Reviewerly/exHarmony huggingface dataset:
Multiple-Kernelized-Correlation-Filter-Based Track-Before-Detect Algorithm for Tracking Weak and Extended Target in Marine Radar Systems

This article addresses the problem of tracking weak and extended targets in the clutter for marine radar systems. In the proposal, multiple kernelized correlation filters (MKCFs) incorporate a low-threshold constant false alarm rate and segmentation model under the principle of multiframe track before detect (MF-TBD). By setting the similarity score of the correlation filter as the test statistic, the maximization of the integration over frames can be solved by the essentially exhaustive searching of the MKCF. Since the correlation filter measures the similarity of the intensity distributions in the extended templates, the proposed method can differentiate the weak target from the surrounding clutter and other targets at a close range. Compared to other amplitude-based MF-TBD methods, it performs better in detecting and tracking extended targets in the real-radar data with heavy clutter caused by floating sea ice and the simulations with Rayleigh and $K$-distributed sea clutters.
A hierarchical blockchain-based electricity market framework for energy transactions in a security-constrained cluster of microgrids

In this paper, a Hierarchical Blockchain-based electricity market framework is proposed for peer-to-peer energy transactions within and between Microgrids in a security-constrained cluster of Microgrids. This market enhances the energy transaction mechanism, market clearing process and security of the power market. The existing blockchain based electricity markets usually check the energy availability through a transaction verification process, however, the feasibility of energy transactions between microgrids from the power system point of view is not evaluated before approving a transaction and creating a new block. In this paper, the unique structure of the market makes it possible to consider reliability security constraints of the power grid during the transaction verification process. Furthermore, the blockchain ledger summarization and microgrid’s equivalent model development are other new features of this framework. To develop and verity this market framework, Java programming language is used to create the hierarchical blockchain-based algorithm, and a modified IEEE 37-bus distribution test feeder system is chosen as the case study. Numerical results will show the effectiveness of this market structure, especially, when several transactions are requested to transfer energy over some congested transmission corridors.
An Online Data-Driven LPV Modeling Method for Turbo-Shaft Engines

The linear parameter-varying (LPV) model is widely used in aero engine control system design. The conventional local modeling method is inaccurate and inefficient in the full flying envelope. Hence, a novel online data-driven LPV modeling method based on the online sequential extreme learning machine (OS-ELM) with an additional multiplying layer (MLOS-ELM) was proposed. An extra multiplying layer was inserted between the hidden layer and the output layer, where the hidden layer outputs were multiplied by the input variables and state variables of the LPV model. Additionally, the input layer was set to the LPV model’s scheduling parameter. With the multiplying layer added, the state space equation matrices of the LPV model could be easily calculated using online gathered data. Simulation results showed that the outputs of the MLOS-ELM matched that of the component level model of a turbo-shaft engine precisely. The maximum approximation error was less than 0.18%. The predictive outputs of the proposed online data-driven LPV model after five samples also matched that of the component level model well, and the maximum predictive error within a large flight envelope was less than 1.1% with measurement noise considered. Thus, the efficiency and accuracy of the proposed method were validated.
Human–Machine Interaction-Oriented Image Coding for Resource-Constrained Visual Monitoring in IoT

Visual monitoring supported by the Internet of Things (IoT) increasingly relies on analyzing a mass of image data with human–machine interactive mechanisms. However, maintaining the efficiency of such a monitoring system in complex environments with energy or bandwidth constraints poses challenges. While both human perception and machine analysis performance should be satisfied, transmitting extra information to satisfy both should be avoided for efficient resource utilization. To this end, we propose a human–machine interaction-oriented image coding (HMI-IC) framework based on deep learning. In this framework, machines should provide early monitoring messages consisting of analysis results and preview images, and then humans can additionally request high-quality images of objects of interest. Each collected image is compressed into a layered data stream by HMI-IC to fulfill the demands of analysis, preview visualization, and high-quality reconstruction. Adaptive coding transmission can fit different demands in two stages, according to resource constraints. Experimental results show that both accuracy and inference speed on compressed images are improved by our method, with entire coding efficiency comparable to JPEG2000. To validate HMI-IC’s efficiency in practical terms, we provide two use cases (energy constrained and bandwidth constrained) for visual monitoring.
A Serial Attention Frame for Multi-Label Waste Bottle Classification

