Reviewerly/exHarmony huggingface dataset:
Super-resolution reconstruction of turbulent flow fields at various Reynolds numbers based on generative adversarial networks

This study presents a deep learning-based framework to reconstruct high-resolution turbulent velocity fields from extremely low-resolution data at various Reynolds numbers using the concept of generative adversarial networks (GANs). A multiscale enhanced super-resolution generative adversarial network (MS-ESRGAN) is applied as a model to reconstruct the high-resolution velocity fields, and direct numerical simulation (DNS) data of turbulent channel flow with large longitudinal ribs at various Reynolds numbers are used to evaluate the performance of the model. The model is found to have the capacity to accurately reproduce high-resolution velocity fields from data at two different low-resolution levels in terms of the quantities of velocity fields and turbulent statistics. The results further reveal that the model is able to reconstruct velocity fields at Reynolds numbers that are not used in the training process.
Local Path Planning: Dynamic Window Approach With Virtual Manipulators Considering Dynamic Obstacles

Local path planning considering static and dynamic obstacles for a mobile robot is one of challenging research topics. Conventional local path planning methods generate path candidates by assuming constant velocities for a certain period time. Therefore, path candidates consist of straight line and arc paths. These path candidates are not suitable for dynamic environments and narrow spaces. This paper proposes a novel local path planning method based on dynamic window approach with virtual manipulators (DWV). DWV consists of dynamic window approach (DWA) and virtual manipulator (VM). DWA is the local path planning method that performs obstacle avoidance for static obstacles under robot constraints. DWA also generates straight line and arc path candidates by assuming constant velocities. VM generates velocities of reflective motion by using virtual manipulators and environmental information. DWV generates path candidates by variable velocities modified by VM and predicted positions of static and dynamic obstacles. Therefore, in an environment with dynamic obstacles, the obstacle-avoidable paths which include non-straight line and non-arc paths are generated. The effectiveness of the proposed method was confirmed from simulation and experimental results.
A Redactable Blockchain Framework for Secure Federated Learning in Industrial Internet of Things

Industrial Internet of Things (IIoT) facilitate private data collecting via (a broad range of) sensors, and the analysis of such data can inform decision making at different levels. Federated learning (FL) can be used to analyze the collected data, in privacy-preserving manner by transmitting model updates instead of private data in IIoT networks. The FL framework is, however, vulnerable because model updates are easily tampered with by malicious agents. Motivated by this observation, we propose a novel chameleon hash scheme with a changeable trapdoor (CHCT) for secure FL in IIoT settings. Our scheme imposes various constraints on the use of trapdoor. We give a rigorous security analysis on our CHCT scheme. We also instantiate the CHCT scheme as a redactable medical blockchain (RMB). The experimental evaluations demonstrate the practical utility of CHCT in terms of accuracy and efficiency.
Research on the Coordinate Attention Mechanism Fuse in a YOLOv5 Deep Learning Detector for the SAR Ship Detection Task

The real-time performance of ship detection is an important index in the marine remote sensing detection task. Due to the computing resources on the satellite being limited by the solar array size and the radiation-resistant electronic components, information extraction tasks are usually implemented after the image is transmitted to the ground. However, in recent years, the one-stage based target detector such as the You Only Look Once Version 5 (YOLOv5) deep learning framework shows powerful performance while being lightweight, and it provides an implementation scheme for on-orbit reasoning to shorten the time delay of ship detention. Optimizing the lightweight model has important research significance for SAR image onboard processing. In this paper, we studied the fusion problem of two lightweight models which are the Coordinate Attention (CA) mechanism module and the YOLOv5 detector. We propose a novel lightweight end-to-end object detection framework fused with a CA module in the backbone of a suitable position: YOLO Coordinate Attention SAR Ship (YOLO-CASS), for the SAR ship target detection task. The experimental results on the SSDD synthetic aperture radar (SAR) remote sensing imagery indicate that our method shows significant gains in both efficiency and performance, and it has the potential to be developed into onboard processing in the SAR satellite platform. The techniques we explored provide a solution to improve the performance of the lightweight deep learning-based object detection framework.
Efficient daily solar radiation prediction with deep learning 4-phase convolutional neural network, dual stage stacked regression and support vector machine CNN-REGST hybrid model

