Reviewerly/exHarmony huggingface dataset:
Novel View Synthesis of Human Interactions from Sparse Multi-view Videos

This paper presents a novel system for generating free-viewpoint videos of multiple human performers from very sparse RGB cameras. The system reconstructs a layered neural representation of the dynamic multi-person scene from multi-view videos with each layer representing a moving instance or static background. Unlike previous work that requires instance segmentation as input, a novel approach is proposed to decompose the multi-person scene into layers and reconstruct neural representations for each layer in a weakly-supervised manner, yielding both high-quality novel view rendering and accurate instance masks. Camera synchronization error is also addressed in the proposed approach. The experiments demonstrate the better view synthesis quality of the proposed system compared to previous ones and the capability of producing an editable free-viewpoint video of a real soccer game using several asynchronous GoPro cameras. The dataset and code are available at https://github.com/zju3dv/EasyMocap .
Learning From Documents in the Wild to Improve Document Unwarping

Document image unwarping is important for document digitization and analysis. The state-of-the-art approach relies on purely synthetic data to train deep networks for unwarping. As a result, the trained networks have generalization limitations when testing on real-world images, often yielding unsatisfying results. In this work, we propose to improve document unwarping performance by incorporating real-world images in training. We collected Document-in-the-Wild (DIW) dataset contains 5000 captured document images with large diversities in content, shape, and capturing environment. We annotate the boundaries of all DIW images and use them for weakly supervised learning. We propose a novel network architecture, PaperEdge, to train with a hybrid of synthetic and real document images. Additionally, we identify and analyze the flaws of popular evaluation metrics, e.g., MS-SSIM and Local Distortion (LD), for document unwarping and propose a more robust and reliable error metric called Aligned Distortion (AD). Training with a combination of synthetic and real-world document images, we demonstrate state-of-the-art performance on popular benchmarks with comprehensive quantitative evaluations and ablation studies. Code and data are available at https://github.com/cvlab-stonybrook/PaperEdge.
Edge Computing and Blockchain in Enterprise Performance and Venture Capital Management

Aiming at improving the enterprise performance and venture capital management, the edge computing is combined with blockchain technology in the construction of the supply chains so as to improve the stability and security of raw material supply chains of venture capital enterprises. Firstly, the relationships among venture capital, enterprise performance, and supply chain integration are analyzed, and the positive impacts of optimization and innovation of supply chain technology and venture capital on enterprise performance improvement are discussed. Secondly, the edge computing and blockchain technology are adopted to optimize the supply chain, and a protocol model is established to improve the security of supply chain information transmission. In addition, the supply chain Internet of Things (IoT) system is designed in layers based on blockchain and edge computing technology. Finally, the designed protocol model is applied to the supply chain and the supplier performance is evaluated by using the fuzzy analytic hierarchy process (FAHP). The results show that the gas value of the designed protocol model is stable at 104,000∼116,000 in blockchain transactions, and the execution time of a single blockchain transaction fluctuates between 4,300 and 6,100 ms. Therefore, the designed protocol model can effectively ensure the security of supply chain data during the transmission. The evaluation results of supplier performance suggest that the cooperative relationship between venture capital enterprises and suppliers is affected by many factors. Optimizing the supply chain structure and production activities can improve the performance of suppliers and venture capital enterprises. This work provides a reference for analyzing the performance of blockchain technology on venture capital enterprises and suppliers.
CubeNet: Array-Based Seismic Phase Picking with Deep Learning

Abstract In recent years, a variety of deep learning (DL) models for seismic phase picking have attracted considerable attention and are widely adopted in many earthquake monitoring projects. However, most current DL models pick P and S arrivals trace by trace without simultaneously considering the spatial coherence of seismic phases among different stations in a seismic array. In this study, we develop a generalized neural network named CubeNet based on 3D U-Net to properly consider the spatial correlation of individual picks at different stations and thus improve the picking accuracy. To deal with data acquired by irregularly distributed stations, seismic data are first regularized into data cubes, which are then fed into CubeNet to calculate probability distributions of P arrivals, S arrivals, and noise. In addition, a variable trace resampling method for optimizing the differential sampling points between P and S arrivals in a trace for varying array apertures is also proposed to further improve the picking accuracy. CubeNet is trained by 47,000 microseismic data cubes and then tested by three data sets from different arrays with varying apertures and station intervals. It is found that CubeNet is rather resilient to impulsive noise and can avoid misidentifying most of the abnormal picks, which are challenging for the signal-trace based phase picking methods such as PhaseNet. We believe the newly proposed CubeNet is especially suitable for processing seismic data collected by large-N arrays.
Comparison of machine learning and the regression-based EHMRG model for predicting early mortality in acute heart failure

