Reviewerly/exHarmony huggingface dataset:
A Robust Airport Runway Detection Network Based on R-CNN Using Remote Sensing Images

Autonomous vision-based taxi, takeoff, and landing systems are gaining popularity in fixed-wing commercial, military, and unmanned aerial vehicles presenting new technical and regulatory challenges. With ongoing advances in deep learning, machine learning, and big data management, the dangers of takeoff and landing operations have greatly decreased, and with less dependence on simultaneous and complex controllers. However, in a real-world scenario, airport runway detection for takeoff and landing operations is still deemed challenging, due to the vast amount of contextual information, complex backgrounds, and dangerous geographical scenarios. In this work, we propose an end-to-end airport runway detection network for automatic runway detection and localization centered on a two-stage region-based convolutional neural network that could significantly improve navigation efficiency and dependence on complex control systems. The first stage is an enhanced region proposal network with domain-specific constraints added, which automatically learns and extracts multiscale (high, mid, and low-level) features using a deep convolutional neural network and filters low-quality positive samples for robust runway detection. The second stage classifies the candidate regions and determines the precise localization of predictions using an enhanced anchor refinement and regression algorithm with a nonmaximum suppression function for bounding box optimization. Transfer learning and data augmentation techniques are used to propagate relevant domain-specific semantic and context information during the training phase to handle the problem of imbalances in the dataset classes. The results show the effectiveness of our algorithmic contributions as well as the benefits of the specific enhancements, in terms of, improved efficiency and accuracy of runway detection in remote sensing images.
A Statistical Analysis of Summarization Evaluation Metrics Using Resampling Methods

Abstract The quality of a summarization evaluation metric is quantified by calculating the correlation between its scores and human annotations across a large number of summaries. Currently, it is unclear how precise these correlation estimates are, nor whether differences between two metrics’ correlations reflect a true difference or if it is due to mere chance. In this work, we address these two problems by proposing methods for calculating confidence intervals and running hypothesis tests for correlations using two resampling methods, bootstrapping and permutation. After evaluating which of the proposed methods is most appropriate for summarization through two simulation experiments, we analyze the results of applying these methods to several different automatic evaluation metrics across three sets of human annotations. We find that the confidence intervals are rather wide, demonstrating high uncertainty in the reliability of automatic metrics. Further, although many metrics fail to show statistical improvements over ROUGE, two recent works, QAEval and BERTScore, do so in some evaluation settings.1
Optimality of spectral clustering in the Gaussian mixture model

Spectral clustering is one of the most popular algorithms to group high- dimensional data. It is easy to implement and computationally efficient. Despite its popularity and successful applications, its theoretical properties have not been fully understood. In this paper, we show that spectral clustering is minimax optimal in the Gaussian mixture model with isotropic covariance matrix, when the number of clusters is fixed and the signal-to-noise ratio is large enough. Spectral gap conditions are widely assumed in the literature to analyze spectral clustering. On the contrary, these conditions are not needed to establish optimality of spectral clustering in this paper.
Numerical modeling of formation of the Maoping Pb-Zn deposit within the Sichuan-Yunnan-Guizhou Metallogenic Province, Southwestern China: Implications for the spatial distribution of concealed Pb mineralization and its controlling factors

The Maoping large Pb-Zn deposit is located in the Sichuan-Yunnan-Guizhou Metallogenic Province (SYGMP), southwestern China, hosting more than 3 mt Pb and Zn minerals, with 140 t associated Ge. This study uses numerical simulation methods to explore the key factors of the mineralizing system that formed the Maoping deposit which traditional analytical methods cannot easily identify, answering questions about the spatial distribution of galena and giving information for future resource exploration within the Maoping district. This Study reveals and highlights the practical value of numerical modeling in quantitatively researching the ore-forming processes of mineral deposits and how this knowledge can be used in future geological research and mineral exploration. Our model interlinks heat transfer, pressure (without deformation process), fluid-flow, and material migration during the mineralizing processes and indicates the presence of a temperature anomaly, location of fluid convergence and the spatial distribution of Pb2+ that match the known distribution of mineralization. We also analyze the whole process and find the key factors which control the spatial distribution of galena, answering the question why there is mineralization with economic value in the northwest limb and none in the southeast limb of the Maoping anticline. All these data can be used in future geological research to gain insights into the ore-forming system that formed the Maoping Pb-Zn deposit as well as other hydrothermal mineralizing systems in the SYGMP.
Automatic Detection and Counting of Blood Cells in Smear Images Using RetinaNet