Optimal utilisation of the sun's freely available energy to generate electricity requires efficient predictive models of global solar radiation (GSR). These are necessary to provide solar energy companies an early and effective market entry to support renewable energy integration into electrical grids. We propose a hybrid deep learning CNN-REGST method where a Convolutional Neural Network is integrated with a dual-stage Stacked Regression (Level-O Learner and Level-O predictor) followed by a Support Vector Machine (Level-1 Learner) with its hyperparameters optimised using the HyperOpt function to predict the daily GSR with high accuracy. Six solar energy farms in Queensland, Australia, are selected as testing sites and the predictive features from Global Climate Models and observations, derived using marine predator algorithm, are employed to build the CNN-REGST prediction model. We include a feature selection process based on meta-heuristic methods to select the optimal predictors used as inputs for the resulting CNN-REGST model. Our hybrid model is rigorously evaluated to analyze its performance over a yearlong, and all four season data. We also compare the proposed CNN-REGST model with several deep learning (i.e., CNN, Long-term Short-term Memory Network LSTM, Deep Neural Network DNN) and conventional ML approaches (Extreme Learning Machine ELM, Stacked Regression REGST, Random Forest Regression RFR, Gradient Boosting Machine GBM, Multivariate Adaptive Regression Splines MARS) using the same test datasets. The simulations carried out show that the proposed hybrid model is significantly accurate in GSR predictions compared with the deep learning and the ML models as well as a commonly used persistence model. We conclude that the CNN-REGST prediction model could be a useful scientific ploy incorporated in modern solar energy monitoring technologies to utilize a greater proportion of sustainable energy resources captured from the sun into consumer electricity for conventional-renewable hybrid energy grid systems.
Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning

Incorporating Knowledge Graphs (KG) into recommeder system as side information has attracted considerable attention. Recently, the technical trend of Knowledge-aware Recommendation (KGR) is to develop end-to-end models based on graph neural networks (GNNs). However, the extremely sparse user-item interactions significantly degrade the performance of the GNN-based models, from the following aspects: 1) the sparse interaction, itself, means inadequate supervision signals and limits the supervised GNN-based models; 2) the combination of sparse interactions (CF part) and redundant KG facts (KG part) further results in an unbalanced information utilization. Besides, the GNN paradigm aggregates local neighbors for node representation learning, while ignoring the non-local KG facts and making the knowledge extraction insufficient. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring contrastive learning in KGR and propose a novel multi-level interactive contrastive learning mechanism, to alleviate the aforementioned challenges. Different from traditional contrastive learning methods which contrast nodes of two generated graph views, interactive contrastive mechanism conducts layer-wise self-supervised learning by contrasting layers of different parts within graphs, which is also an "interaction" action. Specifically, we first construct local and non-local graphs for user/item in KG, exploring more KG facts for KGR. Then an intra-graph level interactive contrastive learning is performed within each local/non-local graph, which contrasts layers of the CF and KG parts, for more consistent information leveraging. Besides, an inter-graph level interactive contrastive learning is performed between the local and non-local graphs, for sufficiently and coherently extracting non-local KG signals. Extensive experiments conducted on three benchmark datasets show the superior performance of our proposed method over the state-of-the-arts. The implementations are available at: https://github.com/CCIIPLab/KGIC.
Artificial intelligence-based methods for fusion of electronic health records and imaging data

Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal data sources contributes to a better understanding of human health and provides optimal personalized healthcare. Advances in artificial intelligence (AI) technologies, particularly machine learning (ML), enable the fusion of these different data modalities to provide multimodal insights. To this end, in this scoping review, we focus on synthesizing and analyzing the literature that uses AI techniques to fuse multimodal medical data for different clinical applications. More specifically, we focus on studies that only fused EHR with medical imaging data to develop various AI methods for clinical applications. We present a comprehensive analysis of the various fusion strategies, the diseases and clinical outcomes for which multimodal fusion was used, the ML algorithms used to perform multimodal fusion for each clinical application, and the available multimodal medical datasets. We followed the PRISMA-ScR guidelines. We searched Embase, PubMed, Scopus, and Google Scholar to retrieve relevant studies. We extracted data from 34 studies that fulfilled the inclusion criteria. In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. Specifically, early fusion was the most used technique in most applications for multimodal learning (22 out of 34 studies). We found that multimodality fusion models outperformed traditional single-modality models for the same task. Disease diagnosis and prediction were the most common clinical outcomes (reported in 20 and 10 studies, respectively) from a clinical outcome perspective.
Offline Planning with Hierarchical Task Networks in Video Games

Artificial intelligence (AI) technology can have a dramatic impact on the quality of video games. AI planning techniques are useful in a wide range of game components, including modules that control the behavior of fully autonomous units. However, planning is computationally expensive, and the CPU and memory resources available to game AI modules at runtime are scarce. Offline planning can be a good strategy to avoid runtime performance bottlenecks. In this work, we apply hierarchical task network (HTN) planning to video games. We describe a system that computes plans offline and then represents them as game scripts. This can be seen as a form of generating game scripts automatically, replacing the traditional approach of composing them by hand. We apply our ideas to the commercial game The Elder Scrolls IV: Oblivion, with encouraging results. Our system generates scripts automatically at a level of complexity that would require a great human effort to achieve.
Inferring Gender from the Content of Tweets: A Region Specific Example

