Reviewerly/exHarmony huggingface dataset:
Earthquake Detection at the Edge: IoT Crowdsensing Network

State-of-the-art Earthquake Early Warning systems rely on a network of sensors connected to a fusion center in a client–server paradigm. The fusion center runs different algorithms on the whole data set to detect earthquakes. Instead, we propose moving computation to the edge, with detector nodes that probe the environment and process information from nearby probes to detect earthquakes locally. Our approach tolerates multiple node faults and partial network disruption and keeps all data locally, enhancing privacy. This paper describes our proposal’s rationale and explains its architecture. We then present an implementation that uses Raspberry, NodeMCU, and the Crowdquake machine learning model.
Structure-Enhanced Heterogeneous Graph Contrastive Learning

Recent years have seen a growing interest in Graph Contrastive Learning (GCL), which trains Graph Neural Network (GNN) model to discriminate similar and dissimilar pairs of nodes without human annotations. Most prior GCL work focuses on homogeneous graphs and little attention has been paid to Heterogeneous Graphs (HGs) that involve different types of nodes and edges. Moreover, earlier studies reveal that the explicit use of structure information of underlying graphs is useful for learning representations. Conventional GCL methods merely measure the likelihood of contrastive pairs according to node representations, which may not align with the true semantic similarities. How to leverage such structure information for GCL is not yet well-understood. To address the aforementioned challenges, this paper presents a novel method dubbed STructure-EnhaNced heterogeneous graph ContrastIve Learning, STENCIL for brevity. At first, we generate multiple semantic views for HGs based on metapaths. Unlike most methods that maximize the consistency among these views, we propose a novel multiview contrastive aggregation objective to adaptively distill information from each view. In addition, we advocate the explicit use of structure embedding, which enriches the model with local structural patterns of the underlying HGs, so as to better mine true and hard negatives for GCL. Empirical studies on three real-world datasets show that our proposed method consistently outperforms existing state-of-the-art methods and even surpasses several supervised counterparts.
Invisible Medical Image Watermarking using Edge Detection And Discrete Wavelet Transform Coefficients

Protection and authentication of medical images is essential for the patient’s disease identification and diagnosis. The watermark in medical imaging application needs to be invisible and it is also required to preserve the low and high frequency features of image data which makes watermarking a difficult assignment. Within this manuscript an unseen medical image watermarking approach is projected apply edge detection in the discrete wavelet transform domain. The wavelet transform is brought into play to decay the medical picture interested in multi-frequency secondary band coefficients. The edge detection applies to high frequency wavelet group in the direction of generating the boundary coefficients used as a key. The Gaussian noise pattern is utilized as watermark as well as embedded within the edge coefficients around the edges. To add the robustness scaled dilated edge coefficient is added with the edge coefficients to generate the watermarked image. Preserving the small frequency secondary band fulfills the information requirement of the medical imaging application. At the same time as adding together the watermark during high frequency sub-bands improve the watermark invisibility. To add additional robustness the dilation is applied on the edged coefficient before being embedded with sub band coefficients. presentation of the technique is experienced on the dissimilar set of medical imagery as well as evaluation of the proposed watermarking method founds it robust not in favor of the different attacks such at the same time as filtering, turning round plus resizing. Parametric study foundation going on Mean Square Error along with Signal to Noise Ratio shows that how good method performs for invisibility.
A two‐stage data cleansing method for bridge global positioning system monitoring data based on bi‐direction long and short term memory anomaly identification and conditional generative adversarial networks data repair

Data cleansing is an essential approach for improving data quality. Therefore, it is the key to avoiding the false alarm of the monitoring system due to the anomaly of the data itself. Data cleansing consists of two parts: anomaly identification and anomaly repair. However, current research on data cleansing has mainly focused on anomaly identification and lacks efficient data repair methods. The key to data repair lies in sensor correlation models based on mapping relationships between sensors. To obtain a good inter-sensor relationship model, it is first necessary to exclude anomalous data from the training data set used for modeling. Therefore, a two-stage data cleansing framework for collaborative multi-sensor repair is proposed. First, based on the analysis of anomalous features of GPS data, a bidirectional long- and short-term memory (Bi-LSTM) neural network model is adopted for data anomalies classification and localization. As a result, the data segment to be repaired is determined. Then, on the basis of all sensor data in the time range of the day before the target repair data segment, the data set for data repair is constructed by excluding the anomaly data segments in the data set with the help of the above anomaly identification results. Then, a conditional generation adversarial network (CGAN) is proposed to achieve data repair. Experimental validation shows that the two-stage data cleansing method of identification followed by repair can accurately identify and repair GPS anomalies. Finally, several factors affecting the repair effect are discussed.
Effective and efficient negative sampling in metric learning based recommendation