A complete blood count is one of the significant clinical tests that evaluates overall human health and provides relevant information for disease diagnosis. The conventional strategies of blood cell counting include manual counting as well as counting using the hemocytometer and are tedious and time-consuming tasks. This research-based paper proposes an automatic software-based alternative method to count blood cells accurately using the RetinaNet deep learning network, which is used to recognize and classify objects in microscopic images. After training, the network automatically recognizes and counts red blood cells, white blood cells, and platelets. We tested a model trained on smear images and found that the trained model has generalized capabilities. We assessed the quality of detection and cell counting using performance measures, such as accuracy, sensitivity, precision, and F1-score. Moreover, we studied the dependence of the confidence thresholds and the number of learning epochs on the obtained results of recognition and counting. We compared the performance of the proposed approach with those obtained by other authors who dealt with the subject of cell counting and show that object detection and labeling can be an additional advantage in the task of counting objects.
Group event recommendation based on graph multi-head attention network combining explicit and implicit information

In event-based social networks (EBSN), group event recommendation has become an important task for groups to quickly find events that they are interested in. Existing methods on group event recommendation either consider just one type of information, explicit or implicit, or separately model the explicit and implicit information. However, these methods often generate a problem of data sparsity or of model vector redundancy. In this paper, we present a Graph Multi-head Attention Network (GMAN) model for group event recommendation that integrates the explicit and implicit information in EBSN. Specifically, we first construct a user-explicit graph based on the user's explicit information, such as gender, age, occupation and the interactions between users and events. Then we build a user-implicit graph based on the user's implicit information, such as friend relationships. The incorporated both explicit and implicit information can effectively describe the user's interests and alleviate the data sparsity problem. Considering that there may be a correlation between the user's explicit and implicit information in EBSN, we take the user's explicit vector representation as the input of the implicit information aggregation when modeling with graph neural networks. This unified user modeling can solve the aforementioned problem of user model vector redundancy and is also suitable for event modeling. Furthermore, we utilize a multi-head attention network to learn richer implicit information vectors of users and events from multiple perspectives. Finally, in order to get a higher level of group vector representation, we use a vanilla attention mechanism to fuse different user vectors in the group. Through experimenting on two real-world Meetup datasets, we demonstrate that GMAN model consistently outperforms state-of-the-art methods on group event recommendation.
SDH: Secure Data Hiding in Fused Medical Image for Smart Healthcare

Fusing the single modality medical images to obtain a distinct multimodality image is needed to ensure a better clinical experience. However, the distribution of these fused images brings the issues of ownership and authentication and has attracted many researchers. Presently, large volumes of medical data are stored on cloud platforms. However, outsourcing medical data to this popular platform may introduce security issues. Following this, we introduce a secure data hiding in fused medical image for smart healthcare, since it is also suitable for applications in the cloud. To achieve this, we first create a fused medical image as a cover by nonsubsampled contourlet transform (NSCT). The method, which is based on NSCT, QR, and Schur decomposition, allows concealing the image and electronic patient records (EPR) mark into the fused image. Importantly, EPR watermark includes a hash value of cover is created first and then embedded into the cover via magic cube-based procedure. Finally, the marked image is encrypted using deoxyribonucleic acid (DNA), chaotic maps, and a hash function-based encryption scheme. The introduced watermarking scheme has been evaluated using 25 pairs of medical images and several embedding/extracting parameters. Apart from being satisfactorily imperceptible, the proposed work is also robust and secure against well-known signal processing attacks and promising results are obtained when compared with similar techniques. It indicates a considerable improvement in robustness of 66.7% and 99.7% over existing discrete wavelet transform (DWT)–singular value decomposition (SVD)-based watermarking schemes.
Mathematical models of CBSC over wireless channels and their analysis by using the LeNN-WOA-NM algorithm

In recent studies, several chaos-based secure communication (CBSC) systems are developed and are assumed to be effective and promising tools for balancing communications in wireless applications. The reliability of a wireless communication system is dependent on the synchronization between receiving and transmitting nodes. Therefore, it is crucial to analyze the mathematical models for different enhancements through CBSC and avoid the difficulty in synchronization. We have designed a novel computational paradigm that uses weighted Legendre polynomials to construct series solutions for mathematical models of the Lorenz Chaotic Attractor (LCA) and Double Scroll Attractor (DSA) (with filter and without filter) by using Chua’s circuits. We design fitness functions for each system of differential equations. Two optimization techniques, the Whale Optimization Algorithm (WOA) and the Nelder–Mead (NM) algorithm are adopted to optimize the objective functions and calculate the best set of unknown weights. We name our soft computing paradigm, the LeNN-WOA-NM algorithm. We have compared our results with state-of-the-art, and we establish that the LeNN-WOA-NM algorithm is fast, accurate, and reliable. We present graphical and statistical analysis to further elaborate on the performance of our algorithm.
Proof of Learning (PoLe): Empowering neural network training with consensus building on blockchains

