification rules is proved by using the law of large numbers. The effectiveness of the SCRM was evaluated and verified using seven UCI datasets. Theoretical analysis and experimental results show that SCRM is generic with good classification ability, thereby improving the classification accuracy of the rules. SCRM has a significant advantage as it provides theoretical and methodological support for the classification rule mining of big data. Therefore, the SCRM can be used in many applications.
Exploration and Petrogenesis of Corundum-Bearing Pegmatites: A Case Study in Migif-Hafafit Area, Egypt

A net of pegmatite veins crosscut gneiss and dismembered ophiolitic ultramafic rocks are abundant in the Migif-Hafafit culmination area, South Eastern Desert, Egypt. Some of these pegmatites are corundum-bearing. Corundum-bearing pegmatites are studied in eight localities around the Migif-Hafafit complex. The present study aims to integrate field investigation, whole rock and corundum chemistry, remote sensing studies, and the conventional methods to detect the corundum-bearing pegmatites, which led to finding out the genesis of the corundum mineralization. In addition, the tectonic regime has been detected using bulk rock geochemistry. False-color composite (FCCs), band ratio (BR), principal component analysis (PCA), minimum noise fraction (MNF), decorrelation stretch (DS), and the constrained energy minimization (CEM) image processing algorithms were proposed and applied on Landsat-8 OLI, and Sentinel-2A data to discriminate the exposed lithological units and to detect the corundum-bearing pegmatites in the study area. These detected pegmatites are structurally controlled following the NW-SE direction. The ultramafic rocks are the host of six areas and the gneisses are the host rocks of the two others. Gneisses and pegmatites are characterized by a steep slope of LREEs relative to HREEs with week Eu negative anomaly. The studied pegmatites have both plumasite and marundite. On the other hand, the studied corundum has Al 2 O 3 content up to 99.72 wt% in pegmatite-gneiss and up to 100 wt.% in pegmatite-ultramafics. Corundum is ascribed to metamorphic/metasomatic type according to its chromosphere. Desilication in pegmatites led to the excess of alumina content in the residual melt producing corundum mineralization.
YOLO V3 + VGG16-based automatic operations monitoring and analysis in a manufacturing workshop under Industry 4.0

Under the background of Industry 4.0 and smart manufacturing, operators are still the core of manufacturing production, and the standardization of their actions greatly affects production efficiency and quality. However, they have not received enough attention. In view of the monitoring and analysis of operators’ actions in the manufacturing field, this paper proposes the YOLO V3 + VGG 16 transfer learning network. First, the region detection of key operators is realized by using YOLO V3, and an action dataset is constructed. Second, using transfer learning to realize the automatic recognition, monitoring and analysis of small sample data, the recognition accuracy of the proposed method is greater than 96%, and the average deviation of the action execution time is less than 1 s. This research is expected to provide guidance for increasing the degree of workshop automation, improving the standardization of operators’ actions, optimizing action processes and ensuring product quality.