Reviewerly/exHarmony huggingface dataset:
Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach

In emergency departments (EDs), early diagnosis and timely rescue, which are supported by prediction modes using ED data, can increase patients' chances of survival. Unfortunately, ED data usually contain missing, imbalanced, and sparse features, which makes it challenging to build early identification models for diseases.This study aims to propose a systematic approach to deal with the problems of missing, imbalanced, and sparse features for developing sudden-death prediction models using emergency medicine (or ED) data.We proposed a 3-step approach to deal with data quality issues: a random forest (RF) for missing values, k-means for imbalanced data, and principal component analysis (PCA) for sparse features. For continuous and discrete variables, the decision coefficient R2 and the κ coefficient were used to evaluate performance, respectively. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) were used to estimate the model's performance. To further evaluate the proposed approach, we carried out a case study using an ED data set obtained from the Hainan Hospital of Chinese PLA General Hospital. A logistic regression (LR) prediction model for patient condition worsening was built.A total of 1085 patients with rescue records and 17,959 patients without rescue records were selected and significantly imbalanced. We extracted 275, 402, and 891 variables from laboratory tests, medications, and diagnosis, respectively. After data preprocessing, the median R2 of the RF continuous variable interpolation was 0.623 (IQR 0.647), and the median of the κ coefficient for discrete variable interpolation was 0.444 (IQR 0.285). The LR model constructed using the initial diagnostic data showed poor performance and variable separation, which was reflected in the abnormally high odds ratio (OR) values of the 2 variables of cardiac arrest and respiratory arrest (201568034532 and 1211118945, respectively) and an abnormal 95% CI. Using processed data, the recall of the model reached 0.746, the F1-score was 0.73, and the AUROC was 0.708.The proposed systematic approach is valid for building a prediction model for emergency patients.
Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution

Due to the sophisticated imaging process, an identical scene captured by different cameras could exhibit distinct imaging patterns, introducing distinct proficiency among the super-resolution (SR) models trained on images from different devices. In this paper, we investigate a novel and practical task coded cross-device SR, which strives to adapt a real-world SR model trained on the paired images captured by one camera to low-resolution (LR) images captured by arbitrary target devices. The proposed task is highly challenging due to the absence of paired data from various imaging devices. To address this issue, we propose an unsupervised domain adaptation mechanism for real-world SR, named Dual ADversarial Adaptation (DADA), which only requires LR images in the target domain with available real paired data from a source camera. DADA employs the Domain-Invariant Attention (DIA) module to establish the basis of target model training even without HR supervision. Furthermore, the dual framework of DADA facilitates an Inter-domain Adversarial Adaptation (InterAA) in one branch for two LR input images from two domains, and an Intra-domain Adversarial Adaptation (IntraAA) in two branches for an LR input image. InterAA and IntraAA together improve the model transferability from the source domain to the target. We empirically conduct experiments under six <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{Real} \rightarrow \text{Real}$</tex> adaptation settings among three different cameras, and achieve superior performance compared with existing state-of-the-art approaches. We also evaluate the proposed DADA to address the adaptation to the video camera, which presents a promising re-search topic to promote the wide applications of real-world super-resolution. Our source code is publicly available at https://github.com/lonelyhopeIDADA.
Learning Object Context for Novel-view Scene Layout Generation

Novel-view prediction of a scene has many applications. Existing works mainly focus on generating novel-view images via pixel-wise prediction in the image space, often resulting in severe ghosting and blurry artifacts. In this paper, we make the first attempt to explore novel-view prediction in the layout space, and introduce the new problem of novel-view scene layout generation. Given a single scene layout and the camera transformation as inputs, our goal is to generate a plausible scene layout for a specified viewpoint. Such a problem is challenging as it involves accurate understanding of the 3D geometry and semantics of the scene from as little as a single 2D scene layout. To tackle this challenging problem, we propose a deep model to capture contextualized object representation by explicitly modeling the object context transformation in the scene. The contextualized object representation is essential in generating geometrically and semantically consistent scene layouts of different views. Experiments show that our model outperforms several strong baselines on many indoor and outdoor scenes, both qualitatively and quantitatively. We also show that our model enables a wide range of applications, including novel-view image synthesis, novel-view image editing, and amodal object estimation.
$$\mu \text {KG}$$: A Library for Multi-source Knowledge Graph Embeddings and Applications