The advent of neural network (NN) based deep learning, especially the recent development of the automatic design of networks, has brought unprecedented performance gains at heavy computational cost. On the other hand, in order to generate a new consensus block, Proof of Work (PoW) based blockchain systems routinely perform a huge amount of computation that does not achieve practical purposes but to solving a difficult cryptographic hash puzzle problem.In this study, we propose a new consensus mechanism, Proof of Learning (PoLe), which directs the computation spent for block consensus toward optimization of neural networks. In our design, the training and testing data are released to the entire blockchain network and the consensus nodes train NN models on the data, which serves as the proof of learning. As a core component of PoLe, we design a secure mapping layer (SML) to prevent consensus nodes from cheating, which can be straightforwardly implemented as a linear NN layer. When the consensus on the blockchain network is achieved, a new block is appended to the blockchain. We experimentally compare the PoLe protocol with Proof of Work (PoW) and show that PoLe can achieve a more stable block generation rate, which leads to more efficient transaction processing. Experimental evaluation also shows the PoLe can achieve a stable block generation rate without significantly sacrificing training performance.
A conversational agent for querying Italian Patient Information Leaflets and improving health literacy

In the last years, the rise of digital technologies has enormously augmented the possibility for people to access health information and consult online versions of Patient Information Leaflets (PILs), enabling them to improve their knowledge about medication and adherence to therapies. However, health information may often be difficult to consult and comprehend due to an excessively lengthy and undersized text, coupled with the presence of many incomprehensible medical terms. To face these issues, this paper proposes a conversational agent as a valuable solution to simplify health information retrieval and improve health literacy in Italian by codifying PILs and making them query-able in natural language. In particular, the system has been devised to: i) comprehend natural language questions on medicines of interest; ii) proactively ask the user or automatically infer from the dialog state all the missing information necessary to generate an answer; iii) extract the answer from a structured knowledge base built from PILs of registered drugs. An experimental study has been carried out to evaluate both the performance and usability of the proposed system. Results showed an adequate ability of the system to handle most of the dialogues started by participants correctly, good users satisfaction, and, thus, proved its feasibility and usefulness.
Measuring consumer-perceived humanness of online organizational agents

As organizations increasingly adopt artificial intelligence (AI) systems for their online communication with publics, conceptualizing and operationalizing perceived humanness presents an important challenge to understanding how consumers and other publics respond to both AI and human agents in live chats with organizations. This study develops a measure of consumer-perceived humanness derived from three related constructs: conversational human voice, anthropomorphism, and social presence. The new measure was tested for reliability and validity, including tests of criterion validity predicting how it would relate to perceived relational investment and trust as relational outcomes. Two online studies (N₁ = 172, N₂ = 375) were conducted in which participants interacted with either human or machine online chat agents. Greater perceived humanness of chat agents was found to correlate with greater perceived relational investment and trust in the organization, with perceived relational investment mediating the relationship between perceived humanness and trust. Perceived humanness can be utilized subsequently to assess both human and machine agents. Its corollary, perceived machineness, was also identified as a separate factor to be taken into consideration in future research and theory building.
RangeLVDet: Boosting 3D Object Detection in LIDAR With Range Image and RGB Image

Camera and LIDAR are both important sensor modalities for real-world applications, especially autonomous driving. The sensors provide complementary information and make sensor fusion possible. However, the progress of early-fusion for both modalities is very slow due to the limitations of viewpoint misalignment, feature misalignment and data volume alignment, so that its performance is also very low. In this work, we propose a novel fusion pipeline: an early-fusion method of range image and RGB image to enhance 3D object detection. It takes full advantage of LIDAR&#x2019;s range view, point view, bird&#x2019;s eye view, and RGB view of the camera: First, it uses a large-scale 2D detection data set to pre-train a 2D detection network for extracting high-level semantic features from the RGB image; Second, it uses a specially designed 2D convolution network to extract high-level geometric features from the range image; Third, it fuses the semantic and geometric features through the point view of point clouds; Finally, the point view is transferred to bird&#x2019;s eye view for 3D object detection. Both the range image and the RGB image are front views, so the multi-modal features are more matched. Experiments show that the proposed method has large improvements on the method of using only the point cloud, and outperforms the state-of-the-art methods on the self-built actual data set. In ablation, we study the dependence of the fusion method on 2D features and the effect of different fusion positions on the performance.
DDUNet: Dense Dense U-Net with Applications in Image Denoising