Although risk stratification of patients with acute decompensated heart failure (HF) is important, it is unknown whether machine learning (ML) or conventional statistical models are optimal. We developed ML algorithms to predict 7-day and 30-day mortality in patients with acute HF and compared these with an existing logistic regression model at the same timepoints.Patients presenting to one of 86 hospitals, who were either admitted to hospital or discharged home directly from the emergency department, were randomly selected using stratified random sampling. ML approaches, including neural networks, random forest, XGBoost, and the Lasso, were compared with a validated logistic regression model for discrimination and calibration.Among 12,608 patients in our analysis, lasso regression (c-statistic 0.774; 95% CI, 0.743, 0.806) performed better than other ML models for 7-day mortality but did not outperform the baseline logistic regression model (0.794; 95% CI, 0.789, 0.800). For 30-day mortality, XGBoost performed better than other ML models (c-statistic 0.759; 95% CI; 0.740, 0.779), but was not significantly better than logistic regression (c-statistic 0.755; 95% CI, 0.750, 0.762). Logistic regression demonstrated better calibration at 7 days (calibration-in-the-large 0.017; 95% CI, -0.657, 0.692, and calibration slope 0.954; 95% CI, 0.769, 1.139), and at 30 days (-0.026; 95% CI, -0.374, 0.322, and 0.964; 95% CI, 0.831, 1.098), and best Brier scores, compared to ML approaches.Logistic regression was comparable to ML in discrimination, but was superior to ML algorithms in calibration overall. ML algorithms for prognosis should routinely report calibration metrics in addition to discrimination.
Transferable regularization and normalization: Towards transferable feature learning for unsupervised domain adaptation

Unsupervised domain adaptation aims at alleviating distribution discrepancy when transferring knowledge from a large-scale labeled source domain to a new unlabeled target domain. A prevailing method is adversarial feature adaptation, which generates transferable features via aligning feature distributions across domains to bound domain discrepancy. However, such approach renders suboptimal performances and vulnerable to negative transfer when target domain has less number of classes compared to source domain as in partial domain adaptation. To address this issue, previous methods adopt a transferable feature norm strategy to adapting feature norms of two domains achieving significant transfer gains. Nonetheless, these works do not consider the intrinsic limitation of the architecture design of deep neural networks which greatly influences the loss function of transferability. In this paper, we propose Transferable Regularization and Normalization ( TRN ), which simultaneously avoids negative transfer via adapting feature norms of both domains and facilitates positive transfer via replacing the existing normalization techniques in mainstream deep backbones. As a general approach, TRN can be simply embedded into deep transfer learning approaches. After a thorough evaluation of proposed method utilizing several benchmark datasets (Office-31, ImageCLEF-DA, Office-Home and VisDA-2017). TRN yielded the state-of-the-art results and outperformed other approaches (IFAN, PADA) by a large margin (0.5%, 7.74%) for various domain adaptation tasks.
Weakly labeled data augmentation for social media named entity recognition

Named entity recognition is a task that extracts entities corresponding to predefined categories. Although NER is important in processing user-generated texts such as those obtained from social media, it remains challenging because such texts tend to contain numerous unseen words or abbreviations. To address this issue, we propose two methods for weakly labeled data generation that can extract named entities from social media texts more effectively: alias augmentation and typo augmentation. Using these methods, weakly labeled data are generated through the automatic annotation of unlabeled Wikipedia texts and Tweets and then trained through transfer learning. Our experimental results suggest that the proposed approach improves NER performance, with our best F1-score of 51.43% representing the highest score ever reported.
Prediction of the Resource Consumption of Distributed Deep Learning Systems

The prediction of the resource consumption for the distributed training of deep learning models is of paramount importance, as it can inform a priori users how long their training would take and also enable users to manage the cost of training. Yet, no such prediction is available for users because the resource consumption itself varies significantly according to "settings" such as GPU types and also by "workloads" like deep learning models. Previous studies have aimed to derive or model such a prediction, but they fall short of accommodating the various combinations of settings and workloads together. This study presents Driple that designs graph neural networks to predict the resource consumption of diverse workloads. Driple also designs transfer learning to extend the graph neural networks to adapt to differences in settings. The evaluation results show that Driple can effectively predict a wide range of workloads and settings. At the same time, Driple can efficiently reduce the time required to tailor the prediction for different settings by up to 7.3×.
AIBLOCK: Blockchain based Lightweight Framework for Serverless Computing using AI