This paper presents $$\mu \text {KG}$$ , an open-source Python library for representation learning over knowledge graphs. $$\mu \text {KG}$$ supports joint representation learning over multi-source knowledge graphs (and also a single knowledge graph), multiple deep learning libraries (PyTorch and TensorFlow2), multiple embedding tasks (link prediction, entity alignment, entity typing, and multi-source link prediction), and multiple parallel computing modes (multi-process and multi-GPU computing). It currently implements 26 popular knowledge graph embedding models and supports 16 benchmark datasets. $$\mu \text {KG}$$ provides advanced implementations of embedding techniques with simplified pipelines of different tasks. It also comes with high-quality documentation for ease of use. $$\mu \text {KG}$$ is more comprehensive than existing knowledge graph embedding libraries. It is useful for a thorough comparison and analysis of various embedding models and tasks. We show that the jointly learned embeddings can greatly help knowledge-powered downstream tasks, such as multi-hop knowledge graph question answering. We will stay abreast of the latest developments in the related fields and incorporate them into $$\mu \text {KG}$$ . Resource Type: Software License: GPL-3.0 License GitHub Repository: https://github.com/nju-websoft/muKG
Over-the-Air Split Learning with MIMO-Based Neural Network and Constellation-Based Activation

This paper investigates a communication-efficient split learning (SL) over multiple-input multiple-output (MIMO) communication system. In particular, we mathematically decompose the inter-layer connection of a neural network (NN) to a series of linear precoding and combining transformations using over-the-air computation (OAC), which synergistically form a linear layer in NNs. The precoding and combining matrices are trainable parameters in such a system, whereas the MIMO channel is implicit. The proposed system eliminates the implicit channel estimation through exploiting the channel reciprocity and properly casting the backpropagation process, significantly saving the system costs and further improving the overall efficiency. The practical constellation diagrams are used as the activation function to avoid sending arbitrary analog signals as in the traditional OAC system. Numerical results are illustrated to demonstrate the effectiveness of the proposed scheme.
Deep Semantic Statistics Matching (D2SM) Denoising Network

The ultimate aim of image restoration like denoising is to find an exact correlation between the noisy and clear image domains. But the optimization of end-to-end denoising learning like pixel-wise losses is performed in a sample-to-sample manner, which ignores the intrinsic correlation of images, especially semantics. In this paper, we introduce the Deep Semantic Statistics Matching (D2SM) Denoising Network. It exploits semantic features of pretrained classification networks, then it implicitly matches the probabilistic distribution of clear images at the semantic feature space. By learning to preserve the semantic distribution of denoised images, we empirically find our method significantly improves the denoising capabilities of networks, and the denoised results can be better understood by high-level vision tasks. Comprehensive experiments conducted on the noisy Cityscapes dataset demonstrate the superiority of our method on both the denoising performance and semantic segmentation accuracy. Moreover, the performance improvement observed on our extended tasks including super-resolution and dehazing experiments shows its potentiality as a new general plug-and-play component.
Synthetic minority over-sampling technique nominal continous logistic regression for imbalanced data

Evaluation is a method that determines the success of a program or policy that has been carried out by an institution,one of which is an educational institution. Service evaluation is carried out by means of a satisfaction survey of students. The resultsof the evaluation are one of the efforts to improve the quality of services in educational institutions. In 2021 in.one of the educationalinstitutions in Surabaya, 5% of students were dissatisfied and 95% of students were satisfied. These data indicate an imbalance in the classification of student satisfaction, where the number of students in the satisfied category dominates more than the number ofstudents who are dissatisfied. The data imbalance has a negative impact on the classification where the minority class is often misclassified. Therefore, this study classifies student satisfaction using the Logistics Regression and the Synthetic Minority OverSampling Technique Nominal Continuous (SMOTE-NC) Logistic Regression. The results of the study using the Logistics Regression have an accuracy of 79.41%, a sensitivity of 84%, a precision of 93% and an error rate of 21%. The SMOTE-NC Logistics Regression has 85.29% accuracy, 91% sensitivity, 94% precision and 15% error rate. The results of this study concludedthat the SMOTE-NC Logistics Regression method can handle the imbalance in the amount of data and has better accuracy, sensitivity, precision and error rate values than Logistics Regression.
Radar-Enhanced Image Fusion-based Object Detection for Autonomous Driving

Accurate and robust object detection is imperative to the implementation of autonomous driving. In real-world scenarios, the effectiveness of image-based detectors is limited by low visibility or harsh circumstances. Owing to the immunity to environmental variability, millimeter-wave (mmWave) radar sensors are complementary to camera sensors, opening up the possibility of radar-camera fusion to improve object detection performance. In this paper, we construct a Radar-Enhanced image Fusion Network (REFNet) for 2D object detection in autonomous driving. Specifically, the radar data is projected onto the camera image plane to unify the data format of heterogeneous sensing modalities. To overcome the sparsity of radar point clouds, we devise an Uncertainty Radar Block (URB) to increase the density of radar points considering the azimuth uncertainty of radar measurements. Additionally, we design an adaptive network architecture which supports multi-level fusion and has the ability to determine the optimal fusion level. Moreover, we incorporate a robust attention module within the fusion network to exploit the synergy of radar and camera information. Evaluated with the canonical nuScenes dataset, our proposed method consistently and significantly outperforms the image-only version under all scenarios, especially in nightly and rainy conditions.
Hyperspectral Dehazing Using Admm-Adam Theory