In this paper, we start by pointing out the problem of a negative sampling (NS) strategy, denoted as nearest-NS (NNS), used in metric learning (ML)-based recommendation methods. NNS samples the items nearer to a user with higher probability among her unrated items. This could move her preferred items far away from her, thereby making the preferred items excluded from top- K recommendation. To address the problem, we first define a concept of a cage for a user , a region that contains the items highly likely preferred by her. Based on the concept, we propose a novel NS strategy, named as cage-based NS (CNS), that makes her preferred items rarely sampled as negative items , thereby improving the accuracy of top- K recommendation. Furthermore, we propose CNS+, an improved version of CNS, that reduces the computation overhead of CNS . CNS+ strategy provides performance than CNS much higher, however, not requiring to sacrifice the accuracy. Through extensive experiments using four real-life datasets, we validate the effectiveness ( i.e. , accuracy) and efficiency ( i.e. , performance) of the proposed approach. We first demonstrate that our CNS strategy addresses successfully the problem of NNS strategy. In addition, we show that applying our CNS strategy to three existing ML-based recommendation methods ( i.e. , CML , LRML, and SML) improves their accuracy consistently and significantly in all datasets and with all metrics. Also, we confirm that CNS+ strategy significantly reduces the execution times with (almost) no loss of accuracy of CNS strategy. Finally, we show that our CNS and CNS+ strategies have a linear scalability with the increasing number of ratings.
Metric Learning Based Interactive Modulation for Real-World Super-Resolution

Interactive image restoration aims to restore images by adjusting several controlling coefficients, which determine the restoration strength. Existing methods are restricted in learning the controllable functions under the supervision of known degradation types and levels. They usually suffer from a severe performance drop when the real degradation is different from their assumptions. Such a limitation is due to the complexity of real-world degradations, which can not provide explicit supervision to the interactive modulation during training. However, how to realize the interactive modulation in real-world super-resolution has not yet been studied. In this work, we present a Metric Learning based Interactive Modulation for Real-World Super-Resolution (MM-RealSR). Specifically, we propose an unsupervised degradation estimation strategy to estimate the degradation level in real-world scenarios. Instead of using known degradation levels as explicit supervision to the interactive mechanism, we propose a metric learning strategy to map the unquantifiable degradation levels in real-world scenarios to a metric space, which is trained in an unsupervised manner. Moreover, we introduce an anchor point strategy in the metric learning process to normalize the distribution of metric space. Extensive experiments demonstrate that the proposed MM-RealSR achieves excellent modulation and restoration performance in real-world super-resolution. Codes are available at https://github.com/TencentARC/MM-RealSR .
dPQL: a lossless distributed algorithm for generalized linear mixed model with application to privacy-preserving hospital profiling

Abstract Objective To develop a lossless distributed algorithm for generalized linear mixed model (GLMM) with application to privacy-preserving hospital profiling. Materials and Methods The GLMM is often fitted to implement hospital profiling, using clinical or administrative claims data. Due to individual patient data (IPD) privacy regulations and the computational complexity of GLMM, a distributed algorithm for hospital profiling is needed. We develop a novel distributed penalized quasi-likelihood (dPQL) algorithm to fit GLMM when only aggregated data, rather than IPD, can be shared across hospitals. We also show that the standardized mortality rates, which are often reported as the results of hospital profiling, can also be calculated distributively without sharing IPD. We demonstrate the applicability of the proposed dPQL algorithm by ranking 929 hospitals for coronavirus disease 2019 (COVID-19) mortality or referral to hospice that have been previously studied. Results The proposed dPQL algorithm is mathematically proven to be lossless, that is, it obtains identical results as if IPD were pooled from all hospitals. In the example of hospital profiling regarding COVID-19 mortality, the dPQL algorithm reached convergence with only 5 iterations, and the estimation of fixed effects, random effects, and mortality rates were identical to that of the PQL from pooled data. Conclusion The dPQL algorithm is lossless, privacy-preserving and fast-converging for fitting GLMM. It provides an extremely suitable and convenient distributed approach for hospital profiling.
Prediction of the driver’s focus of attention based on feature visualization of a deep autonomous driving model