There is growing interest in using social networking sites such as Twitter to gather real-time data on the reactions and opinions of a region's population, including locations in the developing world where social media has played an important role in recent events, such as the 2011 Arab Spring. However, many interesting and important opinions and reactions may differ significantly within a given region depending on the demographics of the subpopulation, including such categories as gender and ethnicity. Unfortunately, the demographic characteristics of social media users are often unknown because such categories are not always captured in user metadata. Twitter, for example, does not capture a user’s gender in their profile, and inferring gender from first names is difficult since Twitter users are not required to give their real names. There is thus a need for automated methods that can infer such hidden attributes of users from other data sources. In this paper we describe a method to infer the gender of Twitter users from only the content of their tweets. Looking at Twitter users from the West African nation of Nigeria, we applied supervised machine learning using features derived from the content of user tweets to train a classifier. Using unigram features alone, we obtained an accuracy of 80% for predicting gender, suggesting that content alone can be a good predictor of gender. An analysis of the highest weighted features shows some interesting distinctions between men and women both topically and emotionally. We argue that approaches such as the one described here can give us a clearer picture of who is utilizing social media when certain user attributes are unreliable or not available.
Border-Peeling Clustering

In this paper, we present a novel non-parametric clustering technique. Our technique is based on the notion that each latent cluster is comprised of layers that surround its core, where the external layers, or border points, implicitly separate the clusters. Unlike previous techniques, such as DBSCAN, where the cores of the clusters are defined directly by their densities, here the latent cores are revealed by a progressive peeling of the border points. Analyzing the density of the local neighborhoods allows identifying the border points and associating them with points of inner layers. We show that the peeling process adapts to the local densities and characteristics to successfully separate adjacent clusters (of possibly different densities). We extensively tested our technique on large sets of labeled data, including high-dimensional datasets of deep features that were trained by a convolutional neural network. We show that our technique is competitive to other state-of-the-art non-parametric methods using a fixed set of parameters throughout the experiments.
A Large Dimensional Analysis of Least Squares Support Vector Machines

In this article, a large dimensional performance analysis of kernel least squares support vector machines (LS-SVMs) is provided under the assumption of a two-class Gaussian mixture model for the input data.Building upon recent advances in random matrix theory, we show, when the dimension of data p and their number n are both large, that the LS-SVM decision function can be well approximated by a normally distributed random variable, the mean and variance of which depend explicitly on a local behavior of the kernel function.This theoretical result is then applied to the MNIST and Fashion-MNIST datasets which, despite their non-Gaussianity, exhibit a convincingly close behavior.Most importantly, our analysis provides a deeper understanding of the mechanism into play in SVM-type methods and in particular of the impact on the choice of the kernel function as well as some of their theoretical limits in separating high dimensional Gaussian vectors.
User Participation in Collaborative Filtering-Based Recommendation Systems: A Game Theoretic Approach

Collaborative filtering is widely used in recommendation systems. A user can get high-quality recommendations only when both the user himself/herself and other users actively participate, i.e., provide sufficient ratings. However, due to the rating cost, rational users tend to provide as few ratings as possible. Therefore, there exists a tradeoff between the rating cost and the recommendation quality. In this paper, we model the interactions among users as a game in satisfaction form and study the corresponding equilibrium, namely satisfaction equilibrium (SE). Considering that accumulated ratings are used for generating recommendations, we design a behavior rule which allows users to achieve an SE via iteratively rating items. We theoretically analyze under what conditions an SE can be learned via the behavior rule. Experimental results on Jester and MovieLens data sets confirm the analysis and demonstrate that, if all users have moderate expectations for recommendation quality and satisfied users are willing to provide more ratings, then all users can get satisfying recommendations without providing many ratings. The SE analysis of the proposed game in this paper is helpful for designing mechanisms to encourage user participation.
Category Preferred Canopy–K-means based Collaborative Filtering algorithm