ADMM-Adam theory, originally invented for hyperspectral remote sensing, is adopted to solve the challenging ill-posed hyperspectral dehazing problem for the first time, aiming to recover lost spatial information in hazy hyperspectral images covered by haze. Under this innovative framework, benefitted from combining deep learning (DL) and convex optimization (CO), we can train the neural network using just small data and still obtain promising results. In this work, we implement a simple Unet model to obtain a rough DL solution, contributing to the complexity reduction of network parameters, besides avoiding the time-consuming training procedure. Furthermore, experimental evidences show that plugging existing peer methods into the powerful ADMM-Adam framework also yields improved dehazing performance. To conclude, we demonstrate the feasibility and flexibility of solving complicated inverse problems (e.g., hyperspectral dehazing) with a dramatic enhancement under the ADMM-Adam framework.
A Lightweight and Efficient Schoolbook Polynomial Multiplier for Saber

Saber is a lattice-based post-quantum cryptography (PQC) algorithm, which is still a candidate in the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> Round of National Institute of Standards and Technology (NIST) PQC standardization process. Saber provides a great advantage of being lightest among all the candidates, so a suitable choice for resource-constraint platforms. Polynomial multiplication occupies most of the resources in hardware implementation of Saber, which needs to be optimized for the efficient hardware implementation. In this work, a lightweight and efficient schoolbook polynomial multiplier is proposed. The architecture includes an efficient multiplication strategy that compute four coefficient-wise multiplication per cycle along with the multiplication operand loading technique being designed for the compact multiplier. The proposed multiplier on Artix-7 FPGA, achieves a frequency of 130 MHz and fits into 201 slices. Compared with the state-of-the-art lightweight schoolbook implementations for Saber, our design has a 30% improved frequency and saves 15.8% of the clock counts at the cost of only 3.7% more LUTs.
WeLDCFNet: Convolutional Neural Network based on Wedgelet Filters and Learnt Deep Correlation Features for depth maps features extraction

With the emergence of depth sensors, extraction of depth maps features is becoming more and more solicited and prominent for several computer vision applications, such as gesture recognition, face recognition and segmentation. These applications can be more accurate thanks to the depth information that provides more precise separate foreground objects from background. In this paper, we propose an automatic depth maps features extraction model based on an optimized Convolutional Neural Network (CNN), trained on a mixture of depth maps and grayscale texture images. The CNN includes a first convolutional layer of pre-defined wedgelet filters, followed by a pre-trained VGG-19 neural network. Then, we opt for an image style classification based on Learnt Deep Correlation Features to capture features distinguishing depth maps from grayscale texture images of the training set. Experimental results demonstrate the potential effectiveness of the proposed wedgelet (We) and Learnt Deep Correlation Features (LDCF) based Network (WeLDCFNet) with a mean accuracy gain up to 32.77%, when compared to existing features extraction approaches. As our aim in this paper is depth maps features extraction and not the texture/depth classification itself, we propose, as a use case, to leverage the proposed WeLDCFNet for depth maps learned compression. If our model succeeds to extract depth features that make them distinguishable from texture, it would be useful to save the main compact depth information. Our WeLDCFNet based autoencoder, tailored to compression needs, shows competitive Rate/Distortion tradeoffs when compared to the latest depth maps compression standard.
Towards Spatially Disentangled Manipulation of Face Images With Pre-Trained StyleGANs

Generative Adversarial Networks with style-based generators could successfully synthesize realistic images from input latent code. Moreover, recent studies have revealed that interpretable translations of generated images could be obtained by linearly traversing in the latent space. However, in most existing latent spaces, linear interpolation often leads to ‘spatially entangled modification’ in the manipulation result, which is undesirable in many real-world applications where local editing is required. To solve this problem, we propose to manipulate the latent code in the ‘style space’ and analyze its advantage in achieving spatial disentanglement. Furthermore, we point out the weakness of simply interpolating in the style space and propose ‘Style Intervention’, a lightweight optimization-based algorithm, to further improve the visual fidelity of manipulation results. The performance of our method is verified with the task of attribute editing on high-resolution face images. Both qualitative and quantitative results demonstrate the advantage of image translation in the style space and the effectiveness of our method on both real and synthetic images.
Learning Consistent Global-Local Representation for Cross-Domain Facial Expression Recognition