Driver focus of attention (DFoA) is a fundamental research problem in human-like autonomous driving systems. However, most existing methods require large amounts of ground-truth DFoA data for training, which are difficult to collect. Inspired by the visual interpretability of neural networks, this study innovatively developed a DFoA prediction method based on the feature visualization of a deep autonomous driving model that does not require ground-truth DFoA data to train. Here, we propose a multimodal spatiotemporal convolutional network with an attention mechanism for DFoA prediction. First, semantic and depth images were generated from RGB video frames to enable the multipath convolutional network to learn the spatiotemporal information of successive images. A parameter-free attention mechanism with 3-D weights was incorporated as an energy function to calculate the importance of each neuron. A graph attention network was used to learn the most driving behavior-relevant semantic context features. The learned features were fused, and a convolutional long short-term memory network (ConvLSTM) was adopted to achieve the evolution of the fused features in successive frames while considering historical scene variation. Finally, a novel feature visualization method was designed to predict the DFoA by visualizing the driving behavior-relevant features. The experimental results demonstrated that the proposed method can accurately predict DFoA.
A Comprehensive Study of Deep Learning-based Covert Communication

Deep learning-based methods have been popular in multimedia analysis tasks, including classification, detection, segmentation, and so on. In addition to conventional applications, this model can be widely used for cover communication, i.e., information hiding. This article presents a review of deep learning-based covert communication scheme for protecting digital contents, devices, and models. In particular, we discuss the background knowledge, current applications, and constraints of existing deep learning-based information hiding schemes, identify recent challenges, and highlight possible research directions. Further, major role of deep learning in the area of information hiding are highlighted. Then, the contribution of surveyed scheme is also summarized and compared in the context of estimation of design objectives, approaches, evaluation metric, and weaknesses. We believe that this survey can pave the way to new research in this crucial field of information hiding in deep-learning environment.
Identifying the key frames: An attention-aware sampling method for action recognition

Deep learning based methods have achieved remarkable progress in action recognition. Existing works mainly focus on designing novel deep architectures to learn video representations for action recognition. Most existing methods treat sampled frames equally and average all the frame-level predictions to generate video-level predictions at the testing stage. However, within a video, discriminative actions may occur sparsely in a few frames whereas most other frames are irrelevant to the ground truth which may even lead to wrong results. As a result, we think that the strategy of selecting relevant frames would be a further important key to enhance the existing deep learning based action recognition. In this paper, we propose an attention-aware sampling method for action recognition, which aims to discard the irrelevant and misleading frames and preserve the most discriminative frames. We formulate the process of mining key frames from videos as a Markov decision process and train the attention agent through deep reinforcement learning without extra labels. The agent takes features and predictions from the baseline model as inputs and generates importance scores for all frames. Moreover, our approach is extensible, which can be applied to different existing deep learning based action recognition models. We achieve very competitive action recognition performance on two widely used action recognition datasets.
A robust receptive field code for optic flow detection and decomposition during self-motion

The perception of optic flow is essential for any visually guided behavior of a moving animal. To mechanistically predict behavior and understand the emergence of self-motion perception in vertebrate brains, it is essential to systematically characterize the motion receptive fields (RFs) of optic-flow-processing neurons. Here, we present the fine-scale RFs of thousands of motion-sensitive neurons studied in the diencephalon and the midbrain of zebrafish. We found neurons that serve as linear filters and robustly encode directional and speed information of translation-induced optic flow. These neurons are topographically arranged in pretectum according to translation direction. The unambiguous encoding of translation enables the decomposition of translational and rotational self-motion information from mixed optic flow. In behavioral experiments, we successfully demonstrated the predicted decomposition in the optokinetic and optomotor responses. Together, our study reveals the algorithm and the neural implementation for self-motion estimation in a vertebrate visual system.
Island Extractions in the Wild: A Corpus Study of Adjunct and Relative Clause Islands in Danish and English