The multi-label recognition of damaged waste bottles has important significance in environmental protection. However, most of the previous methods are known for their poor performance, especially in regards to damaged waste bottle classification. In this paper, we propose the use of a serial attention frame (SAF) to overcome the mentioned drawback. The proposed network architecture includes the following three parts: a residual learning block (RB), a mixed attention block (MAB), and a self-attention block (SAB). The RB uses ResNet to pretrain the SAF to extract more detailed information. To address the effect of the complex background of waste bottle recognition, a serial attention mechanism containing MAB and SAB is presented. MAB is used to extract more salient category information via the simultaneous use of spatial attention and channel attention. SAB exploits the obtained features and its parameters to enable the diverse features to improve the classification results of waste bottles. The experimental results demonstrate that our proposed model exhibited good recognition performance in the collected waste bottle datasets, with eight labels of three classifications, i.e., the color, whether the bottle was damage, and whether the wrapper had been removed, as well as public image classification datasets.
A Cybersecurity Culture Survey Targeting Healthcare Critical Infrastructures

Recent studies report that cybersecurity breaches noticed in hospitals are associated with low levels of personnel's cybersecurity awareness. This work aims to assess the cybersecurity culture in healthcare institutions from middle- to low-income EU countries. The evaluation process was designed and performed via anonymous online surveys targeting individually ICT (internet and communication technology) departments and healthcare professionals. The study was conducted in 2019 for a health region in Greece, with a significant number of hospitals and health centers, a large hospital in Portugal, and a medical clinic in Romania, with 53.6% and 6.71% response rates for the ICT and healthcare professionals, respectively. Its findings indicate the necessity of establishing individual cybersecurity departments to monitor assets and attitudes while underlying the importance of continuous security awareness training programs. The analysis of our results assists in comprehending the countermeasures, which have been implemented in the healthcare institutions, and consequently enhancing cybersecurity defense, while reducing the risk surface.
Multi-Channel HEVC Steganography by Minimizing IPM Steganographic Distortions

Calibration is a common method for steganalysis, and Intra Prediction Mode (IPM) shift is a typical phenomenon used in calibration to detect video steganography. The current HEVC steganography lacks resistance to steganalysis based on this phenomenon because the new technology of HEVC introduces steganographic distortion in addition to providing more potential steganographic space. In this paper, an HEVC steganographic algorithm that resists IPM shift is proposed. First, we introduce the IPM shift in HEVC, and the previous H.264 steganalytic IPM shift feature is modeled and improved. By analyzing the HEVC encoding process, we found that modifying large-size blocks has a more significant impact on compression efficiency, while small ones are more sensitive to IPM optimality. Therefore, we perform the embedding channel division based on the block size and design the distortion function separately. In addition, we discover a unique IPM transition probability distribution in HEVC. According to our analysis, this unique distribution arises due to HEVC's MPM rules and the regularity of IPM direction. Modifying IPM in HEVC will change such distribution, thus, a mapping rule is designed based on this distribution to achieve a better embedding effect. Experimental results show that the channel division and proposed distortion function can effectively improve the overall performance. The proposed steganography outperforms the state-of-the-art steganography in resisting steganalysis, bitrate controlling, and visual quality.
Enhancing Performance of Breast Ultrasound in Opportunistic Screening Women by a Deep Learning-Based System: A Multicenter Prospective Study

To validate the feasibility of S-Detect, an ultrasound computer-aided diagnosis (CAD) system using deep learning, in enhancing the diagnostic performance of breast ultrasound (US) for patients with opportunistic screening-detected breast lesions.Nine medical centers throughout China participated in this prospective study. Asymptomatic patients with US-detected breast masses were enrolled and received conventional US, S-Detect, and strain elastography subsequently. The final pathological results are referred to as the gold standard for classifying breast mass. The diagnostic performances of the three methods and the combination of S-Detect and elastography were evaluated and compared, including sensitivity, specificity, and area under the receiver operating characteristics (AUC) curve. We also compared the diagnostic performances of S-Detect among different study sites.A total of 757 patients were enrolled, including 460 benign and 297 malignant cases. S-Detect exhibited significantly higher AUC and specificity than conventional US (AUC, S-Detect 0.83 [0.80-0.85] vs. US 0.74 [0.70-0.77], p < 0.0001; specificity, S-Detect 74.35% [70.10%-78.28%] vs. US 54.13% [51.42%-60.29%], p < 0.0001), with no decrease in sensitivity. In comparison to that of S-Detect alone, the AUC value significantly was enhanced after combining elastography and S-Detect (0.87 [0.84-0.90]), without compromising specificity (73.93% [68.60%-78.78%]). Significant differences in the S-Detect's performance were also observed across different study sites (AUC of S-Detect in Groups 1-4: 0.89 [0.84-0.93], 0.84 [0.77-0.89], 0.85 [0.76-0.92], 0.75 [0.69-0.80]; p [1 vs. 4] < 0.0001, p [2 vs. 4] = 0.0165, p [3 vs. 4] = 0.0157).Compared with the conventional US, S-Detect presented higher overall accuracy and specificity. After S-Detect and strain elastography were combined, the performance could be further enhanced. The performances of S-Detect also varied among different centers.
Lightweight convolutional neural network for real-time 3D object detection in road and railway environments