The investigation of CNN for image denoising has arrived at a serious bottleneck and it is extremely difficult to design an efficient network for image denoising with better performance and fewer parameters. A nice starting point for this is the cascading U-Nets architecture which has been successfully applied in numerous image-to-image tasks such as image denoising and segmentation. How-ever, the previous related models often focused on the local architecture in each U-Net rather than the connection between U-Nets, which strictly limits their performances. To further improve the connection between U-Nets, we propose a novel cascading U-Nets architecture with multi-scale dense processing, named Dense Dense U-Net (DDUNet). The multi-scale dense processing connects the feature maps in each level cross cascading U-Nets, which has several compelling advantages: they alleviate the vanishing gradient problem, strengthen feature propagation and encourage feature reuse. Furthermore, we develop a series of related important techniques to improve model performance with fewer parameters. Extensive experimental results on both synthetic and real noisy datasets demonstrate that the proposed model achieves outstanding results with fewer parameters. Meanwhile, experimental results show clearly that the proposed DDUNet is good at edge recovery and structure preservation in real noisy image denoising.
A simplified evaluation method of rooftop solar energy potential based on image semantic segmentation of urban streetscapes

The evaluation of rooftop solar energy potential in cities has a fundamental role in the development and utilization of solar energy. The irradiance-based approach of evaluation is a repetitive calculation process combined with the complex geometry of urban forms. This research paper proposes a framework that utilizes urban streetscapes to quickly estimate the urban solar energy potential using a simplified solar energy yield model based on two indicators: sky view factor (SVF) and sun coverage factor (SCF). First, 327 Google Street View images were converted into fisheye images, which were semantically segmented based on the deep-learning Full Convolutional Network (FCN). Then, the SVF and SCF were calculated based on the method of pixel statistics. Finally, a model with the simulated solar energy yield was established. Furthermore, a validation study was conducted using rooftop solar production data, which showed that the maximum estimation error of the proposed model was less than 8% and the average relative error was 6.6%. Generally, the proposed model reduced the required computation time while preserving a satisfactory degree of accuracy.
Large-Scale Spatio-Temporal Person Re-Identification: Algorithms and Benchmark

Person re-identification (re-ID) in the scenario with large spatial and temporal spans has not been fully explored. This fact partially occurs because existing benchmark datasets were mainly collected with limited spatial and temporal ranges, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> using videos recorded in a few days by cameras in a specific region of the campus. Such limited spatial and temporal ranges make it hard to simulate the difficulties of person re-ID in real scenarios. In this work, we contribute a novel Large-scale Spatio-Temporal (LaST) person re-ID dataset, including 10,862 identities with more than 228k images. Compared with existing datasets, LaST presents more challenging and high-diversity re-ID settings and significantly larger spatial and temporal ranges. For instance, each person can appear in different cities or countries, and in various time slots from day to evening, and in different seasons from spring to winter. To our best knowledge, LaST is a novel person re-ID dataset with the largest spatio-temporal ranges. Based on LaST, we verified its challenge by conducting a comprehensive performance evaluation of 14 re-ID algorithms. We further propose an easy-to-implement baseline that works well in such challenging re-ID settings. We also verified that models pre-trained on LaST can generalize well on existing datasets with short-term and cloth-changing scenarios. We expect LaST to inspire future works toward more realistic and challenging re-ID tasks. More information about the dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/shuxjweb/last.git</uri> .
Initialization-Based <i>k</i>-Winners-Take-All Neural Network Model Using Modified Gradient Descent

The k -winners-take-all ( k -WTA) problem refers to the selection of k winners with the first k largest inputs over a group of n neurons, where each neuron has an input. In existing k -WTA neural network models, the positive integer k is explicitly given in the corresponding mathematical models. In this article, we consider another case where the number k in the k -WTA problem is implicitly specified by the initial states of the neurons. Based on the constraint conversion for a classical optimization problem formulation of the k -WTA, via modifying the traditional gradient descent, we propose an initialization-based k -WTA neural network model with only n neurons for n -dimensional inputs, and the dynamics of the neural network model is described by parameterized gradient descent. Theoretical results show that the state vector of the proposed k -WTA neural network model globally asymptotically converges to the theoretical k -WTA solution under mild conditions. Simulative examples demonstrate the effectiveness of the proposed model and indicate that its convergence can be accelerated by readily setting two design parameters.