Artificial intelligence (AI)-based studies have been carried out recently for the early detection of COVID-19. The goal is to prevent the spread of the disease and the number of fatal cases. In AI-based COVID-19 diagnostic studies, the integrity of the data is critical to obtain reliable results. In this paper, we propose a Blockchain-based framework called AIBLOCK, to offer the data integrity required for applications such as Industry 4.0, healthcare, and online banking. In addition, the proposed framework is integrated with Google Cloud Platform (GCP)-Cloud Functions, a serverless computing platform that automatically manages resources by offering dynamic scalability. The performance of five different machine learning models is evaluated and compared in terms of Accuracy, Precision, Recall, F-Score and Area under the curve (AUC). The experimental results show that decision trees gives the best results in terms of accuracy (98.4 %). Further, it has been identified that utilization of Blockchain technology can increase the load on memory.
Telepresence Robots in Higher Education – The Current State of Research

Distance learning may lead to decreased social presence of students in higher education, potentially causing deterioration of both student learning out-comes and their mental health. Telepresence robotics is seen as an approach that, through the embodied agent of a robotic body, enhances student social presence in the classroom, improving the quality of classroom interaction. In the literature review, we analyzed the current state of research on using telepresence robots in higher education, and, based on existing papers, studied the strengths and weaknesses of using these robots in higher education. Based on our findings, we suggest that the literature in this area is scarce, requiring further studies in the future. The current telepresence robots struggle in relaying flawless telepresence experience, but can still be recommended in certain scenarios. We conclude that despite telepresence robotics being a promising technology, longitudinal studies are required to reach better understanding about the features that telepresence robots should have when used in higher education and about the best use methods.
Physics-Constrained Vulnerability Assessment of Deep Reinforcement Learning-Based SCOPF

The decarbonization of energy systems has posed unprecedented challenges in system complexity and operational uncertainty that render it imperative to exploit cutting-edge artificial intelligence (AI) technologies to realize real-time, autonomous power system operation and control. In particular, deep reinforcement learning (DRL)-based approaches in power systems are extensively studied and implemented in several trials worldwide. Nevertheless, the vulnerability of DRL brings new security threats to power systems that have not been well identified and investigated in the literature. To this end, this paper proposes a physics-constrained vulnerability assessment methodological framework for the DRL-based power system operation and control, with a special focus on the problem of security-constrained optimal power flow (SCOPF). In particular, we develop a novel adversarial example generation method, defined as a false data injection attack against the DRL-based SCOPF (FDIAI), to realize a targeted adversarial attack considering the nonlinear physical constraints in power systems via two main stages of constructor function design and unconstrained optimization problem transformation. In this way, the proposed FDIAI can significantly influence the decision-making procedure of DRL while successfully evading the bad data detection mechanism in power systems. Case studies are conducted to explore the stealthiness and effectiveness of FDIAI and then show its severe impacts on system operation and control on the winners' models of the Learning to Run a Power Network (L2RPN) competitions, including L2RPN IJCNN 2019 (IJCNN), L2RPN WCCI 2020 (WCCI), and L2RPN NeurIPS 2020 (NeurIPS).
Aerial Grasping and the Velocity Sufficiency Region

A largely untapped potential for aerial robots is to capture airborne targets in flight. We present an approach in which a simple dynamic model of a quadrotor/target interaction leads to the design of a gripper and associated velocity sufficiency region with a high probability of capture. A model of the interaction dynamics maps the gripper force sufficiency region to an envelope of relative velocities for which capture should be possible without exceeding the capabilities of the quadrotor controller. The approach motivates a gripper design that emphasizes compliance and is passively triggered for a fast response. The resulting gripper is lightweight (23 g) and closes within 12 ms. With this gripper, we demonstrate in-flight experiments that a 550 g drone can capture an 85 g target at various relative velocities between 1 m/s and 2.7 m/s.
AF-GCN: Attribute-Fusing Graph Convolution Network for Recommendation

Graph Convolution Networks (GCNs) are playing important role and widely used in recommendation systems. This is benefited from their capability of capturing the collaborative signals of higher-order neighbors by exploiting the graph structure. GCN-based methods have made great success in improving recommending performance, but still suffer from the severe problem of data sparsity. An effective solution to alleviate the data sparsity is to introduce attribute information. However, existing GCN-based methods hardly capture the complex attribute information of users and items and the complicated relationships between users, items, and attributes simultaneously. To address the above problems, we propose a novel attribute-fusing graph convolution network model called AF-GCN. Specifically, we first propose an attention-based attribute fusion strategy by taking account of different effects of attributes. Then, we construct a complex graph containing four kinds of nodes. Finally, we design a particular Laplacian matrix, which leverages the attribute information through graph structure to learn user and item representations better. Extensive experimental results on three real-world datasets demonstrate that the proposed AF-GCN significantly outperforms state-of-the-art methods. The source codes of this work are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xiaorui-mnaire/af-gcn</uri> .
Feature-based question routing in community question answering platforms