Abstract For smart mobility, and autonomous vehicles (AV), it is necessary to have a very precise perception of the environment to guarantee reliable decision-making, and to be able to extend the results obtained for the road sector to other areas such as rail. To this end, we introduce a new single-stage monocular real-time 3D object detection convolutional neural network (CNN) based on YOLOv5, dedicated to smart mobility applications for both road and rail environments. To perform the 3D parameter regression, we replace YOLOv5’s anchor boxes with our hybrid anchor boxes. Our method is available in different model sizes such as YOLOv5: small, medium, and large. The new model that we propose is optimized for real-time embedded constraints (lightweight, speed, and accuracy) that takes advantage of the improvement brought by split attention (SA) convolutions called small split attention model (Small-SA). To validate our CNN model, we also introduce a new virtual dataset for both road and rail environments by leveraging the video game Grand Theft Auto V (GTAV). We provide extensive results of our different models on both KITTI and our own GTAV datasets. Through our results, we show that our method is the fastest available 3D object detection with accuracy results close to state-of-the-art methods on the KITTI road dataset. We further demonstrate that the pre-training process on our GTAV virtual dataset improves the accuracy on real datasets such as KITTI, thus allowing our method to obtain an even greater accuracy than state-of-the-art approaches with 16.16% 3D average precision on hard car detection with inference time of 11.1 ms/image on an RTX 3080 GPU.
Complementing human effort in online reviews: A deep learning approach to automatic content generation and review synthesis

Online product reviews are ubiquitous and helpful sources of information available to consumers for making purchase decisions. Consumers rely on both the quantitative aspects of reviews such as valence and volume as well as textual descriptions to learn about product quality and fit. In this paper we show how new achievements in natural language processing can provide an important assist for different kinds of review-related writing tasks. Working in the interesting context of wine reviews, we demonstrate that machines are capable of performing the critical marketing task of writing expert reviews directly from a fairly small amount of product attribute data (metadata). We conduct a kind of “Turing Test” to evaluate human response to our machine-written reviews and show strong support for the assertion that machines can write reviews that are indistinguishable from those written by experts. Rather than replacing the human review writer, we envision a workflow wherein machines take the metadata as inputs and generate a human readable review as a first draft of the review and thereby assist an expert reviewer in writing their review. We next modify and apply our machine-writing technology to show how machines can be used to write a synthesis of a set of product reviews. For this last application we work in the context of beer reviews (for which there is a large set of available reviews for each of a large number of products) and produce machine-written review syntheses that do a good job – measured again through human evaluation – of capturing the ideas expressed in the reviews of any given beer. For each of these applications, we adapt the Transformer neural net architecture. The work herein is broadly applicable in marketing, particularly in the context of online reviews. We close with suggestions for additional applications of our model and approach as well as other directions for future research.
Summarization of Text and Image Captioning in Information Retrieval Using Deep Learning Techniques

Automated information retrieval and text summarization concept is a difficult process in natural language processing because of the infrequent structure and high complexity of the documents. The text summarization process creates a summary by paraphrasing a long text. Earlier models on information retrieval and summarization are based on a massive labeled dataset by the use of handcrafted features, leveraging on knowledge for a particular domain, and concentrated on the narrow sub-domain to improve efficiency. This paper presents a new deep learning (DL) based information retrieval with a text summarization model. The proposed model involves three major processes namely information retrieval, template generation, and text summarization. Initially, the bidirectional long short term memory (BiLSTM) approach is employed for retrieving the textual data, which assumes each word in a sentence, extracts the information, and embeds it into the semantic vector. Next, the template generation process takes place using the DL model. The deep belief network (DBN) model is employed as a text summarization tool to summarize the textual content. In addition, the image description is generated for the visualized entities that exist in the images. The design of BiLSTM with the DBN model for the text summarization and image captioning process shows the novelty of the work. The performance of the presented method is validated using Giga word corpus and DUC corpus. The experimental results referred that the proposed DBN model outperformed the compared methods with the maximum precision, recall and F-score. The image captions are compared with a predefined set of captions that exists for the image and the performance is evaluated using the BLEU metric.
Exploiting Transformer-Based Multitask Learning for the Detection of Media Bias in News Articles

Media has a substantial impact on the public perception of events. A one-sided or polarizing perspective on any topic is usually described as media bias. One of the ways how bias in news articles can be introduced is by altering word choice. Biased word choices are not always obvious, nor do they exhibit high context-dependency. Hence, detecting bias is often difficult. We propose a Transformer-based deep learning architecture trained via Multi-Task Learning using six bias-related data sets to tackle the media bias detection problem. Our best-performing implementation achieves a macro $$F_{1}$$ of 0.776, a performance boost of 3% compared to our baseline, outperforming existing methods. Our results indicate Multi-Task Learning as a promising alternative to improve existing baseline models in identifying slanted reporting.
Structure-aware Meta-fusion for Image Super-resolution