Adjuncts and relative clauses are traditionally classified as strong islands for extraction across languages. However, the Mainland Scandinavian (MSc.) languages have been reported to differ from e.g., English in allowing extraction from adjunct and relative clauses. In order to investigate the distribution of possible island extractions in these languages based on naturally produced material, we conducted two exploratory corpus studies on adjunct and relative clause extraction in Danish and in English. Results suggest that both extraction from finite adjuncts and from relative clauses appears at a non-trivial rate in naturally produced Danish, which supports the claim that these structures are not strong islands in Danish. In English, we also found a non-trivial amount of examples displaying extraction from finite adjuncts, as well as a small number of cases of relative clause extraction. This finding presents a potential challenge to the claim that English differs from MSc. in never allowing extraction from strong islands. Furthermore, our results show that both languages appear to share certain trends that can be observed in the extraction examples regarding the type of extraction dependency, the type of adjunct clause featured in adjunct clause extraction, and the type of matrix predicate featured in relative clause extraction.
MRDN: A lightweight Multi-stage residual distillation network for image Super-Resolution

• Constructing a multi-stage residual distillation network for lightweight SR. • Designing an efficient pixel attention module. • Constructing a multi-stage residual distillation block. • Effectively reducing the network parameters. • Superior to the state-of-the-art models. For the deep learning based super-resolution (SR) reconstruction method, researchers try to expand the receptive field to improve the reconstruction quality. With the increase of network depth, the consumption of computing resources is impressive. Therefore, SR technology is challenging to apply to small mobile clients. To deal with this defect, we propose a lightweight multi-stage residual distillation network (MRDN) for the SR task in this paper. The model has made the following two main improvements: First, we design a multi-stage residual distillation block (MRDB). It combines the channel separation and the skip connection to reduce the parameter number and guarantee the network’s performance by residual learning. Secondly, we propose efficient pixel attention (EPA) module, which weights different channels according to their importance so that the network can learn more details of pictures. Experiments show that the proposed MRDN is superior to the state-of-the-art models in subjective visual effects and objective evaluation criteria such as PSNR, SSIM, and computational complexity. Taking the basic data set manga109 as an example, the PSNR of this algorithm on scale ×4 reaches 30.66, and the average inference time is 0.021 s. The performance surpasses RFDN, the champion of AIM 2020 Challenge on Efficient Super-Resolution.
Deep-Learning-Incorporated Augmented Reality Application for Engineering Lab Training

Deep learning (DL) algorithms have achieved significantly high performance in object detection tasks. At the same time, augmented reality (AR) techniques are transforming the ways that we work and connect with people. With the increasing popularity of online and hybrid learning, we propose a new framework for improving students’ learning experiences with electrical engineering lab equipment by incorporating the abovementioned technologies. The DL powered automatic object detection component integrated into the AR application is designed to recognize equipment such as multimeter, oscilloscope, wave generator, and power supply. A deep neural network model, namely MobileNet-SSD v2, is implemented for equipment detection using TensorFlow’s object detection API. When a piece of equipment is detected, the corresponding AR-based tutorial will be displayed on the screen. The mean average precision (mAP) of the developed equipment detection model is 81.4%, while the average recall of the model is 85.3%. Furthermore, to demonstrate practical application of the proposed framework, we develop a multimeter tutorial where virtual models are superimposed on real multimeters. The tutorial includes images and web links as well to help users learn more effectively. The Unity3D game engine is used as the primary development tool for this tutorial to integrate DL and AR frameworks and create immersive scenarios. The proposed framework can be a useful foundation for AR and machine-learning-based frameworks for industrial and educational training.
Software Defect Prediction Using Artificial Neural Networks: A Systematic Literature Review

The demand for automated online software systems is increasing day by day, which triggered the need for high-quality and maintainable softwares at lower cost. Software defect prediction is one of the crucial tasks of the quality assurance process which improves the quality at lower cost by reducing the overall testing and maintenance efforts. Early detection of defects in the software development life cycle (SDLC) leads to the early corrections and ultimately timely delivery of maintainable software, which satisfies the customer and makes him confident towards the development team. In the last decade, many machine learning-based approaches for software defect prediction have been proposed to achieve the higher accuracy. Artificial Neural Network (ANN) is considered as one of the widely used machine learning techniques, which is included in most of the proposed defect prediction frameworks and models. This research provides a critical analysis of the latest literature, published from year 2015 to 2018 on the use of Artificial Neural Networks for software defect prediction. In this study, a systematic research process is followed to extract the literature from three widely used digital libraries including IEEE, Elsevier, and Springer, and then after following a thorough process, 8 most relevant research publications are selected for critical review. This study will serve the researchers by exploring the current trends in software defect prediction with the focus on ANNs and will also provide a baseline for future innovations, comparisons, and reviews.