Community question answering (CQA) platforms are receiving increased attention and are becoming an indispensable source of information in different domains ranging from board games to physics. The success of these platforms dependent on how efficiently new questions are assigned to community experts, known ascalled question routing. In this paper, we address the problem of question routing by adopting a learning to rank approach over five CQA websites in the context of which we introduce 74 features and systematically classify them into content-based and social-based categories. Our extensive experiments on datasets from five real online question answering websites indicate that content-based features related to tags and topics as well as social features that are related to user characteristics and user temporality are effective for question routing. Our work shows the ability to improve performance compared to the state-of-the-art neural matchmaking methods that lack the interpretability offered by our work. The improvement can be as high as on average 2.47% and 1.10% in terms of common ranking metrics, Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP) respectively, compared to our best baselines.
Fairness in Music Recommender Systems: A Stakeholder-Centered Mini Review

The performance of recommender systems highly impacts both music streaming platform users and the artists providing music. As fairness is a fundamental value of human life, there is increasing pressure for these algorithmic decision-making processes to be fair as well. However, many factors make recommender systems prone to biases, resulting in unfair outcomes. Furthermore, several stakeholders are involved, who may all have distinct needs requiring different fairness considerations. While there is an increasing interest in research on recommender system fairness in general, the music domain has received relatively little attention. This mini review, therefore, outlines current literature on music recommender system fairness from the perspective of each relevant stakeholder and the stakeholders combined. For instance, various works address gender fairness: one line of research compares differences in recommendation quality across user gender groups, and another line focuses on the imbalanced representation of artist gender in the recommendations. In addition to gender, popularity bias is frequently addressed; yet, primarily from the user perspective and rarely addressing how it impacts the representation of artists. Overall, this narrative literature review shows that the large majority of works analyze the current situation of fairness in music recommender systems, whereas only a few works propose approaches to improve it. This is, thus, a promising direction for future research.
Random Fourier Feature-Based Deep Learning for Wireless Communications

Deep-learning (DL) has emerged as a powerful machine-learning technique for several problems encountered in generic wireless communications. Also, random Fourier Features (RFF) based DL has emerged as an attractive solution for several machine-learning problems; yet existing works lack rigorous analytical results to justify the viability of RFF based DL. To address this gap, we analytically quantify the viability of RFF based DL in this paper. Precisely, we present analytical proofs which show that the RFF based DL architectures have lower approximation-error and a lower probability of misclassification as compared to classical DL architectures for a fixed dataset-size. In addition, a new distribution-dependent RFF (DDRFF) is proposed to facilitate DL architectures with low training-complexity. The presented analytical contributions and the DDRFF are validated by relevant case-studies such as: a) line of sight (LOS)/non-line of sight (NLOS) classification, and b) message-passing based detection of low-density parity check (LDPC) codes over nonlinear visible light communication (VLC) channels. Especially, in the low training-data regime, the presented simulations depict significant performance gains for RFF based DL. Lastly, in all the presented simulations, it is observed that the proposed DDRFFs significantly outperform RFFs, which make them useful for potential machine-learning/DL applications for communication systems.
Factors Influencing School Teachers’ Continuous Usage Intention of Using VR Technology for Classroom Teaching

Teachers’ continuous usage intention for virtual reality (VR) technology is a prerequisite for achieving its expected educational value in elementary and secondary education. Through a comprehensive model considering task-technology fit (TTF) and usage satisfaction, this study involved an analysis framework for the intention of teachers to continue using VR technology for classroom teaching. A questionnaire survey was conducted on 291 teachers of elementary and secondary schools in Jiangsu Province, China, to identify factors affecting their continuous usage intention for VR technology. The results indicated that perceived usefulness and perceived ease-of-usage significantly impacted usage satisfaction. Surprisingly, usage satisfaction did not promote continuous usage intention. System quality and service quality of VR technology were key factors influencing perceived usefulness and perceived ease-of-use, respectively. Meanwhile, TTF promoted teachers’ continuous usage intention while being positively affected by quality factors. Therefore, teachers are more concerned about optimizing pedagogical effectiveness by VR technology. Thus, further applications of VR technology in teaching should focus on improving the application modes. Moreover, a teaching environment supporting the pedagogical application of VR technology and a more autonomous classroom ecology for teachers to conduct VR-supported classroom teaching reform may be important factors to promote teachers’ usage intention for VR technology.