There are two main categories of image super-resolution algorithms: distortion oriented and perception oriented. Recent evidence shows that reconstruction accuracy and perceptual quality are typically in disagreement with each other. In this article, we present a new image super-resolution framework that is capable of striking a balance between distortion and perception. The core of our framework is a deep fusion network capable of generating a final high-resolution image by fusing a pair of deterministic and stochastic images using spatially varying weights. To make a single fusion model produce images with varying degrees of stochasticity, we further incorporate meta-learning into our fusion network. Once equipped with the kernel produced by a kernel prediction module, our meta fusion network is able to produce final images at any desired level of stochasticity. Experimental results indicate that our meta fusion network outperforms existing state-of-the-art SISR algorithms on widely used datasets, including PIRM-val, DIV2K-val, Set5, Set14, Urban100, Manga109, and B100. In addition, it is capable of producing high-resolution images that achieve low distortion and high perceptual quality simultaneously.
Broad stochastic configuration network for regression

Stochastic configuration network (SCN) is an incremental learning approach with universal approximation property for analyzing high-dimensional and large-scale data. It can start with a small structure and gradually increase hidden layer nodes through the supervision mechanism. Broad learning system (BLS), as a novel structure, has been widely used in various fields because of its high efficiency, generalization performance and scalability. Unlike deep learning models, BLS is proposed based on random vector functional link network (RVFLN) which can map input features to a more appropriate feature space and the mapped features are enhanced as enhancement nodes. Therefore, to enhance the feature learning ability and improve the effectiveness of SCN, inspired by BLS architecture, a novel broad stochastic configuration network (BSCN) is proposed, then we give the structure and theory of BSCN. In this model, the original features are transformed into mapping features in the feature layer and the mapping features are enhanced in the enhancement layer, then the input weights and biases of enhancement nodes are determined according to the supervision mechanism and the output weight matrix can be calculated through the standard least squares. The experimental results on function approximation problems and real-world datasets indicate that BSCN improves the performance of SCN, and it has higher regression accuracy and stability.
A Deep Active Learning Approach to the Automatic Classification of Volcano-Seismic Events

Volcano-seismic event classification represents a fundamental component of volcanic monitoring. Recent advances in techniques for the automatic classification of volcano-seismic events using supervised deep learning models achieve high accuracy. However, these deep learning models require a large, labelled training dataset to successfully train a generalisable model. We develop an approach to volcano-seismic event classification making use of active learning, where a machine learning model actively selects the training data which it learns from. We apply a diversity-based active learning approach, which works by selecting new training points which are most dissimilar from points already in the model according to a distance-based calculation applied to the model features. We combine the active learning with an existing volcano-seismic event classifier and apply the model to data from two volcanoes: Nevado del Ruiz, Colombia and Llaima, Chile. We find that models with data selected using an active learning approach achieve better testing accuracy and AUC (Area Under the Receiver Operating Characteristic Curve) than models with data selected using random sampling. Additionally, active learning decreases the labelling burden for the Nevado del Ruiz dataset but offers no increase in performance for the Llaima dataset. To explain these results, we visualise the features from the two datasets and suggest that active learning can reduce the quantity of labelled data required for less separable data, such as the Nevado del Ruiz dataset. This study represents the first evaluation of an active learning approach in volcano-seismology.
How and What to Learn: Taxonomizing Self-Supervised Learning for 3D Action Recognition

There are two competing standards for self-supervised learning in action recognition from 3D skeletons. Su et al., 2020 [31] used an auto-encoder architecture and an image reconstruction objective function to achieve state-of-the-art performance on the NTU60 C-View benchmark. Rao et al., 2020 [23] used Contrastive learning in the latent space to achieve state-of-the-art performance on the NTU60 C-Sub benchmark. Here, we reconcile these disparate approaches by developing a taxonomy of self-supervised learning for action recognition. We observe that leading approaches generally use one of two types of objective functions: those that seek to reconstruct the input from a latent representation ("Attractive" learning) versus those that also try to maximize the representations distinctiveness ("Contrastive" learning). Independently, leading approaches also differ in how they implement these objective functions: there are those that optimize representations in the decoder output space and those which optimize representations in the network’s latent space (encoder output). We find that combining these approaches leads to larger gains in performance and tolerance to transformation than is achievable by any individual method, leading to state-of-the-art performance on three standard action recognition datasets. We include links to our code and data.