It is the era of information explosion and overload. The recommender systems can help people quickly get the expected information when facing the enormous data flood. Therefore, researchers in both industry and academia are also paying more attention to this area. The Collaborative Filtering Algorithm (CF) is one of the most widely used algorithms in recommender systems. However, it has difficulty in dealing with the problems of sparsity and scalability of data. This paper presents Category Preferred Canopy–K-means based Collaborative Filtering Algorithm (CPCKCF) to solve the challenges of sparsity and scalability of data. In particular, CPCKCF proposes the definition of the User–Item Category Preferred Ratio (UICPR), and use it to compute the UICPR matrix. The results can be applied to cluster the user data and find the nearest users to obtain prediction ratings. Our experimentation results performed using the MovieLens data set demonstrates that compared with traditional user-based Collaborative Filtering algorithm, the proposed CPCKCF algorithm proposed in this paper improved computational efficiency and recommendation accuracy by 2.81%.
Credit card fraud detection strategies with consumer incentives

Credit card fraud increases as ecommerce becomes more prevalent. Current fraud detection techniques, however, are far from accurate, and can result in significant financial losses to merchants and card issuers. Furthermore, the associated high false positive declines can cause inconvenience and dissatisfaction for consumers. We introduce a consumer incentive strategy for merchants to adopt more accurate fraud detection (prevention) techniques, such as secondary verification. We further optimize the strategy by applying secondary verification only to transactions with values higher than certain thresholds. We identify the conditions under which the strategy is attractive to merchants, and show that it may lead to a 'win-win-win' for consumers, merchants, and the card issuer, when the consumer's tolerance of false declines is low or/and the incentive amount is within a certain range. In addition, we show that coordinated decisions by the merchant and the card issuer may generate further benefits. Finally, the effectiveness of various proposed strategies is demonstrated using actual credit card transaction data.
Deep key frame extraction for sport training

For some professional sports, it is highly required to supervise and analyze the athletics pose in training of athletes. Key frame extraction from training videos plays a key role to facilitate the browse of sport training videos. In this paper, we propose a deep key frame extraction method for analyzing weightlifting sport training videos. To alleviate the bias from complex background, Fully Convolutional Networks (FCN) is employed firstly to extract the region of interest (ROI) which contains mainly the athlete and barbell for a more precise pose estimation of frames. Then over the extracted ROI, Convolutional Neural Networks (CNN) are leveraged to estimate the pose probability of each frame. Finally, a variation aware key frame extraction is constructed to extract the key frames considering neighboring probability difference of frames. The experimental results demonstrate that the proposed method achieves good performance in key frame extraction of sport videos, and significantly outperforms the comparisons.
Deep Belief Network for Meteorological Time Series Prediction in the Internet of Things

Seeking to address the challenges associated with high-dimensional complex time series representations of recurrent neural networks, such as low generalization ability and long training time, a hybrid neural network based on a deep belief network (DBN) is proposed in this paper to facilitate time series predictions for the Internet of Things. In our approach, we integrate both a DBN and a recurrent neural network with the gated recurrent unit as the activation unit. First, we implement unsupervised pretraining through the DBN and then supervise the curve fitting using the recurrent neural network. Finally, the hybrid neural network is learned and can make predictions. The experimental results show that the hybrid neural network has a stronger historical learning ability than two other widely used recurrent neural networks and can effectively reduce the number of iterations required by the recurrent neural network, thereby reducing the overall learning time.
Self-supervised Learning of Depth and Camera Motion from 360 $$^\circ $$ Videos

As 360 $$^{\circ }$$ cameras become prevalent in many autonomous systems (e.g., self-driving cars and drones), efficient 360 $$^{\circ }$$ perception becomes more and more important. We propose a novel self-supervised learning approach for predicting the omnidirectional depth and camera motion from a 360 $$^{\circ }$$ video. In particular, starting from the SfMLearner, which is designed for cameras with normal field-of-view, we introduce three key features to process 360 $$^{\circ }$$ images efficiently. Firstly, we convert each image from equirectangular projection to cubic projection in order to avoid image distortion. In each network layer, we use Cube Padding (CP), which pads intermediate features from adjacent faces, to avoid image boundaries. Secondly, we propose a novel “spherical” photometric consistency constraint on the whole viewing sphere. In this way, no pixel will be projected outside the image boundary which typically happens in images with normal field-of-view. Finally, rather than naively estimating six independent camera motions (i.e., naively applying SfM-Learner to each face on a cube), we propose a novel camera pose consistency loss to ensure the estimated camera motions reaching consensus. To train and evaluate our approach, we collect a new PanoSUNCG dataset containing a large amount of 360 $$^{\circ }$$ videos with groundtruth depth and camera motion. Our approach achieves state-of-the-art depth prediction and camera motion estimation on PanoSUNCG with faster inference speed comparing to equirectangular. In real-world indoor videos, our approach can also achieve qualitatively reasonable depth prediction by acquiring model pre-trained on PanoSUNCG.