Domain shift is one of the knotty problems that seriously restricts the accuracy of cross-domain facial expression recognition. Most existing works mainly focus on learning domain-invariant features by global feature adaption, and little works are conducted using the local features which are more transferable across different domains. In this paper, a consistent global-local feature and semantic learning framework is proposed which can learn domain-invariant global and local feature representation, and generate pseudo labels to facilitate cross-domain facial expression recognition. Specifically, the proposed method first simultaneously learns the domain-invariant global and local features via separately adversarial global and local learning. Once those features are acquired, a global and local semantic consistency is introduced to help generate pseudo labels for unlabeled data of the target dataset. By performing such strategy, more efficiency pseudo labels with high accuracy are produced due to the information diversity in global-local features and do without the image transformation. We conduct extensive experiments and analyses on several public datasets to demonstrate the effectiveness of the proposed model.
PointIt: A ROS Toolkit for Interacting with Co-located Robots using Pointing Gestures

We introduce PointIt, a toolkit for the Robot Operating System (ROS2) to build human-robot interfaces based on pointing gestures sensed by a wrist-worn Inertial Measurement Unit, such as a smartwatch. We release the software as open-source with MIT license; docker images and exhaustive instructions simplify its usage in simulated and real-world deployments.
RARN: A Real-Time Skeleton-based Action Recognition Network for Auxiliary Rehabilitation Therapy

Rehabilitation gymnastic training is an effective therapy for degeneration of spine disease in Traditional Chinese Medicine (TCM). In this paper, we propose a lightweight real-time Rehabilitation Action Recognition Network (RARN) using skeleton sequence obtained through 2d pose estimation and build an intelligent auxiliary rehabilitation therapy system. We design a set of training exercises consisting of 8 actions, and construct a dataset called Rehabilitation Action for Degenerative Spine Diseases (RDSD), containing 1012 skeleton sequences. We describe a demo application to conduct real-time action evaluation for rehabilitation therapy. The experimental results on RDSD shows that our system achieves high accuracy while still working under 6ms per frame averagely and the inference costs only about 0. 2ms per frame.
Scale-Aware Siamese Object Tracking for Vision-Based UAM Approaching

In many industrial applications of unmanned aerial manipulator (UAM), visual approaching the object is crucial to subsequent manipulating. In comparison with the widely-studied manipulating, the key to efficient vision-based UAM approaching, i.e., UAM object tracking, is still limited. Since traditional model-based UAM tracking is costly and cannot track arbitrary objects, an intuitive solution is to introduce state-of-the-art model-free Siamese trackers from the visual tracking field. Although Siamese tracking is most suitable for the onboard embedded processors, severe object scale variation in UAM tracking brings formidable challenges. To address these problems, this work proposes a novel model-free scale-aware Siamese tracker (SiamSA). Specifically, a scale attention network is proposed to emphasize scale awareness in feature processing. A scale-aware anchor proposal network is designed to achieve anchor proposing. Besides, two novel UAM tracking benchmarks are first recorded. Comprehensive experiments on benchmarks validate the effectiveness of SiamSA. Furthermore, real-world tests also confirm practicality for industrial UAM approaching tasks with high efficiency and robustness.
ManyTypes4TypeScript

In this paper, we present ManyTypes4TypeScript, a very large corpus for training and evaluating machine-learning models for sequence-based type inference in TypeScript. The dataset includes over 9 million type annotations, across 13,953 projects and 539,571 files. The dataset is approximately 10x larger than analogous type inference datasets for Python, and is the largest available for Type-Script. We also provide API access to the dataset, which can be integrated into any tokenizer and used with any state-of-the-art sequence-based model. Finally, we provide analysis and performance results for state-of-the-art code-specific models, for baselining. ManyTypes4TypeScript is available on Huggingface, Zenodo, and CodeXGLUE.
Privacy Protection in Surveillance Videos Using Block Scrambling-Based Encryption and DCNN-Based Face Detection

Surely surveillance cameras are certainly important in all aspects of life. We have become in an era where we need to use surveillance cameras everywhere, homes, schools, banks, hospitals, and companies, even in the general streets, to monitor everything that happens and follow the progress of those places with all safety by surveillance videos. However, the pervasiveness of surveillance cameras has become an issue for people’s privacy. This paper proposes a novel method for surveillance video privacy protection using block scrambling-based encryption and DCNN-based object detection. An object detection model based on DCNN You Only Look Once version 3 (YOLOv3) is used to detect the faces of the people. Then, the detected faces are scrambled using the fast block scrambling technique. Finally, the scrambled faces are encrypted using a secret key produced from a chaotic logistic map. The bounding boxes that output from the YOLOv3 are modified to include the entire edges of the detected faces to prevent any leaks of the sensitive regions. The simulation results and security analysis confirmed the proposed method’s effectiveness in protecting the surveillance videos’ privacy.